diff --git a/ckpts/universal/global_step40/zero/11.attention.dense.weight/exp_avg_sq.pt b/ckpts/universal/global_step40/zero/11.attention.dense.weight/exp_avg_sq.pt new file mode 100644 index 0000000000000000000000000000000000000000..73a3d8cb07ea9e119a2f012ec498a8601c6f0526 --- /dev/null +++ b/ckpts/universal/global_step40/zero/11.attention.dense.weight/exp_avg_sq.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:bdcca03be094aa3bab46127679c7cab281a4c52284570ae3922ce1fa03665c8f +size 16778411 diff --git a/ckpts/universal/global_step40/zero/11.attention.dense.weight/fp32.pt b/ckpts/universal/global_step40/zero/11.attention.dense.weight/fp32.pt new file mode 100644 index 0000000000000000000000000000000000000000..cacb444bcc360263d203f623262fed0258e12600 --- /dev/null +++ b/ckpts/universal/global_step40/zero/11.attention.dense.weight/fp32.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4f629e604f127ba633c6d5c8975ba28a80aa2096fc11e767b6dc8de7ce548d94 +size 16778317 diff --git a/ckpts/universal/global_step40/zero/11.mlp.dense_h_to_4h_swiglu.weight/fp32.pt b/ckpts/universal/global_step40/zero/11.mlp.dense_h_to_4h_swiglu.weight/fp32.pt new file mode 100644 index 0000000000000000000000000000000000000000..ad2601c9716a05138099697ec3ba33f2328bc144 --- /dev/null +++ b/ckpts/universal/global_step40/zero/11.mlp.dense_h_to_4h_swiglu.weight/fp32.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e12c152189d73a6366dc9d5155c8a7c9140f4fc7b01ec8cd9ada1c675bcd02fc +size 33555533 diff --git a/venv/lib/python3.10/site-packages/annotated_types-0.6.0.dist-info/INSTALLER b/venv/lib/python3.10/site-packages/annotated_types-0.6.0.dist-info/INSTALLER new file mode 100644 index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68 --- /dev/null +++ b/venv/lib/python3.10/site-packages/annotated_types-0.6.0.dist-info/INSTALLER @@ -0,0 +1 @@ +pip diff --git a/venv/lib/python3.10/site-packages/annotated_types-0.6.0.dist-info/METADATA b/venv/lib/python3.10/site-packages/annotated_types-0.6.0.dist-info/METADATA new file mode 100644 index 0000000000000000000000000000000000000000..36ec9b0d6ce27ed5511a80cf0fa4b25c91dae522 --- /dev/null +++ b/venv/lib/python3.10/site-packages/annotated_types-0.6.0.dist-info/METADATA @@ -0,0 +1,242 @@ +Metadata-Version: 2.1 +Name: annotated-types +Version: 0.6.0 +Summary: Reusable constraint types to use with typing.Annotated +Author-email: Samuel Colvin , Adrian Garcia Badaracco <1755071+adriangb@users.noreply.github.com>, Zac Hatfield-Dodds +License-File: LICENSE +Classifier: Development Status :: 4 - Beta +Classifier: Environment :: Console +Classifier: Environment :: MacOS X +Classifier: Intended Audience :: Developers +Classifier: Intended Audience :: Information Technology +Classifier: License :: OSI Approved :: MIT License +Classifier: Operating System :: POSIX :: Linux +Classifier: Operating System :: Unix +Classifier: Programming Language :: Python :: 3 :: Only +Classifier: Programming Language :: Python :: 3.8 +Classifier: Programming Language :: Python :: 3.9 +Classifier: Programming Language :: Python :: 3.10 +Classifier: Programming Language :: Python :: 3.11 +Classifier: Programming Language :: Python :: 3.12 +Classifier: Topic :: Software Development :: Libraries :: Python Modules +Classifier: Typing :: Typed +Requires-Python: >=3.8 +Requires-Dist: typing-extensions>=4.0.0; python_version < '3.9' +Description-Content-Type: text/markdown + +# annotated-types + +[![CI](https://github.com/annotated-types/annotated-types/workflows/CI/badge.svg?event=push)](https://github.com/annotated-types/annotated-types/actions?query=event%3Apush+branch%3Amain+workflow%3ACI) +[![pypi](https://img.shields.io/pypi/v/annotated-types.svg)](https://pypi.python.org/pypi/annotated-types) +[![versions](https://img.shields.io/pypi/pyversions/annotated-types.svg)](https://github.com/annotated-types/annotated-types) +[![license](https://img.shields.io/github/license/annotated-types/annotated-types.svg)](https://github.com/annotated-types/annotated-types/blob/main/LICENSE) + +[PEP-593](https://peps.python.org/pep-0593/) added `typing.Annotated` as a way of +adding context-specific metadata to existing types, and specifies that +`Annotated[T, x]` _should_ be treated as `T` by any tool or library without special +logic for `x`. + +This package provides metadata objects which can be used to represent common +constraints such as upper and lower bounds on scalar values and collection sizes, +a `Predicate` marker for runtime checks, and +descriptions of how we intend these metadata to be interpreted. In some cases, +we also note alternative representations which do not require this package. + +## Install + +```bash +pip install annotated-types +``` + +## Examples + +```python +from typing import Annotated +from annotated_types import Gt, Len, Predicate + +class MyClass: + age: Annotated[int, Gt(18)] # Valid: 19, 20, ... + # Invalid: 17, 18, "19", 19.0, ... + factors: list[Annotated[int, Predicate(is_prime)]] # Valid: 2, 3, 5, 7, 11, ... + # Invalid: 4, 8, -2, 5.0, "prime", ... + + my_list: Annotated[list[int], Len(0, 10)] # Valid: [], [10, 20, 30, 40, 50] + # Invalid: (1, 2), ["abc"], [0] * 20 +``` + +## Documentation + +_While `annotated-types` avoids runtime checks for performance, users should not +construct invalid combinations such as `MultipleOf("non-numeric")` or `Annotated[int, Len(3)]`. +Downstream implementors may choose to raise an error, emit a warning, silently ignore +a metadata item, etc., if the metadata objects described below are used with an +incompatible type - or for any other reason!_ + +### Gt, Ge, Lt, Le + +Express inclusive and/or exclusive bounds on orderable values - which may be numbers, +dates, times, strings, sets, etc. Note that the boundary value need not be of the +same type that was annotated, so long as they can be compared: `Annotated[int, Gt(1.5)]` +is fine, for example, and implies that the value is an integer x such that `x > 1.5`. + +We suggest that implementors may also interpret `functools.partial(operator.le, 1.5)` +as being equivalent to `Gt(1.5)`, for users who wish to avoid a runtime dependency on +the `annotated-types` package. + +To be explicit, these types have the following meanings: + +* `Gt(x)` - value must be "Greater Than" `x` - equivalent to exclusive minimum +* `Ge(x)` - value must be "Greater than or Equal" to `x` - equivalent to inclusive minimum +* `Lt(x)` - value must be "Less Than" `x` - equivalent to exclusive maximum +* `Le(x)` - value must be "Less than or Equal" to `x` - equivalent to inclusive maximum + +### Interval + +`Interval(gt, ge, lt, le)` allows you to specify an upper and lower bound with a single +metadata object. `None` attributes should be ignored, and non-`None` attributes +treated as per the single bounds above. + +### MultipleOf + +`MultipleOf(multiple_of=x)` might be interpreted in two ways: + +1. Python semantics, implying `value % multiple_of == 0`, or +2. [JSONschema semantics](https://json-schema.org/draft/2020-12/json-schema-validation.html#rfc.section.6.2.1), + where `int(value / multiple_of) == value / multiple_of`. + +We encourage users to be aware of these two common interpretations and their +distinct behaviours, especially since very large or non-integer numbers make +it easy to cause silent data corruption due to floating-point imprecision. + +We encourage libraries to carefully document which interpretation they implement. + +### MinLen, MaxLen, Len + +`Len()` implies that `min_length <= len(value) <= max_length` - lower and upper bounds are inclusive. + +As well as `Len()` which can optionally include upper and lower bounds, we also +provide `MinLen(x)` and `MaxLen(y)` which are equivalent to `Len(min_length=x)` +and `Len(max_length=y)` respectively. + +`Len`, `MinLen`, and `MaxLen` may be used with any type which supports `len(value)`. + +Examples of usage: + +* `Annotated[list, MaxLen(10)]` (or `Annotated[list, Len(max_length=10))`) - list must have a length of 10 or less +* `Annotated[str, MaxLen(10)]` - string must have a length of 10 or less +* `Annotated[list, MinLen(3))` (or `Annotated[list, Len(min_length=3))`) - list must have a length of 3 or more +* `Annotated[list, Len(4, 6)]` - list must have a length of 4, 5, or 6 +* `Annotated[list, Len(8, 8)]` - list must have a length of exactly 8 + +#### Changed in v0.4.0 + +* `min_inclusive` has been renamed to `min_length`, no change in meaning +* `max_exclusive` has been renamed to `max_length`, upper bound is now **inclusive** instead of **exclusive** +* The recommendation that slices are interpreted as `Len` has been removed due to ambiguity and different semantic + meaning of the upper bound in slices vs. `Len` + +See [issue #23](https://github.com/annotated-types/annotated-types/issues/23) for discussion. + +### Timezone + +`Timezone` can be used with a `datetime` or a `time` to express which timezones +are allowed. `Annotated[datetime, Timezone(None)]` must be a naive datetime. +`Timezone[...]` ([literal ellipsis](https://docs.python.org/3/library/constants.html#Ellipsis)) +expresses that any timezone-aware datetime is allowed. You may also pass a specific +timezone string or `timezone` object such as `Timezone(timezone.utc)` or +`Timezone("Africa/Abidjan")` to express that you only allow a specific timezone, +though we note that this is often a symptom of fragile design. + +### Predicate + +`Predicate(func: Callable)` expresses that `func(value)` is truthy for valid values. +Users should prefer the statically inspectable metadata above, but if you need +the full power and flexibility of arbitrary runtime predicates... here it is. + +We provide a few predefined predicates for common string constraints: + +* `IsLower = Predicate(str.islower)` +* `IsUpper = Predicate(str.isupper)` +* `IsDigit = Predicate(str.isdigit)` +* `IsFinite = Predicate(math.isfinite)` +* `IsNotFinite = Predicate(Not(math.isfinite))` +* `IsNan = Predicate(math.isnan)` +* `IsNotNan = Predicate(Not(math.isnan))` +* `IsInfinite = Predicate(math.isinf)` +* `IsNotInfinite = Predicate(Not(math.isinf))` + +Some libraries might have special logic to handle known or understandable predicates, +for example by checking for `str.isdigit` and using its presence to both call custom +logic to enforce digit-only strings, and customise some generated external schema. +Users are therefore encouraged to avoid indirection like `lambda s: s.lower()`, in +favor of introspectable methods such as `str.lower` or `re.compile("pattern").search`. + +To enable basic negation of commonly used predicates like `math.isnan` without introducing introspection that makes it impossible for implementers to introspect the predicate we provide a `Not` wrapper that simply negates the predicate in an introspectable manner. Several of the predicates listed above are created in this manner. + +We do not specify what behaviour should be expected for predicates that raise +an exception. For example `Annotated[int, Predicate(str.isdigit)]` might silently +skip invalid constraints, or statically raise an error; or it might try calling it +and then propogate or discard the resulting +`TypeError: descriptor 'isdigit' for 'str' objects doesn't apply to a 'int' object` +exception. We encourage libraries to document the behaviour they choose. + +### Doc + +`doc()` can be used to add documentation information in `Annotated`, for function and method parameters, variables, class attributes, return types, and any place where `Annotated` can be used. + +It expects a value that can be statically analyzed, as the main use case is for static analysis, editors, documentation generators, and similar tools. + +It returns a `DocInfo` class with a single attribute `documentation` containing the value passed to `doc()`. + +This is the early adopter's alternative form of the [`typing-doc` proposal](https://github.com/tiangolo/fastapi/blob/typing-doc/typing_doc.md). + +### Integrating downstream types with `GroupedMetadata` + +Implementers may choose to provide a convenience wrapper that groups multiple pieces of metadata. +This can help reduce verbosity and cognitive overhead for users. +For example, an implementer like Pydantic might provide a `Field` or `Meta` type that accepts keyword arguments and transforms these into low-level metadata: + +```python +from dataclasses import dataclass +from typing import Iterator +from annotated_types import GroupedMetadata, Ge + +@dataclass +class Field(GroupedMetadata): + ge: int | None = None + description: str | None = None + + def __iter__(self) -> Iterator[object]: + # Iterating over a GroupedMetadata object should yield annotated-types + # constraint metadata objects which describe it as fully as possible, + # and may include other unknown objects too. + if self.ge is not None: + yield Ge(self.ge) + if self.description is not None: + yield Description(self.description) +``` + +Libraries consuming annotated-types constraints should check for `GroupedMetadata` and unpack it by iterating over the object and treating the results as if they had been "unpacked" in the `Annotated` type. The same logic should be applied to the [PEP 646 `Unpack` type](https://peps.python.org/pep-0646/), so that `Annotated[T, Field(...)]`, `Annotated[T, Unpack[Field(...)]]` and `Annotated[T, *Field(...)]` are all treated consistently. + +Libraries consuming annotated-types should also ignore any metadata they do not recongize that came from unpacking a `GroupedMetadata`, just like they ignore unrecognized metadata in `Annotated` itself. + +Our own `annotated_types.Interval` class is a `GroupedMetadata` which unpacks itself into `Gt`, `Lt`, etc., so this is not an abstract concern. Similarly, `annotated_types.Len` is a `GroupedMetadata` which unpacks itself into `MinLen` (optionally) and `MaxLen`. + +### Consuming metadata + +We intend to not be prescriptive as to _how_ the metadata and constraints are used, but as an example of how one might parse constraints from types annotations see our [implementation in `test_main.py`](https://github.com/annotated-types/annotated-types/blob/f59cf6d1b5255a0fe359b93896759a180bec30ae/tests/test_main.py#L94-L103). + +It is up to the implementer to determine how this metadata is used. +You could use the metadata for runtime type checking, for generating schemas or to generate example data, amongst other use cases. + +## Design & History + +This package was designed at the PyCon 2022 sprints by the maintainers of Pydantic +and Hypothesis, with the goal of making it as easy as possible for end-users to +provide more informative annotations for use by runtime libraries. + +It is deliberately minimal, and following PEP-593 allows considerable downstream +discretion in what (if anything!) they choose to support. Nonetheless, we expect +that staying simple and covering _only_ the most common use-cases will give users +and maintainers the best experience we can. If you'd like more constraints for your +types - follow our lead, by defining them and documenting them downstream! diff --git a/venv/lib/python3.10/site-packages/annotated_types-0.6.0.dist-info/RECORD b/venv/lib/python3.10/site-packages/annotated_types-0.6.0.dist-info/RECORD new file mode 100644 index 0000000000000000000000000000000000000000..983c17359aa333dc95283fa2e1e72bb40c6238cf --- /dev/null +++ b/venv/lib/python3.10/site-packages/annotated_types-0.6.0.dist-info/RECORD @@ -0,0 +1,10 @@ +annotated_types-0.6.0.dist-info/INSTALLER,sha256=zuuue4knoyJ-UwPPXg8fezS7VCrXJQrAP7zeNuwvFQg,4 +annotated_types-0.6.0.dist-info/METADATA,sha256=78YeruT3b_doh8TbsK7qcbqY4cvCMz_QQRCGUbyAo6M,12879 +annotated_types-0.6.0.dist-info/RECORD,, +annotated_types-0.6.0.dist-info/WHEEL,sha256=9QBuHhg6FNW7lppboF2vKVbCGTVzsFykgRQjjlajrhA,87 +annotated_types-0.6.0.dist-info/licenses/LICENSE,sha256=_hBJiEsaDZNCkB6I4H8ykl0ksxIdmXK2poBfuYJLCV0,1083 +annotated_types/__init__.py,sha256=txLoRt8iSiAc-Su37esWpMI0wFjN92V7gE5HdFIeeKI,12151 +annotated_types/__pycache__/__init__.cpython-310.pyc,, +annotated_types/__pycache__/test_cases.cpython-310.pyc,, +annotated_types/py.typed,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0 +annotated_types/test_cases.py,sha256=LfFyURZwr7X3NVfoCrSXSMMxTxJD4o8Xz-Y8qbmY7JU,6327 diff --git a/venv/lib/python3.10/site-packages/annotated_types-0.6.0.dist-info/WHEEL b/venv/lib/python3.10/site-packages/annotated_types-0.6.0.dist-info/WHEEL new file mode 100644 index 0000000000000000000000000000000000000000..ba1a8af28bcccdacebb8c22dfda1537447a1a58a --- /dev/null +++ b/venv/lib/python3.10/site-packages/annotated_types-0.6.0.dist-info/WHEEL @@ -0,0 +1,4 @@ +Wheel-Version: 1.0 +Generator: hatchling 1.18.0 +Root-Is-Purelib: true +Tag: py3-none-any diff --git a/venv/lib/python3.10/site-packages/annotated_types-0.6.0.dist-info/licenses/LICENSE b/venv/lib/python3.10/site-packages/annotated_types-0.6.0.dist-info/licenses/LICENSE new file mode 100644 index 0000000000000000000000000000000000000000..d99323a9965f146d5b0888c4ca1bf0727e12b04f --- /dev/null +++ b/venv/lib/python3.10/site-packages/annotated_types-0.6.0.dist-info/licenses/LICENSE @@ -0,0 +1,21 @@ +The MIT License (MIT) + +Copyright (c) 2022 the contributors + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/venv/lib/python3.10/site-packages/chardet/__init__.py b/venv/lib/python3.10/site-packages/chardet/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..fe581623d89d67a49eb43f3c3e88f3f450257707 --- /dev/null +++ b/venv/lib/python3.10/site-packages/chardet/__init__.py @@ -0,0 +1,115 @@ +######################## BEGIN LICENSE BLOCK ######################## +# This library is free software; you can redistribute it and/or +# modify it under the terms of the GNU Lesser General Public +# License as published by the Free Software Foundation; either +# version 2.1 of the License, or (at your option) any later version. +# +# This library is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +# Lesser General Public License for more details. +# +# You should have received a copy of the GNU Lesser General Public +# License along with this library; if not, write to the Free Software +# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA +# 02110-1301 USA +######################### END LICENSE BLOCK ######################### + +from typing import List, Union + +from .charsetgroupprober import CharSetGroupProber +from .charsetprober import CharSetProber +from .enums import InputState +from .resultdict import ResultDict +from .universaldetector import UniversalDetector +from .version import VERSION, __version__ + +__all__ = ["UniversalDetector", "detect", "detect_all", "__version__", "VERSION"] + + +def detect( + byte_str: Union[bytes, bytearray], should_rename_legacy: bool = False +) -> ResultDict: + """ + Detect the encoding of the given byte string. + + :param byte_str: The byte sequence to examine. + :type byte_str: ``bytes`` or ``bytearray`` + :param should_rename_legacy: Should we rename legacy encodings + to their more modern equivalents? + :type should_rename_legacy: ``bool`` + """ + if not isinstance(byte_str, bytearray): + if not isinstance(byte_str, bytes): + raise TypeError( + f"Expected object of type bytes or bytearray, got: {type(byte_str)}" + ) + byte_str = bytearray(byte_str) + detector = UniversalDetector(should_rename_legacy=should_rename_legacy) + detector.feed(byte_str) + return detector.close() + + +def detect_all( + byte_str: Union[bytes, bytearray], + ignore_threshold: bool = False, + should_rename_legacy: bool = False, +) -> List[ResultDict]: + """ + Detect all the possible encodings of the given byte string. + + :param byte_str: The byte sequence to examine. + :type byte_str: ``bytes`` or ``bytearray`` + :param ignore_threshold: Include encodings that are below + ``UniversalDetector.MINIMUM_THRESHOLD`` + in results. + :type ignore_threshold: ``bool`` + :param should_rename_legacy: Should we rename legacy encodings + to their more modern equivalents? + :type should_rename_legacy: ``bool`` + """ + if not isinstance(byte_str, bytearray): + if not isinstance(byte_str, bytes): + raise TypeError( + f"Expected object of type bytes or bytearray, got: {type(byte_str)}" + ) + byte_str = bytearray(byte_str) + + detector = UniversalDetector(should_rename_legacy=should_rename_legacy) + detector.feed(byte_str) + detector.close() + + if detector.input_state == InputState.HIGH_BYTE: + results: List[ResultDict] = [] + probers: List[CharSetProber] = [] + for prober in detector.charset_probers: + if isinstance(prober, CharSetGroupProber): + probers.extend(p for p in prober.probers) + else: + probers.append(prober) + for prober in probers: + if ignore_threshold or prober.get_confidence() > detector.MINIMUM_THRESHOLD: + charset_name = prober.charset_name or "" + lower_charset_name = charset_name.lower() + # Use Windows encoding name instead of ISO-8859 if we saw any + # extra Windows-specific bytes + if lower_charset_name.startswith("iso-8859") and detector.has_win_bytes: + charset_name = detector.ISO_WIN_MAP.get( + lower_charset_name, charset_name + ) + # Rename legacy encodings with superset encodings if asked + if should_rename_legacy: + charset_name = detector.LEGACY_MAP.get( + charset_name.lower(), charset_name + ) + results.append( + { + "encoding": charset_name, + "confidence": prober.get_confidence(), + "language": prober.language, + } + ) + if len(results) > 0: + return sorted(results, key=lambda result: -result["confidence"]) + + return [detector.result] diff --git a/venv/lib/python3.10/site-packages/chardet/big5freq.py b/venv/lib/python3.10/site-packages/chardet/big5freq.py new file mode 100644 index 0000000000000000000000000000000000000000..87d9f972edde20d1f8e391b8010703242a8de977 --- /dev/null +++ b/venv/lib/python3.10/site-packages/chardet/big5freq.py @@ -0,0 +1,386 @@ +######################## BEGIN LICENSE BLOCK ######################## +# The Original Code is Mozilla Communicator client code. +# +# The Initial Developer of the Original Code is +# Netscape Communications Corporation. +# Portions created by the Initial Developer are Copyright (C) 1998 +# the Initial Developer. All Rights Reserved. +# +# Contributor(s): +# Mark Pilgrim - port to Python +# +# This library is free software; you can redistribute it and/or +# modify it under the terms of the GNU Lesser General Public +# License as published by the Free Software Foundation; either +# version 2.1 of the License, or (at your option) any later version. +# +# This library is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +# Lesser General Public License for more details. +# +# You should have received a copy of the GNU Lesser General Public +# License along with this library; if not, write to the Free Software +# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA +# 02110-1301 USA +######################### END LICENSE BLOCK ######################### + +# Big5 frequency table +# by Taiwan's Mandarin Promotion Council +# +# +# 128 --> 0.42261 +# 256 --> 0.57851 +# 512 --> 0.74851 +# 1024 --> 0.89384 +# 2048 --> 0.97583 +# +# Ideal Distribution Ratio = 0.74851/(1-0.74851) =2.98 +# Random Distribution Ration = 512/(5401-512)=0.105 +# +# Typical Distribution Ratio about 25% of Ideal one, still much higher than RDR + +BIG5_TYPICAL_DISTRIBUTION_RATIO = 0.75 + +# Char to FreqOrder table +BIG5_TABLE_SIZE = 5376 +# fmt: off +BIG5_CHAR_TO_FREQ_ORDER = ( + 1,1801,1506, 255,1431, 198, 9, 82, 6,5008, 177, 202,3681,1256,2821, 110, # 16 +3814, 33,3274, 261, 76, 44,2114, 16,2946,2187,1176, 659,3971, 26,3451,2653, # 32 +1198,3972,3350,4202, 410,2215, 302, 590, 361,1964, 8, 204, 58,4510,5009,1932, # 48 + 63,5010,5011, 317,1614, 75, 222, 159,4203,2417,1480,5012,3555,3091, 224,2822, # 64 +3682, 3, 10,3973,1471, 29,2787,1135,2866,1940, 873, 130,3275,1123, 312,5013, # 80 +4511,2052, 507, 252, 682,5014, 142,1915, 124, 206,2947, 34,3556,3204, 64, 604, # 96 +5015,2501,1977,1978, 155,1991, 645, 641,1606,5016,3452, 337, 72, 406,5017, 80, # 112 + 630, 238,3205,1509, 263, 939,1092,2654, 756,1440,1094,3453, 449, 69,2987, 591, # 128 + 179,2096, 471, 115,2035,1844, 60, 50,2988, 134, 806,1869, 734,2036,3454, 180, # 144 + 995,1607, 156, 537,2907, 688,5018, 319,1305, 779,2145, 514,2379, 298,4512, 359, # 160 +2502, 90,2716,1338, 663, 11, 906,1099,2553, 20,2441, 182, 532,1716,5019, 732, # 176 +1376,4204,1311,1420,3206, 25,2317,1056, 113, 399, 382,1950, 242,3455,2474, 529, # 192 +3276, 475,1447,3683,5020, 117, 21, 656, 810,1297,2300,2334,3557,5021, 126,4205, # 208 + 706, 456, 150, 613,4513, 71,1118,2037,4206, 145,3092, 85, 835, 486,2115,1246, # 224 +1426, 428, 727,1285,1015, 800, 106, 623, 303,1281,5022,2128,2359, 347,3815, 221, # 240 +3558,3135,5023,1956,1153,4207, 83, 296,1199,3093, 192, 624, 93,5024, 822,1898, # 256 +2823,3136, 795,2065, 991,1554,1542,1592, 27, 43,2867, 859, 139,1456, 860,4514, # 272 + 437, 712,3974, 164,2397,3137, 695, 211,3037,2097, 195,3975,1608,3559,3560,3684, # 288 +3976, 234, 811,2989,2098,3977,2233,1441,3561,1615,2380, 668,2077,1638, 305, 228, # 304 +1664,4515, 467, 415,5025, 262,2099,1593, 239, 108, 300, 200,1033, 512,1247,2078, # 320 +5026,5027,2176,3207,3685,2682, 593, 845,1062,3277, 88,1723,2038,3978,1951, 212, # 336 + 266, 152, 149, 468,1899,4208,4516, 77, 187,5028,3038, 37, 5,2990,5029,3979, # 352 +5030,5031, 39,2524,4517,2908,3208,2079, 55, 148, 74,4518, 545, 483,1474,1029, # 368 +1665, 217,1870,1531,3138,1104,2655,4209, 24, 172,3562, 900,3980,3563,3564,4519, # 384 + 32,1408,2824,1312, 329, 487,2360,2251,2717, 784,2683, 4,3039,3351,1427,1789, # 400 + 188, 109, 499,5032,3686,1717,1790, 888,1217,3040,4520,5033,3565,5034,3352,1520, # 416 +3687,3981, 196,1034, 775,5035,5036, 929,1816, 249, 439, 38,5037,1063,5038, 794, # 432 +3982,1435,2301, 46, 178,3278,2066,5039,2381,5040, 214,1709,4521, 804, 35, 707, # 448 + 324,3688,1601,2554, 140, 459,4210,5041,5042,1365, 839, 272, 978,2262,2580,3456, # 464 +2129,1363,3689,1423, 697, 100,3094, 48, 70,1231, 495,3139,2196,5043,1294,5044, # 480 +2080, 462, 586,1042,3279, 853, 256, 988, 185,2382,3457,1698, 434,1084,5045,3458, # 496 + 314,2625,2788,4522,2335,2336, 569,2285, 637,1817,2525, 757,1162,1879,1616,3459, # 512 + 287,1577,2116, 768,4523,1671,2868,3566,2526,1321,3816, 909,2418,5046,4211, 933, # 528 +3817,4212,2053,2361,1222,4524, 765,2419,1322, 786,4525,5047,1920,1462,1677,2909, # 544 +1699,5048,4526,1424,2442,3140,3690,2600,3353,1775,1941,3460,3983,4213, 309,1369, # 560 +1130,2825, 364,2234,1653,1299,3984,3567,3985,3986,2656, 525,1085,3041, 902,2001, # 576 +1475, 964,4527, 421,1845,1415,1057,2286, 940,1364,3141, 376,4528,4529,1381, 7, # 592 +2527, 983,2383, 336,1710,2684,1846, 321,3461, 559,1131,3042,2752,1809,1132,1313, # 608 + 265,1481,1858,5049, 352,1203,2826,3280, 167,1089, 420,2827, 776, 792,1724,3568, # 624 +4214,2443,3281,5050,4215,5051, 446, 229, 333,2753, 901,3818,1200,1557,4530,2657, # 640 +1921, 395,2754,2685,3819,4216,1836, 125, 916,3209,2626,4531,5052,5053,3820,5054, # 656 +5055,5056,4532,3142,3691,1133,2555,1757,3462,1510,2318,1409,3569,5057,2146, 438, # 672 +2601,2910,2384,3354,1068, 958,3043, 461, 311,2869,2686,4217,1916,3210,4218,1979, # 688 + 383, 750,2755,2627,4219, 274, 539, 385,1278,1442,5058,1154,1965, 384, 561, 210, # 704 + 98,1295,2556,3570,5059,1711,2420,1482,3463,3987,2911,1257, 129,5060,3821, 642, # 720 + 523,2789,2790,2658,5061, 141,2235,1333, 68, 176, 441, 876, 907,4220, 603,2602, # 736 + 710, 171,3464, 404, 549, 18,3143,2398,1410,3692,1666,5062,3571,4533,2912,4534, # 752 +5063,2991, 368,5064, 146, 366, 99, 871,3693,1543, 748, 807,1586,1185, 22,2263, # 768 + 379,3822,3211,5065,3212, 505,1942,2628,1992,1382,2319,5066, 380,2362, 218, 702, # 784 +1818,1248,3465,3044,3572,3355,3282,5067,2992,3694, 930,3283,3823,5068, 59,5069, # 800 + 585, 601,4221, 497,3466,1112,1314,4535,1802,5070,1223,1472,2177,5071, 749,1837, # 816 + 690,1900,3824,1773,3988,1476, 429,1043,1791,2236,2117, 917,4222, 447,1086,1629, # 832 +5072, 556,5073,5074,2021,1654, 844,1090, 105, 550, 966,1758,2828,1008,1783, 686, # 848 +1095,5075,2287, 793,1602,5076,3573,2603,4536,4223,2948,2302,4537,3825, 980,2503, # 864 + 544, 353, 527,4538, 908,2687,2913,5077, 381,2629,1943,1348,5078,1341,1252, 560, # 880 +3095,5079,3467,2870,5080,2054, 973, 886,2081, 143,4539,5081,5082, 157,3989, 496, # 896 +4224, 57, 840, 540,2039,4540,4541,3468,2118,1445, 970,2264,1748,1966,2082,4225, # 912 +3144,1234,1776,3284,2829,3695, 773,1206,2130,1066,2040,1326,3990,1738,1725,4226, # 928 + 279,3145, 51,1544,2604, 423,1578,2131,2067, 173,4542,1880,5083,5084,1583, 264, # 944 + 610,3696,4543,2444, 280, 154,5085,5086,5087,1739, 338,1282,3096, 693,2871,1411, # 960 +1074,3826,2445,5088,4544,5089,5090,1240, 952,2399,5091,2914,1538,2688, 685,1483, # 976 +4227,2475,1436, 953,4228,2055,4545, 671,2400, 79,4229,2446,3285, 608, 567,2689, # 992 +3469,4230,4231,1691, 393,1261,1792,2401,5092,4546,5093,5094,5095,5096,1383,1672, # 1008 +3827,3213,1464, 522,1119, 661,1150, 216, 675,4547,3991,1432,3574, 609,4548,2690, # 1024 +2402,5097,5098,5099,4232,3045, 0,5100,2476, 315, 231,2447, 301,3356,4549,2385, # 1040 +5101, 233,4233,3697,1819,4550,4551,5102, 96,1777,1315,2083,5103, 257,5104,1810, # 1056 +3698,2718,1139,1820,4234,2022,1124,2164,2791,1778,2659,5105,3097, 363,1655,3214, # 1072 +5106,2993,5107,5108,5109,3992,1567,3993, 718, 103,3215, 849,1443, 341,3357,2949, # 1088 +1484,5110,1712, 127, 67, 339,4235,2403, 679,1412, 821,5111,5112, 834, 738, 351, # 1104 +2994,2147, 846, 235,1497,1881, 418,1993,3828,2719, 186,1100,2148,2756,3575,1545, # 1120 +1355,2950,2872,1377, 583,3994,4236,2581,2995,5113,1298,3699,1078,2557,3700,2363, # 1136 + 78,3829,3830, 267,1289,2100,2002,1594,4237, 348, 369,1274,2197,2178,1838,4552, # 1152 +1821,2830,3701,2757,2288,2003,4553,2951,2758, 144,3358, 882,4554,3995,2759,3470, # 1168 +4555,2915,5114,4238,1726, 320,5115,3996,3046, 788,2996,5116,2831,1774,1327,2873, # 1184 +3997,2832,5117,1306,4556,2004,1700,3831,3576,2364,2660, 787,2023, 506, 824,3702, # 1200 + 534, 323,4557,1044,3359,2024,1901, 946,3471,5118,1779,1500,1678,5119,1882,4558, # 1216 + 165, 243,4559,3703,2528, 123, 683,4239, 764,4560, 36,3998,1793, 589,2916, 816, # 1232 + 626,1667,3047,2237,1639,1555,1622,3832,3999,5120,4000,2874,1370,1228,1933, 891, # 1248 +2084,2917, 304,4240,5121, 292,2997,2720,3577, 691,2101,4241,1115,4561, 118, 662, # 1264 +5122, 611,1156, 854,2386,1316,2875, 2, 386, 515,2918,5123,5124,3286, 868,2238, # 1280 +1486, 855,2661, 785,2216,3048,5125,1040,3216,3578,5126,3146, 448,5127,1525,5128, # 1296 +2165,4562,5129,3833,5130,4242,2833,3579,3147, 503, 818,4001,3148,1568, 814, 676, # 1312 +1444, 306,1749,5131,3834,1416,1030, 197,1428, 805,2834,1501,4563,5132,5133,5134, # 1328 +1994,5135,4564,5136,5137,2198, 13,2792,3704,2998,3149,1229,1917,5138,3835,2132, # 1344 +5139,4243,4565,2404,3580,5140,2217,1511,1727,1120,5141,5142, 646,3836,2448, 307, # 1360 +5143,5144,1595,3217,5145,5146,5147,3705,1113,1356,4002,1465,2529,2530,5148, 519, # 1376 +5149, 128,2133, 92,2289,1980,5150,4003,1512, 342,3150,2199,5151,2793,2218,1981, # 1392 +3360,4244, 290,1656,1317, 789, 827,2365,5152,3837,4566, 562, 581,4004,5153, 401, # 1408 +4567,2252, 94,4568,5154,1399,2794,5155,1463,2025,4569,3218,1944,5156, 828,1105, # 1424 +4245,1262,1394,5157,4246, 605,4570,5158,1784,2876,5159,2835, 819,2102, 578,2200, # 1440 +2952,5160,1502, 436,3287,4247,3288,2836,4005,2919,3472,3473,5161,2721,2320,5162, # 1456 +5163,2337,2068, 23,4571, 193, 826,3838,2103, 699,1630,4248,3098, 390,1794,1064, # 1472 +3581,5164,1579,3099,3100,1400,5165,4249,1839,1640,2877,5166,4572,4573, 137,4250, # 1488 + 598,3101,1967, 780, 104, 974,2953,5167, 278, 899, 253, 402, 572, 504, 493,1339, # 1504 +5168,4006,1275,4574,2582,2558,5169,3706,3049,3102,2253, 565,1334,2722, 863, 41, # 1520 +5170,5171,4575,5172,1657,2338, 19, 463,2760,4251, 606,5173,2999,3289,1087,2085, # 1536 +1323,2662,3000,5174,1631,1623,1750,4252,2691,5175,2878, 791,2723,2663,2339, 232, # 1552 +2421,5176,3001,1498,5177,2664,2630, 755,1366,3707,3290,3151,2026,1609, 119,1918, # 1568 +3474, 862,1026,4253,5178,4007,3839,4576,4008,4577,2265,1952,2477,5179,1125, 817, # 1584 +4254,4255,4009,1513,1766,2041,1487,4256,3050,3291,2837,3840,3152,5180,5181,1507, # 1600 +5182,2692, 733, 40,1632,1106,2879, 345,4257, 841,2531, 230,4578,3002,1847,3292, # 1616 +3475,5183,1263, 986,3476,5184, 735, 879, 254,1137, 857, 622,1300,1180,1388,1562, # 1632 +4010,4011,2954, 967,2761,2665,1349, 592,2134,1692,3361,3003,1995,4258,1679,4012, # 1648 +1902,2188,5185, 739,3708,2724,1296,1290,5186,4259,2201,2202,1922,1563,2605,2559, # 1664 +1871,2762,3004,5187, 435,5188, 343,1108, 596, 17,1751,4579,2239,3477,3709,5189, # 1680 +4580, 294,3582,2955,1693, 477, 979, 281,2042,3583, 643,2043,3710,2631,2795,2266, # 1696 +1031,2340,2135,2303,3584,4581, 367,1249,2560,5190,3585,5191,4582,1283,3362,2005, # 1712 + 240,1762,3363,4583,4584, 836,1069,3153, 474,5192,2149,2532, 268,3586,5193,3219, # 1728 +1521,1284,5194,1658,1546,4260,5195,3587,3588,5196,4261,3364,2693,1685,4262, 961, # 1744 +1673,2632, 190,2006,2203,3841,4585,4586,5197, 570,2504,3711,1490,5198,4587,2633, # 1760 +3293,1957,4588, 584,1514, 396,1045,1945,5199,4589,1968,2449,5200,5201,4590,4013, # 1776 + 619,5202,3154,3294, 215,2007,2796,2561,3220,4591,3221,4592, 763,4263,3842,4593, # 1792 +5203,5204,1958,1767,2956,3365,3712,1174, 452,1477,4594,3366,3155,5205,2838,1253, # 1808 +2387,2189,1091,2290,4264, 492,5206, 638,1169,1825,2136,1752,4014, 648, 926,1021, # 1824 +1324,4595, 520,4596, 997, 847,1007, 892,4597,3843,2267,1872,3713,2405,1785,4598, # 1840 +1953,2957,3103,3222,1728,4265,2044,3714,4599,2008,1701,3156,1551, 30,2268,4266, # 1856 +5207,2027,4600,3589,5208, 501,5209,4267, 594,3478,2166,1822,3590,3479,3591,3223, # 1872 + 829,2839,4268,5210,1680,3157,1225,4269,5211,3295,4601,4270,3158,2341,5212,4602, # 1888 +4271,5213,4015,4016,5214,1848,2388,2606,3367,5215,4603, 374,4017, 652,4272,4273, # 1904 + 375,1140, 798,5216,5217,5218,2366,4604,2269, 546,1659, 138,3051,2450,4605,5219, # 1920 +2254, 612,1849, 910, 796,3844,1740,1371, 825,3845,3846,5220,2920,2562,5221, 692, # 1936 + 444,3052,2634, 801,4606,4274,5222,1491, 244,1053,3053,4275,4276, 340,5223,4018, # 1952 +1041,3005, 293,1168, 87,1357,5224,1539, 959,5225,2240, 721, 694,4277,3847, 219, # 1968 +1478, 644,1417,3368,2666,1413,1401,1335,1389,4019,5226,5227,3006,2367,3159,1826, # 1984 + 730,1515, 184,2840, 66,4607,5228,1660,2958, 246,3369, 378,1457, 226,3480, 975, # 2000 +4020,2959,1264,3592, 674, 696,5229, 163,5230,1141,2422,2167, 713,3593,3370,4608, # 2016 +4021,5231,5232,1186, 15,5233,1079,1070,5234,1522,3224,3594, 276,1050,2725, 758, # 2032 +1126, 653,2960,3296,5235,2342, 889,3595,4022,3104,3007, 903,1250,4609,4023,3481, # 2048 +3596,1342,1681,1718, 766,3297, 286, 89,2961,3715,5236,1713,5237,2607,3371,3008, # 2064 +5238,2962,2219,3225,2880,5239,4610,2505,2533, 181, 387,1075,4024, 731,2190,3372, # 2080 +5240,3298, 310, 313,3482,2304, 770,4278, 54,3054, 189,4611,3105,3848,4025,5241, # 2096 +1230,1617,1850, 355,3597,4279,4612,3373, 111,4280,3716,1350,3160,3483,3055,4281, # 2112 +2150,3299,3598,5242,2797,4026,4027,3009, 722,2009,5243,1071, 247,1207,2343,2478, # 2128 +1378,4613,2010, 864,1437,1214,4614, 373,3849,1142,2220, 667,4615, 442,2763,2563, # 2144 +3850,4028,1969,4282,3300,1840, 837, 170,1107, 934,1336,1883,5244,5245,2119,4283, # 2160 +2841, 743,1569,5246,4616,4284, 582,2389,1418,3484,5247,1803,5248, 357,1395,1729, # 2176 +3717,3301,2423,1564,2241,5249,3106,3851,1633,4617,1114,2086,4285,1532,5250, 482, # 2192 +2451,4618,5251,5252,1492, 833,1466,5253,2726,3599,1641,2842,5254,1526,1272,3718, # 2208 +4286,1686,1795, 416,2564,1903,1954,1804,5255,3852,2798,3853,1159,2321,5256,2881, # 2224 +4619,1610,1584,3056,2424,2764, 443,3302,1163,3161,5257,5258,4029,5259,4287,2506, # 2240 +3057,4620,4030,3162,2104,1647,3600,2011,1873,4288,5260,4289, 431,3485,5261, 250, # 2256 + 97, 81,4290,5262,1648,1851,1558, 160, 848,5263, 866, 740,1694,5264,2204,2843, # 2272 +3226,4291,4621,3719,1687, 950,2479, 426, 469,3227,3720,3721,4031,5265,5266,1188, # 2288 + 424,1996, 861,3601,4292,3854,2205,2694, 168,1235,3602,4293,5267,2087,1674,4622, # 2304 +3374,3303, 220,2565,1009,5268,3855, 670,3010, 332,1208, 717,5269,5270,3603,2452, # 2320 +4032,3375,5271, 513,5272,1209,2882,3376,3163,4623,1080,5273,5274,5275,5276,2534, # 2336 +3722,3604, 815,1587,4033,4034,5277,3605,3486,3856,1254,4624,1328,3058,1390,4035, # 2352 +1741,4036,3857,4037,5278, 236,3858,2453,3304,5279,5280,3723,3859,1273,3860,4625, # 2368 +5281, 308,5282,4626, 245,4627,1852,2480,1307,2583, 430, 715,2137,2454,5283, 270, # 2384 + 199,2883,4038,5284,3606,2727,1753, 761,1754, 725,1661,1841,4628,3487,3724,5285, # 2400 +5286, 587, 14,3305, 227,2608, 326, 480,2270, 943,2765,3607, 291, 650,1884,5287, # 2416 +1702,1226, 102,1547, 62,3488, 904,4629,3489,1164,4294,5288,5289,1224,1548,2766, # 2432 + 391, 498,1493,5290,1386,1419,5291,2056,1177,4630, 813, 880,1081,2368, 566,1145, # 2448 +4631,2291,1001,1035,2566,2609,2242, 394,1286,5292,5293,2069,5294, 86,1494,1730, # 2464 +4039, 491,1588, 745, 897,2963, 843,3377,4040,2767,2884,3306,1768, 998,2221,2070, # 2480 + 397,1827,1195,1970,3725,3011,3378, 284,5295,3861,2507,2138,2120,1904,5296,4041, # 2496 +2151,4042,4295,1036,3490,1905, 114,2567,4296, 209,1527,5297,5298,2964,2844,2635, # 2512 +2390,2728,3164, 812,2568,5299,3307,5300,1559, 737,1885,3726,1210, 885, 28,2695, # 2528 +3608,3862,5301,4297,1004,1780,4632,5302, 346,1982,2222,2696,4633,3863,1742, 797, # 2544 +1642,4043,1934,1072,1384,2152, 896,4044,3308,3727,3228,2885,3609,5303,2569,1959, # 2560 +4634,2455,1786,5304,5305,5306,4045,4298,1005,1308,3728,4299,2729,4635,4636,1528, # 2576 +2610, 161,1178,4300,1983, 987,4637,1101,4301, 631,4046,1157,3229,2425,1343,1241, # 2592 +1016,2243,2570, 372, 877,2344,2508,1160, 555,1935, 911,4047,5307, 466,1170, 169, # 2608 +1051,2921,2697,3729,2481,3012,1182,2012,2571,1251,2636,5308, 992,2345,3491,1540, # 2624 +2730,1201,2071,2406,1997,2482,5309,4638, 528,1923,2191,1503,1874,1570,2369,3379, # 2640 +3309,5310, 557,1073,5311,1828,3492,2088,2271,3165,3059,3107, 767,3108,2799,4639, # 2656 +1006,4302,4640,2346,1267,2179,3730,3230, 778,4048,3231,2731,1597,2667,5312,4641, # 2672 +5313,3493,5314,5315,5316,3310,2698,1433,3311, 131, 95,1504,4049, 723,4303,3166, # 2688 +1842,3610,2768,2192,4050,2028,2105,3731,5317,3013,4051,1218,5318,3380,3232,4052, # 2704 +4304,2584, 248,1634,3864, 912,5319,2845,3732,3060,3865, 654, 53,5320,3014,5321, # 2720 +1688,4642, 777,3494,1032,4053,1425,5322, 191, 820,2121,2846, 971,4643, 931,3233, # 2736 + 135, 664, 783,3866,1998, 772,2922,1936,4054,3867,4644,2923,3234, 282,2732, 640, # 2752 +1372,3495,1127, 922, 325,3381,5323,5324, 711,2045,5325,5326,4055,2223,2800,1937, # 2768 +4056,3382,2224,2255,3868,2305,5327,4645,3869,1258,3312,4057,3235,2139,2965,4058, # 2784 +4059,5328,2225, 258,3236,4646, 101,1227,5329,3313,1755,5330,1391,3314,5331,2924, # 2800 +2057, 893,5332,5333,5334,1402,4305,2347,5335,5336,3237,3611,5337,5338, 878,1325, # 2816 +1781,2801,4647, 259,1385,2585, 744,1183,2272,4648,5339,4060,2509,5340, 684,1024, # 2832 +4306,5341, 472,3612,3496,1165,3315,4061,4062, 322,2153, 881, 455,1695,1152,1340, # 2848 + 660, 554,2154,4649,1058,4650,4307, 830,1065,3383,4063,4651,1924,5342,1703,1919, # 2864 +5343, 932,2273, 122,5344,4652, 947, 677,5345,3870,2637, 297,1906,1925,2274,4653, # 2880 +2322,3316,5346,5347,4308,5348,4309, 84,4310, 112, 989,5349, 547,1059,4064, 701, # 2896 +3613,1019,5350,4311,5351,3497, 942, 639, 457,2306,2456, 993,2966, 407, 851, 494, # 2912 +4654,3384, 927,5352,1237,5353,2426,3385, 573,4312, 680, 921,2925,1279,1875, 285, # 2928 + 790,1448,1984, 719,2168,5354,5355,4655,4065,4066,1649,5356,1541, 563,5357,1077, # 2944 +5358,3386,3061,3498, 511,3015,4067,4068,3733,4069,1268,2572,3387,3238,4656,4657, # 2960 +5359, 535,1048,1276,1189,2926,2029,3167,1438,1373,2847,2967,1134,2013,5360,4313, # 2976 +1238,2586,3109,1259,5361, 700,5362,2968,3168,3734,4314,5363,4315,1146,1876,1907, # 2992 +4658,2611,4070, 781,2427, 132,1589, 203, 147, 273,2802,2407, 898,1787,2155,4071, # 3008 +4072,5364,3871,2803,5365,5366,4659,4660,5367,3239,5368,1635,3872, 965,5369,1805, # 3024 +2699,1516,3614,1121,1082,1329,3317,4073,1449,3873, 65,1128,2848,2927,2769,1590, # 3040 +3874,5370,5371, 12,2668, 45, 976,2587,3169,4661, 517,2535,1013,1037,3240,5372, # 3056 +3875,2849,5373,3876,5374,3499,5375,2612, 614,1999,2323,3877,3110,2733,2638,5376, # 3072 +2588,4316, 599,1269,5377,1811,3735,5378,2700,3111, 759,1060, 489,1806,3388,3318, # 3088 +1358,5379,5380,2391,1387,1215,2639,2256, 490,5381,5382,4317,1759,2392,2348,5383, # 3104 +4662,3878,1908,4074,2640,1807,3241,4663,3500,3319,2770,2349, 874,5384,5385,3501, # 3120 +3736,1859, 91,2928,3737,3062,3879,4664,5386,3170,4075,2669,5387,3502,1202,1403, # 3136 +3880,2969,2536,1517,2510,4665,3503,2511,5388,4666,5389,2701,1886,1495,1731,4076, # 3152 +2370,4667,5390,2030,5391,5392,4077,2702,1216, 237,2589,4318,2324,4078,3881,4668, # 3168 +4669,2703,3615,3504, 445,4670,5393,5394,5395,5396,2771, 61,4079,3738,1823,4080, # 3184 +5397, 687,2046, 935, 925, 405,2670, 703,1096,1860,2734,4671,4081,1877,1367,2704, # 3200 +3389, 918,2106,1782,2483, 334,3320,1611,1093,4672, 564,3171,3505,3739,3390, 945, # 3216 +2641,2058,4673,5398,1926, 872,4319,5399,3506,2705,3112, 349,4320,3740,4082,4674, # 3232 +3882,4321,3741,2156,4083,4675,4676,4322,4677,2408,2047, 782,4084, 400, 251,4323, # 3248 +1624,5400,5401, 277,3742, 299,1265, 476,1191,3883,2122,4324,4325,1109, 205,5402, # 3264 +2590,1000,2157,3616,1861,5403,5404,5405,4678,5406,4679,2573, 107,2484,2158,4085, # 3280 +3507,3172,5407,1533, 541,1301, 158, 753,4326,2886,3617,5408,1696, 370,1088,4327, # 3296 +4680,3618, 579, 327, 440, 162,2244, 269,1938,1374,3508, 968,3063, 56,1396,3113, # 3312 +2107,3321,3391,5409,1927,2159,4681,3016,5410,3619,5411,5412,3743,4682,2485,5413, # 3328 +2804,5414,1650,4683,5415,2613,5416,5417,4086,2671,3392,1149,3393,4087,3884,4088, # 3344 +5418,1076, 49,5419, 951,3242,3322,3323, 450,2850, 920,5420,1812,2805,2371,4328, # 3360 +1909,1138,2372,3885,3509,5421,3243,4684,1910,1147,1518,2428,4685,3886,5422,4686, # 3376 +2393,2614, 260,1796,3244,5423,5424,3887,3324, 708,5425,3620,1704,5426,3621,1351, # 3392 +1618,3394,3017,1887, 944,4329,3395,4330,3064,3396,4331,5427,3744, 422, 413,1714, # 3408 +3325, 500,2059,2350,4332,2486,5428,1344,1911, 954,5429,1668,5430,5431,4089,2409, # 3424 +4333,3622,3888,4334,5432,2307,1318,2512,3114, 133,3115,2887,4687, 629, 31,2851, # 3440 +2706,3889,4688, 850, 949,4689,4090,2970,1732,2089,4335,1496,1853,5433,4091, 620, # 3456 +3245, 981,1242,3745,3397,1619,3746,1643,3326,2140,2457,1971,1719,3510,2169,5434, # 3472 +3246,5435,5436,3398,1829,5437,1277,4690,1565,2048,5438,1636,3623,3116,5439, 869, # 3488 +2852, 655,3890,3891,3117,4092,3018,3892,1310,3624,4691,5440,5441,5442,1733, 558, # 3504 +4692,3747, 335,1549,3065,1756,4336,3748,1946,3511,1830,1291,1192, 470,2735,2108, # 3520 +2806, 913,1054,4093,5443,1027,5444,3066,4094,4693, 982,2672,3399,3173,3512,3247, # 3536 +3248,1947,2807,5445, 571,4694,5446,1831,5447,3625,2591,1523,2429,5448,2090, 984, # 3552 +4695,3749,1960,5449,3750, 852, 923,2808,3513,3751, 969,1519, 999,2049,2325,1705, # 3568 +5450,3118, 615,1662, 151, 597,4095,2410,2326,1049, 275,4696,3752,4337, 568,3753, # 3584 +3626,2487,4338,3754,5451,2430,2275, 409,3249,5452,1566,2888,3514,1002, 769,2853, # 3600 + 194,2091,3174,3755,2226,3327,4339, 628,1505,5453,5454,1763,2180,3019,4096, 521, # 3616 +1161,2592,1788,2206,2411,4697,4097,1625,4340,4341, 412, 42,3119, 464,5455,2642, # 3632 +4698,3400,1760,1571,2889,3515,2537,1219,2207,3893,2643,2141,2373,4699,4700,3328, # 3648 +1651,3401,3627,5456,5457,3628,2488,3516,5458,3756,5459,5460,2276,2092, 460,5461, # 3664 +4701,5462,3020, 962, 588,3629, 289,3250,2644,1116, 52,5463,3067,1797,5464,5465, # 3680 +5466,1467,5467,1598,1143,3757,4342,1985,1734,1067,4702,1280,3402, 465,4703,1572, # 3696 + 510,5468,1928,2245,1813,1644,3630,5469,4704,3758,5470,5471,2673,1573,1534,5472, # 3712 +5473, 536,1808,1761,3517,3894,3175,2645,5474,5475,5476,4705,3518,2929,1912,2809, # 3728 +5477,3329,1122, 377,3251,5478, 360,5479,5480,4343,1529, 551,5481,2060,3759,1769, # 3744 +2431,5482,2930,4344,3330,3120,2327,2109,2031,4706,1404, 136,1468,1479, 672,1171, # 3760 +3252,2308, 271,3176,5483,2772,5484,2050, 678,2736, 865,1948,4707,5485,2014,4098, # 3776 +2971,5486,2737,2227,1397,3068,3760,4708,4709,1735,2931,3403,3631,5487,3895, 509, # 3792 +2854,2458,2890,3896,5488,5489,3177,3178,4710,4345,2538,4711,2309,1166,1010, 552, # 3808 + 681,1888,5490,5491,2972,2973,4099,1287,1596,1862,3179, 358, 453, 736, 175, 478, # 3824 +1117, 905,1167,1097,5492,1854,1530,5493,1706,5494,2181,3519,2292,3761,3520,3632, # 3840 +4346,2093,4347,5495,3404,1193,2489,4348,1458,2193,2208,1863,1889,1421,3331,2932, # 3856 +3069,2182,3521, 595,2123,5496,4100,5497,5498,4349,1707,2646, 223,3762,1359, 751, # 3872 +3121, 183,3522,5499,2810,3021, 419,2374, 633, 704,3897,2394, 241,5500,5501,5502, # 3888 + 838,3022,3763,2277,2773,2459,3898,1939,2051,4101,1309,3122,2246,1181,5503,1136, # 3904 +2209,3899,2375,1446,4350,2310,4712,5504,5505,4351,1055,2615, 484,3764,5506,4102, # 3920 + 625,4352,2278,3405,1499,4353,4103,5507,4104,4354,3253,2279,2280,3523,5508,5509, # 3936 +2774, 808,2616,3765,3406,4105,4355,3123,2539, 526,3407,3900,4356, 955,5510,1620, # 3952 +4357,2647,2432,5511,1429,3766,1669,1832, 994, 928,5512,3633,1260,5513,5514,5515, # 3968 +1949,2293, 741,2933,1626,4358,2738,2460, 867,1184, 362,3408,1392,5516,5517,4106, # 3984 +4359,1770,1736,3254,2934,4713,4714,1929,2707,1459,1158,5518,3070,3409,2891,1292, # 4000 +1930,2513,2855,3767,1986,1187,2072,2015,2617,4360,5519,2574,2514,2170,3768,2490, # 4016 +3332,5520,3769,4715,5521,5522, 666,1003,3023,1022,3634,4361,5523,4716,1814,2257, # 4032 + 574,3901,1603, 295,1535, 705,3902,4362, 283, 858, 417,5524,5525,3255,4717,4718, # 4048 +3071,1220,1890,1046,2281,2461,4107,1393,1599, 689,2575, 388,4363,5526,2491, 802, # 4064 +5527,2811,3903,2061,1405,2258,5528,4719,3904,2110,1052,1345,3256,1585,5529, 809, # 4080 +5530,5531,5532, 575,2739,3524, 956,1552,1469,1144,2328,5533,2329,1560,2462,3635, # 4096 +3257,4108, 616,2210,4364,3180,2183,2294,5534,1833,5535,3525,4720,5536,1319,3770, # 4112 +3771,1211,3636,1023,3258,1293,2812,5537,5538,5539,3905, 607,2311,3906, 762,2892, # 4128 +1439,4365,1360,4721,1485,3072,5540,4722,1038,4366,1450,2062,2648,4367,1379,4723, # 4144 +2593,5541,5542,4368,1352,1414,2330,2935,1172,5543,5544,3907,3908,4724,1798,1451, # 4160 +5545,5546,5547,5548,2936,4109,4110,2492,2351, 411,4111,4112,3637,3333,3124,4725, # 4176 +1561,2674,1452,4113,1375,5549,5550, 47,2974, 316,5551,1406,1591,2937,3181,5552, # 4192 +1025,2142,3125,3182, 354,2740, 884,2228,4369,2412, 508,3772, 726,3638, 996,2433, # 4208 +3639, 729,5553, 392,2194,1453,4114,4726,3773,5554,5555,2463,3640,2618,1675,2813, # 4224 + 919,2352,2975,2353,1270,4727,4115, 73,5556,5557, 647,5558,3259,2856,2259,1550, # 4240 +1346,3024,5559,1332, 883,3526,5560,5561,5562,5563,3334,2775,5564,1212, 831,1347, # 4256 +4370,4728,2331,3909,1864,3073, 720,3910,4729,4730,3911,5565,4371,5566,5567,4731, # 4272 +5568,5569,1799,4732,3774,2619,4733,3641,1645,2376,4734,5570,2938, 669,2211,2675, # 4288 +2434,5571,2893,5572,5573,1028,3260,5574,4372,2413,5575,2260,1353,5576,5577,4735, # 4304 +3183, 518,5578,4116,5579,4373,1961,5580,2143,4374,5581,5582,3025,2354,2355,3912, # 4320 + 516,1834,1454,4117,2708,4375,4736,2229,2620,1972,1129,3642,5583,2776,5584,2976, # 4336 +1422, 577,1470,3026,1524,3410,5585,5586, 432,4376,3074,3527,5587,2594,1455,2515, # 4352 +2230,1973,1175,5588,1020,2741,4118,3528,4737,5589,2742,5590,1743,1361,3075,3529, # 4368 +2649,4119,4377,4738,2295, 895, 924,4378,2171, 331,2247,3076, 166,1627,3077,1098, # 4384 +5591,1232,2894,2231,3411,4739, 657, 403,1196,2377, 542,3775,3412,1600,4379,3530, # 4400 +5592,4740,2777,3261, 576, 530,1362,4741,4742,2540,2676,3776,4120,5593, 842,3913, # 4416 +5594,2814,2032,1014,4121, 213,2709,3413, 665, 621,4380,5595,3777,2939,2435,5596, # 4432 +2436,3335,3643,3414,4743,4381,2541,4382,4744,3644,1682,4383,3531,1380,5597, 724, # 4448 +2282, 600,1670,5598,1337,1233,4745,3126,2248,5599,1621,4746,5600, 651,4384,5601, # 4464 +1612,4385,2621,5602,2857,5603,2743,2312,3078,5604, 716,2464,3079, 174,1255,2710, # 4480 +4122,3645, 548,1320,1398, 728,4123,1574,5605,1891,1197,3080,4124,5606,3081,3082, # 4496 +3778,3646,3779, 747,5607, 635,4386,4747,5608,5609,5610,4387,5611,5612,4748,5613, # 4512 +3415,4749,2437, 451,5614,3780,2542,2073,4388,2744,4389,4125,5615,1764,4750,5616, # 4528 +4390, 350,4751,2283,2395,2493,5617,4391,4126,2249,1434,4127, 488,4752, 458,4392, # 4544 +4128,3781, 771,1330,2396,3914,2576,3184,2160,2414,1553,2677,3185,4393,5618,2494, # 4560 +2895,2622,1720,2711,4394,3416,4753,5619,2543,4395,5620,3262,4396,2778,5621,2016, # 4576 +2745,5622,1155,1017,3782,3915,5623,3336,2313, 201,1865,4397,1430,5624,4129,5625, # 4592 +5626,5627,5628,5629,4398,1604,5630, 414,1866, 371,2595,4754,4755,3532,2017,3127, # 4608 +4756,1708, 960,4399, 887, 389,2172,1536,1663,1721,5631,2232,4130,2356,2940,1580, # 4624 +5632,5633,1744,4757,2544,4758,4759,5634,4760,5635,2074,5636,4761,3647,3417,2896, # 4640 +4400,5637,4401,2650,3418,2815, 673,2712,2465, 709,3533,4131,3648,4402,5638,1148, # 4656 + 502, 634,5639,5640,1204,4762,3649,1575,4763,2623,3783,5641,3784,3128, 948,3263, # 4672 + 121,1745,3916,1110,5642,4403,3083,2516,3027,4132,3785,1151,1771,3917,1488,4133, # 4688 +1987,5643,2438,3534,5644,5645,2094,5646,4404,3918,1213,1407,2816, 531,2746,2545, # 4704 +3264,1011,1537,4764,2779,4405,3129,1061,5647,3786,3787,1867,2897,5648,2018, 120, # 4720 +4406,4407,2063,3650,3265,2314,3919,2678,3419,1955,4765,4134,5649,3535,1047,2713, # 4736 +1266,5650,1368,4766,2858, 649,3420,3920,2546,2747,1102,2859,2679,5651,5652,2000, # 4752 +5653,1111,3651,2977,5654,2495,3921,3652,2817,1855,3421,3788,5655,5656,3422,2415, # 4768 +2898,3337,3266,3653,5657,2577,5658,3654,2818,4135,1460, 856,5659,3655,5660,2899, # 4784 +2978,5661,2900,3922,5662,4408, 632,2517, 875,3923,1697,3924,2296,5663,5664,4767, # 4800 +3028,1239, 580,4768,4409,5665, 914, 936,2075,1190,4136,1039,2124,5666,5667,5668, # 4816 +5669,3423,1473,5670,1354,4410,3925,4769,2173,3084,4137, 915,3338,4411,4412,3339, # 4832 +1605,1835,5671,2748, 398,3656,4413,3926,4138, 328,1913,2860,4139,3927,1331,4414, # 4848 +3029, 937,4415,5672,3657,4140,4141,3424,2161,4770,3425, 524, 742, 538,3085,1012, # 4864 +5673,5674,3928,2466,5675, 658,1103, 225,3929,5676,5677,4771,5678,4772,5679,3267, # 4880 +1243,5680,4142, 963,2250,4773,5681,2714,3658,3186,5682,5683,2596,2332,5684,4774, # 4896 +5685,5686,5687,3536, 957,3426,2547,2033,1931,2941,2467, 870,2019,3659,1746,2780, # 4912 +2781,2439,2468,5688,3930,5689,3789,3130,3790,3537,3427,3791,5690,1179,3086,5691, # 4928 +3187,2378,4416,3792,2548,3188,3131,2749,4143,5692,3428,1556,2549,2297, 977,2901, # 4944 +2034,4144,1205,3429,5693,1765,3430,3189,2125,1271, 714,1689,4775,3538,5694,2333, # 4960 +3931, 533,4417,3660,2184, 617,5695,2469,3340,3539,2315,5696,5697,3190,5698,5699, # 4976 +3932,1988, 618, 427,2651,3540,3431,5700,5701,1244,1690,5702,2819,4418,4776,5703, # 4992 +3541,4777,5704,2284,1576, 473,3661,4419,3432, 972,5705,3662,5706,3087,5707,5708, # 5008 +4778,4779,5709,3793,4145,4146,5710, 153,4780, 356,5711,1892,2902,4420,2144, 408, # 5024 + 803,2357,5712,3933,5713,4421,1646,2578,2518,4781,4782,3934,5714,3935,4422,5715, # 5040 +2416,3433, 752,5716,5717,1962,3341,2979,5718, 746,3030,2470,4783,4423,3794, 698, # 5056 +4784,1893,4424,3663,2550,4785,3664,3936,5719,3191,3434,5720,1824,1302,4147,2715, # 5072 +3937,1974,4425,5721,4426,3192, 823,1303,1288,1236,2861,3542,4148,3435, 774,3938, # 5088 +5722,1581,4786,1304,2862,3939,4787,5723,2440,2162,1083,3268,4427,4149,4428, 344, # 5104 +1173, 288,2316, 454,1683,5724,5725,1461,4788,4150,2597,5726,5727,4789, 985, 894, # 5120 +5728,3436,3193,5729,1914,2942,3795,1989,5730,2111,1975,5731,4151,5732,2579,1194, # 5136 + 425,5733,4790,3194,1245,3796,4429,5734,5735,2863,5736, 636,4791,1856,3940, 760, # 5152 +1800,5737,4430,2212,1508,4792,4152,1894,1684,2298,5738,5739,4793,4431,4432,2213, # 5168 + 479,5740,5741, 832,5742,4153,2496,5743,2980,2497,3797, 990,3132, 627,1815,2652, # 5184 +4433,1582,4434,2126,2112,3543,4794,5744, 799,4435,3195,5745,4795,2113,1737,3031, # 5200 +1018, 543, 754,4436,3342,1676,4796,4797,4154,4798,1489,5746,3544,5747,2624,2903, # 5216 +4155,5748,5749,2981,5750,5751,5752,5753,3196,4799,4800,2185,1722,5754,3269,3270, # 5232 +1843,3665,1715, 481, 365,1976,1857,5755,5756,1963,2498,4801,5757,2127,3666,3271, # 5248 + 433,1895,2064,2076,5758, 602,2750,5759,5760,5761,5762,5763,3032,1628,3437,5764, # 5264 +3197,4802,4156,2904,4803,2519,5765,2551,2782,5766,5767,5768,3343,4804,2905,5769, # 5280 +4805,5770,2864,4806,4807,1221,2982,4157,2520,5771,5772,5773,1868,1990,5774,5775, # 5296 +5776,1896,5777,5778,4808,1897,4158, 318,5779,2095,4159,4437,5780,5781, 485,5782, # 5312 + 938,3941, 553,2680, 116,5783,3942,3667,5784,3545,2681,2783,3438,3344,2820,5785, # 5328 +3668,2943,4160,1747,2944,2983,5786,5787, 207,5788,4809,5789,4810,2521,5790,3033, # 5344 + 890,3669,3943,5791,1878,3798,3439,5792,2186,2358,3440,1652,5793,5794,5795, 941, # 5360 +2299, 208,3546,4161,2020, 330,4438,3944,2906,2499,3799,4439,4811,5796,5797,5798, # 5376 +) +# fmt: on diff --git a/venv/lib/python3.10/site-packages/chardet/big5prober.py b/venv/lib/python3.10/site-packages/chardet/big5prober.py new file mode 100644 index 0000000000000000000000000000000000000000..ef09c60e327a0122e32f95f2f10a826a033c573c --- /dev/null +++ b/venv/lib/python3.10/site-packages/chardet/big5prober.py @@ -0,0 +1,47 @@ +######################## BEGIN LICENSE BLOCK ######################## +# The Original Code is Mozilla Communicator client code. +# +# The Initial Developer of the Original Code is +# Netscape Communications Corporation. +# Portions created by the Initial Developer are Copyright (C) 1998 +# the Initial Developer. All Rights Reserved. +# +# Contributor(s): +# Mark Pilgrim - port to Python +# +# This library is free software; you can redistribute it and/or +# modify it under the terms of the GNU Lesser General Public +# License as published by the Free Software Foundation; either +# version 2.1 of the License, or (at your option) any later version. +# +# This library is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +# Lesser General Public License for more details. +# +# You should have received a copy of the GNU Lesser General Public +# License along with this library; if not, write to the Free Software +# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA +# 02110-1301 USA +######################### END LICENSE BLOCK ######################### + +from .chardistribution import Big5DistributionAnalysis +from .codingstatemachine import CodingStateMachine +from .mbcharsetprober import MultiByteCharSetProber +from .mbcssm import BIG5_SM_MODEL + + +class Big5Prober(MultiByteCharSetProber): + def __init__(self) -> None: + super().__init__() + self.coding_sm = CodingStateMachine(BIG5_SM_MODEL) + self.distribution_analyzer = Big5DistributionAnalysis() + self.reset() + + @property + def charset_name(self) -> str: + return "Big5" + + @property + def language(self) -> str: + return "Chinese" diff --git a/venv/lib/python3.10/site-packages/chardet/chardistribution.py b/venv/lib/python3.10/site-packages/chardet/chardistribution.py new file mode 100644 index 0000000000000000000000000000000000000000..176cb996408e6681a88722783919efc0e9dafb29 --- /dev/null +++ b/venv/lib/python3.10/site-packages/chardet/chardistribution.py @@ -0,0 +1,261 @@ +######################## BEGIN LICENSE BLOCK ######################## +# The Original Code is Mozilla Communicator client code. +# +# The Initial Developer of the Original Code is +# Netscape Communications Corporation. +# Portions created by the Initial Developer are Copyright (C) 1998 +# the Initial Developer. All Rights Reserved. +# +# Contributor(s): +# Mark Pilgrim - port to Python +# +# This library is free software; you can redistribute it and/or +# modify it under the terms of the GNU Lesser General Public +# License as published by the Free Software Foundation; either +# version 2.1 of the License, or (at your option) any later version. +# +# This library is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +# Lesser General Public License for more details. +# +# You should have received a copy of the GNU Lesser General Public +# License along with this library; if not, write to the Free Software +# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA +# 02110-1301 USA +######################### END LICENSE BLOCK ######################### + +from typing import Tuple, Union + +from .big5freq import ( + BIG5_CHAR_TO_FREQ_ORDER, + BIG5_TABLE_SIZE, + BIG5_TYPICAL_DISTRIBUTION_RATIO, +) +from .euckrfreq import ( + EUCKR_CHAR_TO_FREQ_ORDER, + EUCKR_TABLE_SIZE, + EUCKR_TYPICAL_DISTRIBUTION_RATIO, +) +from .euctwfreq import ( + EUCTW_CHAR_TO_FREQ_ORDER, + EUCTW_TABLE_SIZE, + EUCTW_TYPICAL_DISTRIBUTION_RATIO, +) +from .gb2312freq import ( + GB2312_CHAR_TO_FREQ_ORDER, + GB2312_TABLE_SIZE, + GB2312_TYPICAL_DISTRIBUTION_RATIO, +) +from .jisfreq import ( + JIS_CHAR_TO_FREQ_ORDER, + JIS_TABLE_SIZE, + JIS_TYPICAL_DISTRIBUTION_RATIO, +) +from .johabfreq import JOHAB_TO_EUCKR_ORDER_TABLE + + +class CharDistributionAnalysis: + ENOUGH_DATA_THRESHOLD = 1024 + SURE_YES = 0.99 + SURE_NO = 0.01 + MINIMUM_DATA_THRESHOLD = 3 + + def __init__(self) -> None: + # Mapping table to get frequency order from char order (get from + # GetOrder()) + self._char_to_freq_order: Tuple[int, ...] = tuple() + self._table_size = 0 # Size of above table + # This is a constant value which varies from language to language, + # used in calculating confidence. See + # http://www.mozilla.org/projects/intl/UniversalCharsetDetection.html + # for further detail. + self.typical_distribution_ratio = 0.0 + self._done = False + self._total_chars = 0 + self._freq_chars = 0 + self.reset() + + def reset(self) -> None: + """reset analyser, clear any state""" + # If this flag is set to True, detection is done and conclusion has + # been made + self._done = False + self._total_chars = 0 # Total characters encountered + # The number of characters whose frequency order is less than 512 + self._freq_chars = 0 + + def feed(self, char: Union[bytes, bytearray], char_len: int) -> None: + """feed a character with known length""" + if char_len == 2: + # we only care about 2-bytes character in our distribution analysis + order = self.get_order(char) + else: + order = -1 + if order >= 0: + self._total_chars += 1 + # order is valid + if order < self._table_size: + if 512 > self._char_to_freq_order[order]: + self._freq_chars += 1 + + def get_confidence(self) -> float: + """return confidence based on existing data""" + # if we didn't receive any character in our consideration range, + # return negative answer + if self._total_chars <= 0 or self._freq_chars <= self.MINIMUM_DATA_THRESHOLD: + return self.SURE_NO + + if self._total_chars != self._freq_chars: + r = self._freq_chars / ( + (self._total_chars - self._freq_chars) * self.typical_distribution_ratio + ) + if r < self.SURE_YES: + return r + + # normalize confidence (we don't want to be 100% sure) + return self.SURE_YES + + def got_enough_data(self) -> bool: + # It is not necessary to receive all data to draw conclusion. + # For charset detection, certain amount of data is enough + return self._total_chars > self.ENOUGH_DATA_THRESHOLD + + def get_order(self, _: Union[bytes, bytearray]) -> int: + # We do not handle characters based on the original encoding string, + # but convert this encoding string to a number, here called order. + # This allows multiple encodings of a language to share one frequency + # table. + return -1 + + +class EUCTWDistributionAnalysis(CharDistributionAnalysis): + def __init__(self) -> None: + super().__init__() + self._char_to_freq_order = EUCTW_CHAR_TO_FREQ_ORDER + self._table_size = EUCTW_TABLE_SIZE + self.typical_distribution_ratio = EUCTW_TYPICAL_DISTRIBUTION_RATIO + + def get_order(self, byte_str: Union[bytes, bytearray]) -> int: + # for euc-TW encoding, we are interested + # first byte range: 0xc4 -- 0xfe + # second byte range: 0xa1 -- 0xfe + # no validation needed here. State machine has done that + first_char = byte_str[0] + if first_char >= 0xC4: + return 94 * (first_char - 0xC4) + byte_str[1] - 0xA1 + return -1 + + +class EUCKRDistributionAnalysis(CharDistributionAnalysis): + def __init__(self) -> None: + super().__init__() + self._char_to_freq_order = EUCKR_CHAR_TO_FREQ_ORDER + self._table_size = EUCKR_TABLE_SIZE + self.typical_distribution_ratio = EUCKR_TYPICAL_DISTRIBUTION_RATIO + + def get_order(self, byte_str: Union[bytes, bytearray]) -> int: + # for euc-KR encoding, we are interested + # first byte range: 0xb0 -- 0xfe + # second byte range: 0xa1 -- 0xfe + # no validation needed here. State machine has done that + first_char = byte_str[0] + if first_char >= 0xB0: + return 94 * (first_char - 0xB0) + byte_str[1] - 0xA1 + return -1 + + +class JOHABDistributionAnalysis(CharDistributionAnalysis): + def __init__(self) -> None: + super().__init__() + self._char_to_freq_order = EUCKR_CHAR_TO_FREQ_ORDER + self._table_size = EUCKR_TABLE_SIZE + self.typical_distribution_ratio = EUCKR_TYPICAL_DISTRIBUTION_RATIO + + def get_order(self, byte_str: Union[bytes, bytearray]) -> int: + first_char = byte_str[0] + if 0x88 <= first_char < 0xD4: + code = first_char * 256 + byte_str[1] + return JOHAB_TO_EUCKR_ORDER_TABLE.get(code, -1) + return -1 + + +class GB2312DistributionAnalysis(CharDistributionAnalysis): + def __init__(self) -> None: + super().__init__() + self._char_to_freq_order = GB2312_CHAR_TO_FREQ_ORDER + self._table_size = GB2312_TABLE_SIZE + self.typical_distribution_ratio = GB2312_TYPICAL_DISTRIBUTION_RATIO + + def get_order(self, byte_str: Union[bytes, bytearray]) -> int: + # for GB2312 encoding, we are interested + # first byte range: 0xb0 -- 0xfe + # second byte range: 0xa1 -- 0xfe + # no validation needed here. State machine has done that + first_char, second_char = byte_str[0], byte_str[1] + if (first_char >= 0xB0) and (second_char >= 0xA1): + return 94 * (first_char - 0xB0) + second_char - 0xA1 + return -1 + + +class Big5DistributionAnalysis(CharDistributionAnalysis): + def __init__(self) -> None: + super().__init__() + self._char_to_freq_order = BIG5_CHAR_TO_FREQ_ORDER + self._table_size = BIG5_TABLE_SIZE + self.typical_distribution_ratio = BIG5_TYPICAL_DISTRIBUTION_RATIO + + def get_order(self, byte_str: Union[bytes, bytearray]) -> int: + # for big5 encoding, we are interested + # first byte range: 0xa4 -- 0xfe + # second byte range: 0x40 -- 0x7e , 0xa1 -- 0xfe + # no validation needed here. State machine has done that + first_char, second_char = byte_str[0], byte_str[1] + if first_char >= 0xA4: + if second_char >= 0xA1: + return 157 * (first_char - 0xA4) + second_char - 0xA1 + 63 + return 157 * (first_char - 0xA4) + second_char - 0x40 + return -1 + + +class SJISDistributionAnalysis(CharDistributionAnalysis): + def __init__(self) -> None: + super().__init__() + self._char_to_freq_order = JIS_CHAR_TO_FREQ_ORDER + self._table_size = JIS_TABLE_SIZE + self.typical_distribution_ratio = JIS_TYPICAL_DISTRIBUTION_RATIO + + def get_order(self, byte_str: Union[bytes, bytearray]) -> int: + # for sjis encoding, we are interested + # first byte range: 0x81 -- 0x9f , 0xe0 -- 0xfe + # second byte range: 0x40 -- 0x7e, 0x81 -- oxfe + # no validation needed here. State machine has done that + first_char, second_char = byte_str[0], byte_str[1] + if 0x81 <= first_char <= 0x9F: + order = 188 * (first_char - 0x81) + elif 0xE0 <= first_char <= 0xEF: + order = 188 * (first_char - 0xE0 + 31) + else: + return -1 + order = order + second_char - 0x40 + if second_char > 0x7F: + order = -1 + return order + + +class EUCJPDistributionAnalysis(CharDistributionAnalysis): + def __init__(self) -> None: + super().__init__() + self._char_to_freq_order = JIS_CHAR_TO_FREQ_ORDER + self._table_size = JIS_TABLE_SIZE + self.typical_distribution_ratio = JIS_TYPICAL_DISTRIBUTION_RATIO + + def get_order(self, byte_str: Union[bytes, bytearray]) -> int: + # for euc-JP encoding, we are interested + # first byte range: 0xa0 -- 0xfe + # second byte range: 0xa1 -- 0xfe + # no validation needed here. State machine has done that + char = byte_str[0] + if char >= 0xA0: + return 94 * (char - 0xA1) + byte_str[1] - 0xA1 + return -1 diff --git a/venv/lib/python3.10/site-packages/chardet/charsetgroupprober.py b/venv/lib/python3.10/site-packages/chardet/charsetgroupprober.py new file mode 100644 index 0000000000000000000000000000000000000000..6def56b4a75f67000ed8181ae2d2c40eefb645fb --- /dev/null +++ b/venv/lib/python3.10/site-packages/chardet/charsetgroupprober.py @@ -0,0 +1,106 @@ +######################## BEGIN LICENSE BLOCK ######################## +# The Original Code is Mozilla Communicator client code. +# +# The Initial Developer of the Original Code is +# Netscape Communications Corporation. +# Portions created by the Initial Developer are Copyright (C) 1998 +# the Initial Developer. All Rights Reserved. +# +# Contributor(s): +# Mark Pilgrim - port to Python +# +# This library is free software; you can redistribute it and/or +# modify it under the terms of the GNU Lesser General Public +# License as published by the Free Software Foundation; either +# version 2.1 of the License, or (at your option) any later version. +# +# This library is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +# Lesser General Public License for more details. +# +# You should have received a copy of the GNU Lesser General Public +# License along with this library; if not, write to the Free Software +# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA +# 02110-1301 USA +######################### END LICENSE BLOCK ######################### + +from typing import List, Optional, Union + +from .charsetprober import CharSetProber +from .enums import LanguageFilter, ProbingState + + +class CharSetGroupProber(CharSetProber): + def __init__(self, lang_filter: LanguageFilter = LanguageFilter.NONE) -> None: + super().__init__(lang_filter=lang_filter) + self._active_num = 0 + self.probers: List[CharSetProber] = [] + self._best_guess_prober: Optional[CharSetProber] = None + + def reset(self) -> None: + super().reset() + self._active_num = 0 + for prober in self.probers: + prober.reset() + prober.active = True + self._active_num += 1 + self._best_guess_prober = None + + @property + def charset_name(self) -> Optional[str]: + if not self._best_guess_prober: + self.get_confidence() + if not self._best_guess_prober: + return None + return self._best_guess_prober.charset_name + + @property + def language(self) -> Optional[str]: + if not self._best_guess_prober: + self.get_confidence() + if not self._best_guess_prober: + return None + return self._best_guess_prober.language + + def feed(self, byte_str: Union[bytes, bytearray]) -> ProbingState: + for prober in self.probers: + if not prober.active: + continue + state = prober.feed(byte_str) + if not state: + continue + if state == ProbingState.FOUND_IT: + self._best_guess_prober = prober + self._state = ProbingState.FOUND_IT + return self.state + if state == ProbingState.NOT_ME: + prober.active = False + self._active_num -= 1 + if self._active_num <= 0: + self._state = ProbingState.NOT_ME + return self.state + return self.state + + def get_confidence(self) -> float: + state = self.state + if state == ProbingState.FOUND_IT: + return 0.99 + if state == ProbingState.NOT_ME: + return 0.01 + best_conf = 0.0 + self._best_guess_prober = None + for prober in self.probers: + if not prober.active: + self.logger.debug("%s not active", prober.charset_name) + continue + conf = prober.get_confidence() + self.logger.debug( + "%s %s confidence = %s", prober.charset_name, prober.language, conf + ) + if best_conf < conf: + best_conf = conf + self._best_guess_prober = prober + if not self._best_guess_prober: + return 0.0 + return best_conf diff --git a/venv/lib/python3.10/site-packages/chardet/codingstatemachine.py b/venv/lib/python3.10/site-packages/chardet/codingstatemachine.py new file mode 100644 index 0000000000000000000000000000000000000000..8ed4a8773b8404c2705aa8728e5fd692362ba168 --- /dev/null +++ b/venv/lib/python3.10/site-packages/chardet/codingstatemachine.py @@ -0,0 +1,90 @@ +######################## BEGIN LICENSE BLOCK ######################## +# The Original Code is mozilla.org code. +# +# The Initial Developer of the Original Code is +# Netscape Communications Corporation. +# Portions created by the Initial Developer are Copyright (C) 1998 +# the Initial Developer. All Rights Reserved. +# +# Contributor(s): +# Mark Pilgrim - port to Python +# +# This library is free software; you can redistribute it and/or +# modify it under the terms of the GNU Lesser General Public +# License as published by the Free Software Foundation; either +# version 2.1 of the License, or (at your option) any later version. +# +# This library is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +# Lesser General Public License for more details. +# +# You should have received a copy of the GNU Lesser General Public +# License along with this library; if not, write to the Free Software +# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA +# 02110-1301 USA +######################### END LICENSE BLOCK ######################### + +import logging + +from .codingstatemachinedict import CodingStateMachineDict +from .enums import MachineState + + +class CodingStateMachine: + """ + A state machine to verify a byte sequence for a particular encoding. For + each byte the detector receives, it will feed that byte to every active + state machine available, one byte at a time. The state machine changes its + state based on its previous state and the byte it receives. There are 3 + states in a state machine that are of interest to an auto-detector: + + START state: This is the state to start with, or a legal byte sequence + (i.e. a valid code point) for character has been identified. + + ME state: This indicates that the state machine identified a byte sequence + that is specific to the charset it is designed for and that + there is no other possible encoding which can contain this byte + sequence. This will to lead to an immediate positive answer for + the detector. + + ERROR state: This indicates the state machine identified an illegal byte + sequence for that encoding. This will lead to an immediate + negative answer for this encoding. Detector will exclude this + encoding from consideration from here on. + """ + + def __init__(self, sm: CodingStateMachineDict) -> None: + self._model = sm + self._curr_byte_pos = 0 + self._curr_char_len = 0 + self._curr_state = MachineState.START + self.active = True + self.logger = logging.getLogger(__name__) + self.reset() + + def reset(self) -> None: + self._curr_state = MachineState.START + + def next_state(self, c: int) -> int: + # for each byte we get its class + # if it is first byte, we also get byte length + byte_class = self._model["class_table"][c] + if self._curr_state == MachineState.START: + self._curr_byte_pos = 0 + self._curr_char_len = self._model["char_len_table"][byte_class] + # from byte's class and state_table, we get its next state + curr_state = self._curr_state * self._model["class_factor"] + byte_class + self._curr_state = self._model["state_table"][curr_state] + self._curr_byte_pos += 1 + return self._curr_state + + def get_current_charlen(self) -> int: + return self._curr_char_len + + def get_coding_state_machine(self) -> str: + return self._model["name"] + + @property + def language(self) -> str: + return self._model["language"] diff --git a/venv/lib/python3.10/site-packages/chardet/codingstatemachinedict.py b/venv/lib/python3.10/site-packages/chardet/codingstatemachinedict.py new file mode 100644 index 0000000000000000000000000000000000000000..7a3c4c7e3fe16e91225a87cbc58b8bbd798f9cc1 --- /dev/null +++ b/venv/lib/python3.10/site-packages/chardet/codingstatemachinedict.py @@ -0,0 +1,19 @@ +from typing import TYPE_CHECKING, Tuple + +if TYPE_CHECKING: + # TypedDict was introduced in Python 3.8. + # + # TODO: Remove the else block and TYPE_CHECKING check when dropping support + # for Python 3.7. + from typing import TypedDict + + class CodingStateMachineDict(TypedDict, total=False): + class_table: Tuple[int, ...] + class_factor: int + state_table: Tuple[int, ...] + char_len_table: Tuple[int, ...] + name: str + language: str # Optional key + +else: + CodingStateMachineDict = dict diff --git a/venv/lib/python3.10/site-packages/chardet/enums.py b/venv/lib/python3.10/site-packages/chardet/enums.py new file mode 100644 index 0000000000000000000000000000000000000000..5e3e198233698f2b007489dd299cecb87d971067 --- /dev/null +++ b/venv/lib/python3.10/site-packages/chardet/enums.py @@ -0,0 +1,85 @@ +""" +All of the Enums that are used throughout the chardet package. + +:author: Dan Blanchard (dan.blanchard@gmail.com) +""" + +from enum import Enum, Flag + + +class InputState: + """ + This enum represents the different states a universal detector can be in. + """ + + PURE_ASCII = 0 + ESC_ASCII = 1 + HIGH_BYTE = 2 + + +class LanguageFilter(Flag): + """ + This enum represents the different language filters we can apply to a + ``UniversalDetector``. + """ + + NONE = 0x00 + CHINESE_SIMPLIFIED = 0x01 + CHINESE_TRADITIONAL = 0x02 + JAPANESE = 0x04 + KOREAN = 0x08 + NON_CJK = 0x10 + ALL = 0x1F + CHINESE = CHINESE_SIMPLIFIED | CHINESE_TRADITIONAL + CJK = CHINESE | JAPANESE | KOREAN + + +class ProbingState(Enum): + """ + This enum represents the different states a prober can be in. + """ + + DETECTING = 0 + FOUND_IT = 1 + NOT_ME = 2 + + +class MachineState: + """ + This enum represents the different states a state machine can be in. + """ + + START = 0 + ERROR = 1 + ITS_ME = 2 + + +class SequenceLikelihood: + """ + This enum represents the likelihood of a character following the previous one. + """ + + NEGATIVE = 0 + UNLIKELY = 1 + LIKELY = 2 + POSITIVE = 3 + + @classmethod + def get_num_categories(cls) -> int: + """:returns: The number of likelihood categories in the enum.""" + return 4 + + +class CharacterCategory: + """ + This enum represents the different categories language models for + ``SingleByteCharsetProber`` put characters into. + + Anything less than CONTROL is considered a letter. + """ + + UNDEFINED = 255 + LINE_BREAK = 254 + SYMBOL = 253 + DIGIT = 252 + CONTROL = 251 diff --git a/venv/lib/python3.10/site-packages/chardet/escprober.py b/venv/lib/python3.10/site-packages/chardet/escprober.py new file mode 100644 index 0000000000000000000000000000000000000000..fd713830d36cabc6a0fb4ab4e8cf426a84decdc6 --- /dev/null +++ b/venv/lib/python3.10/site-packages/chardet/escprober.py @@ -0,0 +1,102 @@ +######################## BEGIN LICENSE BLOCK ######################## +# The Original Code is mozilla.org code. +# +# The Initial Developer of the Original Code is +# Netscape Communications Corporation. +# Portions created by the Initial Developer are Copyright (C) 1998 +# the Initial Developer. All Rights Reserved. +# +# Contributor(s): +# Mark Pilgrim - port to Python +# +# This library is free software; you can redistribute it and/or +# modify it under the terms of the GNU Lesser General Public +# License as published by the Free Software Foundation; either +# version 2.1 of the License, or (at your option) any later version. +# +# This library is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +# Lesser General Public License for more details. +# +# You should have received a copy of the GNU Lesser General Public +# License along with this library; if not, write to the Free Software +# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA +# 02110-1301 USA +######################### END LICENSE BLOCK ######################### + +from typing import Optional, Union + +from .charsetprober import CharSetProber +from .codingstatemachine import CodingStateMachine +from .enums import LanguageFilter, MachineState, ProbingState +from .escsm import ( + HZ_SM_MODEL, + ISO2022CN_SM_MODEL, + ISO2022JP_SM_MODEL, + ISO2022KR_SM_MODEL, +) + + +class EscCharSetProber(CharSetProber): + """ + This CharSetProber uses a "code scheme" approach for detecting encodings, + whereby easily recognizable escape or shift sequences are relied on to + identify these encodings. + """ + + def __init__(self, lang_filter: LanguageFilter = LanguageFilter.NONE) -> None: + super().__init__(lang_filter=lang_filter) + self.coding_sm = [] + if self.lang_filter & LanguageFilter.CHINESE_SIMPLIFIED: + self.coding_sm.append(CodingStateMachine(HZ_SM_MODEL)) + self.coding_sm.append(CodingStateMachine(ISO2022CN_SM_MODEL)) + if self.lang_filter & LanguageFilter.JAPANESE: + self.coding_sm.append(CodingStateMachine(ISO2022JP_SM_MODEL)) + if self.lang_filter & LanguageFilter.KOREAN: + self.coding_sm.append(CodingStateMachine(ISO2022KR_SM_MODEL)) + self.active_sm_count = 0 + self._detected_charset: Optional[str] = None + self._detected_language: Optional[str] = None + self._state = ProbingState.DETECTING + self.reset() + + def reset(self) -> None: + super().reset() + for coding_sm in self.coding_sm: + coding_sm.active = True + coding_sm.reset() + self.active_sm_count = len(self.coding_sm) + self._detected_charset = None + self._detected_language = None + + @property + def charset_name(self) -> Optional[str]: + return self._detected_charset + + @property + def language(self) -> Optional[str]: + return self._detected_language + + def get_confidence(self) -> float: + return 0.99 if self._detected_charset else 0.00 + + def feed(self, byte_str: Union[bytes, bytearray]) -> ProbingState: + for c in byte_str: + for coding_sm in self.coding_sm: + if not coding_sm.active: + continue + coding_state = coding_sm.next_state(c) + if coding_state == MachineState.ERROR: + coding_sm.active = False + self.active_sm_count -= 1 + if self.active_sm_count <= 0: + self._state = ProbingState.NOT_ME + return self.state + elif coding_state == MachineState.ITS_ME: + self._state = ProbingState.FOUND_IT + self._detected_charset = coding_sm.get_coding_state_machine() + self._detected_language = coding_sm.language + return self.state + + return self.state diff --git a/venv/lib/python3.10/site-packages/chardet/escsm.py b/venv/lib/python3.10/site-packages/chardet/escsm.py new file mode 100644 index 0000000000000000000000000000000000000000..11d4adf771f3f90bb5f1cc11043599b48e955c22 --- /dev/null +++ b/venv/lib/python3.10/site-packages/chardet/escsm.py @@ -0,0 +1,261 @@ +######################## BEGIN LICENSE BLOCK ######################## +# The Original Code is mozilla.org code. +# +# The Initial Developer of the Original Code is +# Netscape Communications Corporation. +# Portions created by the Initial Developer are Copyright (C) 1998 +# the Initial Developer. All Rights Reserved. +# +# Contributor(s): +# Mark Pilgrim - port to Python +# +# This library is free software; you can redistribute it and/or +# modify it under the terms of the GNU Lesser General Public +# License as published by the Free Software Foundation; either +# version 2.1 of the License, or (at your option) any later version. +# +# This library is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +# Lesser General Public License for more details. +# +# You should have received a copy of the GNU Lesser General Public +# License along with this library; if not, write to the Free Software +# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA +# 02110-1301 USA +######################### END LICENSE BLOCK ######################### + +from .codingstatemachinedict import CodingStateMachineDict +from .enums import MachineState + +# fmt: off +HZ_CLS = ( + 1, 0, 0, 0, 0, 0, 0, 0, # 00 - 07 + 0, 0, 0, 0, 0, 0, 0, 0, # 08 - 0f + 0, 0, 0, 0, 0, 0, 0, 0, # 10 - 17 + 0, 0, 0, 1, 0, 0, 0, 0, # 18 - 1f + 0, 0, 0, 0, 0, 0, 0, 0, # 20 - 27 + 0, 0, 0, 0, 0, 0, 0, 0, # 28 - 2f + 0, 0, 0, 0, 0, 0, 0, 0, # 30 - 37 + 0, 0, 0, 0, 0, 0, 0, 0, # 38 - 3f + 0, 0, 0, 0, 0, 0, 0, 0, # 40 - 47 + 0, 0, 0, 0, 0, 0, 0, 0, # 48 - 4f + 0, 0, 0, 0, 0, 0, 0, 0, # 50 - 57 + 0, 0, 0, 0, 0, 0, 0, 0, # 58 - 5f + 0, 0, 0, 0, 0, 0, 0, 0, # 60 - 67 + 0, 0, 0, 0, 0, 0, 0, 0, # 68 - 6f + 0, 0, 0, 0, 0, 0, 0, 0, # 70 - 77 + 0, 0, 0, 4, 0, 5, 2, 0, # 78 - 7f + 1, 1, 1, 1, 1, 1, 1, 1, # 80 - 87 + 1, 1, 1, 1, 1, 1, 1, 1, # 88 - 8f + 1, 1, 1, 1, 1, 1, 1, 1, # 90 - 97 + 1, 1, 1, 1, 1, 1, 1, 1, # 98 - 9f + 1, 1, 1, 1, 1, 1, 1, 1, # a0 - a7 + 1, 1, 1, 1, 1, 1, 1, 1, # a8 - af + 1, 1, 1, 1, 1, 1, 1, 1, # b0 - b7 + 1, 1, 1, 1, 1, 1, 1, 1, # b8 - bf + 1, 1, 1, 1, 1, 1, 1, 1, # c0 - c7 + 1, 1, 1, 1, 1, 1, 1, 1, # c8 - cf + 1, 1, 1, 1, 1, 1, 1, 1, # d0 - d7 + 1, 1, 1, 1, 1, 1, 1, 1, # d8 - df + 1, 1, 1, 1, 1, 1, 1, 1, # e0 - e7 + 1, 1, 1, 1, 1, 1, 1, 1, # e8 - ef + 1, 1, 1, 1, 1, 1, 1, 1, # f0 - f7 + 1, 1, 1, 1, 1, 1, 1, 1, # f8 - ff +) + +HZ_ST = ( +MachineState.START, MachineState.ERROR, 3, MachineState.START, MachineState.START, MachineState.START, MachineState.ERROR, MachineState.ERROR, # 00-07 +MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, MachineState.ITS_ME, MachineState.ITS_ME, MachineState.ITS_ME, MachineState.ITS_ME, # 08-0f +MachineState.ITS_ME, MachineState.ITS_ME, MachineState.ERROR, MachineState.ERROR, MachineState.START, MachineState.START, 4, MachineState.ERROR, # 10-17 + 5, MachineState.ERROR, 6, MachineState.ERROR, 5, 5, 4, MachineState.ERROR, # 18-1f + 4, MachineState.ERROR, 4, 4, 4, MachineState.ERROR, 4, MachineState.ERROR, # 20-27 + 4, MachineState.ITS_ME, MachineState.START, MachineState.START, MachineState.START, MachineState.START, MachineState.START, MachineState.START, # 28-2f +) +# fmt: on + +HZ_CHAR_LEN_TABLE = (0, 0, 0, 0, 0, 0) + +HZ_SM_MODEL: CodingStateMachineDict = { + "class_table": HZ_CLS, + "class_factor": 6, + "state_table": HZ_ST, + "char_len_table": HZ_CHAR_LEN_TABLE, + "name": "HZ-GB-2312", + "language": "Chinese", +} + +# fmt: off +ISO2022CN_CLS = ( + 2, 0, 0, 0, 0, 0, 0, 0, # 00 - 07 + 0, 0, 0, 0, 0, 0, 0, 0, # 08 - 0f + 0, 0, 0, 0, 0, 0, 0, 0, # 10 - 17 + 0, 0, 0, 1, 0, 0, 0, 0, # 18 - 1f + 0, 0, 0, 0, 0, 0, 0, 0, # 20 - 27 + 0, 3, 0, 0, 0, 0, 0, 0, # 28 - 2f + 0, 0, 0, 0, 0, 0, 0, 0, # 30 - 37 + 0, 0, 0, 0, 0, 0, 0, 0, # 38 - 3f + 0, 0, 0, 4, 0, 0, 0, 0, # 40 - 47 + 0, 0, 0, 0, 0, 0, 0, 0, # 48 - 4f + 0, 0, 0, 0, 0, 0, 0, 0, # 50 - 57 + 0, 0, 0, 0, 0, 0, 0, 0, # 58 - 5f + 0, 0, 0, 0, 0, 0, 0, 0, # 60 - 67 + 0, 0, 0, 0, 0, 0, 0, 0, # 68 - 6f + 0, 0, 0, 0, 0, 0, 0, 0, # 70 - 77 + 0, 0, 0, 0, 0, 0, 0, 0, # 78 - 7f + 2, 2, 2, 2, 2, 2, 2, 2, # 80 - 87 + 2, 2, 2, 2, 2, 2, 2, 2, # 88 - 8f + 2, 2, 2, 2, 2, 2, 2, 2, # 90 - 97 + 2, 2, 2, 2, 2, 2, 2, 2, # 98 - 9f + 2, 2, 2, 2, 2, 2, 2, 2, # a0 - a7 + 2, 2, 2, 2, 2, 2, 2, 2, # a8 - af + 2, 2, 2, 2, 2, 2, 2, 2, # b0 - b7 + 2, 2, 2, 2, 2, 2, 2, 2, # b8 - bf + 2, 2, 2, 2, 2, 2, 2, 2, # c0 - c7 + 2, 2, 2, 2, 2, 2, 2, 2, # c8 - cf + 2, 2, 2, 2, 2, 2, 2, 2, # d0 - d7 + 2, 2, 2, 2, 2, 2, 2, 2, # d8 - df + 2, 2, 2, 2, 2, 2, 2, 2, # e0 - e7 + 2, 2, 2, 2, 2, 2, 2, 2, # e8 - ef + 2, 2, 2, 2, 2, 2, 2, 2, # f0 - f7 + 2, 2, 2, 2, 2, 2, 2, 2, # f8 - ff +) + +ISO2022CN_ST = ( + MachineState.START, 3, MachineState.ERROR, MachineState.START, MachineState.START, MachineState.START, MachineState.START, MachineState.START, # 00-07 + MachineState.START, MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, # 08-0f + MachineState.ERROR, MachineState.ERROR, MachineState.ITS_ME, MachineState.ITS_ME, MachineState.ITS_ME, MachineState.ITS_ME, MachineState.ITS_ME, MachineState.ITS_ME, # 10-17 + MachineState.ITS_ME, MachineState.ITS_ME, MachineState.ITS_ME, MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, 4, MachineState.ERROR, # 18-1f + MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, MachineState.ITS_ME, MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, # 20-27 + 5, 6, MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, # 28-2f + MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, MachineState.ITS_ME, MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, # 30-37 + MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, MachineState.ITS_ME, MachineState.ERROR, MachineState.START, # 38-3f +) +# fmt: on + +ISO2022CN_CHAR_LEN_TABLE = (0, 0, 0, 0, 0, 0, 0, 0, 0) + +ISO2022CN_SM_MODEL: CodingStateMachineDict = { + "class_table": ISO2022CN_CLS, + "class_factor": 9, + "state_table": ISO2022CN_ST, + "char_len_table": ISO2022CN_CHAR_LEN_TABLE, + "name": "ISO-2022-CN", + "language": "Chinese", +} + +# fmt: off +ISO2022JP_CLS = ( + 2, 0, 0, 0, 0, 0, 0, 0, # 00 - 07 + 0, 0, 0, 0, 0, 0, 2, 2, # 08 - 0f + 0, 0, 0, 0, 0, 0, 0, 0, # 10 - 17 + 0, 0, 0, 1, 0, 0, 0, 0, # 18 - 1f + 0, 0, 0, 0, 7, 0, 0, 0, # 20 - 27 + 3, 0, 0, 0, 0, 0, 0, 0, # 28 - 2f + 0, 0, 0, 0, 0, 0, 0, 0, # 30 - 37 + 0, 0, 0, 0, 0, 0, 0, 0, # 38 - 3f + 6, 0, 4, 0, 8, 0, 0, 0, # 40 - 47 + 0, 9, 5, 0, 0, 0, 0, 0, # 48 - 4f + 0, 0, 0, 0, 0, 0, 0, 0, # 50 - 57 + 0, 0, 0, 0, 0, 0, 0, 0, # 58 - 5f + 0, 0, 0, 0, 0, 0, 0, 0, # 60 - 67 + 0, 0, 0, 0, 0, 0, 0, 0, # 68 - 6f + 0, 0, 0, 0, 0, 0, 0, 0, # 70 - 77 + 0, 0, 0, 0, 0, 0, 0, 0, # 78 - 7f + 2, 2, 2, 2, 2, 2, 2, 2, # 80 - 87 + 2, 2, 2, 2, 2, 2, 2, 2, # 88 - 8f + 2, 2, 2, 2, 2, 2, 2, 2, # 90 - 97 + 2, 2, 2, 2, 2, 2, 2, 2, # 98 - 9f + 2, 2, 2, 2, 2, 2, 2, 2, # a0 - a7 + 2, 2, 2, 2, 2, 2, 2, 2, # a8 - af + 2, 2, 2, 2, 2, 2, 2, 2, # b0 - b7 + 2, 2, 2, 2, 2, 2, 2, 2, # b8 - bf + 2, 2, 2, 2, 2, 2, 2, 2, # c0 - c7 + 2, 2, 2, 2, 2, 2, 2, 2, # c8 - cf + 2, 2, 2, 2, 2, 2, 2, 2, # d0 - d7 + 2, 2, 2, 2, 2, 2, 2, 2, # d8 - df + 2, 2, 2, 2, 2, 2, 2, 2, # e0 - e7 + 2, 2, 2, 2, 2, 2, 2, 2, # e8 - ef + 2, 2, 2, 2, 2, 2, 2, 2, # f0 - f7 + 2, 2, 2, 2, 2, 2, 2, 2, # f8 - ff +) + +ISO2022JP_ST = ( + MachineState.START, 3, MachineState.ERROR, MachineState.START, MachineState.START, MachineState.START, MachineState.START, MachineState.START, # 00-07 + MachineState.START, MachineState.START, MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, # 08-0f + MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, MachineState.ITS_ME, MachineState.ITS_ME, MachineState.ITS_ME, MachineState.ITS_ME, # 10-17 + MachineState.ITS_ME, MachineState.ITS_ME, MachineState.ITS_ME, MachineState.ITS_ME, MachineState.ITS_ME, MachineState.ITS_ME, MachineState.ERROR, MachineState.ERROR, # 18-1f + MachineState.ERROR, 5, MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, 4, MachineState.ERROR, MachineState.ERROR, # 20-27 + MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, 6, MachineState.ITS_ME, MachineState.ERROR, MachineState.ITS_ME, MachineState.ERROR, # 28-2f + MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, MachineState.ITS_ME, MachineState.ITS_ME, # 30-37 + MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, MachineState.ITS_ME, MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, # 38-3f + MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, MachineState.ITS_ME, MachineState.ERROR, MachineState.START, MachineState.START, # 40-47 +) +# fmt: on + +ISO2022JP_CHAR_LEN_TABLE = (0, 0, 0, 0, 0, 0, 0, 0, 0, 0) + +ISO2022JP_SM_MODEL: CodingStateMachineDict = { + "class_table": ISO2022JP_CLS, + "class_factor": 10, + "state_table": ISO2022JP_ST, + "char_len_table": ISO2022JP_CHAR_LEN_TABLE, + "name": "ISO-2022-JP", + "language": "Japanese", +} + +# fmt: off +ISO2022KR_CLS = ( + 2, 0, 0, 0, 0, 0, 0, 0, # 00 - 07 + 0, 0, 0, 0, 0, 0, 0, 0, # 08 - 0f + 0, 0, 0, 0, 0, 0, 0, 0, # 10 - 17 + 0, 0, 0, 1, 0, 0, 0, 0, # 18 - 1f + 0, 0, 0, 0, 3, 0, 0, 0, # 20 - 27 + 0, 4, 0, 0, 0, 0, 0, 0, # 28 - 2f + 0, 0, 0, 0, 0, 0, 0, 0, # 30 - 37 + 0, 0, 0, 0, 0, 0, 0, 0, # 38 - 3f + 0, 0, 0, 5, 0, 0, 0, 0, # 40 - 47 + 0, 0, 0, 0, 0, 0, 0, 0, # 48 - 4f + 0, 0, 0, 0, 0, 0, 0, 0, # 50 - 57 + 0, 0, 0, 0, 0, 0, 0, 0, # 58 - 5f + 0, 0, 0, 0, 0, 0, 0, 0, # 60 - 67 + 0, 0, 0, 0, 0, 0, 0, 0, # 68 - 6f + 0, 0, 0, 0, 0, 0, 0, 0, # 70 - 77 + 0, 0, 0, 0, 0, 0, 0, 0, # 78 - 7f + 2, 2, 2, 2, 2, 2, 2, 2, # 80 - 87 + 2, 2, 2, 2, 2, 2, 2, 2, # 88 - 8f + 2, 2, 2, 2, 2, 2, 2, 2, # 90 - 97 + 2, 2, 2, 2, 2, 2, 2, 2, # 98 - 9f + 2, 2, 2, 2, 2, 2, 2, 2, # a0 - a7 + 2, 2, 2, 2, 2, 2, 2, 2, # a8 - af + 2, 2, 2, 2, 2, 2, 2, 2, # b0 - b7 + 2, 2, 2, 2, 2, 2, 2, 2, # b8 - bf + 2, 2, 2, 2, 2, 2, 2, 2, # c0 - c7 + 2, 2, 2, 2, 2, 2, 2, 2, # c8 - cf + 2, 2, 2, 2, 2, 2, 2, 2, # d0 - d7 + 2, 2, 2, 2, 2, 2, 2, 2, # d8 - df + 2, 2, 2, 2, 2, 2, 2, 2, # e0 - e7 + 2, 2, 2, 2, 2, 2, 2, 2, # e8 - ef + 2, 2, 2, 2, 2, 2, 2, 2, # f0 - f7 + 2, 2, 2, 2, 2, 2, 2, 2, # f8 - ff +) + +ISO2022KR_ST = ( + MachineState.START, 3, MachineState.ERROR, MachineState.START, MachineState.START, MachineState.START, MachineState.ERROR, MachineState.ERROR, # 00-07 + MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, MachineState.ITS_ME, MachineState.ITS_ME, MachineState.ITS_ME, MachineState.ITS_ME, # 08-0f + MachineState.ITS_ME, MachineState.ITS_ME, MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, 4, MachineState.ERROR, MachineState.ERROR, # 10-17 + MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, 5, MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, # 18-1f + MachineState.ERROR, MachineState.ERROR, MachineState.ERROR, MachineState.ITS_ME, MachineState.START, MachineState.START, MachineState.START, MachineState.START, # 20-27 +) +# fmt: on + +ISO2022KR_CHAR_LEN_TABLE = (0, 0, 0, 0, 0, 0) + +ISO2022KR_SM_MODEL: CodingStateMachineDict = { + "class_table": ISO2022KR_CLS, + "class_factor": 6, + "state_table": ISO2022KR_ST, + "char_len_table": ISO2022KR_CHAR_LEN_TABLE, + "name": "ISO-2022-KR", + "language": "Korean", +} diff --git a/venv/lib/python3.10/site-packages/chardet/eucjpprober.py b/venv/lib/python3.10/site-packages/chardet/eucjpprober.py new file mode 100644 index 0000000000000000000000000000000000000000..39487f4098d7c2068b67d7d3dd85b61848974a23 --- /dev/null +++ b/venv/lib/python3.10/site-packages/chardet/eucjpprober.py @@ -0,0 +1,102 @@ +######################## BEGIN LICENSE BLOCK ######################## +# The Original Code is mozilla.org code. +# +# The Initial Developer of the Original Code is +# Netscape Communications Corporation. +# Portions created by the Initial Developer are Copyright (C) 1998 +# the Initial Developer. All Rights Reserved. +# +# Contributor(s): +# Mark Pilgrim - port to Python +# +# This library is free software; you can redistribute it and/or +# modify it under the terms of the GNU Lesser General Public +# License as published by the Free Software Foundation; either +# version 2.1 of the License, or (at your option) any later version. +# +# This library is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +# Lesser General Public License for more details. +# +# You should have received a copy of the GNU Lesser General Public +# License along with this library; if not, write to the Free Software +# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA +# 02110-1301 USA +######################### END LICENSE BLOCK ######################### + +from typing import Union + +from .chardistribution import EUCJPDistributionAnalysis +from .codingstatemachine import CodingStateMachine +from .enums import MachineState, ProbingState +from .jpcntx import EUCJPContextAnalysis +from .mbcharsetprober import MultiByteCharSetProber +from .mbcssm import EUCJP_SM_MODEL + + +class EUCJPProber(MultiByteCharSetProber): + def __init__(self) -> None: + super().__init__() + self.coding_sm = CodingStateMachine(EUCJP_SM_MODEL) + self.distribution_analyzer = EUCJPDistributionAnalysis() + self.context_analyzer = EUCJPContextAnalysis() + self.reset() + + def reset(self) -> None: + super().reset() + self.context_analyzer.reset() + + @property + def charset_name(self) -> str: + return "EUC-JP" + + @property + def language(self) -> str: + return "Japanese" + + def feed(self, byte_str: Union[bytes, bytearray]) -> ProbingState: + assert self.coding_sm is not None + assert self.distribution_analyzer is not None + + for i, byte in enumerate(byte_str): + # PY3K: byte_str is a byte array, so byte is an int, not a byte + coding_state = self.coding_sm.next_state(byte) + if coding_state == MachineState.ERROR: + self.logger.debug( + "%s %s prober hit error at byte %s", + self.charset_name, + self.language, + i, + ) + self._state = ProbingState.NOT_ME + break + if coding_state == MachineState.ITS_ME: + self._state = ProbingState.FOUND_IT + break + if coding_state == MachineState.START: + char_len = self.coding_sm.get_current_charlen() + if i == 0: + self._last_char[1] = byte + self.context_analyzer.feed(self._last_char, char_len) + self.distribution_analyzer.feed(self._last_char, char_len) + else: + self.context_analyzer.feed(byte_str[i - 1 : i + 1], char_len) + self.distribution_analyzer.feed(byte_str[i - 1 : i + 1], char_len) + + self._last_char[0] = byte_str[-1] + + if self.state == ProbingState.DETECTING: + if self.context_analyzer.got_enough_data() and ( + self.get_confidence() > self.SHORTCUT_THRESHOLD + ): + self._state = ProbingState.FOUND_IT + + return self.state + + def get_confidence(self) -> float: + assert self.distribution_analyzer is not None + + context_conf = self.context_analyzer.get_confidence() + distrib_conf = self.distribution_analyzer.get_confidence() + return max(context_conf, distrib_conf) diff --git a/venv/lib/python3.10/site-packages/chardet/euckrfreq.py b/venv/lib/python3.10/site-packages/chardet/euckrfreq.py new file mode 100644 index 0000000000000000000000000000000000000000..7dc3b10387d1c3d2da8b4e27e917ee2a85086e0c --- /dev/null +++ b/venv/lib/python3.10/site-packages/chardet/euckrfreq.py @@ -0,0 +1,196 @@ +######################## BEGIN LICENSE BLOCK ######################## +# The Original Code is Mozilla Communicator client code. +# +# The Initial Developer of the Original Code is +# Netscape Communications Corporation. +# Portions created by the Initial Developer are Copyright (C) 1998 +# the Initial Developer. All Rights Reserved. +# +# Contributor(s): +# Mark Pilgrim - port to Python +# +# This library is free software; you can redistribute it and/or +# modify it under the terms of the GNU Lesser General Public +# License as published by the Free Software Foundation; either +# version 2.1 of the License, or (at your option) any later version. +# +# This library is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +# Lesser General Public License for more details. +# +# You should have received a copy of the GNU Lesser General Public +# License along with this library; if not, write to the Free Software +# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA +# 02110-1301 USA +######################### END LICENSE BLOCK ######################### + +# Sampling from about 20M text materials include literature and computer technology + +# 128 --> 0.79 +# 256 --> 0.92 +# 512 --> 0.986 +# 1024 --> 0.99944 +# 2048 --> 0.99999 +# +# Idea Distribution Ratio = 0.98653 / (1-0.98653) = 73.24 +# Random Distribution Ration = 512 / (2350-512) = 0.279. +# +# Typical Distribution Ratio + +EUCKR_TYPICAL_DISTRIBUTION_RATIO = 6.0 + +EUCKR_TABLE_SIZE = 2352 + +# Char to FreqOrder table , +# fmt: off +EUCKR_CHAR_TO_FREQ_ORDER = ( + 13, 130, 120,1396, 481,1719,1720, 328, 609, 212,1721, 707, 400, 299,1722, 87, +1397,1723, 104, 536,1117,1203,1724,1267, 685,1268, 508,1725,1726,1727,1728,1398, +1399,1729,1730,1731, 141, 621, 326,1057, 368,1732, 267, 488, 20,1733,1269,1734, + 945,1400,1735, 47, 904,1270,1736,1737, 773, 248,1738, 409, 313, 786, 429,1739, + 116, 987, 813,1401, 683, 75,1204, 145,1740,1741,1742,1743, 16, 847, 667, 622, + 708,1744,1745,1746, 966, 787, 304, 129,1747, 60, 820, 123, 676,1748,1749,1750, +1751, 617,1752, 626,1753,1754,1755,1756, 653,1757,1758,1759,1760,1761,1762, 856, + 344,1763,1764,1765,1766, 89, 401, 418, 806, 905, 848,1767,1768,1769, 946,1205, + 709,1770,1118,1771, 241,1772,1773,1774,1271,1775, 569,1776, 999,1777,1778,1779, +1780, 337, 751,1058, 28, 628, 254,1781, 177, 906, 270, 349, 891,1079,1782, 19, +1783, 379,1784, 315,1785, 629, 754,1402, 559,1786, 636, 203,1206,1787, 710, 567, +1788, 935, 814,1789,1790,1207, 766, 528,1791,1792,1208,1793,1794,1795,1796,1797, +1403,1798,1799, 533,1059,1404,1405,1156,1406, 936, 884,1080,1800, 351,1801,1802, +1803,1804,1805, 801,1806,1807,1808,1119,1809,1157, 714, 474,1407,1810, 298, 899, + 885,1811,1120, 802,1158,1812, 892,1813,1814,1408, 659,1815,1816,1121,1817,1818, +1819,1820,1821,1822, 319,1823, 594, 545,1824, 815, 937,1209,1825,1826, 573,1409, +1022,1827,1210,1828,1829,1830,1831,1832,1833, 556, 722, 807,1122,1060,1834, 697, +1835, 900, 557, 715,1836,1410, 540,1411, 752,1159, 294, 597,1211, 976, 803, 770, +1412,1837,1838, 39, 794,1413, 358,1839, 371, 925,1840, 453, 661, 788, 531, 723, + 544,1023,1081, 869, 91,1841, 392, 430, 790, 602,1414, 677,1082, 457,1415,1416, +1842,1843, 475, 327,1024,1417, 795, 121,1844, 733, 403,1418,1845,1846,1847, 300, + 119, 711,1212, 627,1848,1272, 207,1849,1850, 796,1213, 382,1851, 519,1852,1083, + 893,1853,1854,1855, 367, 809, 487, 671,1856, 663,1857,1858, 956, 471, 306, 857, +1859,1860,1160,1084,1861,1862,1863,1864,1865,1061,1866,1867,1868,1869,1870,1871, + 282, 96, 574,1872, 502,1085,1873,1214,1874, 907,1875,1876, 827, 977,1419,1420, +1421, 268,1877,1422,1878,1879,1880, 308,1881, 2, 537,1882,1883,1215,1884,1885, + 127, 791,1886,1273,1423,1887, 34, 336, 404, 643,1888, 571, 654, 894, 840,1889, + 0, 886,1274, 122, 575, 260, 908, 938,1890,1275, 410, 316,1891,1892, 100,1893, +1894,1123, 48,1161,1124,1025,1895, 633, 901,1276,1896,1897, 115, 816,1898, 317, +1899, 694,1900, 909, 734,1424, 572, 866,1425, 691, 85, 524,1010, 543, 394, 841, +1901,1902,1903,1026,1904,1905,1906,1907,1908,1909, 30, 451, 651, 988, 310,1910, +1911,1426, 810,1216, 93,1912,1913,1277,1217,1914, 858, 759, 45, 58, 181, 610, + 269,1915,1916, 131,1062, 551, 443,1000, 821,1427, 957, 895,1086,1917,1918, 375, +1919, 359,1920, 687,1921, 822,1922, 293,1923,1924, 40, 662, 118, 692, 29, 939, + 887, 640, 482, 174,1925, 69,1162, 728,1428, 910,1926,1278,1218,1279, 386, 870, + 217, 854,1163, 823,1927,1928,1929,1930, 834,1931, 78,1932, 859,1933,1063,1934, +1935,1936,1937, 438,1164, 208, 595,1938,1939,1940,1941,1219,1125,1942, 280, 888, +1429,1430,1220,1431,1943,1944,1945,1946,1947,1280, 150, 510,1432,1948,1949,1950, +1951,1952,1953,1954,1011,1087,1955,1433,1043,1956, 881,1957, 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793,2575,2576,2577,2578,2579,2580,1531, 251,2581,2582,2583, +2584,1532, 54, 612, 237,1327,2585,2586, 275, 408, 647, 111,2587,1533,1106, 465, + 3, 458, 9, 38,2588, 107, 110, 890, 209, 26, 737, 498,2589,1534,2590, 431, + 202, 88,1535, 356, 287,1107, 660,1149,2591, 381,1536, 986,1150, 445,1248,1151, + 974,2592,2593, 846,2594, 446, 953, 184,1249,1250, 727,2595, 923, 193, 883,2596, +2597,2598, 102, 324, 539, 817,2599, 421,1041,2600, 832,2601, 94, 175, 197, 406, +2602, 459,2603,2604,2605,2606,2607, 330, 555,2608,2609,2610, 706,1108, 389,2611, +2612,2613,2614, 233,2615, 833, 558, 931, 954,1251,2616,2617,1537, 546,2618,2619, +1009,2620,2621,2622,1538, 690,1328,2623, 955,2624,1539,2625,2626, 772,2627,2628, +2629,2630,2631, 924, 648, 863, 603,2632,2633, 934,1540, 864, 865,2634, 642,1042, + 670,1190,2635,2636,2637,2638, 168,2639, 652, 873, 542,1054,1541,2640,2641,2642, # 512, 256 +) +# fmt: on diff --git a/venv/lib/python3.10/site-packages/chardet/gb2312freq.py b/venv/lib/python3.10/site-packages/chardet/gb2312freq.py new file mode 100644 index 0000000000000000000000000000000000000000..b32bfc74213d93d434f1f3a47cb5d7d0bf4863d3 --- /dev/null +++ b/venv/lib/python3.10/site-packages/chardet/gb2312freq.py @@ -0,0 +1,284 @@ +######################## BEGIN LICENSE BLOCK ######################## +# The Original Code is Mozilla Communicator client code. +# +# The Initial Developer of the Original Code is +# Netscape Communications Corporation. +# Portions created by the Initial Developer are Copyright (C) 1998 +# the Initial Developer. All Rights Reserved. +# +# Contributor(s): +# Mark Pilgrim - port to Python +# +# This library is free software; you can redistribute it and/or +# modify it under the terms of the GNU Lesser General Public +# License as published by the Free Software Foundation; either +# version 2.1 of the License, or (at your option) any later version. +# +# This library is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +# Lesser General Public License for more details. +# +# You should have received a copy of the GNU Lesser General Public +# License along with this library; if not, write to the Free Software +# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA +# 02110-1301 USA +######################### END LICENSE BLOCK ######################### + +# GB2312 most frequently used character table +# +# Char to FreqOrder table , from hz6763 + +# 512 --> 0.79 -- 0.79 +# 1024 --> 0.92 -- 0.13 +# 2048 --> 0.98 -- 0.06 +# 6768 --> 1.00 -- 0.02 +# +# Ideal Distribution Ratio = 0.79135/(1-0.79135) = 3.79 +# Random Distribution Ration = 512 / (3755 - 512) = 0.157 +# +# Typical Distribution Ratio about 25% of Ideal one, still much higher that RDR + +GB2312_TYPICAL_DISTRIBUTION_RATIO = 0.9 + +GB2312_TABLE_SIZE = 3760 + +# fmt: off +GB2312_CHAR_TO_FREQ_ORDER = ( +1671, 749,1443,2364,3924,3807,2330,3921,1704,3463,2691,1511,1515, 572,3191,2205, +2361, 224,2558, 479,1711, 963,3162, 440,4060,1905,2966,2947,3580,2647,3961,3842, +2204, 869,4207, 970,2678,5626,2944,2956,1479,4048, 514,3595, 588,1346,2820,3409, + 249,4088,1746,1873,2047,1774, 581,1813, 358,1174,3590,1014,1561,4844,2245, 670, +1636,3112, 889,1286, 953, 556,2327,3060,1290,3141, 613, 185,3477,1367, 850,3820, +1715,2428,2642,2303,2732,3041,2562,2648,3566,3946,1349, 388,3098,2091,1360,3585, + 152,1687,1539, 738,1559, 59,1232,2925,2267,1388,1249,1741,1679,2960, 151,1566, +1125,1352,4271, 924,4296, 385,3166,4459, 310,1245,2850, 70,3285,2729,3534,3575, +2398,3298,3466,1960,2265, 217,3647, 864,1909,2084,4401,2773,1010,3269,5152, 853, +3051,3121,1244,4251,1895, 364,1499,1540,2313,1180,3655,2268, 562, 715,2417,3061, + 544, 336,3768,2380,1752,4075, 950, 280,2425,4382, 183,2759,3272, 333,4297,2155, +1688,2356,1444,1039,4540, 736,1177,3349,2443,2368,2144,2225, 565, 196,1482,3406, + 927,1335,4147, 692, 878,1311,1653,3911,3622,1378,4200,1840,2969,3149,2126,1816, +2534,1546,2393,2760, 737,2494, 13, 447, 245,2747, 38,2765,2129,2589,1079, 606, + 360, 471,3755,2890, 404, 848, 699,1785,1236, 370,2221,1023,3746,2074,2026,2023, +2388,1581,2119, 812,1141,3091,2536,1519, 804,2053, 406,1596,1090, 784, 548,4414, +1806,2264,2936,1100, 343,4114,5096, 622,3358, 743,3668,1510,1626,5020,3567,2513, +3195,4115,5627,2489,2991, 24,2065,2697,1087,2719, 48,1634, 315, 68, 985,2052, + 198,2239,1347,1107,1439, 597,2366,2172, 871,3307, 919,2487,2790,1867, 236,2570, +1413,3794, 906,3365,3381,1701,1982,1818,1524,2924,1205, 616,2586,2072,2004, 575, + 253,3099, 32,1365,1182, 197,1714,2454,1201, 554,3388,3224,2748, 756,2587, 250, +2567,1507,1517,3529,1922,2761,2337,3416,1961,1677,2452,2238,3153, 615, 911,1506, +1474,2495,1265,1906,2749,3756,3280,2161, 898,2714,1759,3450,2243,2444, 563, 26, +3286,2266,3769,3344,2707,3677, 611,1402, 531,1028,2871,4548,1375, 261,2948, 835, +1190,4134, 353, 840,2684,1900,3082,1435,2109,1207,1674, 329,1872,2781,4055,2686, +2104, 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466,1705,1095, 900,3423, 880,2667, +3751,5258,2317,3109,2571,4317,2766,1503,1342, 866,4447,1118, 63,2076, 314,1881, +1348,1061, 172, 978,3515,1747, 532, 511,3970, 6, 601, 905,2699,3300,1751, 276, +1467,3725,2668, 65,4239,2544,2779,2556,1604, 578,2451,1802, 992,2331,2624,1320, +3446, 713,1513,1013, 103,2786,2447,1661, 886,1702, 916, 654,3574,2031,1556, 751, +2178,2821,2179,1498,1538,2176, 271, 914,2251,2080,1325, 638,1953,2937,3877,2432, +2754, 95,3265,1716, 260,1227,4083, 775, 106,1357,3254, 426,1607, 555,2480, 772, +1985, 244,2546, 474, 495,1046,2611,1851,2061, 71,2089,1675,2590, 742,3758,2843, +3222,1433, 267,2180,2576,2826,2233,2092,3913,2435, 956,1745,3075, 856,2113,1116, + 451, 3,1988,2896,1398, 993,2463,1878,2049,1341,2718,2721,2870,2108, 712,2904, +4363,2753,2324, 277,2872,2349,2649, 384, 987, 435, 691,3000, 922, 164,3939, 652, +1500,1184,4153,2482,3373,2165,4848,2335,3775,3508,3154,2806,2830,1554,2102,1664, +2530,1434,2408, 893,1547,2623,3447,2832,2242,2532,3169,2856,3223,2078, 49,3770, +3469, 462, 318, 656,2259,3250,3069, 679,1629,2758, 344,1138,1104,3120,1836,1283, +3115,2154,1437,4448, 934, 759,1999, 794,2862,1038, 533,2560,1722,2342, 855,2626, +1197,1663,4476,3127, 85,4240,2528, 25,1111,1181,3673, 407,3470,4561,2679,2713, + 768,1925,2841,3986,1544,1165, 932, 373,1240,2146,1930,2673, 721,4766, 354,4333, + 391,2963, 187, 61,3364,1442,1102, 330,1940,1767, 341,3809,4118, 393,2496,2062, +2211, 105, 331, 300, 439, 913,1332, 626, 379,3304,1557, 328, 689,3952, 309,1555, + 931, 317,2517,3027, 325, 569, 686,2107,3084, 60,1042,1333,2794, 264,3177,4014, +1628, 258,3712, 7,4464,1176,1043,1778, 683, 114,1975, 78,1492, 383,1886, 510, + 386, 645,5291,2891,2069,3305,4138,3867,2939,2603,2493,1935,1066,1848,3588,1015, +1282,1289,4609, 697,1453,3044,2666,3611,1856,2412, 54, 719,1330, 568,3778,2459, +1748, 788, 492, 551,1191,1000, 488,3394,3763, 282,1799, 348,2016,1523,3155,2390, +1049, 382,2019,1788,1170, 729,2968,3523, 897,3926,2785,2938,3292, 350,2319,3238, +1718,1717,2655,3453,3143,4465, 161,2889,2980,2009,1421, 56,1908,1640,2387,2232, +1917,1874,2477,4921, 148, 83,3438, 592,4245,2882,1822,1055, 741, 115,1496,1624, + 381,1638,4592,1020, 516,3214, 458, 947,4575,1432, 211,1514,2926,1865,2142, 189, + 852,1221,1400,1486, 882,2299,4036, 351, 28,1122, 700,6479,6480,6481,6482,6483, #last 512 +) +# fmt: on diff --git a/venv/lib/python3.10/site-packages/chardet/gb2312prober.py b/venv/lib/python3.10/site-packages/chardet/gb2312prober.py new file mode 100644 index 0000000000000000000000000000000000000000..d423e7311e2fbd9a014de808c107e96ad11c66e5 --- /dev/null +++ b/venv/lib/python3.10/site-packages/chardet/gb2312prober.py @@ -0,0 +1,47 @@ +######################## BEGIN LICENSE BLOCK ######################## +# The Original Code is mozilla.org code. +# +# The Initial Developer of the Original Code is +# Netscape Communications Corporation. +# Portions created by the Initial Developer are Copyright (C) 1998 +# the Initial Developer. All Rights Reserved. +# +# Contributor(s): +# Mark Pilgrim - port to Python +# +# This library is free software; you can redistribute it and/or +# modify it under the terms of the GNU Lesser General Public +# License as published by the Free Software Foundation; either +# version 2.1 of the License, or (at your option) any later version. +# +# This library is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +# Lesser General Public License for more details. +# +# You should have received a copy of the GNU Lesser General Public +# License along with this library; if not, write to the Free Software +# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA +# 02110-1301 USA +######################### END LICENSE BLOCK ######################### + +from .chardistribution import GB2312DistributionAnalysis +from .codingstatemachine import CodingStateMachine +from .mbcharsetprober import MultiByteCharSetProber +from .mbcssm import GB2312_SM_MODEL + + +class GB2312Prober(MultiByteCharSetProber): + def __init__(self) -> None: + super().__init__() + self.coding_sm = CodingStateMachine(GB2312_SM_MODEL) + self.distribution_analyzer = GB2312DistributionAnalysis() + self.reset() + + @property + def charset_name(self) -> str: + return "GB2312" + + @property + def language(self) -> str: + return "Chinese" diff --git a/venv/lib/python3.10/site-packages/chardet/hebrewprober.py b/venv/lib/python3.10/site-packages/chardet/hebrewprober.py new file mode 100644 index 0000000000000000000000000000000000000000..785d0057bcc0ea74a4b8d65ab7a0de78474bf892 --- /dev/null +++ b/venv/lib/python3.10/site-packages/chardet/hebrewprober.py @@ -0,0 +1,316 @@ +######################## BEGIN LICENSE BLOCK ######################## +# The Original Code is Mozilla Universal charset detector code. +# +# The Initial Developer of the Original Code is +# Shy Shalom +# Portions created by the Initial Developer are Copyright (C) 2005 +# the Initial Developer. All Rights Reserved. +# +# Contributor(s): +# Mark Pilgrim - port to Python +# +# This library is free software; you can redistribute it and/or +# modify it under the terms of the GNU Lesser General Public +# License as published by the Free Software Foundation; either +# version 2.1 of the License, or (at your option) any later version. +# +# This library is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +# Lesser General Public License for more details. +# +# You should have received a copy of the GNU Lesser General Public +# License along with this library; if not, write to the Free Software +# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA +# 02110-1301 USA +######################### END LICENSE BLOCK ######################### + +from typing import Optional, Union + +from .charsetprober import CharSetProber +from .enums import ProbingState +from .sbcharsetprober import SingleByteCharSetProber + +# This prober doesn't actually recognize a language or a charset. +# It is a helper prober for the use of the Hebrew model probers + +### General ideas of the Hebrew charset recognition ### +# +# Four main charsets exist in Hebrew: +# "ISO-8859-8" - Visual Hebrew +# "windows-1255" - Logical Hebrew +# "ISO-8859-8-I" - Logical Hebrew +# "x-mac-hebrew" - ?? Logical Hebrew ?? +# +# Both "ISO" charsets use a completely identical set of code points, whereas +# "windows-1255" and "x-mac-hebrew" are two different proper supersets of +# these code points. windows-1255 defines additional characters in the range +# 0x80-0x9F as some misc punctuation marks as well as some Hebrew-specific +# diacritics and additional 'Yiddish' ligature letters in the range 0xc0-0xd6. +# x-mac-hebrew defines similar additional code points but with a different +# mapping. +# +# As far as an average Hebrew text with no diacritics is concerned, all four +# charsets are identical with respect to code points. Meaning that for the +# main Hebrew alphabet, all four map the same values to all 27 Hebrew letters +# (including final letters). +# +# The dominant difference between these charsets is their directionality. +# "Visual" directionality means that the text is ordered as if the renderer is +# not aware of a BIDI rendering algorithm. The renderer sees the text and +# draws it from left to right. The text itself when ordered naturally is read +# backwards. A buffer of Visual Hebrew generally looks like so: +# "[last word of first line spelled backwards] [whole line ordered backwards +# and spelled backwards] [first word of first line spelled backwards] +# [end of line] [last word of second line] ... etc' " +# adding punctuation marks, numbers and English text to visual text is +# naturally also "visual" and from left to right. +# +# "Logical" directionality means the text is ordered "naturally" according to +# the order it is read. It is the responsibility of the renderer to display +# the text from right to left. A BIDI algorithm is used to place general +# punctuation marks, numbers and English text in the text. +# +# Texts in x-mac-hebrew are almost impossible to find on the Internet. From +# what little evidence I could find, it seems that its general directionality +# is Logical. +# +# To sum up all of the above, the Hebrew probing mechanism knows about two +# charsets: +# Visual Hebrew - "ISO-8859-8" - backwards text - Words and sentences are +# backwards while line order is natural. For charset recognition purposes +# the line order is unimportant (In fact, for this implementation, even +# word order is unimportant). +# Logical Hebrew - "windows-1255" - normal, naturally ordered text. +# +# "ISO-8859-8-I" is a subset of windows-1255 and doesn't need to be +# specifically identified. +# "x-mac-hebrew" is also identified as windows-1255. A text in x-mac-hebrew +# that contain special punctuation marks or diacritics is displayed with +# some unconverted characters showing as question marks. This problem might +# be corrected using another model prober for x-mac-hebrew. Due to the fact +# that x-mac-hebrew texts are so rare, writing another model prober isn't +# worth the effort and performance hit. +# +#### The Prober #### +# +# The prober is divided between two SBCharSetProbers and a HebrewProber, +# all of which are managed, created, fed data, inquired and deleted by the +# SBCSGroupProber. The two SBCharSetProbers identify that the text is in +# fact some kind of Hebrew, Logical or Visual. The final decision about which +# one is it is made by the HebrewProber by combining final-letter scores +# with the scores of the two SBCharSetProbers to produce a final answer. +# +# The SBCSGroupProber is responsible for stripping the original text of HTML +# tags, English characters, numbers, low-ASCII punctuation characters, spaces +# and new lines. It reduces any sequence of such characters to a single space. +# The buffer fed to each prober in the SBCS group prober is pure text in +# high-ASCII. +# The two SBCharSetProbers (model probers) share the same language model: +# Win1255Model. +# The first SBCharSetProber uses the model normally as any other +# SBCharSetProber does, to recognize windows-1255, upon which this model was +# built. The second SBCharSetProber is told to make the pair-of-letter +# lookup in the language model backwards. This in practice exactly simulates +# a visual Hebrew model using the windows-1255 logical Hebrew model. +# +# The HebrewProber is not using any language model. All it does is look for +# final-letter evidence suggesting the text is either logical Hebrew or visual +# Hebrew. Disjointed from the model probers, the results of the HebrewProber +# alone are meaningless. HebrewProber always returns 0.00 as confidence +# since it never identifies a charset by itself. Instead, the pointer to the +# HebrewProber is passed to the model probers as a helper "Name Prober". +# When the Group prober receives a positive identification from any prober, +# it asks for the name of the charset identified. If the prober queried is a +# Hebrew model prober, the model prober forwards the call to the +# HebrewProber to make the final decision. In the HebrewProber, the +# decision is made according to the final-letters scores maintained and Both +# model probers scores. The answer is returned in the form of the name of the +# charset identified, either "windows-1255" or "ISO-8859-8". + + +class HebrewProber(CharSetProber): + SPACE = 0x20 + # windows-1255 / ISO-8859-8 code points of interest + FINAL_KAF = 0xEA + NORMAL_KAF = 0xEB + FINAL_MEM = 0xED + NORMAL_MEM = 0xEE + FINAL_NUN = 0xEF + NORMAL_NUN = 0xF0 + FINAL_PE = 0xF3 + NORMAL_PE = 0xF4 + FINAL_TSADI = 0xF5 + NORMAL_TSADI = 0xF6 + + # Minimum Visual vs Logical final letter score difference. + # If the difference is below this, don't rely solely on the final letter score + # distance. + MIN_FINAL_CHAR_DISTANCE = 5 + + # Minimum Visual vs Logical model score difference. + # If the difference is below this, don't rely at all on the model score + # distance. + MIN_MODEL_DISTANCE = 0.01 + + VISUAL_HEBREW_NAME = "ISO-8859-8" + LOGICAL_HEBREW_NAME = "windows-1255" + + def __init__(self) -> None: + super().__init__() + self._final_char_logical_score = 0 + self._final_char_visual_score = 0 + self._prev = self.SPACE + self._before_prev = self.SPACE + self._logical_prober: Optional[SingleByteCharSetProber] = None + self._visual_prober: Optional[SingleByteCharSetProber] = None + self.reset() + + def reset(self) -> None: + self._final_char_logical_score = 0 + self._final_char_visual_score = 0 + # The two last characters seen in the previous buffer, + # mPrev and mBeforePrev are initialized to space in order to simulate + # a word delimiter at the beginning of the data + self._prev = self.SPACE + self._before_prev = self.SPACE + # These probers are owned by the group prober. + + def set_model_probers( + self, + logical_prober: SingleByteCharSetProber, + visual_prober: SingleByteCharSetProber, + ) -> None: + self._logical_prober = logical_prober + self._visual_prober = visual_prober + + def is_final(self, c: int) -> bool: + return c in [ + self.FINAL_KAF, + self.FINAL_MEM, + self.FINAL_NUN, + self.FINAL_PE, + self.FINAL_TSADI, + ] + + def is_non_final(self, c: int) -> bool: + # The normal Tsadi is not a good Non-Final letter due to words like + # 'lechotet' (to chat) containing an apostrophe after the tsadi. This + # apostrophe is converted to a space in FilterWithoutEnglishLetters + # causing the Non-Final tsadi to appear at an end of a word even + # though this is not the case in the original text. + # The letters Pe and Kaf rarely display a related behavior of not being + # a good Non-Final letter. Words like 'Pop', 'Winamp' and 'Mubarak' + # for example legally end with a Non-Final Pe or Kaf. However, the + # benefit of these letters as Non-Final letters outweighs the damage + # since these words are quite rare. + return c in [self.NORMAL_KAF, self.NORMAL_MEM, self.NORMAL_NUN, self.NORMAL_PE] + + def feed(self, byte_str: Union[bytes, bytearray]) -> ProbingState: + # Final letter analysis for logical-visual decision. + # Look for evidence that the received buffer is either logical Hebrew + # or visual Hebrew. + # The following cases are checked: + # 1) A word longer than 1 letter, ending with a final letter. This is + # an indication that the text is laid out "naturally" since the + # final letter really appears at the end. +1 for logical score. + # 2) A word longer than 1 letter, ending with a Non-Final letter. In + # normal Hebrew, words ending with Kaf, Mem, Nun, Pe or Tsadi, + # should not end with the Non-Final form of that letter. Exceptions + # to this rule are mentioned above in isNonFinal(). This is an + # indication that the text is laid out backwards. +1 for visual + # score + # 3) A word longer than 1 letter, starting with a final letter. Final + # letters should not appear at the beginning of a word. This is an + # indication that the text is laid out backwards. +1 for visual + # score. + # + # The visual score and logical score are accumulated throughout the + # text and are finally checked against each other in GetCharSetName(). + # No checking for final letters in the middle of words is done since + # that case is not an indication for either Logical or Visual text. + # + # We automatically filter out all 7-bit characters (replace them with + # spaces) so the word boundary detection works properly. [MAP] + + if self.state == ProbingState.NOT_ME: + # Both model probers say it's not them. No reason to continue. + return ProbingState.NOT_ME + + byte_str = self.filter_high_byte_only(byte_str) + + for cur in byte_str: + if cur == self.SPACE: + # We stand on a space - a word just ended + if self._before_prev != self.SPACE: + # next-to-last char was not a space so self._prev is not a + # 1 letter word + if self.is_final(self._prev): + # case (1) [-2:not space][-1:final letter][cur:space] + self._final_char_logical_score += 1 + elif self.is_non_final(self._prev): + # case (2) [-2:not space][-1:Non-Final letter][ + # cur:space] + self._final_char_visual_score += 1 + else: + # Not standing on a space + if ( + (self._before_prev == self.SPACE) + and (self.is_final(self._prev)) + and (cur != self.SPACE) + ): + # case (3) [-2:space][-1:final letter][cur:not space] + self._final_char_visual_score += 1 + self._before_prev = self._prev + self._prev = cur + + # Forever detecting, till the end or until both model probers return + # ProbingState.NOT_ME (handled above) + return ProbingState.DETECTING + + @property + def charset_name(self) -> str: + assert self._logical_prober is not None + assert self._visual_prober is not None + + # Make the decision: is it Logical or Visual? + # If the final letter score distance is dominant enough, rely on it. + finalsub = self._final_char_logical_score - self._final_char_visual_score + if finalsub >= self.MIN_FINAL_CHAR_DISTANCE: + return self.LOGICAL_HEBREW_NAME + if finalsub <= -self.MIN_FINAL_CHAR_DISTANCE: + return self.VISUAL_HEBREW_NAME + + # It's not dominant enough, try to rely on the model scores instead. + modelsub = ( + self._logical_prober.get_confidence() - self._visual_prober.get_confidence() + ) + if modelsub > self.MIN_MODEL_DISTANCE: + return self.LOGICAL_HEBREW_NAME + if modelsub < -self.MIN_MODEL_DISTANCE: + return self.VISUAL_HEBREW_NAME + + # Still no good, back to final letter distance, maybe it'll save the + # day. + if finalsub < 0.0: + return self.VISUAL_HEBREW_NAME + + # (finalsub > 0 - Logical) or (don't know what to do) default to + # Logical. + return self.LOGICAL_HEBREW_NAME + + @property + def language(self) -> str: + return "Hebrew" + + @property + def state(self) -> ProbingState: + assert self._logical_prober is not None + assert self._visual_prober is not None + + # Remain active as long as any of the model probers are active. + if (self._logical_prober.state == ProbingState.NOT_ME) and ( + self._visual_prober.state == ProbingState.NOT_ME + ): + return ProbingState.NOT_ME + return ProbingState.DETECTING diff --git a/venv/lib/python3.10/site-packages/chardet/jisfreq.py b/venv/lib/python3.10/site-packages/chardet/jisfreq.py new file mode 100644 index 0000000000000000000000000000000000000000..3293576e012a1c931b5e89ebc065c67b65941084 --- /dev/null +++ b/venv/lib/python3.10/site-packages/chardet/jisfreq.py @@ -0,0 +1,325 @@ +######################## BEGIN LICENSE BLOCK ######################## +# The Original Code is Mozilla Communicator client code. +# +# The Initial Developer of the Original Code is +# Netscape Communications Corporation. +# Portions created by the Initial Developer are Copyright (C) 1998 +# the Initial Developer. All Rights Reserved. +# +# Contributor(s): +# Mark Pilgrim - port to Python +# +# This library is free software; you can redistribute it and/or +# modify it under the terms of the GNU Lesser General Public +# License as published by the Free Software Foundation; either +# version 2.1 of the License, or (at your option) any later version. +# +# This library is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +# Lesser General Public License for more details. +# +# You should have received a copy of the GNU Lesser General Public +# License along with this library; if not, write to the Free Software +# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA +# 02110-1301 USA +######################### END LICENSE BLOCK ######################### + +# Sampling from about 20M text materials include literature and computer technology +# +# Japanese frequency table, applied to both S-JIS and EUC-JP +# They are sorted in order. + +# 128 --> 0.77094 +# 256 --> 0.85710 +# 512 --> 0.92635 +# 1024 --> 0.97130 +# 2048 --> 0.99431 +# +# Ideal Distribution Ratio = 0.92635 / (1-0.92635) = 12.58 +# Random Distribution Ration = 512 / (2965+62+83+86-512) = 0.191 +# +# Typical Distribution Ratio, 25% of IDR + +JIS_TYPICAL_DISTRIBUTION_RATIO = 3.0 + +# Char to FreqOrder table , +JIS_TABLE_SIZE = 4368 + +# fmt: off +JIS_CHAR_TO_FREQ_ORDER = ( + 40, 1, 6, 182, 152, 180, 295,2127, 285, 381,3295,4304,3068,4606,3165,3510, # 16 +3511,1822,2785,4607,1193,2226,5070,4608, 171,2996,1247, 18, 179,5071, 856,1661, # 32 +1262,5072, 619, 127,3431,3512,3230,1899,1700, 232, 228,1294,1298, 284, 283,2041, # 48 +2042,1061,1062, 48, 49, 44, 45, 433, 434,1040,1041, 996, 787,2997,1255,4305, # 64 +2108,4609,1684,1648,5073,5074,5075,5076,5077,5078,3687,5079,4610,5080,3927,3928, # 80 +5081,3296,3432, 290,2285,1471,2187,5082,2580,2825,1303,2140,1739,1445,2691,3375, # 96 +1691,3297,4306,4307,4611, 452,3376,1182,2713,3688,3069,4308,5083,5084,5085,5086, # 112 +5087,5088,5089,5090,5091,5092,5093,5094,5095,5096,5097,5098,5099,5100,5101,5102, # 128 +5103,5104,5105,5106,5107,5108,5109,5110,5111,5112,4097,5113,5114,5115,5116,5117, # 144 +5118,5119,5120,5121,5122,5123,5124,5125,5126,5127,5128,5129,5130,5131,5132,5133, # 160 +5134,5135,5136,5137,5138,5139,5140,5141,5142,5143,5144,5145,5146,5147,5148,5149, # 176 +5150,5151,5152,4612,5153,5154,5155,5156,5157,5158,5159,5160,5161,5162,5163,5164, # 192 +5165,5166,5167,5168,5169,5170,5171,5172,5173,5174,5175,1472, 598, 618, 820,1205, # 208 +1309,1412,1858,1307,1692,5176,5177,5178,5179,5180,5181,5182,1142,1452,1234,1172, # 224 +1875,2043,2149,1793,1382,2973, 925,2404,1067,1241, 960,1377,2935,1491, 919,1217, # 240 +1865,2030,1406,1499,2749,4098,5183,5184,5185,5186,5187,5188,2561,4099,3117,1804, # 256 +2049,3689,4309,3513,1663,5189,3166,3118,3298,1587,1561,3433,5190,3119,1625,2998, # 272 +3299,4613,1766,3690,2786,4614,5191,5192,5193,5194,2161, 26,3377, 2,3929, 20, # 288 +3691, 47,4100, 50, 17, 16, 35, 268, 27, 243, 42, 155, 24, 154, 29, 184, # 304 + 4, 91, 14, 92, 53, 396, 33, 289, 9, 37, 64, 620, 21, 39, 321, 5, # 320 + 12, 11, 52, 13, 3, 208, 138, 0, 7, 60, 526, 141, 151,1069, 181, 275, # 336 +1591, 83, 132,1475, 126, 331, 829, 15, 69, 160, 59, 22, 157, 55,1079, 312, # 352 + 109, 38, 23, 25, 10, 19, 79,5195, 61, 382,1124, 8, 30,5196,5197,5198, # 368 +5199,5200,5201,5202,5203,5204,5205,5206, 89, 62, 74, 34,2416, 112, 139, 196, # 384 + 271, 149, 84, 607, 131, 765, 46, 88, 153, 683, 76, 874, 101, 258, 57, 80, # 400 + 32, 364, 121,1508, 169,1547, 68, 235, 145,2999, 41, 360,3027, 70, 63, 31, # 416 + 43, 259, 262,1383, 99, 533, 194, 66, 93, 846, 217, 192, 56, 106, 58, 565, # 432 + 280, 272, 311, 256, 146, 82, 308, 71, 100, 128, 214, 655, 110, 261, 104,1140, # 448 + 54, 51, 36, 87, 67,3070, 185,2618,2936,2020, 28,1066,2390,2059,5207,5208, # 464 +5209,5210,5211,5212,5213,5214,5215,5216,4615,5217,5218,5219,5220,5221,5222,5223, # 480 +5224,5225,5226,5227,5228,5229,5230,5231,5232,5233,5234,5235,5236,3514,5237,5238, # 496 +5239,5240,5241,5242,5243,5244,2297,2031,4616,4310,3692,5245,3071,5246,3598,5247, # 512 +4617,3231,3515,5248,4101,4311,4618,3808,4312,4102,5249,4103,4104,3599,5250,5251, # 528 +5252,5253,5254,5255,5256,5257,5258,5259,5260,5261,5262,5263,5264,5265,5266,5267, # 544 +5268,5269,5270,5271,5272,5273,5274,5275,5276,5277,5278,5279,5280,5281,5282,5283, # 560 +5284,5285,5286,5287,5288,5289,5290,5291,5292,5293,5294,5295,5296,5297,5298,5299, # 576 +5300,5301,5302,5303,5304,5305,5306,5307,5308,5309,5310,5311,5312,5313,5314,5315, # 592 +5316,5317,5318,5319,5320,5321,5322,5323,5324,5325,5326,5327,5328,5329,5330,5331, # 608 +5332,5333,5334,5335,5336,5337,5338,5339,5340,5341,5342,5343,5344,5345,5346,5347, # 624 +5348,5349,5350,5351,5352,5353,5354,5355,5356,5357,5358,5359,5360,5361,5362,5363, # 640 +5364,5365,5366,5367,5368,5369,5370,5371,5372,5373,5374,5375,5376,5377,5378,5379, # 656 +5380,5381, 363, 642,2787,2878,2788,2789,2316,3232,2317,3434,2011, 165,1942,3930, # 672 +3931,3932,3933,5382,4619,5383,4620,5384,5385,5386,5387,5388,5389,5390,5391,5392, # 688 +5393,5394,5395,5396,5397,5398,5399,5400,5401,5402,5403,5404,5405,5406,5407,5408, # 704 +5409,5410,5411,5412,5413,5414,5415,5416,5417,5418,5419,5420,5421,5422,5423,5424, # 720 +5425,5426,5427,5428,5429,5430,5431,5432,5433,5434,5435,5436,5437,5438,5439,5440, # 736 +5441,5442,5443,5444,5445,5446,5447,5448,5449,5450,5451,5452,5453,5454,5455,5456, # 752 +5457,5458,5459,5460,5461,5462,5463,5464,5465,5466,5467,5468,5469,5470,5471,5472, # 768 +5473,5474,5475,5476,5477,5478,5479,5480,5481,5482,5483,5484,5485,5486,5487,5488, # 784 +5489,5490,5491,5492,5493,5494,5495,5496,5497,5498,5499,5500,5501,5502,5503,5504, # 800 +5505,5506,5507,5508,5509,5510,5511,5512,5513,5514,5515,5516,5517,5518,5519,5520, # 816 +5521,5522,5523,5524,5525,5526,5527,5528,5529,5530,5531,5532,5533,5534,5535,5536, # 832 +5537,5538,5539,5540,5541,5542,5543,5544,5545,5546,5547,5548,5549,5550,5551,5552, # 848 +5553,5554,5555,5556,5557,5558,5559,5560,5561,5562,5563,5564,5565,5566,5567,5568, # 864 +5569,5570,5571,5572,5573,5574,5575,5576,5577,5578,5579,5580,5581,5582,5583,5584, # 880 +5585,5586,5587,5588,5589,5590,5591,5592,5593,5594,5595,5596,5597,5598,5599,5600, # 896 +5601,5602,5603,5604,5605,5606,5607,5608,5609,5610,5611,5612,5613,5614,5615,5616, # 912 +5617,5618,5619,5620,5621,5622,5623,5624,5625,5626,5627,5628,5629,5630,5631,5632, # 928 +5633,5634,5635,5636,5637,5638,5639,5640,5641,5642,5643,5644,5645,5646,5647,5648, # 944 +5649,5650,5651,5652,5653,5654,5655,5656,5657,5658,5659,5660,5661,5662,5663,5664, # 960 +5665,5666,5667,5668,5669,5670,5671,5672,5673,5674,5675,5676,5677,5678,5679,5680, # 976 +5681,5682,5683,5684,5685,5686,5687,5688,5689,5690,5691,5692,5693,5694,5695,5696, # 992 +5697,5698,5699,5700,5701,5702,5703,5704,5705,5706,5707,5708,5709,5710,5711,5712, # 1008 +5713,5714,5715,5716,5717,5718,5719,5720,5721,5722,5723,5724,5725,5726,5727,5728, # 1024 +5729,5730,5731,5732,5733,5734,5735,5736,5737,5738,5739,5740,5741,5742,5743,5744, # 1040 +5745,5746,5747,5748,5749,5750,5751,5752,5753,5754,5755,5756,5757,5758,5759,5760, # 1056 +5761,5762,5763,5764,5765,5766,5767,5768,5769,5770,5771,5772,5773,5774,5775,5776, # 1072 +5777,5778,5779,5780,5781,5782,5783,5784,5785,5786,5787,5788,5789,5790,5791,5792, # 1088 +5793,5794,5795,5796,5797,5798,5799,5800,5801,5802,5803,5804,5805,5806,5807,5808, # 1104 +5809,5810,5811,5812,5813,5814,5815,5816,5817,5818,5819,5820,5821,5822,5823,5824, # 1120 +5825,5826,5827,5828,5829,5830,5831,5832,5833,5834,5835,5836,5837,5838,5839,5840, # 1136 +5841,5842,5843,5844,5845,5846,5847,5848,5849,5850,5851,5852,5853,5854,5855,5856, # 1152 +5857,5858,5859,5860,5861,5862,5863,5864,5865,5866,5867,5868,5869,5870,5871,5872, # 1168 +5873,5874,5875,5876,5877,5878,5879,5880,5881,5882,5883,5884,5885,5886,5887,5888, # 1184 +5889,5890,5891,5892,5893,5894,5895,5896,5897,5898,5899,5900,5901,5902,5903,5904, # 1200 +5905,5906,5907,5908,5909,5910,5911,5912,5913,5914,5915,5916,5917,5918,5919,5920, # 1216 +5921,5922,5923,5924,5925,5926,5927,5928,5929,5930,5931,5932,5933,5934,5935,5936, # 1232 +5937,5938,5939,5940,5941,5942,5943,5944,5945,5946,5947,5948,5949,5950,5951,5952, # 1248 +5953,5954,5955,5956,5957,5958,5959,5960,5961,5962,5963,5964,5965,5966,5967,5968, # 1264 +5969,5970,5971,5972,5973,5974,5975,5976,5977,5978,5979,5980,5981,5982,5983,5984, # 1280 +5985,5986,5987,5988,5989,5990,5991,5992,5993,5994,5995,5996,5997,5998,5999,6000, # 1296 +6001,6002,6003,6004,6005,6006,6007,6008,6009,6010,6011,6012,6013,6014,6015,6016, # 1312 +6017,6018,6019,6020,6021,6022,6023,6024,6025,6026,6027,6028,6029,6030,6031,6032, # 1328 +6033,6034,6035,6036,6037,6038,6039,6040,6041,6042,6043,6044,6045,6046,6047,6048, # 1344 +6049,6050,6051,6052,6053,6054,6055,6056,6057,6058,6059,6060,6061,6062,6063,6064, # 1360 +6065,6066,6067,6068,6069,6070,6071,6072,6073,6074,6075,6076,6077,6078,6079,6080, # 1376 +6081,6082,6083,6084,6085,6086,6087,6088,6089,6090,6091,6092,6093,6094,6095,6096, # 1392 +6097,6098,6099,6100,6101,6102,6103,6104,6105,6106,6107,6108,6109,6110,6111,6112, # 1408 +6113,6114,2044,2060,4621, 997,1235, 473,1186,4622, 920,3378,6115,6116, 379,1108, # 1424 +4313,2657,2735,3934,6117,3809, 636,3233, 573,1026,3693,3435,2974,3300,2298,4105, # 1440 + 854,2937,2463, 393,2581,2417, 539, 752,1280,2750,2480, 140,1161, 440, 708,1569, # 1456 + 665,2497,1746,1291,1523,3000, 164,1603, 847,1331, 537,1997, 486, 508,1693,2418, # 1472 +1970,2227, 878,1220, 299,1030, 969, 652,2751, 624,1137,3301,2619, 65,3302,2045, # 1488 +1761,1859,3120,1930,3694,3516, 663,1767, 852, 835,3695, 269, 767,2826,2339,1305, # 1504 + 896,1150, 770,1616,6118, 506,1502,2075,1012,2519, 775,2520,2975,2340,2938,4314, # 1520 +3028,2086,1224,1943,2286,6119,3072,4315,2240,1273,1987,3935,1557, 175, 597, 985, # 1536 +3517,2419,2521,1416,3029, 585, 938,1931,1007,1052,1932,1685,6120,3379,4316,4623, # 1552 + 804, 599,3121,1333,2128,2539,1159,1554,2032,3810, 687,2033,2904, 952, 675,1467, # 1568 +3436,6121,2241,1096,1786,2440,1543,1924, 980,1813,2228, 781,2692,1879, 728,1918, # 1584 +3696,4624, 548,1950,4625,1809,1088,1356,3303,2522,1944, 502, 972, 373, 513,2827, # 1600 + 586,2377,2391,1003,1976,1631,6122,2464,1084, 648,1776,4626,2141, 324, 962,2012, # 1616 +2177,2076,1384, 742,2178,1448,1173,1810, 222, 102, 301, 445, 125,2420, 662,2498, # 1632 + 277, 200,1476,1165,1068, 224,2562,1378,1446, 450,1880, 659, 791, 582,4627,2939, # 1648 +3936,1516,1274, 555,2099,3697,1020,1389,1526,3380,1762,1723,1787,2229, 412,2114, # 1664 +1900,2392,3518, 512,2597, 427,1925,2341,3122,1653,1686,2465,2499, 697, 330, 273, # 1680 + 380,2162, 951, 832, 780, 991,1301,3073, 965,2270,3519, 668,2523,2636,1286, 535, # 1696 +1407, 518, 671, 957,2658,2378, 267, 611,2197,3030,6123, 248,2299, 967,1799,2356, # 1712 + 850,1418,3437,1876,1256,1480,2828,1718,6124,6125,1755,1664,2405,6126,4628,2879, # 1728 +2829, 499,2179, 676,4629, 557,2329,2214,2090, 325,3234, 464, 811,3001, 992,2342, # 1744 +2481,1232,1469, 303,2242, 466,1070,2163, 603,1777,2091,4630,2752,4631,2714, 322, # 1760 +2659,1964,1768, 481,2188,1463,2330,2857,3600,2092,3031,2421,4632,2318,2070,1849, # 1776 +2598,4633,1302,2254,1668,1701,2422,3811,2905,3032,3123,2046,4106,1763,1694,4634, # 1792 +1604, 943,1724,1454, 917, 868,2215,1169,2940, 552,1145,1800,1228,1823,1955, 316, # 1808 +1080,2510, 361,1807,2830,4107,2660,3381,1346,1423,1134,4108,6127, 541,1263,1229, # 1824 +1148,2540, 545, 465,1833,2880,3438,1901,3074,2482, 816,3937, 713,1788,2500, 122, # 1840 +1575, 195,1451,2501,1111,6128, 859, 374,1225,2243,2483,4317, 390,1033,3439,3075, # 1856 +2524,1687, 266, 793,1440,2599, 946, 779, 802, 507, 897,1081, 528,2189,1292, 711, # 1872 +1866,1725,1167,1640, 753, 398,2661,1053, 246, 348,4318, 137,1024,3440,1600,2077, # 1888 +2129, 825,4319, 698, 238, 521, 187,2300,1157,2423,1641,1605,1464,1610,1097,2541, # 1904 +1260,1436, 759,2255,1814,2150, 705,3235, 409,2563,3304, 561,3033,2005,2564, 726, # 1920 +1956,2343,3698,4109, 949,3812,3813,3520,1669, 653,1379,2525, 881,2198, 632,2256, # 1936 +1027, 778,1074, 733,1957, 514,1481,2466, 554,2180, 702,3938,1606,1017,1398,6129, # 1952 +1380,3521, 921, 993,1313, 594, 449,1489,1617,1166, 768,1426,1360, 495,1794,3601, # 1968 +1177,3602,1170,4320,2344, 476, 425,3167,4635,3168,1424, 401,2662,1171,3382,1998, # 1984 +1089,4110, 477,3169, 474,6130,1909, 596,2831,1842, 494, 693,1051,1028,1207,3076, # 2000 + 606,2115, 727,2790,1473,1115, 743,3522, 630, 805,1532,4321,2021, 366,1057, 838, # 2016 + 684,1114,2142,4322,2050,1492,1892,1808,2271,3814,2424,1971,1447,1373,3305,1090, # 2032 +1536,3939,3523,3306,1455,2199, 336, 369,2331,1035, 584,2393, 902, 718,2600,6131, # 2048 +2753, 463,2151,1149,1611,2467, 715,1308,3124,1268, 343,1413,3236,1517,1347,2663, # 2064 +2093,3940,2022,1131,1553,2100,2941,1427,3441,2942,1323,2484,6132,1980, 872,2368, # 2080 +2441,2943, 320,2369,2116,1082, 679,1933,3941,2791,3815, 625,1143,2023, 422,2200, # 2096 +3816,6133, 730,1695, 356,2257,1626,2301,2858,2637,1627,1778, 937, 883,2906,2693, # 2112 +3002,1769,1086, 400,1063,1325,3307,2792,4111,3077, 456,2345,1046, 747,6134,1524, # 2128 + 884,1094,3383,1474,2164,1059, 974,1688,2181,2258,1047, 345,1665,1187, 358, 875, # 2144 +3170, 305, 660,3524,2190,1334,1135,3171,1540,1649,2542,1527, 927, 968,2793, 885, # 2160 +1972,1850, 482, 500,2638,1218,1109,1085,2543,1654,2034, 876, 78,2287,1482,1277, # 2176 + 861,1675,1083,1779, 724,2754, 454, 397,1132,1612,2332, 893, 672,1237, 257,2259, # 2192 +2370, 135,3384, 337,2244, 547, 352, 340, 709,2485,1400, 788,1138,2511, 540, 772, # 2208 +1682,2260,2272,2544,2013,1843,1902,4636,1999,1562,2288,4637,2201,1403,1533, 407, # 2224 + 576,3308,1254,2071, 978,3385, 170, 136,1201,3125,2664,3172,2394, 213, 912, 873, # 2240 +3603,1713,2202, 699,3604,3699, 813,3442, 493, 531,1054, 468,2907,1483, 304, 281, # 2256 +4112,1726,1252,2094, 339,2319,2130,2639, 756,1563,2944, 748, 571,2976,1588,2425, # 2272 +2715,1851,1460,2426,1528,1392,1973,3237, 288,3309, 685,3386, 296, 892,2716,2216, # 2288 +1570,2245, 722,1747,2217, 905,3238,1103,6135,1893,1441,1965, 251,1805,2371,3700, # 2304 +2601,1919,1078, 75,2182,1509,1592,1270,2640,4638,2152,6136,3310,3817, 524, 706, # 2320 +1075, 292,3818,1756,2602, 317, 98,3173,3605,3525,1844,2218,3819,2502, 814, 567, # 2336 + 385,2908,1534,6137, 534,1642,3239, 797,6138,1670,1529, 953,4323, 188,1071, 538, # 2352 + 178, 729,3240,2109,1226,1374,2000,2357,2977, 731,2468,1116,2014,2051,6139,1261, # 2368 +1593, 803,2859,2736,3443, 556, 682, 823,1541,6140,1369,2289,1706,2794, 845, 462, # 2384 +2603,2665,1361, 387, 162,2358,1740, 739,1770,1720,1304,1401,3241,1049, 627,1571, # 2400 +2427,3526,1877,3942,1852,1500, 431,1910,1503, 677, 297,2795, 286,1433,1038,1198, # 2416 +2290,1133,1596,4113,4639,2469,1510,1484,3943,6141,2442, 108, 712,4640,2372, 866, # 2432 +3701,2755,3242,1348, 834,1945,1408,3527,2395,3243,1811, 824, 994,1179,2110,1548, # 2448 +1453, 790,3003, 690,4324,4325,2832,2909,3820,1860,3821, 225,1748, 310, 346,1780, # 2464 +2470, 821,1993,2717,2796, 828, 877,3528,2860,2471,1702,2165,2910,2486,1789, 453, # 2480 + 359,2291,1676, 73,1164,1461,1127,3311, 421, 604, 314,1037, 589, 116,2487, 737, # 2496 + 837,1180, 111, 244, 735,6142,2261,1861,1362, 986, 523, 418, 581,2666,3822, 103, # 2512 + 855, 503,1414,1867,2488,1091, 657,1597, 979, 605,1316,4641,1021,2443,2078,2001, # 2528 +1209, 96, 587,2166,1032, 260,1072,2153, 173, 94, 226,3244, 819,2006,4642,4114, # 2544 +2203, 231,1744, 782, 97,2667, 786,3387, 887, 391, 442,2219,4326,1425,6143,2694, # 2560 + 633,1544,1202, 483,2015, 592,2052,1958,2472,1655, 419, 129,4327,3444,3312,1714, # 2576 +1257,3078,4328,1518,1098, 865,1310,1019,1885,1512,1734, 469,2444, 148, 773, 436, # 2592 +1815,1868,1128,1055,4329,1245,2756,3445,2154,1934,1039,4643, 579,1238, 932,2320, # 2608 + 353, 205, 801, 115,2428, 944,2321,1881, 399,2565,1211, 678, 766,3944, 335,2101, # 2624 +1459,1781,1402,3945,2737,2131,1010, 844, 981,1326,1013, 550,1816,1545,2620,1335, # 2640 +1008, 371,2881, 936,1419,1613,3529,1456,1395,2273,1834,2604,1317,2738,2503, 416, # 2656 +1643,4330, 806,1126, 229, 591,3946,1314,1981,1576,1837,1666, 347,1790, 977,3313, # 2672 + 764,2861,1853, 688,2429,1920,1462, 77, 595, 415,2002,3034, 798,1192,4115,6144, # 2688 +2978,4331,3035,2695,2582,2072,2566, 430,2430,1727, 842,1396,3947,3702, 613, 377, # 2704 + 278, 236,1417,3388,3314,3174, 757,1869, 107,3530,6145,1194, 623,2262, 207,1253, # 2720 +2167,3446,3948, 492,1117,1935, 536,1838,2757,1246,4332, 696,2095,2406,1393,1572, # 2736 +3175,1782, 583, 190, 253,1390,2230, 830,3126,3389, 934,3245,1703,1749,2979,1870, # 2752 +2545,1656,2204, 869,2346,4116,3176,1817, 496,1764,4644, 942,1504, 404,1903,1122, # 2768 +1580,3606,2945,1022, 515, 372,1735, 955,2431,3036,6146,2797,1110,2302,2798, 617, # 2784 +6147, 441, 762,1771,3447,3607,3608,1904, 840,3037, 86, 939,1385, 572,1370,2445, # 2800 +1336, 114,3703, 898, 294, 203,3315, 703,1583,2274, 429, 961,4333,1854,1951,3390, # 2816 +2373,3704,4334,1318,1381, 966,1911,2322,1006,1155, 309, 989, 458,2718,1795,1372, # 2832 +1203, 252,1689,1363,3177, 517,1936, 168,1490, 562, 193,3823,1042,4117,1835, 551, # 2848 + 470,4645, 395, 489,3448,1871,1465,2583,2641, 417,1493, 279,1295, 511,1236,1119, # 2864 + 72,1231,1982,1812,3004, 871,1564, 984,3449,1667,2696,2096,4646,2347,2833,1673, # 2880 +3609, 695,3246,2668, 807,1183,4647, 890, 388,2333,1801,1457,2911,1765,1477,1031, # 2896 +3316,3317,1278,3391,2799,2292,2526, 163,3450,4335,2669,1404,1802,6148,2323,2407, # 2912 +1584,1728,1494,1824,1269, 298, 909,3318,1034,1632, 375, 776,1683,2061, 291, 210, # 2928 +1123, 809,1249,1002,2642,3038, 206,1011,2132, 144, 975, 882,1565, 342, 667, 754, # 2944 +1442,2143,1299,2303,2062, 447, 626,2205,1221,2739,2912,1144,1214,2206,2584, 760, # 2960 +1715, 614, 950,1281,2670,2621, 810, 577,1287,2546,4648, 242,2168, 250,2643, 691, # 2976 + 123,2644, 647, 313,1029, 689,1357,2946,1650, 216, 771,1339,1306, 808,2063, 549, # 2992 + 913,1371,2913,2914,6149,1466,1092,1174,1196,1311,2605,2396,1783,1796,3079, 406, # 3008 +2671,2117,3949,4649, 487,1825,2220,6150,2915, 448,2348,1073,6151,2397,1707, 130, # 3024 + 900,1598, 329, 176,1959,2527,1620,6152,2275,4336,3319,1983,2191,3705,3610,2155, # 3040 +3706,1912,1513,1614,6153,1988, 646, 392,2304,1589,3320,3039,1826,1239,1352,1340, # 3056 +2916, 505,2567,1709,1437,2408,2547, 906,6154,2672, 384,1458,1594,1100,1329, 710, # 3072 + 423,3531,2064,2231,2622,1989,2673,1087,1882, 333, 841,3005,1296,2882,2379, 580, # 3088 +1937,1827,1293,2585, 601, 574, 249,1772,4118,2079,1120, 645, 901,1176,1690, 795, # 3104 +2207, 478,1434, 516,1190,1530, 761,2080, 930,1264, 355, 435,1552, 644,1791, 987, # 3120 + 220,1364,1163,1121,1538, 306,2169,1327,1222, 546,2645, 218, 241, 610,1704,3321, # 3136 +1984,1839,1966,2528, 451,6155,2586,3707,2568, 907,3178, 254,2947, 186,1845,4650, # 3152 + 745, 432,1757, 428,1633, 888,2246,2221,2489,3611,2118,1258,1265, 956,3127,1784, # 3168 +4337,2490, 319, 510, 119, 457,3612, 274,2035,2007,4651,1409,3128, 970,2758, 590, # 3184 +2800, 661,2247,4652,2008,3950,1420,1549,3080,3322,3951,1651,1375,2111, 485,2491, # 3200 +1429,1156,6156,2548,2183,1495, 831,1840,2529,2446, 501,1657, 307,1894,3247,1341, # 3216 + 666, 899,2156,1539,2549,1559, 886, 349,2208,3081,2305,1736,3824,2170,2759,1014, # 3232 +1913,1386, 542,1397,2948, 490, 368, 716, 362, 159, 282,2569,1129,1658,1288,1750, # 3248 +2674, 276, 649,2016, 751,1496, 658,1818,1284,1862,2209,2087,2512,3451, 622,2834, # 3264 + 376, 117,1060,2053,1208,1721,1101,1443, 247,1250,3179,1792,3952,2760,2398,3953, # 3280 +6157,2144,3708, 446,2432,1151,2570,3452,2447,2761,2835,1210,2448,3082, 424,2222, # 3296 +1251,2449,2119,2836, 504,1581,4338, 602, 817, 857,3825,2349,2306, 357,3826,1470, # 3312 +1883,2883, 255, 958, 929,2917,3248, 302,4653,1050,1271,1751,2307,1952,1430,2697, # 3328 +2719,2359, 354,3180, 777, 158,2036,4339,1659,4340,4654,2308,2949,2248,1146,2232, # 3344 +3532,2720,1696,2623,3827,6158,3129,1550,2698,1485,1297,1428, 637, 931,2721,2145, # 3360 + 914,2550,2587, 81,2450, 612, 827,2646,1242,4655,1118,2884, 472,1855,3181,3533, # 3376 +3534, 569,1353,2699,1244,1758,2588,4119,2009,2762,2171,3709,1312,1531,6159,1152, # 3392 +1938, 134,1830, 471,3710,2276,1112,1535,3323,3453,3535, 982,1337,2950, 488, 826, # 3408 + 674,1058,1628,4120,2017, 522,2399, 211, 568,1367,3454, 350, 293,1872,1139,3249, # 3424 +1399,1946,3006,1300,2360,3324, 588, 736,6160,2606, 744, 669,3536,3828,6161,1358, # 3440 + 199, 723, 848, 933, 851,1939,1505,1514,1338,1618,1831,4656,1634,3613, 443,2740, # 3456 +3829, 717,1947, 491,1914,6162,2551,1542,4121,1025,6163,1099,1223, 198,3040,2722, # 3472 + 370, 410,1905,2589, 998,1248,3182,2380, 519,1449,4122,1710, 947, 928,1153,4341, # 3488 +2277, 344,2624,1511, 615, 105, 161,1212,1076,1960,3130,2054,1926,1175,1906,2473, # 3504 + 414,1873,2801,6164,2309, 315,1319,3325, 318,2018,2146,2157, 963, 631, 223,4342, # 3520 +4343,2675, 479,3711,1197,2625,3712,2676,2361,6165,4344,4123,6166,2451,3183,1886, # 3536 +2184,1674,1330,1711,1635,1506, 799, 219,3250,3083,3954,1677,3713,3326,2081,3614, # 3552 +1652,2073,4657,1147,3041,1752, 643,1961, 147,1974,3955,6167,1716,2037, 918,3007, # 3568 +1994, 120,1537, 118, 609,3184,4345, 740,3455,1219, 332,1615,3830,6168,1621,2980, # 3584 +1582, 783, 212, 553,2350,3714,1349,2433,2082,4124, 889,6169,2310,1275,1410, 973, # 3600 + 166,1320,3456,1797,1215,3185,2885,1846,2590,2763,4658, 629, 822,3008, 763, 940, # 3616 +1990,2862, 439,2409,1566,1240,1622, 926,1282,1907,2764, 654,2210,1607, 327,1130, # 3632 +3956,1678,1623,6170,2434,2192, 686, 608,3831,3715, 903,3957,3042,6171,2741,1522, # 3648 +1915,1105,1555,2552,1359, 323,3251,4346,3457, 738,1354,2553,2311,2334,1828,2003, # 3664 +3832,1753,2351,1227,6172,1887,4125,1478,6173,2410,1874,1712,1847, 520,1204,2607, # 3680 + 264,4659, 836,2677,2102, 600,4660,3833,2278,3084,6174,4347,3615,1342, 640, 532, # 3696 + 543,2608,1888,2400,2591,1009,4348,1497, 341,1737,3616,2723,1394, 529,3252,1321, # 3712 + 983,4661,1515,2120, 971,2592, 924, 287,1662,3186,4349,2700,4350,1519, 908,1948, # 3728 +2452, 156, 796,1629,1486,2223,2055, 694,4126,1259,1036,3392,1213,2249,2742,1889, # 3744 +1230,3958,1015, 910, 408, 559,3617,4662, 746, 725, 935,4663,3959,3009,1289, 563, # 3760 + 867,4664,3960,1567,2981,2038,2626, 988,2263,2381,4351, 143,2374, 704,1895,6175, # 3776 +1188,3716,2088, 673,3085,2362,4352, 484,1608,1921,2765,2918, 215, 904,3618,3537, # 3792 + 894, 509, 976,3043,2701,3961,4353,2837,2982, 498,6176,6177,1102,3538,1332,3393, # 3808 +1487,1636,1637, 233, 245,3962, 383, 650, 995,3044, 460,1520,1206,2352, 749,3327, # 3824 + 530, 700, 389,1438,1560,1773,3963,2264, 719,2951,2724,3834, 870,1832,1644,1000, # 3840 + 839,2474,3717, 197,1630,3394, 365,2886,3964,1285,2133, 734, 922, 818,1106, 732, # 3856 + 480,2083,1774,3458, 923,2279,1350, 221,3086, 85,2233,2234,3835,1585,3010,2147, # 3872 +1387,1705,2382,1619,2475, 133, 239,2802,1991,1016,2084,2383, 411,2838,1113, 651, # 3888 +1985,1160,3328, 990,1863,3087,1048,1276,2647, 265,2627,1599,3253,2056, 150, 638, # 3904 +2019, 656, 853, 326,1479, 680,1439,4354,1001,1759, 413,3459,3395,2492,1431, 459, # 3920 +4355,1125,3329,2265,1953,1450,2065,2863, 849, 351,2678,3131,3254,3255,1104,1577, # 3936 + 227,1351,1645,2453,2193,1421,2887, 812,2121, 634, 95,2435, 201,2312,4665,1646, # 3952 +1671,2743,1601,2554,2702,2648,2280,1315,1366,2089,3132,1573,3718,3965,1729,1189, # 3968 + 328,2679,1077,1940,1136, 558,1283, 964,1195, 621,2074,1199,1743,3460,3619,1896, # 3984 +1916,1890,3836,2952,1154,2112,1064, 862, 378,3011,2066,2113,2803,1568,2839,6178, # 4000 +3088,2919,1941,1660,2004,1992,2194, 142, 707,1590,1708,1624,1922,1023,1836,1233, # 4016 +1004,2313, 789, 741,3620,6179,1609,2411,1200,4127,3719,3720,4666,2057,3721, 593, # 4032 +2840, 367,2920,1878,6180,3461,1521, 628,1168, 692,2211,2649, 300, 720,2067,2571, # 4048 +2953,3396, 959,2504,3966,3539,3462,1977, 701,6181, 954,1043, 800, 681, 183,3722, # 4064 +1803,1730,3540,4128,2103, 815,2314, 174, 467, 230,2454,1093,2134, 755,3541,3397, # 4080 +1141,1162,6182,1738,2039, 270,3256,2513,1005,1647,2185,3837, 858,1679,1897,1719, # 4096 +2954,2324,1806, 402, 670, 167,4129,1498,2158,2104, 750,6183, 915, 189,1680,1551, # 4112 + 455,4356,1501,2455, 405,1095,2955, 338,1586,1266,1819, 570, 641,1324, 237,1556, # 4128 +2650,1388,3723,6184,1368,2384,1343,1978,3089,2436, 879,3724, 792,1191, 758,3012, # 4144 +1411,2135,1322,4357, 240,4667,1848,3725,1574,6185, 420,3045,1546,1391, 714,4358, # 4160 +1967, 941,1864, 863, 664, 426, 560,1731,2680,1785,2864,1949,2363, 403,3330,1415, # 4176 +1279,2136,1697,2335, 204, 721,2097,3838, 90,6186,2085,2505, 191,3967, 124,2148, # 4192 +1376,1798,1178,1107,1898,1405, 860,4359,1243,1272,2375,2983,1558,2456,1638, 113, # 4208 +3621, 578,1923,2609, 880, 386,4130, 784,2186,2266,1422,2956,2172,1722, 497, 263, # 4224 +2514,1267,2412,2610, 177,2703,3542, 774,1927,1344, 616,1432,1595,1018, 172,4360, # 4240 +2325, 911,4361, 438,1468,3622, 794,3968,2024,2173,1681,1829,2957, 945, 895,3090, # 4256 + 575,2212,2476, 475,2401,2681, 785,2744,1745,2293,2555,1975,3133,2865, 394,4668, # 4272 +3839, 635,4131, 639, 202,1507,2195,2766,1345,1435,2572,3726,1908,1184,1181,2457, # 4288 +3727,3134,4362, 843,2611, 437, 916,4669, 234, 769,1884,3046,3047,3623, 833,6187, # 4304 +1639,2250,2402,1355,1185,2010,2047, 999, 525,1732,1290,1488,2612, 948,1578,3728, # 4320 +2413,2477,1216,2725,2159, 334,3840,1328,3624,2921,1525,4132, 564,1056, 891,4363, # 4336 +1444,1698,2385,2251,3729,1365,2281,2235,1717,6188, 864,3841,2515, 444, 527,2767, # 4352 +2922,3625, 544, 461,6189, 566, 209,2437,3398,2098,1065,2068,3331,3626,3257,2137, # 4368 #last 512 +) +# fmt: on diff --git a/venv/lib/python3.10/site-packages/chardet/langbulgarianmodel.py b/venv/lib/python3.10/site-packages/chardet/langbulgarianmodel.py new file mode 100644 index 0000000000000000000000000000000000000000..2f771bb8170c1ea374bbfb5156c6d4949ed6e59a --- /dev/null +++ b/venv/lib/python3.10/site-packages/chardet/langbulgarianmodel.py @@ -0,0 +1,4649 @@ +from chardet.sbcharsetprober import SingleByteCharSetModel + +# 3: Positive +# 2: Likely +# 1: Unlikely +# 0: Negative + +BULGARIAN_LANG_MODEL = { + 63: { # 'e' + 63: 1, # 'e' + 45: 0, # '\xad' + 31: 0, # 'А' + 32: 0, # 'Б' + 35: 0, # 'В' + 43: 0, # 'Г' + 37: 0, # 'Д' + 44: 0, # 'Е' + 55: 0, # 'Ж' + 47: 0, # 'З' + 40: 0, # 'И' + 59: 0, # 'Й' + 33: 0, # 'К' + 46: 0, # 'Л' + 38: 0, # 'М' + 36: 0, # 'Н' + 41: 0, # 'О' + 30: 0, # 'П' + 39: 0, # 'Р' + 28: 0, # 'С' + 34: 0, # 'Т' + 51: 0, # 'У' + 48: 0, # 'Ф' + 49: 0, # 'Х' + 53: 0, # 'Ц' + 50: 0, # 'Ч' + 54: 0, # 'Ш' + 57: 0, # 'Щ' + 61: 0, # 'Ъ' + 60: 0, # 'Ю' + 56: 0, # 'Я' + 1: 0, # 'а' + 18: 1, # 'б' + 9: 1, # 'в' + 20: 1, # 'г' + 11: 1, # 'д' + 3: 1, # 'е' + 23: 1, # 'ж' + 15: 1, # 'з' + 2: 0, # 'и' + 26: 1, # 'й' + 12: 1, # 'к' + 10: 1, # 'л' + 14: 1, # 'м' + 6: 1, # 'н' + 4: 1, # 'о' + 13: 1, # 'п' + 7: 1, # 'р' + 8: 1, # 'с' + 5: 1, # 'т' + 19: 0, # 'у' + 29: 1, # 'ф' + 25: 1, # 'х' + 22: 0, # 'ц' + 21: 1, # 'ч' + 27: 1, # 'ш' + 24: 1, # 'щ' + 17: 0, # 'ъ' + 52: 0, # 'ь' + 42: 0, # 'ю' + 16: 1, # 'я' + 58: 0, # 'є' + 62: 0, # '№' + }, + 45: { # '\xad' + 63: 0, # 'e' + 45: 0, # '\xad' + 31: 0, # 'А' + 32: 1, # 'Б' + 35: 1, # 'В' + 43: 0, # 'Г' + 37: 1, # 'Д' + 44: 0, # 'Е' + 55: 0, # 'Ж' + 47: 0, # 'З' + 40: 1, # 'И' + 59: 0, # 'Й' + 33: 1, # 'К' + 46: 0, # 'Л' + 38: 1, # 'М' + 36: 0, # 'Н' + 41: 1, # 'О' + 30: 1, # 'П' + 39: 1, # 'Р' + 28: 1, # 'С' + 34: 0, # 'Т' + 51: 0, # 'У' + 48: 0, # 'Ф' + 49: 1, # 'Х' + 53: 0, # 'Ц' + 50: 0, # 'Ч' + 54: 0, # 'Ш' + 57: 0, # 'Щ' + 61: 0, # 'Ъ' + 60: 0, # 'Ю' + 56: 0, # 'Я' + 1: 0, # 'а' + 18: 0, # 'б' + 9: 0, # 'в' + 20: 0, # 'г' + 11: 0, # 'д' + 3: 0, # 'е' + 23: 0, # 'ж' + 15: 0, # 'з' + 2: 0, # 'и' + 26: 0, # 'й' + 12: 0, # 'к' + 10: 0, # 'л' + 14: 0, # 'м' + 6: 0, # 'н' + 4: 0, # 'о' + 13: 0, # 'п' + 7: 0, # 'р' + 8: 0, # 'с' + 5: 0, # 'т' + 19: 0, # 'у' + 29: 0, # 'ф' + 25: 0, # 'х' + 22: 0, # 'ц' + 21: 0, # 'ч' + 27: 0, # 'ш' + 24: 0, # 'щ' + 17: 0, # 'ъ' + 52: 0, # 'ь' + 42: 0, # 'ю' + 16: 0, # 'я' + 58: 0, # 'є' + 62: 0, # '№' + }, + 31: { # 'А' + 63: 0, # 'e' + 45: 1, # '\xad' + 31: 1, # 'А' + 32: 1, # 'Б' + 35: 2, # 'В' + 43: 1, # 'Г' + 37: 2, # 'Д' + 44: 2, # 'Е' + 55: 1, # 'Ж' + 47: 2, # 'З' + 40: 1, # 'И' + 59: 1, # 'Й' + 33: 1, # 'К' + 46: 2, # 'Л' + 38: 1, # 'М' + 36: 2, # 'Н' + 41: 1, # 'О' + 30: 2, # 'П' + 39: 2, # 'Р' + 28: 2, # 'С' + 34: 2, # 'Т' + 51: 1, # 'У' + 48: 2, # 'Ф' + 49: 1, # 'Х' + 53: 1, # 'Ц' + 50: 1, # 'Ч' + 54: 1, # 'Ш' + 57: 2, # 'Щ' + 61: 0, # 'Ъ' + 60: 0, # 'Ю' + 56: 1, # 'Я' + 1: 1, # 'а' + 18: 2, # 'б' + 9: 2, # 'в' + 20: 2, # 'г' + 11: 2, # 'д' + 3: 1, # 'е' + 23: 1, # 'ж' + 15: 2, # 'з' + 2: 0, # 'и' + 26: 2, # 'й' + 12: 2, # 'к' + 10: 3, # 'л' + 14: 2, # 'м' + 6: 3, # 'н' + 4: 0, # 'о' + 13: 2, # 'п' + 7: 2, # 'р' + 8: 2, # 'с' + 5: 2, # 'т' + 19: 1, # 'у' + 29: 2, # 'ф' + 25: 1, # 'х' + 22: 1, # 'ц' + 21: 1, # 'ч' + 27: 1, # 'ш' + 24: 0, # 'щ' + 17: 0, # 'ъ' + 52: 0, # 'ь' + 42: 0, # 'ю' + 16: 1, # 'я' + 58: 0, # 'є' + 62: 0, # '№' + }, + 32: { # 'Б' + 63: 0, # 'e' + 45: 0, # '\xad' + 31: 2, # 'А' + 32: 2, # 'Б' + 35: 1, # 'В' + 43: 1, # 'Г' + 37: 2, # 'Д' + 44: 1, # 'Е' + 55: 1, # 'Ж' + 47: 2, # 'З' + 40: 1, # 'И' + 59: 0, # 'Й' + 33: 1, # 'К' + 46: 1, # 'Л' + 38: 1, # 'М' + 36: 2, # 'Н' + 41: 2, # 'О' + 30: 1, # 'П' + 39: 1, # 'Р' + 28: 2, # 'С' + 34: 2, # 'Т' + 51: 1, # 'У' + 48: 2, # 'Ф' + 49: 1, # 'Х' + 53: 1, # 'Ц' + 50: 1, # 'Ч' + 54: 0, # 'Ш' + 57: 1, # 'Щ' + 61: 2, # 'Ъ' + 60: 1, # 'Ю' + 56: 1, # 'Я' + 1: 3, # 'а' + 18: 0, # 'б' + 9: 0, # 'в' + 20: 0, # 'г' + 11: 1, # 'д' + 3: 3, # 'е' + 23: 0, # 'ж' + 15: 0, # 'з' + 2: 2, # 'и' + 26: 0, # 'й' + 12: 0, # 'к' + 10: 2, # 'л' + 14: 0, # 'м' + 6: 0, # 'н' + 4: 3, # 'о' + 13: 0, # 'п' + 7: 2, # 'р' + 8: 1, # 'с' + 5: 0, # 'т' + 19: 2, # 'у' + 29: 0, # 'ф' + 25: 1, # 'х' + 22: 0, # 'ц' + 21: 0, # 'ч' + 27: 0, # 'ш' + 24: 0, # 'щ' + 17: 3, # 'ъ' + 52: 1, # 'ь' + 42: 1, # 'ю' + 16: 2, # 'я' + 58: 0, # 'є' + 62: 0, # '№' + }, + 35: { # 'В' + 63: 0, # 'e' + 45: 0, # '\xad' + 31: 2, # 'А' + 32: 1, # 'Б' + 35: 1, # 'В' + 43: 0, # 'Г' + 37: 1, # 'Д' + 44: 2, # 'Е' + 55: 0, # 'Ж' + 47: 0, # 'З' + 40: 2, # 'И' + 59: 0, # 'Й' + 33: 1, # 'К' + 46: 1, # 'Л' + 38: 1, # 'М' + 36: 1, # 'Н' + 41: 1, # 'О' + 30: 1, # 'П' + 39: 2, # 'Р' + 28: 2, # 'С' + 34: 1, # 'Т' + 51: 1, # 'У' + 48: 2, # 'Ф' + 49: 0, # 'Х' + 53: 1, # 'Ц' + 50: 0, # 'Ч' + 54: 0, # 'Ш' + 57: 0, # 'Щ' + 61: 1, # 'Ъ' + 60: 1, # 'Ю' + 56: 2, # 'Я' + 1: 3, # 'а' + 18: 1, # 'б' + 9: 0, # 'в' + 20: 0, # 'г' + 11: 1, # 'д' + 3: 3, # 'е' + 23: 1, # 'ж' + 15: 2, # 'з' + 2: 3, # 'и' + 26: 0, # 'й' + 12: 1, # 'к' + 10: 2, # 'л' + 14: 1, # 'м' + 6: 2, # 'н' + 4: 2, # 'о' + 13: 1, # 'п' + 7: 2, # 'р' + 8: 2, # 'с' + 5: 2, # 'т' + 19: 1, # 'у' + 29: 0, # 'ф' + 25: 1, # 'х' + 22: 0, # 'ц' + 21: 2, # 'ч' + 27: 0, # 'ш' + 24: 0, # 'щ' + 17: 2, # 'ъ' + 52: 1, # 'ь' + 42: 1, # 'ю' + 16: 1, # 'я' + 58: 0, # 'є' + 62: 0, # '№' + }, + 43: { # 'Г' + 63: 0, # 'e' + 45: 0, # '\xad' + 31: 2, # 'А' + 32: 1, # 'Б' + 35: 0, # 'В' + 43: 0, # 'Г' + 37: 1, # 'Д' + 44: 2, # 'Е' + 55: 0, # 'Ж' + 47: 1, # 'З' + 40: 1, # 'И' + 59: 0, # 'Й' + 33: 1, # 'К' + 46: 1, # 'Л' + 38: 0, # 'М' + 36: 1, # 'Н' + 41: 1, # 'О' + 30: 0, # 'П' + 39: 1, # 'Р' + 28: 1, # 'С' + 34: 0, # 'Т' + 51: 1, # 'У' + 48: 1, # 'Ф' + 49: 0, # 'Х' + 53: 0, # 'Ц' + 50: 0, # 'Ч' + 54: 0, # 'Ш' + 57: 1, # 'Щ' + 61: 1, # 'Ъ' + 60: 0, # 'Ю' + 56: 0, # 'Я' + 1: 2, # 'а' + 18: 1, # 'б' + 9: 1, # 'в' + 20: 0, # 'г' + 11: 1, # 'д' + 3: 3, # 'е' + 23: 1, # 'ж' + 15: 0, # 'з' + 2: 2, # 'и' + 26: 0, # 'й' + 12: 1, # 'к' + 10: 2, # 'л' + 14: 1, # 'м' + 6: 1, # 'н' + 4: 2, # 'о' + 13: 0, # 'п' + 7: 2, # 'р' + 8: 0, # 'с' + 5: 0, # 'т' + 19: 2, # 'у' + 29: 0, # 'ф' + 25: 0, # 'х' + 22: 0, # 'ц' + 21: 0, # 'ч' + 27: 0, # 'ш' + 24: 1, # 'щ' + 17: 2, # 'ъ' + 52: 1, # 'ь' + 42: 1, # 'ю' + 16: 1, # 'я' + 58: 0, # 'є' + 62: 0, # '№' + }, + 37: { # 'Д' + 63: 0, # 'e' + 45: 0, # '\xad' + 31: 2, # 'А' + 32: 1, # 'Б' + 35: 2, # 'В' + 43: 1, # 'Г' + 37: 2, # 'Д' + 44: 2, # 'Е' + 55: 2, # 'Ж' + 47: 1, # 'З' + 40: 2, # 'И' + 59: 0, # 'Й' + 33: 1, # 'К' + 46: 1, # 'Л' + 38: 1, # 'М' + 36: 1, # 'Н' + 41: 2, # 'О' + 30: 2, # 'П' + 39: 1, # 'Р' + 28: 2, # 'С' + 34: 1, # 'Т' + 51: 1, # 'У' + 48: 1, # 'Ф' + 49: 0, # 'Х' + 53: 1, # 'Ц' + 50: 1, # 'Ч' + 54: 0, # 'Ш' + 57: 0, # 'Щ' + 61: 1, # 'Ъ' + 60: 1, # 'Ю' + 56: 1, # 'Я' + 1: 3, # 'а' + 18: 0, # 'б' + 9: 2, # 'в' + 20: 0, # 'г' + 11: 0, # 'д' + 3: 3, # 'е' + 23: 3, # 'ж' + 15: 1, # 'з' + 2: 3, # 'и' + 26: 0, # 'й' + 12: 0, # 'к' + 10: 1, # 'л' + 14: 1, # 'м' + 6: 2, # 'н' + 4: 3, # 'о' + 13: 0, # 'п' + 7: 2, # 'р' + 8: 0, # 'с' + 5: 0, # 'т' + 19: 2, # 'у' + 29: 0, # 'ф' + 25: 0, # 'х' + 22: 0, # 'ц' + 21: 0, # 'ч' + 27: 0, # 'ш' + 24: 0, # 'щ' + 17: 2, # 'ъ' + 52: 1, # 'ь' + 42: 2, # 'ю' + 16: 1, # 'я' + 58: 0, # 'є' + 62: 0, # '№' + }, + 44: { # 'Е' + 63: 0, # 'e' + 45: 0, # '\xad' + 31: 1, # 'А' + 32: 1, # 'Б' + 35: 2, # 'В' + 43: 1, # 'Г' + 37: 1, # 'Д' + 44: 1, # 'Е' + 55: 1, # 'Ж' + 47: 1, # 'З' + 40: 1, # 'И' + 59: 1, # 'Й' + 33: 2, # 'К' + 46: 2, # 'Л' + 38: 1, # 'М' + 36: 2, # 'Н' + 41: 2, # 'О' + 30: 1, # 'П' + 39: 2, # 'Р' + 28: 2, # 'С' + 34: 2, # 'Т' + 51: 1, # 'У' + 48: 2, # 'Ф' + 49: 1, # 'Х' + 53: 2, # 'Ц' + 50: 1, # 'Ч' + 54: 1, # 'Ш' + 57: 1, # 'Щ' + 61: 0, # 'Ъ' + 60: 0, # 'Ю' + 56: 1, # 'Я' + 1: 0, # 'а' + 18: 1, # 'б' + 9: 2, # 'в' + 20: 1, # 'г' + 11: 2, # 'д' + 3: 0, # 'е' + 23: 1, # 'ж' + 15: 1, # 'з' + 2: 0, # 'и' + 26: 1, # 'й' + 12: 2, # 'к' + 10: 2, # 'л' + 14: 2, # 'м' + 6: 2, # 'н' + 4: 0, # 'о' + 13: 1, # 'п' + 7: 2, # 'р' + 8: 2, # 'с' + 5: 1, # 'т' + 19: 1, # 'у' + 29: 1, # 'ф' + 25: 1, # 'х' + 22: 0, # 'ц' + 21: 1, # 'ч' + 27: 1, # 'ш' + 24: 1, # 'щ' + 17: 1, # 'ъ' + 52: 0, # 'ь' + 42: 1, # 'ю' + 16: 1, # 'я' + 58: 0, # 'є' + 62: 0, # '№' + }, + 55: { # 'Ж' + 63: 0, # 'e' + 45: 0, # '\xad' + 31: 1, # 'А' + 32: 0, # 'Б' + 35: 1, # 'В' + 43: 0, # 'Г' + 37: 1, # 'Д' + 44: 1, # 'Е' + 55: 0, # 'Ж' + 47: 0, # 'З' + 40: 1, # 'И' + 59: 0, # 'Й' + 33: 1, # 'К' + 46: 0, # 'Л' + 38: 0, # 'М' + 36: 1, # 'Н' + 41: 1, # 'О' + 30: 0, # 'П' + 39: 0, # 'Р' + 28: 0, # 'С' + 34: 0, # 'Т' + 51: 1, # 'У' + 48: 0, # 'Ф' + 49: 0, # 'Х' + 53: 0, # 'Ц' + 50: 0, # 'Ч' + 54: 0, # 'Ш' + 57: 0, # 'Щ' + 61: 0, # 'Ъ' + 60: 0, # 'Ю' + 56: 0, # 'Я' + 1: 2, # 'а' + 18: 0, # 'б' + 9: 0, # 'в' + 20: 0, # 'г' + 11: 1, # 'д' + 3: 2, # 'е' + 23: 0, # 'ж' + 15: 0, # 'з' + 2: 2, # 'и' + 26: 0, # 'й' + 12: 0, # 'к' + 10: 0, # 'л' + 14: 0, # 'м' + 6: 0, # 'н' + 4: 2, # 'о' + 13: 1, # 'п' + 7: 1, # 'р' + 8: 0, # 'с' + 5: 0, # 'т' + 19: 1, # 'у' + 29: 0, # 'ф' + 25: 0, # 'х' + 22: 0, # 'ц' + 21: 0, # 'ч' + 27: 0, # 'ш' + 24: 0, # 'щ' + 17: 1, # 'ъ' + 52: 1, # 'ь' + 42: 1, # 'ю' + 16: 0, # 'я' + 58: 0, # 'є' + 62: 0, # '№' + }, + 47: { # 'З' + 63: 0, # 'e' + 45: 0, # '\xad' + 31: 2, # 'А' + 32: 1, # 'Б' + 35: 1, # 'В' + 43: 1, # 'Г' + 37: 1, # 'Д' + 44: 1, # 'Е' + 55: 0, # 'Ж' + 47: 1, # 'З' + 40: 1, # 'И' + 59: 0, # 'Й' + 33: 1, # 'К' + 46: 1, # 'Л' + 38: 1, # 'М' + 36: 2, # 'Н' + 41: 1, # 'О' + 30: 1, # 'П' + 39: 1, # 'Р' + 28: 1, # 'С' + 34: 1, # 'Т' + 51: 1, # 'У' + 48: 0, # 'Ф' + 49: 1, # 'Х' + 53: 1, # 'Ц' + 50: 0, # 'Ч' + 54: 0, # 'Ш' + 57: 0, # 'Щ' + 61: 1, # 'Ъ' + 60: 0, # 'Ю' + 56: 1, # 'Я' + 1: 3, # 'а' + 18: 1, # 'б' + 9: 2, # 'в' + 20: 1, # 'г' + 11: 2, # 'д' + 3: 2, # 'е' + 23: 0, # 'ж' + 15: 0, # 'з' + 2: 1, # 'и' + 26: 0, # 'й' + 12: 0, # 'к' + 10: 2, # 'л' + 14: 1, # 'м' + 6: 1, # 'н' + 4: 1, # 'о' + 13: 0, # 'п' + 7: 1, # 'р' + 8: 0, # 'с' + 5: 0, # 'т' + 19: 1, # 'у' + 29: 0, # 'ф' + 25: 0, # 'х' + 22: 0, # 'ц' + 21: 0, # 'ч' + 27: 0, # 'ш' + 24: 0, # 'щ' + 17: 1, # 'ъ' + 52: 0, # 'ь' + 42: 1, # 'ю' + 16: 0, # 'я' + 58: 0, # 'є' + 62: 0, # '№' + }, + 40: { # 'И' + 63: 0, # 'e' + 45: 1, # '\xad' + 31: 1, # 'А' + 32: 1, # 'Б' + 35: 1, # 'В' + 43: 1, # 'Г' + 37: 1, # 'Д' + 44: 2, # 'Е' + 55: 1, # 'Ж' + 47: 2, # 'З' + 40: 1, # 'И' + 59: 1, # 'Й' + 33: 2, # 'К' + 46: 2, # 'Л' + 38: 2, # 'М' + 36: 2, # 'Н' + 41: 1, # 'О' + 30: 1, # 'П' + 39: 2, # 'Р' + 28: 2, # 'С' + 34: 2, # 'Т' + 51: 0, # 'У' + 48: 1, # 'Ф' + 49: 1, # 'Х' + 53: 1, # 'Ц' + 50: 1, # 'Ч' + 54: 1, # 'Ш' + 57: 1, # 'Щ' + 61: 0, # 'Ъ' + 60: 0, # 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'7' + 56: 252, # '8' + 57: 252, # '9' + 58: 253, # ':' + 59: 253, # ';' + 60: 253, # '<' + 61: 253, # '=' + 62: 253, # '>' + 63: 253, # '?' + 64: 253, # '@' + 65: 77, # 'A' + 66: 90, # 'B' + 67: 99, # 'C' + 68: 100, # 'D' + 69: 72, # 'E' + 70: 109, # 'F' + 71: 107, # 'G' + 72: 101, # 'H' + 73: 79, # 'I' + 74: 185, # 'J' + 75: 81, # 'K' + 76: 102, # 'L' + 77: 76, # 'M' + 78: 94, # 'N' + 79: 82, # 'O' + 80: 110, # 'P' + 81: 186, # 'Q' + 82: 108, # 'R' + 83: 91, # 'S' + 84: 74, # 'T' + 85: 119, # 'U' + 86: 84, # 'V' + 87: 96, # 'W' + 88: 111, # 'X' + 89: 187, # 'Y' + 90: 115, # 'Z' + 91: 253, # '[' + 92: 253, # '\\' + 93: 253, # ']' + 94: 253, # '^' + 95: 253, # '_' + 96: 253, # '`' + 97: 65, # 'a' + 98: 69, # 'b' + 99: 70, # 'c' + 100: 66, # 'd' + 101: 63, # 'e' + 102: 68, # 'f' + 103: 112, # 'g' + 104: 103, # 'h' + 105: 92, # 'i' + 106: 194, # 'j' + 107: 104, # 'k' + 108: 95, # 'l' + 109: 86, # 'm' + 110: 87, # 'n' + 111: 71, # 'o' + 112: 116, # 'p' + 113: 195, # 'q' + 114: 85, # 'r' + 115: 93, # 's' + 116: 97, # 't' + 117: 113, # 'u' + 118: 196, # 'v' + 119: 197, # 'w' + 120: 198, # 'x' + 121: 199, # 'y' + 122: 200, # 'z' + 123: 253, # '{' + 124: 253, # '|' + 125: 253, # '}' + 126: 253, # '~' + 127: 253, # '\x7f' + 128: 206, # 'Ђ' + 129: 207, # 'Ѓ' + 130: 208, # '‚' + 131: 209, # 'ѓ' + 132: 210, # '„' + 133: 211, # '…' + 134: 212, # '†' + 135: 213, # '‡' + 136: 120, # '€' + 137: 214, # '‰' + 138: 215, # 'Љ' + 139: 216, # '‹' + 140: 217, # 'Њ' + 141: 218, # 'Ќ' + 142: 219, # 'Ћ' + 143: 220, # 'Џ' + 144: 221, # 'ђ' + 145: 78, # '‘' + 146: 64, # '’' + 147: 83, # '“' + 148: 121, # '”' + 149: 98, # '•' + 150: 117, # '–' + 151: 105, # '—' + 152: 222, # None + 153: 223, # '™' + 154: 224, # 'љ' + 155: 225, # '›' + 156: 226, # 'њ' + 157: 227, # 'ќ' + 158: 228, # 'ћ' + 159: 229, # 'џ' + 160: 88, # '\xa0' + 161: 230, # 'Ў' + 162: 231, # 'ў' + 163: 232, # 'Ј' + 164: 233, # '¤' + 165: 122, # 'Ґ' + 166: 89, # '¦' + 167: 106, # '§' + 168: 234, # 'Ё' + 169: 235, # '©' + 170: 236, # 'Є' + 171: 237, # '«' + 172: 238, # '¬' + 173: 45, # '\xad' + 174: 239, # '®' + 175: 240, # 'Ї' + 176: 73, # '°' + 177: 80, # '±' + 178: 118, # 'І' + 179: 114, # 'і' + 180: 241, # 'ґ' + 181: 242, # 'µ' + 182: 243, # '¶' + 183: 244, # '·' + 184: 245, # 'ё' + 185: 62, # '№' + 186: 58, # 'є' + 187: 246, # '»' + 188: 247, # 'ј' + 189: 248, # 'Ѕ' + 190: 249, # 'ѕ' + 191: 250, # 'ї' + 192: 31, # 'А' + 193: 32, # 'Б' + 194: 35, # 'В' + 195: 43, # 'Г' + 196: 37, # 'Д' + 197: 44, # 'Е' + 198: 55, # 'Ж' + 199: 47, # 'З' + 200: 40, # 'И' + 201: 59, # 'Й' + 202: 33, # 'К' + 203: 46, # 'Л' + 204: 38, # 'М' + 205: 36, # 'Н' + 206: 41, # 'О' + 207: 30, # 'П' + 208: 39, # 'Р' + 209: 28, # 'С' + 210: 34, # 'Т' + 211: 51, # 'У' + 212: 48, # 'Ф' + 213: 49, # 'Х' + 214: 53, # 'Ц' + 215: 50, # 'Ч' + 216: 54, # 'Ш' + 217: 57, # 'Щ' + 218: 61, # 'Ъ' + 219: 251, # 'Ы' + 220: 67, # 'Ь' + 221: 252, # 'Э' + 222: 60, # 'Ю' + 223: 56, # 'Я' + 224: 1, # 'а' + 225: 18, # 'б' + 226: 9, # 'в' + 227: 20, # 'г' + 228: 11, # 'д' + 229: 3, # 'е' + 230: 23, # 'ж' + 231: 15, # 'з' + 232: 2, # 'и' + 233: 26, # 'й' + 234: 12, # 'к' + 235: 10, # 'л' + 236: 14, # 'м' + 237: 6, # 'н' + 238: 4, # 'о' + 239: 13, # 'п' + 240: 7, # 'р' + 241: 8, # 'с' + 242: 5, # 'т' + 243: 19, # 'у' + 244: 29, # 'ф' + 245: 25, # 'х' + 246: 22, # 'ц' + 247: 21, # 'ч' + 248: 27, # 'ш' + 249: 24, # 'щ' + 250: 17, # 'ъ' + 251: 75, # 'ы' + 252: 52, # 'ь' + 253: 253, # 'э' + 254: 42, # 'ю' + 255: 16, # 'я' +} + +WINDOWS_1251_BULGARIAN_MODEL = SingleByteCharSetModel( + charset_name="windows-1251", + language="Bulgarian", + char_to_order_map=WINDOWS_1251_BULGARIAN_CHAR_TO_ORDER, + language_model=BULGARIAN_LANG_MODEL, + typical_positive_ratio=0.969392, + keep_ascii_letters=False, + alphabet="АБВГДЕЖЗИЙКЛМНОПРСТУФХЦЧШЩЪЬЮЯабвгдежзийклмнопрстуфхцчшщъьюя", +) diff --git a/venv/lib/python3.10/site-packages/chardet/langhungarianmodel.py b/venv/lib/python3.10/site-packages/chardet/langhungarianmodel.py new file mode 100644 index 0000000000000000000000000000000000000000..bd6630a0513447bb56e1ffbed7aa07e173f62f5b --- /dev/null +++ b/venv/lib/python3.10/site-packages/chardet/langhungarianmodel.py @@ -0,0 +1,4649 @@ +from chardet.sbcharsetprober import SingleByteCharSetModel + +# 3: Positive +# 2: Likely +# 1: Unlikely +# 0: Negative + +HUNGARIAN_LANG_MODEL = { + 28: { # 'A' + 28: 0, # 'A' + 40: 1, # 'B' + 54: 1, # 'C' + 45: 2, # 'D' + 32: 1, # 'E' + 50: 1, # 'F' + 49: 2, # 'G' + 38: 1, # 'H' + 39: 2, # 'I' + 53: 1, # 'J' + 36: 2, # 'K' + 41: 2, # 'L' + 34: 1, # 'M' + 35: 2, # 'N' + 47: 1, # 'O' + 46: 2, # 'P' + 43: 2, # 'R' + 33: 2, # 'S' + 37: 2, # 'T' + 57: 1, # 'U' + 48: 1, # 'V' + 55: 1, # 'Y' + 52: 2, # 'Z' + 2: 0, # 'a' + 18: 1, # 'b' + 26: 1, # 'c' + 17: 2, # 'd' + 1: 1, # 'e' + 27: 1, # 'f' + 12: 1, # 'g' + 20: 1, # 'h' + 9: 1, # 'i' + 22: 1, # 'j' + 7: 2, # 'k' + 6: 2, # 'l' + 13: 2, # 'm' + 4: 2, # 'n' + 8: 0, # 'o' + 23: 2, # 'p' + 10: 2, # 'r' + 5: 1, # 's' + 3: 1, # 't' + 21: 1, # 'u' + 19: 1, # 'v' + 62: 1, # 'x' + 16: 0, # 'y' + 11: 3, # 'z' + 51: 1, # 'Á' + 44: 0, # 'É' + 61: 1, # 'Í' + 58: 0, # 'Ó' + 59: 0, # 'Ö' + 60: 0, # 'Ú' + 63: 0, # 'Ü' + 14: 0, # 'á' + 15: 0, # 'é' + 30: 0, # 'í' + 25: 0, # 'ó' + 24: 0, # 'ö' + 31: 0, # 'ú' + 29: 0, # 'ü' + 42: 0, # 'ő' + 56: 0, # 'ű' + }, + 40: { # 'B' + 28: 2, # 'A' + 40: 1, # 'B' + 54: 1, # 'C' + 45: 1, # 'D' + 32: 2, # 'E' + 50: 0, # 'F' + 49: 0, # 'G' + 38: 0, # 'H' + 39: 1, # 'I' + 53: 1, # 'J' + 36: 1, # 'K' + 41: 1, # 'L' + 34: 0, # 'M' + 35: 1, # 'N' + 47: 2, # 'O' + 46: 0, # 'P' + 43: 1, # 'R' + 33: 1, # 'S' + 37: 1, # 'T' + 57: 1, # 'U' + 48: 1, # 'V' + 55: 0, # 'Y' + 52: 0, # 'Z' + 2: 2, # 'a' + 18: 0, # 'b' + 26: 0, # 'c' + 17: 0, # 'd' + 1: 3, # 'e' + 27: 0, # 'f' + 12: 0, # 'g' + 20: 0, # 'h' + 9: 2, # 'i' + 22: 1, # 'j' + 7: 0, # 'k' + 6: 1, # 'l' + 13: 0, # 'm' + 4: 0, # 'n' + 8: 2, # 'o' + 23: 1, # 'p' + 10: 2, # 'r' + 5: 0, # 's' + 3: 0, # 't' + 21: 3, # 'u' + 19: 0, # 'v' + 62: 0, # 'x' + 16: 1, # 'y' + 11: 0, # 'z' + 51: 1, # 'Á' + 44: 1, # 'É' + 61: 1, # 'Í' + 58: 1, # 'Ó' + 59: 1, # 'Ö' + 60: 1, # 'Ú' + 63: 1, # 'Ü' + 14: 2, # 'á' + 15: 2, # 'é' + 30: 1, # 'í' + 25: 1, # 'ó' + 24: 1, # 'ö' + 31: 1, # 'ú' + 29: 1, # 'ü' + 42: 1, # 'ő' + 56: 1, # 'ű' + }, + 54: { # 'C' + 28: 1, # 'A' + 40: 1, # 'B' + 54: 1, # 'C' + 45: 1, # 'D' + 32: 1, # 'E' + 50: 0, # 'F' + 49: 0, # 'G' + 38: 1, # 'H' + 39: 2, # 'I' + 53: 1, # 'J' + 36: 1, # 'K' + 41: 1, # 'L' + 34: 1, # 'M' + 35: 0, # 'N' + 47: 1, # 'O' + 46: 1, # 'P' + 43: 1, # 'R' + 33: 2, # 'S' + 37: 1, # 'T' + 57: 1, # 'U' + 48: 0, # 'V' + 55: 1, # 'Y' + 52: 1, # 'Z' + 2: 2, # 'a' + 18: 0, # 'b' + 26: 0, # 'c' + 17: 0, # 'd' + 1: 1, # 'e' + 27: 0, # 'f' + 12: 0, # 'g' + 20: 1, # 'h' + 9: 1, # 'i' + 22: 0, # 'j' + 7: 0, # 'k' + 6: 1, # 'l' + 13: 0, # 'm' + 4: 0, # 'n' + 8: 2, # 'o' + 23: 0, # 'p' + 10: 1, # 'r' + 5: 3, # 's' + 3: 0, # 't' + 21: 1, # 'u' + 19: 0, # 'v' + 62: 0, # 'x' + 16: 1, # 'y' + 11: 1, # 'z' + 51: 1, # 'Á' + 44: 1, # 'É' + 61: 1, # 'Í' + 58: 0, # 'Ó' + 59: 0, # 'Ö' + 60: 0, # 'Ú' + 63: 0, # 'Ü' + 14: 1, # 'á' + 15: 1, # 'é' + 30: 1, # 'í' + 25: 1, # 'ó' + 24: 0, # 'ö' + 31: 0, # 'ú' + 29: 0, # 'ü' + 42: 0, # 'ő' + 56: 0, # 'ű' + }, + 45: { # 'D' + 28: 2, # 'A' + 40: 1, # 'B' + 54: 0, # 'C' + 45: 1, # 'D' + 32: 2, # 'E' + 50: 1, # 'F' + 49: 1, # 'G' + 38: 1, # 'H' + 39: 2, # 'I' + 53: 1, # 'J' + 36: 1, # 'K' + 41: 0, # 'L' + 34: 1, # 'M' + 35: 1, # 'N' + 47: 2, # 'O' + 46: 0, # 'P' + 43: 1, # 'R' + 33: 1, # 'S' + 37: 1, # 'T' + 57: 1, # 'U' + 48: 1, # 'V' + 55: 1, # 'Y' + 52: 1, # 'Z' + 2: 2, # 'a' + 18: 0, # 'b' + 26: 0, # 'c' + 17: 0, # 'd' + 1: 3, # 'e' + 27: 0, # 'f' + 12: 0, # 'g' + 20: 0, # 'h' + 9: 1, # 'i' + 22: 0, # 'j' + 7: 0, # 'k' + 6: 0, # 'l' + 13: 0, # 'm' + 4: 0, # 'n' + 8: 1, # 'o' + 23: 0, # 'p' + 10: 2, # 'r' + 5: 0, # 's' + 3: 0, # 't' + 21: 2, # 'u' + 19: 0, # 'v' + 62: 0, # 'x' + 16: 1, # 'y' + 11: 1, # 'z' + 51: 1, # 'Á' + 44: 1, # 'É' + 61: 1, # 'Í' + 58: 1, # 'Ó' + 59: 1, # 'Ö' + 60: 1, # 'Ú' + 63: 1, # 'Ü' + 14: 1, # 'á' + 15: 1, # 'é' + 30: 1, # 'í' + 25: 1, # 'ó' + 24: 1, # 'ö' + 31: 1, # 'ú' + 29: 1, # 'ü' + 42: 1, # 'ő' + 56: 0, # 'ű' + }, + 32: { # 'E' + 28: 1, # 'A' + 40: 1, # 'B' + 54: 1, # 'C' + 45: 1, # 'D' + 32: 1, # 'E' + 50: 1, # 'F' + 49: 2, # 'G' + 38: 1, # 'H' + 39: 1, # 'I' + 53: 1, # 'J' + 36: 2, # 'K' + 41: 2, # 'L' + 34: 2, # 'M' + 35: 2, # 'N' + 47: 1, # 'O' + 46: 1, # 'P' + 43: 2, # 'R' + 33: 2, # 'S' + 37: 2, # 'T' + 57: 1, # 'U' + 48: 1, # 'V' + 55: 1, # 'Y' + 52: 1, # 'Z' + 2: 1, # 'a' + 18: 1, # 'b' + 26: 1, # 'c' + 17: 2, # 'd' + 1: 1, # 'e' + 27: 1, # 'f' + 12: 3, # 'g' + 20: 1, # 'h' + 9: 1, # 'i' + 22: 1, # 'j' + 7: 1, # 'k' + 6: 2, # 'l' + 13: 2, # 'm' + 4: 2, # 'n' + 8: 0, # 'o' + 23: 1, # 'p' + 10: 2, # 'r' + 5: 2, # 's' + 3: 1, # 't' + 21: 2, # 'u' + 19: 1, # 'v' + 62: 1, # 'x' + 16: 0, # 'y' + 11: 3, # 'z' + 51: 1, # 'Á' + 44: 1, # 'É' + 61: 0, # 'Í' + 58: 1, # 'Ó' + 59: 1, # 'Ö' + 60: 0, # 'Ú' + 63: 1, # 'Ü' + 14: 0, # 'á' + 15: 0, # 'é' + 30: 0, # 'í' + 25: 0, # 'ó' + 24: 1, # 'ö' + 31: 0, # 'ú' + 29: 0, # 'ü' + 42: 0, # 'ő' + 56: 0, # 'ű' + }, + 50: { # 'F' + 28: 1, # 'A' + 40: 0, # 'B' + 54: 0, # 'C' + 45: 0, # 'D' + 32: 1, # 'E' + 50: 1, # 'F' + 49: 0, # 'G' + 38: 1, # 'H' + 39: 1, # 'I' + 53: 1, # 'J' + 36: 1, # 'K' + 41: 1, # 'L' + 34: 1, # 'M' + 35: 1, # 'N' + 47: 1, # 'O' + 46: 0, # 'P' + 43: 1, # 'R' + 33: 0, # 'S' + 37: 1, # 'T' + 57: 1, # 'U' + 48: 0, # 'V' + 55: 1, # 'Y' + 52: 0, # 'Z' + 2: 2, # 'a' + 18: 0, # 'b' + 26: 0, # 'c' + 17: 0, # 'd' + 1: 2, # 'e' + 27: 1, # 'f' + 12: 0, # 'g' + 20: 0, # 'h' + 9: 2, # 'i' + 22: 1, # 'j' + 7: 0, # 'k' + 6: 1, # 'l' + 13: 0, # 'm' + 4: 0, # 'n' + 8: 2, # 'o' + 23: 0, # 'p' + 10: 2, # 'r' + 5: 0, # 's' + 3: 0, # 't' + 21: 1, # 'u' + 19: 0, # 'v' + 62: 0, # 'x' + 16: 0, # 'y' + 11: 0, # 'z' + 51: 1, # 'Á' + 44: 1, # 'É' + 61: 0, # 'Í' + 58: 1, # 'Ó' + 59: 1, # 'Ö' + 60: 0, # 'Ú' + 63: 1, # 'Ü' + 14: 1, # 'á' + 15: 1, # 'é' + 30: 0, # 'í' + 25: 0, # 'ó' + 24: 2, # 'ö' + 31: 1, # 'ú' + 29: 1, # 'ü' + 42: 1, # 'ő' + 56: 1, # 'ű' + }, + 49: { # 'G' + 28: 2, # 'A' + 40: 1, # 'B' + 54: 1, # 'C' + 45: 1, # 'D' + 32: 2, # 'E' + 50: 1, # 'F' + 49: 1, # 'G' + 38: 1, # 'H' + 39: 1, # 'I' + 53: 1, # 'J' + 36: 1, # 'K' + 41: 1, # 'L' + 34: 1, # 'M' + 35: 1, # 'N' + 47: 1, # 'O' + 46: 1, # 'P' + 43: 1, # 'R' + 33: 1, # 'S' + 37: 1, # 'T' + 57: 1, # 'U' + 48: 1, # 'V' + 55: 2, # 'Y' + 52: 1, # 'Z' + 2: 2, # 'a' + 18: 0, # 'b' + 26: 0, # 'c' + 17: 0, # 'd' + 1: 2, # 'e' + 27: 0, # 'f' + 12: 0, # 'g' + 20: 0, # 'h' + 9: 1, # 'i' + 22: 0, # 'j' + 7: 0, # 'k' + 6: 1, # 'l' + 13: 0, # 'm' + 4: 0, # 'n' + 8: 2, # 'o' + 23: 0, # 'p' + 10: 2, # 'r' + 5: 0, # 's' + 3: 0, # 't' + 21: 1, # 'u' + 19: 0, # 'v' + 62: 0, # 'x' + 16: 2, # 'y' + 11: 0, # 'z' + 51: 1, # 'Á' + 44: 1, # 'É' + 61: 1, # 'Í' + 58: 1, # 'Ó' + 59: 1, # 'Ö' + 60: 1, # 'Ú' + 63: 1, # 'Ü' + 14: 1, # 'á' + 15: 1, # 'é' + 30: 0, # 'í' + 25: 1, # 'ó' + 24: 1, # 'ö' + 31: 1, # 'ú' + 29: 1, # 'ü' + 42: 1, # 'ő' + 56: 0, # 'ű' + }, + 38: { # 'H' + 28: 2, # 'A' + 40: 1, # 'B' + 54: 1, # 'C' + 45: 0, # 'D' + 32: 1, # 'E' + 50: 0, # 'F' + 49: 0, # 'G' + 38: 0, # 'H' + 39: 1, # 'I' + 53: 0, # 'J' + 36: 0, # 'K' + 41: 1, # 'L' + 34: 0, # 'M' + 35: 0, # 'N' + 47: 1, # 'O' + 46: 0, # 'P' + 43: 1, # 'R' + 33: 1, # 'S' + 37: 1, # 'T' + 57: 1, # 'U' + 48: 0, # 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'¬' + 173: 203, # '\xad' + 174: 204, # '®' + 175: 205, # 'Ż' + 176: 81, # '°' + 177: 206, # '±' + 178: 207, # '˛' + 179: 208, # 'ł' + 180: 209, # '´' + 181: 210, # 'µ' + 182: 211, # '¶' + 183: 212, # '·' + 184: 213, # '¸' + 185: 214, # 'ą' + 186: 215, # 'ş' + 187: 216, # '»' + 188: 217, # 'Ľ' + 189: 218, # '˝' + 190: 219, # 'ľ' + 191: 220, # 'ż' + 192: 221, # 'Ŕ' + 193: 51, # 'Á' + 194: 83, # 'Â' + 195: 222, # 'Ă' + 196: 80, # 'Ä' + 197: 223, # 'Ĺ' + 198: 224, # 'Ć' + 199: 225, # 'Ç' + 200: 226, # 'Č' + 201: 44, # 'É' + 202: 227, # 'Ę' + 203: 228, # 'Ë' + 204: 229, # 'Ě' + 205: 61, # 'Í' + 206: 230, # 'Î' + 207: 231, # 'Ď' + 208: 232, # 'Đ' + 209: 233, # 'Ń' + 210: 234, # 'Ň' + 211: 58, # 'Ó' + 212: 235, # 'Ô' + 213: 66, # 'Ő' + 214: 59, # 'Ö' + 215: 236, # '×' + 216: 237, # 'Ř' + 217: 238, # 'Ů' + 218: 60, # 'Ú' + 219: 70, # 'Ű' + 220: 63, # 'Ü' + 221: 239, # 'Ý' + 222: 240, # 'Ţ' + 223: 241, # 'ß' + 224: 84, # 'ŕ' + 225: 14, # 'á' + 226: 75, # 'â' + 227: 242, # 'ă' + 228: 71, # 'ä' + 229: 82, # 'ĺ' + 230: 243, # 'ć' + 231: 73, # 'ç' + 232: 244, # 'č' + 233: 15, # 'é' + 234: 85, # 'ę' + 235: 79, # 'ë' + 236: 86, # 'ě' + 237: 30, # 'í' + 238: 77, # 'î' + 239: 87, # 'ď' + 240: 245, # 'đ' + 241: 246, # 'ń' + 242: 247, # 'ň' + 243: 25, # 'ó' + 244: 74, # 'ô' + 245: 42, # 'ő' + 246: 24, # 'ö' + 247: 248, # '÷' + 248: 249, # 'ř' + 249: 250, # 'ů' + 250: 31, # 'ú' + 251: 56, # 'ű' + 252: 29, # 'ü' + 253: 251, # 'ý' + 254: 252, # 'ţ' + 255: 253, # '˙' +} + +WINDOWS_1250_HUNGARIAN_MODEL = SingleByteCharSetModel( + charset_name="windows-1250", + language="Hungarian", + char_to_order_map=WINDOWS_1250_HUNGARIAN_CHAR_TO_ORDER, + language_model=HUNGARIAN_LANG_MODEL, + typical_positive_ratio=0.947368, + keep_ascii_letters=True, + alphabet="ABCDEFGHIJKLMNOPRSTUVZabcdefghijklmnoprstuvzÁÉÍÓÖÚÜáéíóöúüŐőŰű", +) + +ISO_8859_2_HUNGARIAN_CHAR_TO_ORDER = { + 0: 255, # '\x00' + 1: 255, # '\x01' + 2: 255, # '\x02' + 3: 255, # '\x03' + 4: 255, # '\x04' + 5: 255, # '\x05' + 6: 255, # '\x06' + 7: 255, # '\x07' + 8: 255, # '\x08' + 9: 255, # '\t' + 10: 254, # '\n' + 11: 255, # '\x0b' + 12: 255, # '\x0c' + 13: 254, # '\r' + 14: 255, # '\x0e' + 15: 255, # '\x0f' + 16: 255, # '\x10' + 17: 255, # '\x11' + 18: 255, # '\x12' + 19: 255, # '\x13' + 20: 255, # '\x14' + 21: 255, # '\x15' + 22: 255, # '\x16' + 23: 255, # '\x17' + 24: 255, # '\x18' + 25: 255, # '\x19' + 26: 255, # '\x1a' + 27: 255, # '\x1b' + 28: 255, # '\x1c' + 29: 255, # '\x1d' + 30: 255, # '\x1e' + 31: 255, # '\x1f' + 32: 253, # ' ' + 33: 253, # '!' + 34: 253, # '"' + 35: 253, # '#' + 36: 253, # '$' + 37: 253, # '%' + 38: 253, # '&' + 39: 253, # "'" + 40: 253, # '(' + 41: 253, # ')' + 42: 253, # '*' + 43: 253, # '+' + 44: 253, # ',' + 45: 253, # '-' + 46: 253, # '.' + 47: 253, # '/' + 48: 252, # '0' + 49: 252, # '1' + 50: 252, # '2' + 51: 252, # '3' + 52: 252, # '4' + 53: 252, # '5' + 54: 252, # '6' + 55: 252, # '7' + 56: 252, # '8' + 57: 252, # '9' + 58: 253, # ':' + 59: 253, # ';' + 60: 253, # '<' + 61: 253, # '=' + 62: 253, # '>' + 63: 253, # '?' + 64: 253, # '@' + 65: 28, # 'A' + 66: 40, # 'B' + 67: 54, # 'C' + 68: 45, # 'D' + 69: 32, # 'E' + 70: 50, # 'F' + 71: 49, # 'G' + 72: 38, # 'H' + 73: 39, # 'I' + 74: 53, # 'J' + 75: 36, # 'K' + 76: 41, # 'L' + 77: 34, # 'M' + 78: 35, # 'N' + 79: 47, # 'O' + 80: 46, # 'P' + 81: 71, # 'Q' + 82: 43, # 'R' + 83: 33, # 'S' + 84: 37, # 'T' + 85: 57, # 'U' + 86: 48, # 'V' + 87: 64, # 'W' + 88: 68, # 'X' + 89: 55, # 'Y' + 90: 52, # 'Z' + 91: 253, # '[' + 92: 253, # '\\' + 93: 253, # ']' + 94: 253, # '^' + 95: 253, # '_' + 96: 253, # '`' + 97: 2, # 'a' + 98: 18, # 'b' + 99: 26, # 'c' + 100: 17, # 'd' + 101: 1, # 'e' + 102: 27, # 'f' + 103: 12, # 'g' + 104: 20, # 'h' + 105: 9, # 'i' + 106: 22, # 'j' + 107: 7, # 'k' + 108: 6, # 'l' + 109: 13, # 'm' + 110: 4, # 'n' + 111: 8, # 'o' + 112: 23, # 'p' + 113: 67, # 'q' + 114: 10, # 'r' + 115: 5, # 's' + 116: 3, # 't' + 117: 21, # 'u' + 118: 19, # 'v' + 119: 65, # 'w' + 120: 62, # 'x' + 121: 16, # 'y' + 122: 11, # 'z' + 123: 253, # '{' + 124: 253, # '|' + 125: 253, # '}' + 126: 253, # '~' + 127: 253, # '\x7f' + 128: 159, # '\x80' + 129: 160, # '\x81' + 130: 161, # '\x82' + 131: 162, # '\x83' + 132: 163, # '\x84' + 133: 164, # '\x85' + 134: 165, # '\x86' + 135: 166, # '\x87' + 136: 167, # '\x88' + 137: 168, # '\x89' + 138: 169, # '\x8a' + 139: 170, # '\x8b' + 140: 171, # '\x8c' + 141: 172, # '\x8d' + 142: 173, # '\x8e' + 143: 174, # '\x8f' + 144: 175, # '\x90' + 145: 176, # '\x91' + 146: 177, # '\x92' + 147: 178, # '\x93' + 148: 179, # '\x94' + 149: 180, # '\x95' + 150: 181, # '\x96' + 151: 182, # '\x97' + 152: 183, # '\x98' + 153: 184, # '\x99' + 154: 185, # '\x9a' + 155: 186, # '\x9b' + 156: 187, # '\x9c' + 157: 188, # '\x9d' + 158: 189, # '\x9e' + 159: 190, # '\x9f' + 160: 191, # '\xa0' + 161: 192, # 'Ą' + 162: 193, # '˘' + 163: 194, # 'Ł' + 164: 195, # '¤' + 165: 196, # 'Ľ' + 166: 197, # 'Ś' + 167: 75, # '§' + 168: 198, # '¨' + 169: 199, # 'Š' + 170: 200, # 'Ş' + 171: 201, # 'Ť' + 172: 202, # 'Ź' + 173: 203, # '\xad' + 174: 204, # 'Ž' + 175: 205, # 'Ż' + 176: 79, # '°' + 177: 206, # 'ą' + 178: 207, # '˛' + 179: 208, # 'ł' + 180: 209, # '´' + 181: 210, # 'ľ' + 182: 211, # 'ś' + 183: 212, # 'ˇ' + 184: 213, # '¸' + 185: 214, # 'š' + 186: 215, # 'ş' + 187: 216, # 'ť' + 188: 217, # 'ź' + 189: 218, # '˝' + 190: 219, # 'ž' + 191: 220, # 'ż' + 192: 221, # 'Ŕ' + 193: 51, # 'Á' + 194: 81, # 'Â' + 195: 222, # 'Ă' + 196: 78, # 'Ä' + 197: 223, # 'Ĺ' + 198: 224, # 'Ć' + 199: 225, # 'Ç' + 200: 226, # 'Č' + 201: 44, # 'É' + 202: 227, # 'Ę' + 203: 228, # 'Ë' + 204: 229, # 'Ě' + 205: 61, # 'Í' + 206: 230, # 'Î' + 207: 231, # 'Ď' + 208: 232, # 'Đ' + 209: 233, # 'Ń' + 210: 234, # 'Ň' + 211: 58, # 'Ó' + 212: 235, # 'Ô' + 213: 66, # 'Ő' + 214: 59, # 'Ö' + 215: 236, # '×' + 216: 237, # 'Ř' + 217: 238, # 'Ů' + 218: 60, # 'Ú' + 219: 69, # 'Ű' + 220: 63, # 'Ü' + 221: 239, # 'Ý' + 222: 240, # 'Ţ' + 223: 241, # 'ß' + 224: 82, # 'ŕ' + 225: 14, # 'á' + 226: 74, # 'â' + 227: 242, # 'ă' + 228: 70, # 'ä' + 229: 80, # 'ĺ' + 230: 243, # 'ć' + 231: 72, # 'ç' + 232: 244, # 'č' + 233: 15, # 'é' + 234: 83, # 'ę' + 235: 77, # 'ë' + 236: 84, # 'ě' + 237: 30, # 'í' + 238: 76, # 'î' + 239: 85, # 'ď' + 240: 245, # 'đ' + 241: 246, # 'ń' + 242: 247, # 'ň' + 243: 25, # 'ó' + 244: 73, # 'ô' + 245: 42, # 'ő' + 246: 24, # 'ö' + 247: 248, # '÷' + 248: 249, # 'ř' + 249: 250, # 'ů' + 250: 31, # 'ú' + 251: 56, # 'ű' + 252: 29, # 'ü' + 253: 251, # 'ý' + 254: 252, # 'ţ' + 255: 253, # '˙' +} + +ISO_8859_2_HUNGARIAN_MODEL = SingleByteCharSetModel( + charset_name="ISO-8859-2", + language="Hungarian", + char_to_order_map=ISO_8859_2_HUNGARIAN_CHAR_TO_ORDER, + language_model=HUNGARIAN_LANG_MODEL, + typical_positive_ratio=0.947368, + keep_ascii_letters=True, + alphabet="ABCDEFGHIJKLMNOPRSTUVZabcdefghijklmnoprstuvzÁÉÍÓÖÚÜáéíóöúüŐőŰű", +) diff --git a/venv/lib/python3.10/site-packages/chardet/langrussianmodel.py b/venv/lib/python3.10/site-packages/chardet/langrussianmodel.py new file mode 100644 index 0000000000000000000000000000000000000000..0d5b178446d243dbd98bd1e9ebb0a89e19d94258 --- /dev/null +++ b/venv/lib/python3.10/site-packages/chardet/langrussianmodel.py @@ -0,0 +1,5725 @@ +from chardet.sbcharsetprober import SingleByteCharSetModel + +# 3: Positive +# 2: Likely +# 1: Unlikely +# 0: Negative + +RUSSIAN_LANG_MODEL = { + 37: { # 'А' + 37: 0, # 'А' + 44: 1, # 'Б' + 33: 1, # 'В' + 46: 1, # 'Г' + 41: 1, # 'Д' + 48: 1, # 'Е' + 56: 1, # 'Ж' + 51: 1, # 'З' + 42: 1, # 'И' + 60: 1, # 'Й' + 36: 1, # 'К' + 49: 1, # 'Л' + 38: 1, # 'М' + 31: 2, # 'Н' + 34: 1, # 'О' + 35: 1, # 'П' + 45: 1, # 'Р' + 32: 1, # 'С' + 40: 1, # 'Т' + 52: 1, # 'У' + 53: 1, # 'Ф' + 55: 1, # 'Х' + 58: 1, # 'Ц' + 50: 1, # 'Ч' + 57: 1, # 'Ш' + 63: 1, # 'Щ' + 62: 0, # 'Ы' + 61: 0, # 'Ь' + 47: 0, # 'Э' + 59: 1, # 'Ю' + 43: 1, # 'Я' + 3: 1, # 'а' + 21: 2, # 'б' + 10: 2, # 'в' + 19: 2, # 'г' + 13: 2, # 'д' + 2: 0, # 'е' + 24: 1, # 'ж' + 20: 1, # 'з' + 4: 0, # 'и' + 23: 1, # 'й' + 11: 2, # 'к' + 8: 3, # 'л' + 12: 2, # 'м' + 5: 2, # 'н' + 1: 0, # 'о' + 15: 2, # 'п' + 9: 2, # 'р' + 7: 2, # 'с' + 6: 2, # 'т' + 14: 2, # 'у' + 39: 2, # 'ф' + 26: 2, # 'х' + 28: 0, # 'ц' + 22: 1, # 'ч' + 25: 2, # 'ш' + 29: 0, # 'щ' + 54: 0, # 'ъ' + 18: 0, # 'ы' + 17: 0, # 'ь' + 30: 1, # 'э' + 27: 0, # 'ю' + 16: 0, # 'я' + }, + 44: { # 'Б' + 37: 1, # 'А' + 44: 0, # 'Б' + 33: 1, # 'В' + 46: 1, # 'Г' + 41: 0, # 'Д' + 48: 1, # 'Е' + 56: 0, # 'Ж' + 51: 0, # 'З' + 42: 1, # 'И' + 60: 0, # 'Й' + 36: 0, # 'К' + 49: 1, # 'Л' + 38: 1, # 'М' + 31: 1, # 'Н' + 34: 1, # 'О' + 35: 0, # 'П' + 45: 1, # 'Р' + 32: 0, # 'С' + 40: 0, # 'Т' + 52: 1, # 'У' + 53: 0, # 'Ф' + 55: 0, # 'Х' + 58: 0, # 'Ц' + 50: 0, # 'Ч' + 57: 0, # 'Ш' + 63: 0, # 'Щ' + 62: 1, # 'Ы' + 61: 0, # 'Ь' + 47: 0, # 'Э' + 59: 0, # 'Ю' + 43: 1, # 'Я' + 3: 2, # 'а' + 21: 0, # 'б' + 10: 0, # 'в' + 19: 0, # 'г' + 13: 1, # 'д' + 2: 3, # 'е' + 24: 0, # 'ж' + 20: 0, # 'з' + 4: 2, # 'и' + 23: 0, # 'й' + 11: 0, # 'к' + 8: 2, # 'л' + 12: 0, # 'м' + 5: 0, # 'н' + 1: 3, # 'о' + 15: 0, # 'п' + 9: 2, # 'р' + 7: 0, # 'с' + 6: 0, # 'т' + 14: 2, # 'у' + 39: 0, # 'ф' + 26: 0, # 'х' + 28: 0, # 'ц' + 22: 0, # 'ч' + 25: 0, # 'ш' + 29: 0, # 'щ' + 54: 0, # 'ъ' + 18: 2, # 'ы' + 17: 1, # 'ь' + 30: 2, # 'э' + 27: 1, # 'ю' + 16: 1, # 'я' + }, + 33: { # 'В' + 37: 2, # 'А' + 44: 0, # 'Б' + 33: 1, # 'В' + 46: 0, # 'Г' + 41: 1, # 'Д' + 48: 1, # 'Е' + 56: 0, # 'Ж' + 51: 0, # 'З' + 42: 1, # 'И' + 60: 0, # 'Й' + 36: 1, # 'К' + 49: 1, # 'Л' + 38: 1, # 'М' + 31: 1, # 'Н' + 34: 1, # 'О' + 35: 1, # 'П' + 45: 1, # 'Р' + 32: 1, # 'С' + 40: 1, # 'Т' + 52: 1, # 'У' + 53: 0, # 'Ф' + 55: 0, # 'Х' + 58: 0, # 'Ц' + 50: 0, # 'Ч' + 57: 1, # 'Ш' + 63: 0, # 'Щ' + 62: 1, # 'Ы' + 61: 1, # 'Ь' + 47: 0, # 'Э' + 59: 0, # 'Ю' + 43: 1, # 'Я' + 3: 2, # 'а' + 21: 1, # 'б' + 10: 1, # 'в' + 19: 1, # 'г' + 13: 2, # 'д' + 2: 3, # 'е' + 24: 0, # 'ж' + 20: 2, # 'з' + 4: 2, # 'и' + 23: 0, # 'й' + 11: 1, # 'к' + 8: 2, # 'л' + 12: 2, # 'м' + 5: 2, # 'н' + 1: 3, # 'о' + 15: 2, # 'п' + 9: 2, # 'р' + 7: 3, # 'с' + 6: 2, # 'т' + 14: 2, # 'у' + 39: 0, # 'ф' + 26: 1, # 'х' + 28: 1, # 'ц' + 22: 2, # 'ч' + 25: 1, # 'ш' + 29: 0, # 'щ' + 54: 1, # 'ъ' + 18: 3, # 'ы' + 17: 1, # 'ь' + 30: 2, # 'э' + 27: 0, # 'ю' + 16: 1, # 'я' + }, + 46: { # 'Г' + 37: 1, # 'А' + 44: 1, # 'Б' + 33: 0, # 'В' + 46: 0, # 'Г' + 41: 1, # 'Д' + 48: 1, # 'Е' + 56: 0, # 'Ж' + 51: 0, # 'З' + 42: 1, # 'И' + 60: 0, # 'Й' + 36: 0, # 'К' + 49: 1, # 'Л' + 38: 1, # 'М' + 31: 1, # 'Н' + 34: 1, # 'О' + 35: 1, # 'П' + 45: 1, # 'Р' + 32: 0, # 'С' + 40: 0, # 'Т' + 52: 1, # 'У' + 53: 0, # 'Ф' + 55: 0, # 'Х' + 58: 0, # 'Ц' + 50: 0, # 'Ч' + 57: 0, # 'Ш' + 63: 0, # 'Щ' + 62: 0, # 'Ы' + 61: 0, # 'Ь' + 47: 0, # 'Э' + 59: 0, # 'Ю' + 43: 0, # 'Я' + 3: 2, # 'а' + 21: 0, # 'б' + 10: 1, # 'в' + 19: 0, # 'г' + 13: 2, # 'д' + 2: 2, # 'е' + 24: 0, # 'ж' + 20: 0, # 'з' + 4: 2, # 'и' + 23: 0, # 'й' + 11: 0, # 'к' + 8: 2, # 'л' + 12: 1, # 'м' + 5: 1, # 'н' + 1: 3, # 'о' + 15: 0, # 'п' + 9: 2, # 'р' + 7: 0, # 'с' + 6: 0, # 'т' + 14: 2, # 'у' + 39: 0, # 'ф' + 26: 0, # 'х' + 28: 0, # 'ц' + 22: 0, # 'ч' + 25: 0, # 'ш' + 29: 0, # 'щ' + 54: 0, # 'ъ' + 18: 0, # 'ы' + 17: 1, # 'ь' + 30: 1, # 'э' + 27: 1, # 'ю' + 16: 0, # 'я' + }, + 41: { # 'Д' + 37: 1, # 'А' + 44: 0, # 'Б' + 33: 1, # 'В' + 46: 0, # 'Г' + 41: 0, # 'Д' + 48: 2, # 'Е' + 56: 1, # 'Ж' + 51: 0, # 'З' + 42: 1, # 'И' + 60: 0, # 'Й' + 36: 1, # 'К' + 49: 1, # 'Л' + 38: 0, # 'М' + 31: 1, # 'Н' + 34: 1, # 'О' + 35: 0, # 'П' + 45: 1, # 'Р' + 32: 1, # 'С' + 40: 0, # 'Т' + 52: 1, # 'У' + 53: 0, # 'Ф' + 55: 0, # 'Х' + 58: 1, # 'Ц' + 50: 1, # 'Ч' + 57: 0, # 'Ш' + 63: 0, # 'Щ' + 62: 1, # 'Ы' + 61: 1, # 'Ь' + 47: 0, # 'Э' + 59: 0, # 'Ю' + 43: 1, # 'Я' + 3: 3, # 'а' + 21: 0, # 'б' + 10: 2, # 'в' + 19: 0, # 'г' + 13: 0, # 'д' + 2: 2, # 'е' + 24: 3, # 'ж' + 20: 1, # 'з' + 4: 2, # 'и' + 23: 0, # 'й' + 11: 0, # 'к' + 8: 2, # 'л' + 12: 1, # 'м' + 5: 1, # 'н' + 1: 3, # 'о' + 15: 0, # 'п' + 9: 2, # 'р' + 7: 0, # 'с' + 6: 0, # 'т' + 14: 2, # 'у' + 39: 0, # 'ф' + 26: 1, # 'х' + 28: 0, # 'ц' + 22: 0, # 'ч' + 25: 0, # 'ш' + 29: 0, # 'щ' + 54: 0, # 'ъ' + 18: 1, # 'ы' + 17: 1, # 'ь' + 30: 2, # 'э' + 27: 1, # 'ю' + 16: 1, # 'я' + }, + 48: { # 'Е' + 37: 1, # 'А' + 44: 1, # 'Б' + 33: 1, # 'В' + 46: 1, # 'Г' + 41: 1, # 'Д' + 48: 1, # 'Е' + 56: 1, # 'Ж' + 51: 1, # 'З' + 42: 1, # 'И' + 60: 1, # 'Й' + 36: 1, # 'К' + 49: 1, # 'Л' + 38: 1, # 'М' + 31: 2, # 'Н' + 34: 1, # 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'r' + 115: 73, # 's' + 116: 180, # 't' + 117: 181, # 'u' + 118: 79, # 'v' + 119: 182, # 'w' + 120: 183, # 'x' + 121: 184, # 'y' + 122: 185, # 'z' + 123: 253, # '{' + 124: 253, # '|' + 125: 253, # '}' + 126: 253, # '~' + 127: 253, # '\x7f' + 128: 37, # 'А' + 129: 44, # 'Б' + 130: 33, # 'В' + 131: 46, # 'Г' + 132: 41, # 'Д' + 133: 48, # 'Е' + 134: 56, # 'Ж' + 135: 51, # 'З' + 136: 42, # 'И' + 137: 60, # 'Й' + 138: 36, # 'К' + 139: 49, # 'Л' + 140: 38, # 'М' + 141: 31, # 'Н' + 142: 34, # 'О' + 143: 35, # 'П' + 144: 45, # 'Р' + 145: 32, # 'С' + 146: 40, # 'Т' + 147: 52, # 'У' + 148: 53, # 'Ф' + 149: 55, # 'Х' + 150: 58, # 'Ц' + 151: 50, # 'Ч' + 152: 57, # 'Ш' + 153: 63, # 'Щ' + 154: 70, # 'Ъ' + 155: 62, # 'Ы' + 156: 61, # 'Ь' + 157: 47, # 'Э' + 158: 59, # 'Ю' + 159: 43, # 'Я' + 160: 3, # 'а' + 161: 21, # 'б' + 162: 10, # 'в' + 163: 19, # 'г' + 164: 13, # 'д' + 165: 2, # 'е' + 166: 24, # 'ж' + 167: 20, # 'з' + 168: 4, # 'и' + 169: 23, # 'й' + 170: 11, # 'к' + 171: 8, # 'л' + 172: 12, # 'м' + 173: 5, # 'н' + 174: 1, # 'о' + 175: 15, # 'п' + 176: 191, # '░' + 177: 192, # '▒' + 178: 193, # '▓' + 179: 194, # '│' + 180: 195, # '┤' + 181: 196, # '╡' + 182: 197, # '╢' + 183: 198, # '╖' + 184: 199, # '╕' + 185: 200, # '╣' + 186: 201, # '║' + 187: 202, # '╗' + 188: 203, # '╝' + 189: 204, # '╜' + 190: 205, # '╛' + 191: 206, # '┐' + 192: 207, # '└' + 193: 208, # '┴' + 194: 209, # '┬' + 195: 210, # '├' + 196: 211, # '─' + 197: 212, # '┼' + 198: 213, # '╞' + 199: 214, # '╟' + 200: 215, # '╚' + 201: 216, # '╔' + 202: 217, # '╩' + 203: 218, # '╦' + 204: 219, # '╠' + 205: 220, # '═' + 206: 221, # '╬' + 207: 222, # '╧' + 208: 223, # '╨' + 209: 224, # '╤' + 210: 225, # '╥' + 211: 226, # '╙' + 212: 227, # '╘' + 213: 228, # '╒' + 214: 229, # '╓' + 215: 230, # '╫' + 216: 231, # '╪' + 217: 232, # '┘' + 218: 233, # '┌' + 219: 234, # '█' + 220: 235, # '▄' + 221: 236, # '▌' + 222: 237, # '▐' + 223: 238, # '▀' + 224: 9, # 'р' + 225: 7, # 'с' + 226: 6, # 'т' + 227: 14, # 'у' + 228: 39, # 'ф' + 229: 26, # 'х' + 230: 28, # 'ц' + 231: 22, # 'ч' + 232: 25, # 'ш' + 233: 29, # 'щ' + 234: 54, # 'ъ' + 235: 18, # 'ы' + 236: 17, # 'ь' + 237: 30, # 'э' + 238: 27, # 'ю' + 239: 16, # 'я' + 240: 239, # 'Ё' + 241: 68, # 'ё' + 242: 240, # 'Є' + 243: 241, # 'є' + 244: 242, # 'Ї' + 245: 243, # 'ї' + 246: 244, # 'Ў' + 247: 245, # 'ў' + 248: 246, # '°' + 249: 247, # '∙' + 250: 248, # '·' + 251: 249, # '√' + 252: 250, # '№' + 253: 251, # '¤' + 254: 252, # '■' + 255: 255, # '\xa0' +} + +IBM866_RUSSIAN_MODEL = SingleByteCharSetModel( + charset_name="IBM866", + language="Russian", + char_to_order_map=IBM866_RUSSIAN_CHAR_TO_ORDER, + language_model=RUSSIAN_LANG_MODEL, + typical_positive_ratio=0.976601, + keep_ascii_letters=False, + alphabet="ЁАБВГДЕЖЗИЙКЛМНОПРСТУФХЦЧШЩЪЫЬЭЮЯабвгдежзийклмнопрстуфхцчшщъыьэюяё", +) + +WINDOWS_1251_RUSSIAN_CHAR_TO_ORDER = { + 0: 255, # '\x00' + 1: 255, # '\x01' + 2: 255, # '\x02' + 3: 255, # '\x03' + 4: 255, # '\x04' + 5: 255, # '\x05' + 6: 255, # '\x06' + 7: 255, # '\x07' + 8: 255, # '\x08' + 9: 255, # '\t' + 10: 254, # '\n' + 11: 255, # '\x0b' + 12: 255, # '\x0c' + 13: 254, # '\r' + 14: 255, # '\x0e' + 15: 255, # '\x0f' + 16: 255, # '\x10' + 17: 255, # '\x11' + 18: 255, # '\x12' + 19: 255, # '\x13' + 20: 255, # '\x14' + 21: 255, # '\x15' + 22: 255, # '\x16' + 23: 255, # '\x17' + 24: 255, # '\x18' + 25: 255, # '\x19' + 26: 255, # '\x1a' + 27: 255, # '\x1b' + 28: 255, # '\x1c' + 29: 255, # '\x1d' + 30: 255, # '\x1e' + 31: 255, # '\x1f' + 32: 253, # ' ' + 33: 253, # '!' + 34: 253, # '"' + 35: 253, # '#' + 36: 253, # '$' + 37: 253, # '%' + 38: 253, # '&' + 39: 253, # "'" + 40: 253, # '(' + 41: 253, # ')' + 42: 253, # '*' + 43: 253, # '+' + 44: 253, # ',' + 45: 253, # '-' + 46: 253, # '.' + 47: 253, # '/' + 48: 252, # '0' + 49: 252, # '1' + 50: 252, # '2' + 51: 252, # '3' + 52: 252, # '4' + 53: 252, # '5' + 54: 252, # '6' + 55: 252, # '7' + 56: 252, # '8' + 57: 252, # '9' + 58: 253, # ':' + 59: 253, # ';' + 60: 253, # '<' + 61: 253, # '=' + 62: 253, # '>' + 63: 253, # '?' + 64: 253, # '@' + 65: 142, # 'A' + 66: 143, # 'B' + 67: 144, # 'C' + 68: 145, # 'D' + 69: 146, # 'E' + 70: 147, # 'F' + 71: 148, # 'G' + 72: 149, # 'H' + 73: 150, # 'I' + 74: 151, # 'J' + 75: 152, # 'K' + 76: 74, # 'L' + 77: 153, # 'M' + 78: 75, # 'N' + 79: 154, # 'O' + 80: 155, # 'P' + 81: 156, # 'Q' + 82: 157, # 'R' + 83: 158, # 'S' + 84: 159, # 'T' + 85: 160, # 'U' + 86: 161, # 'V' + 87: 162, # 'W' + 88: 163, # 'X' + 89: 164, # 'Y' + 90: 165, # 'Z' + 91: 253, # '[' + 92: 253, # '\\' + 93: 253, # ']' + 94: 253, # '^' + 95: 253, # '_' + 96: 253, # '`' + 97: 71, # 'a' + 98: 172, # 'b' + 99: 66, # 'c' + 100: 173, # 'd' + 101: 65, # 'e' + 102: 174, # 'f' + 103: 76, # 'g' + 104: 175, # 'h' + 105: 64, # 'i' + 106: 176, # 'j' + 107: 177, # 'k' + 108: 77, # 'l' + 109: 72, # 'm' + 110: 178, # 'n' + 111: 69, # 'o' + 112: 67, # 'p' + 113: 179, # 'q' + 114: 78, # 'r' + 115: 73, # 's' + 116: 180, # 't' + 117: 181, # 'u' + 118: 79, # 'v' + 119: 182, # 'w' + 120: 183, # 'x' + 121: 184, # 'y' + 122: 185, # 'z' + 123: 253, # '{' + 124: 253, # '|' + 125: 253, # '}' + 126: 253, # '~' + 127: 253, # '\x7f' + 128: 191, # 'Ђ' + 129: 192, # 'Ѓ' + 130: 193, # '‚' + 131: 194, # 'ѓ' + 132: 195, # '„' + 133: 196, # '…' + 134: 197, # '†' + 135: 198, # '‡' + 136: 199, # '€' + 137: 200, # '‰' + 138: 201, # 'Љ' + 139: 202, # '‹' + 140: 203, # 'Њ' + 141: 204, # 'Ќ' + 142: 205, # 'Ћ' + 143: 206, # 'Џ' + 144: 207, # 'ђ' + 145: 208, # '‘' + 146: 209, # '’' + 147: 210, # '“' + 148: 211, # '”' + 149: 212, # '•' + 150: 213, # '–' + 151: 214, # '—' + 152: 215, # None + 153: 216, # '™' + 154: 217, # 'љ' + 155: 218, # '›' + 156: 219, # 'њ' + 157: 220, # 'ќ' + 158: 221, # 'ћ' + 159: 222, # 'џ' + 160: 223, # '\xa0' + 161: 224, # 'Ў' + 162: 225, # 'ў' + 163: 226, # 'Ј' + 164: 227, # '¤' + 165: 228, # 'Ґ' + 166: 229, # '¦' + 167: 230, # '§' + 168: 231, # 'Ё' + 169: 232, # '©' + 170: 233, # 'Є' + 171: 234, # '«' + 172: 235, # '¬' + 173: 236, # '\xad' + 174: 237, # '®' + 175: 238, # 'Ї' + 176: 239, # '°' + 177: 240, # '±' + 178: 241, # 'І' + 179: 242, # 'і' + 180: 243, # 'ґ' + 181: 244, # 'µ' + 182: 245, # '¶' + 183: 246, # '·' + 184: 68, # 'ё' + 185: 247, # '№' + 186: 248, # 'є' + 187: 249, # '»' + 188: 250, # 'ј' + 189: 251, # 'Ѕ' + 190: 252, # 'ѕ' + 191: 253, # 'ї' + 192: 37, # 'А' + 193: 44, # 'Б' + 194: 33, # 'В' + 195: 46, # 'Г' + 196: 41, # 'Д' + 197: 48, # 'Е' + 198: 56, # 'Ж' + 199: 51, # 'З' + 200: 42, # 'И' + 201: 60, # 'Й' + 202: 36, # 'К' + 203: 49, # 'Л' + 204: 38, # 'М' + 205: 31, # 'Н' + 206: 34, # 'О' + 207: 35, # 'П' + 208: 45, # 'Р' + 209: 32, # 'С' + 210: 40, # 'Т' + 211: 52, # 'У' + 212: 53, # 'Ф' + 213: 55, # 'Х' + 214: 58, # 'Ц' + 215: 50, # 'Ч' + 216: 57, # 'Ш' + 217: 63, # 'Щ' + 218: 70, # 'Ъ' + 219: 62, # 'Ы' + 220: 61, # 'Ь' + 221: 47, # 'Э' + 222: 59, # 'Ю' + 223: 43, # 'Я' + 224: 3, # 'а' + 225: 21, # 'б' + 226: 10, # 'в' + 227: 19, # 'г' + 228: 13, # 'д' + 229: 2, # 'е' + 230: 24, # 'ж' + 231: 20, # 'з' + 232: 4, # 'и' + 233: 23, # 'й' + 234: 11, # 'к' + 235: 8, # 'л' + 236: 12, # 'м' + 237: 5, # 'н' + 238: 1, # 'о' + 239: 15, # 'п' + 240: 9, # 'р' + 241: 7, # 'с' + 242: 6, # 'т' + 243: 14, # 'у' + 244: 39, # 'ф' + 245: 26, # 'х' + 246: 28, # 'ц' + 247: 22, # 'ч' + 248: 25, # 'ш' + 249: 29, # 'щ' + 250: 54, # 'ъ' + 251: 18, # 'ы' + 252: 17, # 'ь' + 253: 30, # 'э' + 254: 27, # 'ю' + 255: 16, # 'я' +} + +WINDOWS_1251_RUSSIAN_MODEL = SingleByteCharSetModel( + charset_name="windows-1251", + language="Russian", + char_to_order_map=WINDOWS_1251_RUSSIAN_CHAR_TO_ORDER, + language_model=RUSSIAN_LANG_MODEL, + typical_positive_ratio=0.976601, + keep_ascii_letters=False, + alphabet="ЁАБВГДЕЖЗИЙКЛМНОПРСТУФХЦЧШЩЪЫЬЭЮЯабвгдежзийклмнопрстуфхцчшщъыьэюяё", +) + +IBM855_RUSSIAN_CHAR_TO_ORDER = { + 0: 255, # '\x00' + 1: 255, # '\x01' + 2: 255, # '\x02' + 3: 255, # '\x03' + 4: 255, # '\x04' + 5: 255, # '\x05' + 6: 255, # '\x06' + 7: 255, # '\x07' + 8: 255, # '\x08' + 9: 255, # '\t' + 10: 254, # '\n' + 11: 255, # '\x0b' + 12: 255, # '\x0c' + 13: 254, # '\r' + 14: 255, # '\x0e' + 15: 255, # '\x0f' + 16: 255, # '\x10' + 17: 255, # '\x11' + 18: 255, # '\x12' + 19: 255, # '\x13' + 20: 255, # '\x14' + 21: 255, # '\x15' + 22: 255, # '\x16' + 23: 255, # '\x17' + 24: 255, # '\x18' + 25: 255, # '\x19' + 26: 255, # '\x1a' + 27: 255, # '\x1b' + 28: 255, # '\x1c' + 29: 255, # '\x1d' + 30: 255, # '\x1e' + 31: 255, # '\x1f' + 32: 253, # ' ' + 33: 253, # '!' + 34: 253, # '"' + 35: 253, # '#' + 36: 253, # '$' + 37: 253, # '%' + 38: 253, # '&' + 39: 253, # "'" + 40: 253, # '(' + 41: 253, # ')' + 42: 253, # '*' + 43: 253, # '+' + 44: 253, # ',' + 45: 253, # '-' + 46: 253, # '.' + 47: 253, # '/' + 48: 252, # '0' + 49: 252, # '1' + 50: 252, # '2' + 51: 252, # '3' + 52: 252, # '4' + 53: 252, # '5' + 54: 252, # '6' + 55: 252, # '7' + 56: 252, # '8' + 57: 252, # '9' + 58: 253, # ':' + 59: 253, # ';' + 60: 253, # '<' + 61: 253, # '=' + 62: 253, # '>' + 63: 253, # '?' + 64: 253, # '@' + 65: 142, # 'A' + 66: 143, # 'B' + 67: 144, # 'C' + 68: 145, # 'D' + 69: 146, # 'E' + 70: 147, # 'F' + 71: 148, # 'G' + 72: 149, # 'H' + 73: 150, # 'I' + 74: 151, # 'J' + 75: 152, # 'K' + 76: 74, # 'L' + 77: 153, # 'M' + 78: 75, # 'N' + 79: 154, # 'O' + 80: 155, # 'P' + 81: 156, # 'Q' + 82: 157, # 'R' + 83: 158, # 'S' + 84: 159, # 'T' + 85: 160, # 'U' + 86: 161, # 'V' + 87: 162, # 'W' + 88: 163, # 'X' + 89: 164, # 'Y' + 90: 165, # 'Z' + 91: 253, # '[' + 92: 253, # '\\' + 93: 253, # ']' + 94: 253, # '^' + 95: 253, # '_' + 96: 253, # '`' + 97: 71, # 'a' + 98: 172, # 'b' + 99: 66, # 'c' + 100: 173, # 'd' + 101: 65, # 'e' + 102: 174, # 'f' + 103: 76, # 'g' + 104: 175, # 'h' + 105: 64, # 'i' + 106: 176, # 'j' + 107: 177, # 'k' + 108: 77, # 'l' + 109: 72, # 'm' + 110: 178, # 'n' + 111: 69, # 'o' + 112: 67, # 'p' + 113: 179, # 'q' + 114: 78, # 'r' + 115: 73, # 's' + 116: 180, # 't' + 117: 181, # 'u' + 118: 79, # 'v' + 119: 182, # 'w' + 120: 183, # 'x' + 121: 184, # 'y' + 122: 185, # 'z' + 123: 253, # '{' + 124: 253, # '|' + 125: 253, # '}' + 126: 253, # '~' + 127: 253, # '\x7f' + 128: 191, # 'ђ' + 129: 192, # 'Ђ' + 130: 193, # 'ѓ' + 131: 194, # 'Ѓ' + 132: 68, # 'ё' + 133: 195, # 'Ё' + 134: 196, # 'є' + 135: 197, # 'Є' + 136: 198, # 'ѕ' + 137: 199, # 'Ѕ' + 138: 200, # 'і' + 139: 201, # 'І' + 140: 202, # 'ї' + 141: 203, # 'Ї' + 142: 204, # 'ј' + 143: 205, # 'Ј' + 144: 206, # 'љ' + 145: 207, # 'Љ' + 146: 208, # 'њ' + 147: 209, # 'Њ' + 148: 210, # 'ћ' + 149: 211, # 'Ћ' + 150: 212, # 'ќ' + 151: 213, # 'Ќ' + 152: 214, # 'ў' + 153: 215, # 'Ў' + 154: 216, # 'џ' + 155: 217, # 'Џ' + 156: 27, # 'ю' + 157: 59, # 'Ю' + 158: 54, # 'ъ' + 159: 70, # 'Ъ' + 160: 3, # 'а' + 161: 37, # 'А' + 162: 21, # 'б' + 163: 44, # 'Б' + 164: 28, # 'ц' + 165: 58, # 'Ц' + 166: 13, # 'д' + 167: 41, # 'Д' + 168: 2, # 'е' + 169: 48, # 'Е' + 170: 39, # 'ф' + 171: 53, # 'Ф' + 172: 19, # 'г' + 173: 46, # 'Г' + 174: 218, # '«' + 175: 219, # '»' + 176: 220, # '░' + 177: 221, # '▒' + 178: 222, # '▓' + 179: 223, # '│' + 180: 224, # '┤' + 181: 26, # 'х' + 182: 55, # 'Х' + 183: 4, # 'и' + 184: 42, # 'И' + 185: 225, # '╣' + 186: 226, # '║' + 187: 227, # '╗' + 188: 228, # '╝' + 189: 23, # 'й' + 190: 60, # 'Й' + 191: 229, # '┐' + 192: 230, # '└' + 193: 231, # '┴' + 194: 232, # '┬' + 195: 233, # '├' + 196: 234, # '─' + 197: 235, # '┼' + 198: 11, # 'к' + 199: 36, # 'К' + 200: 236, # '╚' + 201: 237, # '╔' + 202: 238, # '╩' + 203: 239, # '╦' + 204: 240, # '╠' + 205: 241, # '═' + 206: 242, # '╬' + 207: 243, # '¤' + 208: 8, # 'л' + 209: 49, # 'Л' + 210: 12, # 'м' + 211: 38, # 'М' + 212: 5, # 'н' + 213: 31, # 'Н' + 214: 1, # 'о' + 215: 34, # 'О' + 216: 15, # 'п' + 217: 244, # '┘' + 218: 245, # '┌' + 219: 246, # '█' + 220: 247, # '▄' + 221: 35, # 'П' + 222: 16, # 'я' + 223: 248, # '▀' + 224: 43, # 'Я' + 225: 9, # 'р' + 226: 45, # 'Р' + 227: 7, # 'с' + 228: 32, # 'С' + 229: 6, # 'т' + 230: 40, # 'Т' + 231: 14, # 'у' + 232: 52, # 'У' + 233: 24, # 'ж' + 234: 56, # 'Ж' + 235: 10, # 'в' + 236: 33, # 'В' + 237: 17, # 'ь' + 238: 61, # 'Ь' + 239: 249, # '№' + 240: 250, # '\xad' + 241: 18, # 'ы' + 242: 62, # 'Ы' + 243: 20, # 'з' + 244: 51, # 'З' + 245: 25, # 'ш' + 246: 57, # 'Ш' + 247: 30, # 'э' + 248: 47, # 'Э' + 249: 29, # 'щ' + 250: 63, # 'Щ' + 251: 22, # 'ч' + 252: 50, # 'Ч' + 253: 251, # '§' + 254: 252, # '■' + 255: 255, # '\xa0' +} + +IBM855_RUSSIAN_MODEL = SingleByteCharSetModel( + charset_name="IBM855", + language="Russian", + char_to_order_map=IBM855_RUSSIAN_CHAR_TO_ORDER, + language_model=RUSSIAN_LANG_MODEL, + typical_positive_ratio=0.976601, + keep_ascii_letters=False, + alphabet="ЁАБВГДЕЖЗИЙКЛМНОПРСТУФХЦЧШЩЪЫЬЭЮЯабвгдежзийклмнопрстуфхцчшщъыьэюяё", +) + +KOI8_R_RUSSIAN_CHAR_TO_ORDER = { + 0: 255, # '\x00' + 1: 255, # '\x01' + 2: 255, # '\x02' + 3: 255, # '\x03' + 4: 255, # '\x04' + 5: 255, # '\x05' + 6: 255, # '\x06' + 7: 255, # '\x07' + 8: 255, # '\x08' + 9: 255, # '\t' + 10: 254, # '\n' + 11: 255, # '\x0b' + 12: 255, # '\x0c' + 13: 254, # '\r' + 14: 255, # '\x0e' + 15: 255, # '\x0f' + 16: 255, # '\x10' + 17: 255, # '\x11' + 18: 255, # '\x12' + 19: 255, # '\x13' + 20: 255, # '\x14' + 21: 255, # '\x15' + 22: 255, # '\x16' + 23: 255, # '\x17' + 24: 255, # '\x18' + 25: 255, # '\x19' + 26: 255, # '\x1a' + 27: 255, # '\x1b' + 28: 255, # '\x1c' + 29: 255, # '\x1d' + 30: 255, # '\x1e' + 31: 255, # '\x1f' + 32: 253, # ' ' + 33: 253, # '!' + 34: 253, # '"' + 35: 253, # '#' + 36: 253, # '$' + 37: 253, # '%' + 38: 253, # '&' + 39: 253, # "'" + 40: 253, # '(' + 41: 253, # ')' + 42: 253, # '*' + 43: 253, # '+' + 44: 253, # ',' + 45: 253, # '-' + 46: 253, # '.' + 47: 253, # '/' + 48: 252, # '0' + 49: 252, # '1' + 50: 252, # '2' + 51: 252, # '3' + 52: 252, # '4' + 53: 252, # '5' + 54: 252, # '6' + 55: 252, # '7' + 56: 252, # '8' + 57: 252, # '9' + 58: 253, # ':' + 59: 253, # ';' + 60: 253, # '<' + 61: 253, # '=' + 62: 253, # '>' + 63: 253, # '?' + 64: 253, # '@' + 65: 142, # 'A' + 66: 143, # 'B' + 67: 144, # 'C' + 68: 145, # 'D' + 69: 146, # 'E' + 70: 147, # 'F' + 71: 148, # 'G' + 72: 149, # 'H' + 73: 150, # 'I' + 74: 151, # 'J' + 75: 152, # 'K' + 76: 74, # 'L' + 77: 153, # 'M' + 78: 75, # 'N' + 79: 154, # 'O' + 80: 155, # 'P' + 81: 156, # 'Q' + 82: 157, # 'R' + 83: 158, # 'S' + 84: 159, # 'T' + 85: 160, # 'U' + 86: 161, # 'V' + 87: 162, # 'W' + 88: 163, # 'X' + 89: 164, # 'Y' + 90: 165, # 'Z' + 91: 253, # '[' + 92: 253, # '\\' + 93: 253, # ']' + 94: 253, # '^' + 95: 253, # '_' + 96: 253, # '`' + 97: 71, # 'a' + 98: 172, # 'b' + 99: 66, # 'c' + 100: 173, # 'd' + 101: 65, # 'e' + 102: 174, # 'f' + 103: 76, # 'g' + 104: 175, # 'h' + 105: 64, # 'i' + 106: 176, # 'j' + 107: 177, # 'k' + 108: 77, # 'l' + 109: 72, # 'm' + 110: 178, # 'n' + 111: 69, # 'o' + 112: 67, # 'p' + 113: 179, # 'q' + 114: 78, # 'r' + 115: 73, # 's' + 116: 180, # 't' + 117: 181, # 'u' + 118: 79, # 'v' + 119: 182, # 'w' + 120: 183, # 'x' + 121: 184, # 'y' + 122: 185, # 'z' + 123: 253, # '{' + 124: 253, # '|' + 125: 253, # '}' + 126: 253, # '~' + 127: 253, # '\x7f' + 128: 191, # '─' + 129: 192, # '│' + 130: 193, # '┌' + 131: 194, # '┐' + 132: 195, # '└' + 133: 196, # '┘' + 134: 197, # '├' + 135: 198, # '┤' + 136: 199, # '┬' + 137: 200, # '┴' + 138: 201, # '┼' + 139: 202, # '▀' + 140: 203, # '▄' + 141: 204, # '█' + 142: 205, # '▌' + 143: 206, # '▐' + 144: 207, # '░' + 145: 208, # '▒' + 146: 209, # '▓' + 147: 210, # '⌠' + 148: 211, # '■' + 149: 212, # '∙' + 150: 213, # '√' + 151: 214, # '≈' + 152: 215, # '≤' + 153: 216, # '≥' + 154: 217, # '\xa0' + 155: 218, # '⌡' + 156: 219, # '°' + 157: 220, # '²' + 158: 221, # '·' + 159: 222, # '÷' + 160: 223, # '═' + 161: 224, # '║' + 162: 225, # '╒' + 163: 68, # 'ё' + 164: 226, # '╓' + 165: 227, # '╔' + 166: 228, # '╕' + 167: 229, # '╖' + 168: 230, # '╗' + 169: 231, # '╘' + 170: 232, # '╙' + 171: 233, # '╚' + 172: 234, # '╛' + 173: 235, # '╜' + 174: 236, # '╝' + 175: 237, # '╞' + 176: 238, # '╟' + 177: 239, # '╠' + 178: 240, # '╡' + 179: 241, # 'Ё' + 180: 242, # '╢' + 181: 243, # '╣' + 182: 244, # '╤' + 183: 245, # '╥' + 184: 246, # '╦' + 185: 247, # '╧' + 186: 248, # '╨' + 187: 249, # '╩' + 188: 250, # '╪' + 189: 251, # '╫' + 190: 252, # '╬' + 191: 253, # '©' + 192: 27, # 'ю' + 193: 3, # 'а' + 194: 21, # 'б' + 195: 28, # 'ц' + 196: 13, # 'д' + 197: 2, # 'е' + 198: 39, # 'ф' + 199: 19, # 'г' + 200: 26, # 'х' + 201: 4, # 'и' + 202: 23, # 'й' + 203: 11, # 'к' + 204: 8, # 'л' + 205: 12, # 'м' + 206: 5, # 'н' + 207: 1, # 'о' + 208: 15, # 'п' + 209: 16, # 'я' + 210: 9, # 'р' + 211: 7, # 'с' + 212: 6, # 'т' + 213: 14, # 'у' + 214: 24, # 'ж' + 215: 10, # 'в' + 216: 17, # 'ь' + 217: 18, # 'ы' + 218: 20, # 'з' + 219: 25, # 'ш' + 220: 30, # 'э' + 221: 29, # 'щ' + 222: 22, # 'ч' + 223: 54, # 'ъ' + 224: 59, # 'Ю' + 225: 37, # 'А' + 226: 44, # 'Б' + 227: 58, # 'Ц' + 228: 41, # 'Д' + 229: 48, # 'Е' + 230: 53, # 'Ф' + 231: 46, # 'Г' + 232: 55, # 'Х' + 233: 42, # 'И' + 234: 60, # 'Й' + 235: 36, # 'К' + 236: 49, # 'Л' + 237: 38, # 'М' + 238: 31, # 'Н' + 239: 34, # 'О' + 240: 35, # 'П' + 241: 43, # 'Я' + 242: 45, # 'Р' + 243: 32, # 'С' + 244: 40, # 'Т' + 245: 52, # 'У' + 246: 56, # 'Ж' + 247: 33, # 'В' + 248: 61, # 'Ь' + 249: 62, # 'Ы' + 250: 51, # 'З' + 251: 57, # 'Ш' + 252: 47, # 'Э' + 253: 63, # 'Щ' + 254: 50, # 'Ч' + 255: 70, # 'Ъ' +} + +KOI8_R_RUSSIAN_MODEL = SingleByteCharSetModel( + charset_name="KOI8-R", + language="Russian", + char_to_order_map=KOI8_R_RUSSIAN_CHAR_TO_ORDER, + language_model=RUSSIAN_LANG_MODEL, + typical_positive_ratio=0.976601, + keep_ascii_letters=False, + alphabet="ЁАБВГДЕЖЗИЙКЛМНОПРСТУФХЦЧШЩЪЫЬЭЮЯабвгдежзийклмнопрстуфхцчшщъыьэюяё", +) + +MACCYRILLIC_RUSSIAN_CHAR_TO_ORDER = { + 0: 255, # '\x00' + 1: 255, # '\x01' + 2: 255, # '\x02' + 3: 255, # '\x03' + 4: 255, # '\x04' + 5: 255, # '\x05' + 6: 255, # '\x06' + 7: 255, # '\x07' + 8: 255, # '\x08' + 9: 255, # '\t' + 10: 254, # '\n' + 11: 255, # '\x0b' + 12: 255, # '\x0c' + 13: 254, # '\r' + 14: 255, # '\x0e' + 15: 255, # '\x0f' + 16: 255, # '\x10' + 17: 255, # '\x11' + 18: 255, # '\x12' + 19: 255, # '\x13' + 20: 255, # '\x14' + 21: 255, # '\x15' + 22: 255, # '\x16' + 23: 255, # '\x17' + 24: 255, # '\x18' + 25: 255, # '\x19' + 26: 255, # '\x1a' + 27: 255, # '\x1b' + 28: 255, # '\x1c' + 29: 255, # '\x1d' + 30: 255, # '\x1e' + 31: 255, # '\x1f' + 32: 253, # ' ' + 33: 253, # '!' + 34: 253, # '"' + 35: 253, # '#' + 36: 253, # '$' + 37: 253, # '%' + 38: 253, # '&' + 39: 253, # "'" + 40: 253, # '(' + 41: 253, # ')' + 42: 253, # '*' + 43: 253, # '+' + 44: 253, # ',' + 45: 253, # '-' + 46: 253, # '.' + 47: 253, # '/' + 48: 252, # '0' + 49: 252, # '1' + 50: 252, # '2' + 51: 252, # '3' + 52: 252, # '4' + 53: 252, # '5' + 54: 252, # '6' + 55: 252, # '7' + 56: 252, # '8' + 57: 252, # '9' + 58: 253, # ':' + 59: 253, # ';' + 60: 253, # '<' + 61: 253, # '=' + 62: 253, # '>' + 63: 253, # '?' + 64: 253, # '@' + 65: 142, # 'A' + 66: 143, # 'B' + 67: 144, # 'C' + 68: 145, # 'D' + 69: 146, # 'E' + 70: 147, # 'F' + 71: 148, # 'G' + 72: 149, # 'H' + 73: 150, # 'I' + 74: 151, # 'J' + 75: 152, # 'K' + 76: 74, # 'L' + 77: 153, # 'M' + 78: 75, # 'N' + 79: 154, # 'O' + 80: 155, # 'P' + 81: 156, # 'Q' + 82: 157, # 'R' + 83: 158, # 'S' + 84: 159, # 'T' + 85: 160, # 'U' + 86: 161, # 'V' + 87: 162, # 'W' + 88: 163, # 'X' + 89: 164, # 'Y' + 90: 165, # 'Z' + 91: 253, # '[' + 92: 253, # '\\' + 93: 253, # ']' + 94: 253, # '^' + 95: 253, # '_' + 96: 253, # '`' + 97: 71, # 'a' + 98: 172, # 'b' + 99: 66, # 'c' + 100: 173, # 'd' + 101: 65, # 'e' + 102: 174, # 'f' + 103: 76, # 'g' + 104: 175, # 'h' + 105: 64, # 'i' + 106: 176, # 'j' + 107: 177, # 'k' + 108: 77, # 'l' + 109: 72, # 'm' + 110: 178, # 'n' + 111: 69, # 'o' + 112: 67, # 'p' + 113: 179, # 'q' + 114: 78, # 'r' + 115: 73, # 's' + 116: 180, # 't' + 117: 181, # 'u' + 118: 79, # 'v' + 119: 182, # 'w' + 120: 183, # 'x' + 121: 184, # 'y' + 122: 185, # 'z' + 123: 253, # '{' + 124: 253, # '|' + 125: 253, # '}' + 126: 253, # '~' + 127: 253, # '\x7f' + 128: 37, # 'А' + 129: 44, # 'Б' + 130: 33, # 'В' + 131: 46, # 'Г' + 132: 41, # 'Д' + 133: 48, # 'Е' + 134: 56, # 'Ж' + 135: 51, # 'З' + 136: 42, # 'И' + 137: 60, # 'Й' + 138: 36, # 'К' + 139: 49, # 'Л' + 140: 38, # 'М' + 141: 31, # 'Н' + 142: 34, # 'О' + 143: 35, # 'П' + 144: 45, # 'Р' + 145: 32, # 'С' + 146: 40, # 'Т' + 147: 52, # 'У' + 148: 53, # 'Ф' + 149: 55, # 'Х' + 150: 58, # 'Ц' + 151: 50, # 'Ч' + 152: 57, # 'Ш' + 153: 63, # 'Щ' + 154: 70, # 'Ъ' + 155: 62, # 'Ы' + 156: 61, # 'Ь' + 157: 47, # 'Э' + 158: 59, # 'Ю' + 159: 43, # 'Я' + 160: 191, # '†' + 161: 192, # '°' + 162: 193, # 'Ґ' + 163: 194, # '£' + 164: 195, # '§' + 165: 196, # '•' + 166: 197, # '¶' + 167: 198, # 'І' + 168: 199, # '®' + 169: 200, # '©' + 170: 201, # '™' + 171: 202, # 'Ђ' + 172: 203, # 'ђ' + 173: 204, # '≠' + 174: 205, # 'Ѓ' + 175: 206, # 'ѓ' + 176: 207, # '∞' + 177: 208, # '±' + 178: 209, # '≤' + 179: 210, # '≥' + 180: 211, # 'і' + 181: 212, # 'µ' + 182: 213, # 'ґ' + 183: 214, # 'Ј' + 184: 215, # 'Є' + 185: 216, # 'є' + 186: 217, # 'Ї' + 187: 218, # 'ї' + 188: 219, # 'Љ' + 189: 220, # 'љ' + 190: 221, # 'Њ' + 191: 222, # 'њ' + 192: 223, # 'ј' + 193: 224, # 'Ѕ' + 194: 225, # '¬' + 195: 226, # '√' + 196: 227, # 'ƒ' + 197: 228, # '≈' + 198: 229, # '∆' + 199: 230, # '«' + 200: 231, # '»' + 201: 232, # '…' + 202: 233, # '\xa0' + 203: 234, # 'Ћ' + 204: 235, # 'ћ' + 205: 236, # 'Ќ' + 206: 237, # 'ќ' + 207: 238, # 'ѕ' + 208: 239, # '–' + 209: 240, # '—' + 210: 241, # '“' + 211: 242, # '”' + 212: 243, # '‘' + 213: 244, # '’' + 214: 245, # '÷' + 215: 246, # '„' + 216: 247, # 'Ў' + 217: 248, # 'ў' + 218: 249, # 'Џ' + 219: 250, # 'џ' + 220: 251, # '№' + 221: 252, # 'Ё' + 222: 68, # 'ё' + 223: 16, # 'я' + 224: 3, # 'а' + 225: 21, # 'б' + 226: 10, # 'в' + 227: 19, # 'г' + 228: 13, # 'д' + 229: 2, # 'е' + 230: 24, # 'ж' + 231: 20, # 'з' + 232: 4, # 'и' + 233: 23, # 'й' + 234: 11, # 'к' + 235: 8, # 'л' + 236: 12, # 'м' + 237: 5, # 'н' + 238: 1, # 'о' + 239: 15, # 'п' + 240: 9, # 'р' + 241: 7, # 'с' + 242: 6, # 'т' + 243: 14, # 'у' + 244: 39, # 'ф' + 245: 26, # 'х' + 246: 28, # 'ц' + 247: 22, # 'ч' + 248: 25, # 'ш' + 249: 29, # 'щ' + 250: 54, # 'ъ' + 251: 18, # 'ы' + 252: 17, # 'ь' + 253: 30, # 'э' + 254: 27, # 'ю' + 255: 255, # '€' +} + +MACCYRILLIC_RUSSIAN_MODEL = SingleByteCharSetModel( + charset_name="MacCyrillic", + language="Russian", + char_to_order_map=MACCYRILLIC_RUSSIAN_CHAR_TO_ORDER, + language_model=RUSSIAN_LANG_MODEL, + typical_positive_ratio=0.976601, + keep_ascii_letters=False, + alphabet="ЁАБВГДЕЖЗИЙКЛМНОПРСТУФХЦЧШЩЪЫЬЭЮЯабвгдежзийклмнопрстуфхцчшщъыьэюяё", +) + +ISO_8859_5_RUSSIAN_CHAR_TO_ORDER = { + 0: 255, # '\x00' + 1: 255, # '\x01' + 2: 255, # '\x02' + 3: 255, # '\x03' + 4: 255, # '\x04' + 5: 255, # '\x05' + 6: 255, # '\x06' + 7: 255, # '\x07' + 8: 255, # '\x08' + 9: 255, # '\t' + 10: 254, # '\n' + 11: 255, # '\x0b' + 12: 255, # '\x0c' + 13: 254, # '\r' + 14: 255, # '\x0e' + 15: 255, # '\x0f' + 16: 255, # '\x10' + 17: 255, # '\x11' + 18: 255, # '\x12' + 19: 255, # '\x13' + 20: 255, # '\x14' + 21: 255, # '\x15' + 22: 255, # '\x16' + 23: 255, # '\x17' + 24: 255, # '\x18' + 25: 255, # '\x19' + 26: 255, # '\x1a' + 27: 255, # '\x1b' + 28: 255, # '\x1c' + 29: 255, # '\x1d' + 30: 255, # '\x1e' + 31: 255, # '\x1f' + 32: 253, # ' ' + 33: 253, # '!' + 34: 253, # '"' + 35: 253, # '#' + 36: 253, # '$' + 37: 253, # '%' + 38: 253, # '&' + 39: 253, # "'" + 40: 253, # '(' + 41: 253, # ')' + 42: 253, # '*' + 43: 253, # '+' + 44: 253, # ',' + 45: 253, # '-' + 46: 253, # '.' + 47: 253, # '/' + 48: 252, # '0' + 49: 252, # '1' + 50: 252, # '2' + 51: 252, # '3' + 52: 252, # '4' + 53: 252, # '5' + 54: 252, # '6' + 55: 252, # '7' + 56: 252, # '8' + 57: 252, # '9' + 58: 253, # ':' + 59: 253, # ';' + 60: 253, # '<' + 61: 253, # '=' + 62: 253, # '>' + 63: 253, # '?' + 64: 253, # '@' + 65: 142, # 'A' + 66: 143, # 'B' + 67: 144, # 'C' + 68: 145, # 'D' + 69: 146, # 'E' + 70: 147, # 'F' + 71: 148, # 'G' + 72: 149, # 'H' + 73: 150, # 'I' + 74: 151, # 'J' + 75: 152, # 'K' + 76: 74, # 'L' + 77: 153, # 'M' + 78: 75, # 'N' + 79: 154, # 'O' + 80: 155, # 'P' + 81: 156, # 'Q' + 82: 157, # 'R' + 83: 158, # 'S' + 84: 159, # 'T' + 85: 160, # 'U' + 86: 161, # 'V' + 87: 162, # 'W' + 88: 163, # 'X' + 89: 164, # 'Y' + 90: 165, # 'Z' + 91: 253, # '[' + 92: 253, # '\\' + 93: 253, # ']' + 94: 253, # '^' + 95: 253, # '_' + 96: 253, # '`' + 97: 71, # 'a' + 98: 172, # 'b' + 99: 66, # 'c' + 100: 173, # 'd' + 101: 65, # 'e' + 102: 174, # 'f' + 103: 76, # 'g' + 104: 175, # 'h' + 105: 64, # 'i' + 106: 176, # 'j' + 107: 177, # 'k' + 108: 77, # 'l' + 109: 72, # 'm' + 110: 178, # 'n' + 111: 69, # 'o' + 112: 67, # 'p' + 113: 179, # 'q' + 114: 78, # 'r' + 115: 73, # 's' + 116: 180, # 't' + 117: 181, # 'u' + 118: 79, # 'v' + 119: 182, # 'w' + 120: 183, # 'x' + 121: 184, # 'y' + 122: 185, # 'z' + 123: 253, # '{' + 124: 253, # '|' + 125: 253, # '}' + 126: 253, # '~' + 127: 253, # '\x7f' + 128: 191, # '\x80' + 129: 192, # '\x81' + 130: 193, # '\x82' + 131: 194, # '\x83' + 132: 195, # '\x84' + 133: 196, # '\x85' + 134: 197, # '\x86' + 135: 198, # '\x87' + 136: 199, # '\x88' + 137: 200, # '\x89' + 138: 201, # '\x8a' + 139: 202, # '\x8b' + 140: 203, # '\x8c' + 141: 204, # '\x8d' + 142: 205, # '\x8e' + 143: 206, # '\x8f' + 144: 207, # '\x90' + 145: 208, # '\x91' + 146: 209, # '\x92' + 147: 210, # '\x93' + 148: 211, # '\x94' + 149: 212, # '\x95' + 150: 213, # '\x96' + 151: 214, # '\x97' + 152: 215, # '\x98' + 153: 216, # '\x99' + 154: 217, # '\x9a' + 155: 218, # '\x9b' + 156: 219, # '\x9c' + 157: 220, # '\x9d' + 158: 221, # '\x9e' + 159: 222, # '\x9f' + 160: 223, # '\xa0' + 161: 224, # 'Ё' + 162: 225, # 'Ђ' + 163: 226, # 'Ѓ' + 164: 227, # 'Є' + 165: 228, # 'Ѕ' + 166: 229, # 'І' + 167: 230, # 'Ї' + 168: 231, # 'Ј' + 169: 232, # 'Љ' + 170: 233, # 'Њ' + 171: 234, # 'Ћ' + 172: 235, # 'Ќ' + 173: 236, # '\xad' + 174: 237, # 'Ў' + 175: 238, # 'Џ' + 176: 37, # 'А' + 177: 44, # 'Б' + 178: 33, # 'В' + 179: 46, # 'Г' + 180: 41, # 'Д' + 181: 48, # 'Е' + 182: 56, # 'Ж' + 183: 51, # 'З' + 184: 42, # 'И' + 185: 60, # 'Й' + 186: 36, # 'К' + 187: 49, # 'Л' + 188: 38, # 'М' + 189: 31, # 'Н' + 190: 34, # 'О' + 191: 35, # 'П' + 192: 45, # 'Р' + 193: 32, # 'С' + 194: 40, # 'Т' + 195: 52, # 'У' + 196: 53, # 'Ф' + 197: 55, # 'Х' + 198: 58, # 'Ц' + 199: 50, # 'Ч' + 200: 57, # 'Ш' + 201: 63, # 'Щ' + 202: 70, # 'Ъ' + 203: 62, # 'Ы' + 204: 61, # 'Ь' + 205: 47, # 'Э' + 206: 59, # 'Ю' + 207: 43, # 'Я' + 208: 3, # 'а' + 209: 21, # 'б' + 210: 10, # 'в' + 211: 19, # 'г' + 212: 13, # 'д' + 213: 2, # 'е' + 214: 24, # 'ж' + 215: 20, # 'з' + 216: 4, # 'и' + 217: 23, # 'й' + 218: 11, # 'к' + 219: 8, # 'л' + 220: 12, # 'м' + 221: 5, # 'н' + 222: 1, # 'о' + 223: 15, # 'п' + 224: 9, # 'р' + 225: 7, # 'с' + 226: 6, # 'т' + 227: 14, # 'у' + 228: 39, # 'ф' + 229: 26, # 'х' + 230: 28, # 'ц' + 231: 22, # 'ч' + 232: 25, # 'ш' + 233: 29, # 'щ' + 234: 54, # 'ъ' + 235: 18, # 'ы' + 236: 17, # 'ь' + 237: 30, # 'э' + 238: 27, # 'ю' + 239: 16, # 'я' + 240: 239, # '№' + 241: 68, # 'ё' + 242: 240, # 'ђ' + 243: 241, # 'ѓ' + 244: 242, # 'є' + 245: 243, # 'ѕ' + 246: 244, # 'і' + 247: 245, # 'ї' + 248: 246, # 'ј' + 249: 247, # 'љ' + 250: 248, # 'њ' + 251: 249, # 'ћ' + 252: 250, # 'ќ' + 253: 251, # '§' + 254: 252, # 'ў' + 255: 255, # 'џ' +} + +ISO_8859_5_RUSSIAN_MODEL = SingleByteCharSetModel( + charset_name="ISO-8859-5", + language="Russian", + char_to_order_map=ISO_8859_5_RUSSIAN_CHAR_TO_ORDER, + language_model=RUSSIAN_LANG_MODEL, + typical_positive_ratio=0.976601, + keep_ascii_letters=False, + alphabet="ЁАБВГДЕЖЗИЙКЛМНОПРСТУФХЦЧШЩЪЫЬЭЮЯабвгдежзийклмнопрстуфхцчшщъыьэюяё", +) diff --git a/venv/lib/python3.10/site-packages/chardet/langthaimodel.py b/venv/lib/python3.10/site-packages/chardet/langthaimodel.py new file mode 100644 index 0000000000000000000000000000000000000000..883fdb1eafea7c7122846bdd478da800375a55bd --- /dev/null +++ b/venv/lib/python3.10/site-packages/chardet/langthaimodel.py @@ -0,0 +1,4380 @@ +from chardet.sbcharsetprober import SingleByteCharSetModel + +# 3: Positive +# 2: Likely +# 1: Unlikely +# 0: Negative + +THAI_LANG_MODEL = { + 5: { # 'ก' + 5: 2, # 'ก' + 30: 2, # 'ข' + 24: 2, # 'ค' + 8: 2, # 'ง' + 26: 2, # 'จ' + 52: 0, # 'ฉ' + 34: 1, # 'ช' + 51: 1, # 'ซ' + 47: 0, # 'ญ' + 58: 3, # 'ฎ' + 57: 2, # 'ฏ' + 49: 0, # 'ฐ' + 53: 0, # 'ฑ' + 55: 0, # 'ฒ' + 43: 2, # 'ณ' + 20: 2, # 'ด' + 19: 3, # 'ต' + 44: 0, # 'ถ' + 14: 2, # 'ท' + 48: 0, # 'ธ' + 3: 2, # 'น' + 17: 1, # 'บ' + 25: 2, # 'ป' + 39: 1, # 'ผ' + 62: 1, # 'ฝ' + 31: 1, # 'พ' + 54: 0, # 'ฟ' + 45: 1, # 'ภ' + 9: 2, # 'ม' + 16: 1, # 'ย' + 2: 3, # 'ร' + 61: 2, # 'ฤ' + 15: 3, # 'ล' + 12: 3, # 'ว' + 42: 2, # 'ศ' + 46: 3, # 'ษ' + 18: 2, # 'ส' + 21: 2, # 'ห' + 4: 3, # 'อ' + 63: 1, # 'ฯ' + 22: 2, # 'ะ' + 10: 3, # 'ั' + 1: 3, # 'า' + 36: 3, # 'ำ' + 23: 3, # 'ิ' + 13: 3, # 'ี' + 40: 0, # 'ึ' + 27: 2, # 'ื' + 32: 2, # 'ุ' + 35: 1, # 'ู' + 11: 2, # 'เ' + 28: 2, # 'แ' + 41: 1, # 'โ' + 29: 1, # 'ใ' + 33: 2, # 'ไ' + 50: 1, # 'ๆ' + 37: 3, # '็' + 6: 3, # '่' + 7: 3, # '้' + 38: 2, # '์' + 56: 0, # '๑' + 59: 0, # '๒' + 60: 0, # '๕' + }, + 30: { # 'ข' + 5: 1, # 'ก' + 30: 0, # 'ข' + 24: 1, # 'ค' + 8: 1, # 'ง' + 26: 1, # 'จ' + 52: 0, # 'ฉ' + 34: 0, # 'ช' + 51: 0, # 'ซ' + 47: 0, # 'ญ' + 58: 0, # 'ฎ' + 57: 0, # 'ฏ' + 49: 0, # 'ฐ' + 53: 0, # 'ฑ' + 55: 0, # 'ฒ' + 43: 2, # 'ณ' + 20: 0, # 'ด' + 19: 2, # 'ต' + 44: 0, # 'ถ' + 14: 1, # 'ท' + 48: 0, # 'ธ' + 3: 2, # 'น' + 17: 1, # 'บ' + 25: 1, # 'ป' + 39: 0, # 'ผ' + 62: 0, # 'ฝ' + 31: 0, # 'พ' + 54: 0, # 'ฟ' + 45: 0, # 'ภ' + 9: 0, # 'ม' + 16: 2, # 'ย' + 2: 1, # 'ร' + 61: 0, # 'ฤ' + 15: 0, # 'ล' + 12: 2, # 'ว' + 42: 0, # 'ศ' + 46: 0, # 'ษ' + 18: 1, # 'ส' + 21: 1, # 'ห' + 4: 3, # 'อ' + 63: 0, # 'ฯ' + 22: 0, # 'ะ' + 10: 3, # 'ั' + 1: 3, # 'า' + 36: 0, # 'ำ' + 23: 0, # 'ิ' + 13: 2, # 'ี' + 40: 3, # 'ึ' + 27: 1, # 'ื' + 32: 1, # 'ุ' + 35: 0, # 'ู' + 11: 0, # 'เ' + 28: 0, # 'แ' + 41: 0, # 'โ' + 29: 1, # 'ใ' + 33: 0, # 'ไ' + 50: 0, # 'ๆ' + 37: 1, # '็' + 6: 2, # '่' + 7: 3, # '้' + 38: 1, # '์' + 56: 0, # '๑' + 59: 0, # '๒' + 60: 0, # '๕' + }, + 24: { # 'ค' + 5: 0, # 'ก' + 30: 0, # 'ข' + 24: 2, # 'ค' + 8: 2, # 'ง' + 26: 0, # 'จ' + 52: 0, # 'ฉ' + 34: 0, # 'ช' + 51: 0, # 'ซ' + 47: 0, # 'ญ' + 58: 0, # 'ฎ' + 57: 0, # 'ฏ' + 49: 0, # 'ฐ' + 53: 0, # 'ฑ' + 55: 0, # 'ฒ' + 43: 2, # 'ณ' + 20: 2, # 'ด' + 19: 2, # 'ต' + 44: 0, # 'ถ' + 14: 1, # 'ท' + 48: 0, # 'ธ' + 3: 3, # 'น' + 17: 0, # 'บ' + 25: 1, # 'ป' + 39: 0, # 'ผ' + 62: 0, # 'ฝ' + 31: 0, # 'พ' + 54: 0, # 'ฟ' + 45: 0, # 'ภ' + 9: 2, # 'ม' + 16: 2, # 'ย' + 2: 3, # 'ร' + 61: 0, # 'ฤ' + 15: 3, # 'ล' + 12: 3, # 'ว' + 42: 0, # 'ศ' + 46: 0, # 'ษ' + 18: 1, # 'ส' + 21: 0, # 'ห' + 4: 2, # 'อ' + 63: 0, # 'ฯ' + 22: 2, # 'ะ' + 10: 3, # 'ั' + 1: 2, # 'า' + 36: 3, # 'ำ' + 23: 3, # 'ิ' + 13: 2, # 'ี' + 40: 0, # 'ึ' + 27: 3, # 'ื' + 32: 3, # 'ุ' + 35: 2, # 'ู' + 11: 1, # 'เ' + 28: 0, # 'แ' + 41: 3, # 'โ' + 29: 0, # 'ใ' + 33: 0, # 'ไ' + 50: 0, # 'ๆ' + 37: 1, # '็' + 6: 3, # '่' + 7: 3, # '้' + 38: 3, # '์' + 56: 0, # '๑' + 59: 0, # '๒' + 60: 0, # '๕' + }, + 8: { # 'ง' + 5: 3, # 'ก' + 30: 2, # 'ข' + 24: 3, # 'ค' + 8: 2, # 'ง' + 26: 2, # 'จ' + 52: 1, # 'ฉ' + 34: 2, # 'ช' + 51: 1, # 'ซ' + 47: 0, # 'ญ' + 58: 0, # 'ฎ' + 57: 0, # 'ฏ' + 49: 0, # 'ฐ' + 53: 0, # 'ฑ' + 55: 0, # 'ฒ' + 43: 0, # 'ณ' + 20: 2, # 'ด' + 19: 2, # 'ต' + 44: 1, # 'ถ' + 14: 3, # 'ท' + 48: 1, # 'ธ' + 3: 3, # 'น' + 17: 2, # 'บ' + 25: 2, # 'ป' + 39: 2, # 'ผ' + 62: 1, # 'ฝ' + 31: 2, # 'พ' + 54: 0, # 'ฟ' + 45: 1, # 'ภ' + 9: 2, # 'ม' + 16: 1, # 'ย' + 2: 2, # 'ร' + 61: 0, # 'ฤ' + 15: 2, # 'ล' + 12: 2, # 'ว' + 42: 2, # 'ศ' + 46: 1, # 'ษ' + 18: 3, # 'ส' + 21: 3, # 'ห' + 4: 2, # 'อ' + 63: 0, # 'ฯ' + 22: 0, # 'ะ' + 10: 1, # 'ั' + 1: 3, # 'า' + 36: 0, # 'ำ' + 23: 2, # 'ิ' + 13: 1, # 'ี' + 40: 0, # 'ึ' + 27: 1, # 'ื' + 32: 1, # 'ุ' + 35: 0, # 'ู' + 11: 3, # 'เ' + 28: 2, # 'แ' + 41: 1, # 'โ' + 29: 2, # 'ใ' + 33: 2, # 'ไ' + 50: 3, # 'ๆ' + 37: 0, # '็' + 6: 2, # '่' + 7: 0, # '้' + 38: 0, # '์' + 56: 0, # '๑' + 59: 0, # '๒' + 60: 0, # '๕' + }, + 26: { # 'จ' + 5: 2, # 'ก' + 30: 1, # 'ข' + 24: 0, # 'ค' + 8: 2, # 'ง' + 26: 3, # 'จ' + 52: 0, # 'ฉ' + 34: 0, # 'ช' + 51: 0, # 'ซ' + 47: 0, # 'ญ' + 58: 0, # 'ฎ' + 57: 0, # 'ฏ' + 49: 0, # 'ฐ' + 53: 0, # 'ฑ' + 55: 0, # 'ฒ' + 43: 0, # 'ณ' + 20: 2, # 'ด' + 19: 1, # 'ต' + 44: 1, # 'ถ' + 14: 2, # 'ท' + 48: 0, # 'ธ' + 3: 3, # 'น' + 17: 1, # 'บ' + 25: 0, # 'ป' + 39: 0, # 'ผ' + 62: 0, # 'ฝ' + 31: 1, # 'พ' + 54: 0, # 'ฟ' + 45: 0, # 'ภ' + 9: 1, # 'ม' + 16: 1, # 'ย' + 2: 3, # 'ร' + 61: 0, # 'ฤ' + 15: 0, # 'ล' + 12: 1, # 'ว' + 42: 0, # 'ศ' + 46: 0, # 'ษ' + 18: 2, # 'ส' + 21: 1, # 'ห' + 4: 2, # 'อ' + 63: 0, # 'ฯ' + 22: 3, # 'ะ' + 10: 3, # 'ั' + 1: 3, # 'า' + 36: 3, # 'ำ' + 23: 2, # 'ิ' + 13: 1, # 'ี' + 40: 3, # 'ึ' + 27: 1, # 'ื' + 32: 3, # 'ุ' + 35: 2, # 'ู' + 11: 1, # 'เ' + 28: 1, # 'แ' + 41: 0, # 'โ' + 29: 1, # 'ใ' + 33: 1, # 'ไ' + 50: 0, # 'ๆ' + 37: 0, # '็' + 6: 2, # '่' + 7: 2, # '้' + 38: 0, # '์' + 56: 0, # '๑' + 59: 0, # '๒' + 60: 0, # '๕' + }, + 52: { # 'ฉ' + 5: 0, # 'ก' + 30: 0, # 'ข' + 24: 0, # 'ค' + 8: 0, # 'ง' + 26: 0, # 'จ' + 52: 0, # 'ฉ' + 34: 0, # 'ช' + 51: 0, # 'ซ' + 47: 0, # 'ญ' + 58: 0, # 'ฎ' + 57: 0, # 'ฏ' + 49: 0, # 'ฐ' + 53: 0, # 'ฑ' + 55: 0, # 'ฒ' + 43: 0, # 'ณ' + 20: 0, # 'ด' + 19: 0, # 'ต' + 44: 0, # 'ถ' + 14: 0, # 'ท' + 48: 0, # 'ธ' + 3: 0, # 'น' + 17: 3, # 'บ' + 25: 0, # 'ป' + 39: 0, # 'ผ' + 62: 0, # 'ฝ' + 31: 3, # 'พ' + 54: 0, # 'ฟ' + 45: 0, # 'ภ' + 9: 1, # 'ม' + 16: 1, # 'ย' + 2: 0, # 'ร' + 61: 0, # 'ฤ' + 15: 2, # 'ล' + 12: 1, # 'ว' + 42: 0, # 'ศ' + 46: 0, # 'ษ' + 18: 0, # 'ส' + 21: 0, # 'ห' + 4: 0, # 'อ' + 63: 0, # 'ฯ' + 22: 1, # 'ะ' + 10: 1, # 'ั' + 1: 1, # 'า' + 36: 0, # 'ำ' + 23: 1, # 'ิ' + 13: 1, # 'ี' + 40: 0, # 'ึ' + 27: 0, # 'ื' + 32: 1, # 'ุ' + 35: 0, # 'ู' + 11: 0, # 'เ' + 28: 0, # 'แ' + 41: 0, # 'โ' + 29: 0, # 'ใ' + 33: 0, # 'ไ' + 50: 0, # 'ๆ' + 37: 0, # '็' + 6: 0, # '่' + 7: 0, # '้' + 38: 0, # '์' + 56: 0, # '๑' + 59: 0, # '๒' + 60: 0, # '๕' + }, + 34: { # 'ช' + 5: 1, # 'ก' + 30: 0, # 'ข' + 24: 0, # 'ค' + 8: 1, # 'ง' + 26: 0, # 'จ' + 52: 0, # 'ฉ' + 34: 0, # 'ช' + 51: 0, # 'ซ' + 47: 1, # 'ญ' + 58: 0, # 'ฎ' + 57: 0, # 'ฏ' + 49: 0, # 'ฐ' + 53: 0, # 'ฑ' + 55: 0, # 'ฒ' + 43: 0, # 'ณ' + 20: 0, # 'ด' + 19: 0, # 'ต' + 44: 0, # 'ถ' + 14: 1, # 'ท' + 48: 0, # 'ธ' + 3: 3, # 'น' + 17: 2, # 'บ' + 25: 0, # 'ป' + 39: 0, # 'ผ' + 62: 0, # 'ฝ' + 31: 0, # 'พ' + 54: 0, # 'ฟ' + 45: 0, # 'ภ' + 9: 2, # 'ม' + 16: 1, # 'ย' + 2: 1, # 'ร' + 61: 0, # 'ฤ' + 15: 0, # 'ล' + 12: 1, # 'ว' + 42: 0, # 'ศ' + 46: 0, # 'ษ' + 18: 0, # 'ส' + 21: 0, # 'ห' + 4: 2, # 'อ' + 63: 0, # 'ฯ' + 22: 0, # 'ะ' + 10: 2, # 'ั' + 1: 3, # 'า' + 36: 1, # 'ำ' + 23: 3, # 'ิ' + 13: 2, # 'ี' + 40: 0, # 'ึ' + 27: 3, # 'ื' + 32: 3, # 'ุ' + 35: 1, # 'ู' + 11: 0, # 'เ' + 28: 0, # 'แ' + 41: 0, # 'โ' + 29: 0, # 'ใ' + 33: 0, # 'ไ' + 50: 0, # 'ๆ' + 37: 1, # '็' + 6: 3, # '่' + 7: 3, # '้' + 38: 0, # '์' + 56: 0, # '๑' + 59: 0, # '๒' + 60: 0, # '๕' + }, + 51: { # 'ซ' + 5: 0, # 'ก' + 30: 0, # 'ข' + 24: 0, # 'ค' + 8: 0, # 'ง' + 26: 0, # 'จ' + 52: 0, # 'ฉ' + 34: 0, # 'ช' + 51: 0, # 'ซ' + 47: 0, # 'ญ' + 58: 0, # 'ฎ' + 57: 0, # 'ฏ' + 49: 0, # 'ฐ' + 53: 0, # 'ฑ' + 55: 0, # 'ฒ' + 43: 0, # 'ณ' + 20: 0, # 'ด' + 19: 0, # 'ต' + 44: 0, # 'ถ' + 14: 0, # 'ท' + 48: 0, # 'ธ' + 3: 1, # 'น' + 17: 0, # 'บ' + 25: 0, # 'ป' + 39: 0, # 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'ง' + 26: 0, # 'จ' + 52: 0, # 'ฉ' + 34: 0, # 'ช' + 51: 0, # 'ซ' + 47: 0, # 'ญ' + 58: 0, # 'ฎ' + 57: 0, # 'ฏ' + 49: 0, # 'ฐ' + 53: 0, # 'ฑ' + 55: 0, # 'ฒ' + 43: 0, # 'ณ' + 20: 0, # 'ด' + 19: 0, # 'ต' + 44: 0, # 'ถ' + 14: 0, # 'ท' + 48: 0, # 'ธ' + 3: 0, # 'น' + 17: 0, # 'บ' + 25: 0, # 'ป' + 39: 0, # 'ผ' + 62: 0, # 'ฝ' + 31: 0, # 'พ' + 54: 0, # 'ฟ' + 45: 0, # 'ภ' + 9: 0, # 'ม' + 16: 0, # 'ย' + 2: 0, # 'ร' + 61: 0, # 'ฤ' + 15: 0, # 'ล' + 12: 0, # 'ว' + 42: 0, # 'ศ' + 46: 0, # 'ษ' + 18: 0, # 'ส' + 21: 0, # 'ห' + 4: 0, # 'อ' + 63: 0, # 'ฯ' + 22: 0, # 'ะ' + 10: 0, # 'ั' + 1: 0, # 'า' + 36: 0, # 'ำ' + 23: 0, # 'ิ' + 13: 0, # 'ี' + 40: 0, # 'ึ' + 27: 0, # 'ื' + 32: 0, # 'ุ' + 35: 0, # 'ู' + 11: 0, # 'เ' + 28: 0, # 'แ' + 41: 0, # 'โ' + 29: 0, # 'ใ' + 33: 0, # 'ไ' + 50: 0, # 'ๆ' + 37: 0, # '็' + 6: 0, # '่' + 7: 0, # '้' + 38: 0, # '์' + 56: 2, # '๑' + 59: 1, # '๒' + 60: 1, # '๕' + }, + 59: { # '๒' + 5: 0, # 'ก' + 30: 0, # 'ข' + 24: 0, # 'ค' + 8: 0, # 'ง' + 26: 0, # 'จ' + 52: 0, # 'ฉ' + 34: 0, # 'ช' + 51: 0, # 'ซ' + 47: 0, # 'ญ' + 58: 0, # 'ฎ' + 57: 0, # 'ฏ' + 49: 0, # 'ฐ' + 53: 0, # 'ฑ' + 55: 0, # 'ฒ' + 43: 0, # 'ณ' + 20: 0, # 'ด' + 19: 0, # 'ต' + 44: 0, # 'ถ' + 14: 0, # 'ท' + 48: 0, # 'ธ' + 3: 0, # 'น' + 17: 0, # 'บ' + 25: 0, # 'ป' + 39: 0, # 'ผ' + 62: 0, # 'ฝ' + 31: 0, # 'พ' + 54: 0, # 'ฟ' + 45: 0, # 'ภ' + 9: 0, # 'ม' + 16: 0, # 'ย' + 2: 0, # 'ร' + 61: 0, # 'ฤ' + 15: 0, # 'ล' + 12: 0, # 'ว' + 42: 0, # 'ศ' + 46: 0, # 'ษ' + 18: 0, # 'ส' + 21: 0, # 'ห' + 4: 0, # 'อ' + 63: 0, # 'ฯ' + 22: 0, # 'ะ' + 10: 0, # 'ั' + 1: 0, # 'า' + 36: 0, # 'ำ' + 23: 0, # 'ิ' + 13: 0, # 'ี' + 40: 0, # 'ึ' + 27: 0, # 'ื' + 32: 0, # 'ุ' + 35: 0, # 'ู' + 11: 0, # 'เ' + 28: 0, # 'แ' + 41: 0, # 'โ' + 29: 0, # 'ใ' + 33: 0, # 'ไ' + 50: 0, # 'ๆ' + 37: 0, # '็' + 6: 0, # '่' + 7: 0, # '้' + 38: 0, # '์' + 56: 1, # '๑' + 59: 1, # '๒' + 60: 3, # '๕' + }, + 60: { # '๕' + 5: 0, # 'ก' + 30: 0, # 'ข' + 24: 0, # 'ค' + 8: 0, # 'ง' + 26: 0, # 'จ' + 52: 0, # 'ฉ' + 34: 0, # 'ช' + 51: 0, # 'ซ' + 47: 0, # 'ญ' + 58: 0, # 'ฎ' + 57: 0, # 'ฏ' + 49: 0, # 'ฐ' + 53: 0, # 'ฑ' + 55: 0, # 'ฒ' + 43: 0, # 'ณ' + 20: 0, # 'ด' + 19: 0, # 'ต' + 44: 0, # 'ถ' + 14: 0, # 'ท' + 48: 0, # 'ธ' + 3: 0, # 'น' + 17: 0, # 'บ' + 25: 0, # 'ป' + 39: 0, # 'ผ' + 62: 0, # 'ฝ' + 31: 0, # 'พ' + 54: 0, # 'ฟ' + 45: 0, # 'ภ' + 9: 0, # 'ม' + 16: 0, # 'ย' + 2: 0, # 'ร' + 61: 0, # 'ฤ' + 15: 0, # 'ล' + 12: 0, # 'ว' + 42: 0, # 'ศ' + 46: 0, # 'ษ' + 18: 0, # 'ส' + 21: 0, # 'ห' + 4: 0, # 'อ' + 63: 0, # 'ฯ' + 22: 0, # 'ะ' + 10: 0, # 'ั' + 1: 0, # 'า' + 36: 0, # 'ำ' + 23: 0, # 'ิ' + 13: 0, # 'ี' + 40: 0, # 'ึ' + 27: 0, # 'ื' + 32: 0, # 'ุ' + 35: 0, # 'ู' + 11: 0, # 'เ' + 28: 0, # 'แ' + 41: 0, # 'โ' + 29: 0, # 'ใ' + 33: 0, # 'ไ' + 50: 0, # 'ๆ' + 37: 0, # '็' + 6: 0, # '่' + 7: 0, # '้' + 38: 0, # '์' + 56: 2, # '๑' + 59: 1, # '๒' + 60: 0, # '๕' + }, +} + +# 255: Undefined characters that did not exist in training text +# 254: Carriage/Return +# 253: symbol (punctuation) that does not belong to word +# 252: 0 - 9 +# 251: Control characters + +# Character Mapping Table(s): +TIS_620_THAI_CHAR_TO_ORDER = { + 0: 255, # '\x00' + 1: 255, # '\x01' + 2: 255, # '\x02' + 3: 255, # '\x03' + 4: 255, # '\x04' + 5: 255, # '\x05' + 6: 255, # '\x06' + 7: 255, # '\x07' + 8: 255, # '\x08' + 9: 255, # '\t' + 10: 254, # '\n' + 11: 255, # '\x0b' + 12: 255, # '\x0c' + 13: 254, # '\r' + 14: 255, # '\x0e' + 15: 255, # '\x0f' + 16: 255, # '\x10' + 17: 255, # '\x11' + 18: 255, # '\x12' + 19: 255, # '\x13' + 20: 255, # '\x14' + 21: 255, # '\x15' + 22: 255, # '\x16' + 23: 255, # '\x17' + 24: 255, # '\x18' + 25: 255, # '\x19' + 26: 255, # '\x1a' + 27: 255, # '\x1b' + 28: 255, # '\x1c' + 29: 255, # '\x1d' + 30: 255, # '\x1e' + 31: 255, # '\x1f' + 32: 253, # ' ' + 33: 253, # '!' + 34: 253, # '"' + 35: 253, # '#' + 36: 253, # '$' + 37: 253, # '%' + 38: 253, # '&' + 39: 253, # "'" + 40: 253, # '(' + 41: 253, # ')' + 42: 253, # '*' + 43: 253, # '+' + 44: 253, # ',' + 45: 253, # '-' + 46: 253, # '.' + 47: 253, # '/' + 48: 252, # '0' + 49: 252, # '1' + 50: 252, # '2' + 51: 252, # '3' + 52: 252, # '4' + 53: 252, # '5' + 54: 252, # '6' + 55: 252, # '7' + 56: 252, # '8' + 57: 252, # '9' + 58: 253, # ':' + 59: 253, # ';' + 60: 253, # '<' + 61: 253, # '=' + 62: 253, # '>' + 63: 253, # '?' + 64: 253, # '@' + 65: 182, # 'A' + 66: 106, # 'B' + 67: 107, # 'C' + 68: 100, # 'D' + 69: 183, # 'E' + 70: 184, # 'F' + 71: 185, # 'G' + 72: 101, # 'H' + 73: 94, # 'I' + 74: 186, # 'J' + 75: 187, # 'K' + 76: 108, # 'L' + 77: 109, # 'M' + 78: 110, # 'N' + 79: 111, # 'O' + 80: 188, # 'P' + 81: 189, # 'Q' + 82: 190, # 'R' + 83: 89, # 'S' + 84: 95, # 'T' + 85: 112, # 'U' + 86: 113, # 'V' + 87: 191, # 'W' + 88: 192, # 'X' + 89: 193, # 'Y' + 90: 194, # 'Z' + 91: 253, # '[' + 92: 253, # '\\' + 93: 253, # ']' + 94: 253, # '^' + 95: 253, # '_' + 96: 253, # '`' + 97: 64, # 'a' + 98: 72, # 'b' + 99: 73, # 'c' + 100: 114, # 'd' + 101: 74, # 'e' + 102: 115, # 'f' + 103: 116, # 'g' + 104: 102, # 'h' + 105: 81, # 'i' + 106: 201, # 'j' + 107: 117, # 'k' + 108: 90, # 'l' + 109: 103, # 'm' + 110: 78, # 'n' + 111: 82, # 'o' + 112: 96, # 'p' + 113: 202, # 'q' + 114: 91, # 'r' + 115: 79, # 's' + 116: 84, # 't' + 117: 104, # 'u' + 118: 105, # 'v' + 119: 97, # 'w' + 120: 98, # 'x' + 121: 92, # 'y' + 122: 203, # 'z' + 123: 253, # '{' + 124: 253, # '|' + 125: 253, # '}' + 126: 253, # '~' + 127: 253, # '\x7f' + 128: 209, # '\x80' + 129: 210, # '\x81' + 130: 211, # '\x82' + 131: 212, # '\x83' + 132: 213, # '\x84' + 133: 88, # '\x85' + 134: 214, # '\x86' + 135: 215, # '\x87' + 136: 216, # '\x88' + 137: 217, # '\x89' + 138: 218, # '\x8a' + 139: 219, # '\x8b' + 140: 220, # '\x8c' + 141: 118, # '\x8d' + 142: 221, # '\x8e' + 143: 222, # '\x8f' + 144: 223, # '\x90' + 145: 224, # '\x91' + 146: 99, # '\x92' + 147: 85, # '\x93' + 148: 83, # '\x94' + 149: 225, # '\x95' + 150: 226, # '\x96' + 151: 227, # '\x97' + 152: 228, # '\x98' + 153: 229, # '\x99' + 154: 230, # '\x9a' + 155: 231, # '\x9b' + 156: 232, # '\x9c' + 157: 233, # '\x9d' + 158: 234, # '\x9e' + 159: 235, # '\x9f' + 160: 236, # None + 161: 5, # 'ก' + 162: 30, # 'ข' + 163: 237, # 'ฃ' + 164: 24, # 'ค' + 165: 238, # 'ฅ' + 166: 75, # 'ฆ' + 167: 8, # 'ง' + 168: 26, # 'จ' + 169: 52, # 'ฉ' + 170: 34, # 'ช' + 171: 51, # 'ซ' + 172: 119, # 'ฌ' + 173: 47, # 'ญ' + 174: 58, # 'ฎ' + 175: 57, # 'ฏ' + 176: 49, # 'ฐ' + 177: 53, # 'ฑ' + 178: 55, # 'ฒ' + 179: 43, # 'ณ' + 180: 20, # 'ด' + 181: 19, # 'ต' + 182: 44, # 'ถ' + 183: 14, # 'ท' + 184: 48, # 'ธ' + 185: 3, # 'น' + 186: 17, # 'บ' + 187: 25, # 'ป' + 188: 39, # 'ผ' + 189: 62, # 'ฝ' + 190: 31, # 'พ' + 191: 54, # 'ฟ' + 192: 45, # 'ภ' + 193: 9, # 'ม' + 194: 16, # 'ย' + 195: 2, # 'ร' + 196: 61, # 'ฤ' + 197: 15, # 'ล' + 198: 239, # 'ฦ' + 199: 12, # 'ว' + 200: 42, # 'ศ' + 201: 46, # 'ษ' + 202: 18, # 'ส' + 203: 21, # 'ห' + 204: 76, # 'ฬ' + 205: 4, # 'อ' + 206: 66, # 'ฮ' + 207: 63, # 'ฯ' + 208: 22, # 'ะ' + 209: 10, # 'ั' + 210: 1, # 'า' + 211: 36, # 'ำ' + 212: 23, # 'ิ' + 213: 13, # 'ี' + 214: 40, # 'ึ' + 215: 27, # 'ื' + 216: 32, # 'ุ' + 217: 35, # 'ู' + 218: 86, # 'ฺ' + 219: 240, # None + 220: 241, # None + 221: 242, # None + 222: 243, # None + 223: 244, # '฿' + 224: 11, # 'เ' + 225: 28, # 'แ' + 226: 41, # 'โ' + 227: 29, # 'ใ' + 228: 33, # 'ไ' + 229: 245, # 'ๅ' + 230: 50, # 'ๆ' + 231: 37, # '็' + 232: 6, # '่' + 233: 7, # '้' + 234: 67, # '๊' + 235: 77, # '๋' + 236: 38, # '์' + 237: 93, # 'ํ' + 238: 246, # '๎' + 239: 247, # '๏' + 240: 68, # '๐' + 241: 56, # '๑' + 242: 59, # '๒' + 243: 65, # '๓' + 244: 69, # '๔' + 245: 60, # '๕' + 246: 70, # '๖' + 247: 80, # '๗' + 248: 71, # '๘' + 249: 87, # '๙' + 250: 248, # '๚' + 251: 249, # '๛' + 252: 250, # None + 253: 251, # None + 254: 252, # None + 255: 253, # None +} + +TIS_620_THAI_MODEL = SingleByteCharSetModel( + charset_name="TIS-620", + language="Thai", + char_to_order_map=TIS_620_THAI_CHAR_TO_ORDER, + language_model=THAI_LANG_MODEL, + typical_positive_ratio=0.926386, + keep_ascii_letters=False, + alphabet="กขฃคฅฆงจฉชซฌญฎฏฐฑฒณดตถทธนบปผฝพฟภมยรฤลฦวศษสหฬอฮฯะัาำิีึืฺุู฿เแโใไๅๆ็่้๊๋์ํ๎๏๐๑๒๓๔๕๖๗๘๙๚๛", +) diff --git a/venv/lib/python3.10/site-packages/chardet/macromanprober.py b/venv/lib/python3.10/site-packages/chardet/macromanprober.py new file mode 100644 index 0000000000000000000000000000000000000000..1425d10ecaa59a9e49b73cea2b8b4747de73f6b5 --- /dev/null +++ b/venv/lib/python3.10/site-packages/chardet/macromanprober.py @@ -0,0 +1,162 @@ +######################## BEGIN LICENSE BLOCK ######################## +# This code was modified from latin1prober.py by Rob Speer . +# The Original Code is Mozilla Universal charset detector code. +# +# The Initial Developer of the Original Code is +# Netscape Communications Corporation. +# Portions created by the Initial Developer are Copyright (C) 2001 +# the Initial Developer. All Rights Reserved. +# +# Contributor(s): +# Rob Speer - adapt to MacRoman encoding +# Mark Pilgrim - port to Python +# Shy Shalom - original C code +# +# This library is free software; you can redistribute it and/or +# modify it under the terms of the GNU Lesser General Public +# License as published by the Free Software Foundation; either +# version 2.1 of the License, or (at your option) any later version. +# +# This library is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +# Lesser General Public License for more details. +# +# You should have received a copy of the GNU Lesser General Public +# License along with this library; if not, write to the Free Software +# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA +# 02110-1301 USA +######################### END LICENSE BLOCK ######################### + +from typing import List, Union + +from .charsetprober import CharSetProber +from .enums import ProbingState + +FREQ_CAT_NUM = 4 + +UDF = 0 # undefined +OTH = 1 # other +ASC = 2 # ascii capital letter +ASS = 3 # ascii small letter +ACV = 4 # accent capital vowel +ACO = 5 # accent capital other +ASV = 6 # accent small vowel +ASO = 7 # accent small other +ODD = 8 # character that is unlikely to appear +CLASS_NUM = 9 # total classes + +# The change from Latin1 is that we explicitly look for extended characters +# that are infrequently-occurring symbols, and consider them to always be +# improbable. This should let MacRoman get out of the way of more likely +# encodings in most situations. + +# fmt: off +MacRoman_CharToClass = ( + OTH, OTH, OTH, OTH, OTH, OTH, OTH, OTH, # 00 - 07 + OTH, OTH, OTH, OTH, OTH, OTH, OTH, OTH, # 08 - 0F + OTH, OTH, OTH, OTH, OTH, OTH, OTH, OTH, # 10 - 17 + OTH, OTH, OTH, OTH, OTH, OTH, OTH, OTH, # 18 - 1F + OTH, OTH, OTH, OTH, OTH, OTH, OTH, OTH, # 20 - 27 + OTH, OTH, OTH, OTH, OTH, OTH, OTH, OTH, # 28 - 2F + OTH, OTH, OTH, OTH, OTH, OTH, OTH, OTH, # 30 - 37 + OTH, OTH, OTH, OTH, OTH, OTH, OTH, OTH, # 38 - 3F + OTH, ASC, ASC, ASC, ASC, ASC, ASC, ASC, # 40 - 47 + ASC, ASC, ASC, ASC, ASC, ASC, ASC, ASC, # 48 - 4F + ASC, ASC, ASC, ASC, ASC, ASC, ASC, ASC, # 50 - 57 + ASC, ASC, ASC, OTH, OTH, OTH, OTH, OTH, # 58 - 5F + OTH, ASS, ASS, ASS, ASS, ASS, ASS, ASS, # 60 - 67 + ASS, ASS, ASS, ASS, ASS, ASS, ASS, ASS, # 68 - 6F + ASS, ASS, ASS, ASS, ASS, ASS, ASS, ASS, # 70 - 77 + ASS, ASS, ASS, OTH, OTH, OTH, OTH, OTH, # 78 - 7F + ACV, ACV, ACO, ACV, ACO, ACV, ACV, ASV, # 80 - 87 + ASV, ASV, ASV, ASV, ASV, ASO, ASV, ASV, # 88 - 8F + ASV, ASV, ASV, ASV, ASV, ASV, ASO, ASV, # 90 - 97 + ASV, ASV, ASV, ASV, ASV, ASV, ASV, ASV, # 98 - 9F + OTH, OTH, OTH, OTH, OTH, OTH, OTH, ASO, # A0 - A7 + OTH, OTH, ODD, ODD, OTH, OTH, ACV, ACV, # A8 - AF + OTH, OTH, OTH, OTH, OTH, OTH, OTH, OTH, # B0 - B7 + OTH, OTH, OTH, OTH, OTH, OTH, ASV, ASV, # B8 - BF + OTH, OTH, ODD, OTH, ODD, OTH, OTH, OTH, # C0 - C7 + OTH, OTH, OTH, ACV, ACV, ACV, ACV, ASV, # C8 - CF + OTH, OTH, OTH, OTH, OTH, OTH, OTH, ODD, # D0 - D7 + ASV, ACV, ODD, OTH, OTH, OTH, OTH, OTH, # D8 - DF + OTH, OTH, OTH, OTH, OTH, ACV, ACV, ACV, # E0 - E7 + ACV, ACV, ACV, ACV, ACV, ACV, ACV, ACV, # E8 - EF + ODD, ACV, ACV, ACV, ACV, ASV, ODD, ODD, # F0 - F7 + ODD, ODD, ODD, ODD, ODD, ODD, ODD, ODD, # F8 - FF +) + +# 0 : illegal +# 1 : very unlikely +# 2 : normal +# 3 : very likely +MacRomanClassModel = ( +# UDF OTH ASC ASS ACV ACO ASV ASO ODD + 0, 0, 0, 0, 0, 0, 0, 0, 0, # UDF + 0, 3, 3, 3, 3, 3, 3, 3, 1, # OTH + 0, 3, 3, 3, 3, 3, 3, 3, 1, # ASC + 0, 3, 3, 3, 1, 1, 3, 3, 1, # ASS + 0, 3, 3, 3, 1, 2, 1, 2, 1, # ACV + 0, 3, 3, 3, 3, 3, 3, 3, 1, # ACO + 0, 3, 1, 3, 1, 1, 1, 3, 1, # ASV + 0, 3, 1, 3, 1, 1, 3, 3, 1, # ASO + 0, 1, 1, 1, 1, 1, 1, 1, 1, # ODD +) +# fmt: on + + +class MacRomanProber(CharSetProber): + def __init__(self) -> None: + super().__init__() + self._last_char_class = OTH + self._freq_counter: List[int] = [] + self.reset() + + def reset(self) -> None: + self._last_char_class = OTH + self._freq_counter = [0] * FREQ_CAT_NUM + + # express the prior that MacRoman is a somewhat rare encoding; + # this can be done by starting out in a slightly improbable state + # that must be overcome + self._freq_counter[2] = 10 + + super().reset() + + @property + def charset_name(self) -> str: + return "MacRoman" + + @property + def language(self) -> str: + return "" + + def feed(self, byte_str: Union[bytes, bytearray]) -> ProbingState: + byte_str = self.remove_xml_tags(byte_str) + for c in byte_str: + char_class = MacRoman_CharToClass[c] + freq = MacRomanClassModel[(self._last_char_class * CLASS_NUM) + char_class] + if freq == 0: + self._state = ProbingState.NOT_ME + break + self._freq_counter[freq] += 1 + self._last_char_class = char_class + + return self.state + + def get_confidence(self) -> float: + if self.state == ProbingState.NOT_ME: + return 0.01 + + total = sum(self._freq_counter) + confidence = ( + 0.0 + if total < 0.01 + else (self._freq_counter[3] - self._freq_counter[1] * 20.0) / total + ) + confidence = max(confidence, 0.0) + # lower the confidence of MacRoman so that other more accurate + # detector can take priority. + confidence *= 0.73 + return confidence diff --git a/venv/lib/python3.10/site-packages/chardet/mbcharsetprober.py b/venv/lib/python3.10/site-packages/chardet/mbcharsetprober.py new file mode 100644 index 0000000000000000000000000000000000000000..666307e8fe0608c69f2b6578a49794e1e20a139a --- /dev/null +++ b/venv/lib/python3.10/site-packages/chardet/mbcharsetprober.py @@ -0,0 +1,95 @@ +######################## BEGIN LICENSE BLOCK ######################## +# The Original Code is Mozilla Universal charset detector code. +# +# The Initial Developer of the Original Code is +# Netscape Communications Corporation. +# Portions created by the Initial Developer are Copyright (C) 2001 +# the Initial Developer. All Rights Reserved. +# +# Contributor(s): +# Mark Pilgrim - port to Python +# Shy Shalom - original C code +# Proofpoint, Inc. +# +# This library is free software; you can redistribute it and/or +# modify it under the terms of the GNU Lesser General Public +# License as published by the Free Software Foundation; either +# version 2.1 of the License, or (at your option) any later version. +# +# This library is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +# Lesser General Public License for more details. +# +# You should have received a copy of the GNU Lesser General Public +# License along with this library; if not, write to the Free Software +# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA +# 02110-1301 USA +######################### END LICENSE BLOCK ######################### + +from typing import Optional, Union + +from .chardistribution import CharDistributionAnalysis +from .charsetprober import CharSetProber +from .codingstatemachine import CodingStateMachine +from .enums import LanguageFilter, MachineState, ProbingState + + +class MultiByteCharSetProber(CharSetProber): + """ + MultiByteCharSetProber + """ + + def __init__(self, lang_filter: LanguageFilter = LanguageFilter.NONE) -> None: + super().__init__(lang_filter=lang_filter) + self.distribution_analyzer: Optional[CharDistributionAnalysis] = None + self.coding_sm: Optional[CodingStateMachine] = None + self._last_char = bytearray(b"\0\0") + + def reset(self) -> None: + super().reset() + if self.coding_sm: + self.coding_sm.reset() + if self.distribution_analyzer: + self.distribution_analyzer.reset() + self._last_char = bytearray(b"\0\0") + + def feed(self, byte_str: Union[bytes, bytearray]) -> ProbingState: + assert self.coding_sm is not None + assert self.distribution_analyzer is not None + + for i, byte in enumerate(byte_str): + coding_state = self.coding_sm.next_state(byte) + if coding_state == MachineState.ERROR: + self.logger.debug( + "%s %s prober hit error at byte %s", + self.charset_name, + self.language, + i, + ) + self._state = ProbingState.NOT_ME + break + if coding_state == MachineState.ITS_ME: + self._state = ProbingState.FOUND_IT + break + if coding_state == MachineState.START: + char_len = self.coding_sm.get_current_charlen() + if i == 0: + self._last_char[1] = byte + self.distribution_analyzer.feed(self._last_char, char_len) + else: + self.distribution_analyzer.feed(byte_str[i - 1 : i + 1], char_len) + + self._last_char[0] = byte_str[-1] + + if self.state == ProbingState.DETECTING: + if self.distribution_analyzer.got_enough_data() and ( + self.get_confidence() > self.SHORTCUT_THRESHOLD + ): + self._state = ProbingState.FOUND_IT + + return self.state + + def get_confidence(self) -> float: + assert self.distribution_analyzer is not None + return self.distribution_analyzer.get_confidence() diff --git a/venv/lib/python3.10/site-packages/chardet/mbcsgroupprober.py b/venv/lib/python3.10/site-packages/chardet/mbcsgroupprober.py new file mode 100644 index 0000000000000000000000000000000000000000..6cb9cc7b3bc751fbb5a54ba06eaaf953bf14ed8d --- /dev/null +++ b/venv/lib/python3.10/site-packages/chardet/mbcsgroupprober.py @@ -0,0 +1,57 @@ +######################## BEGIN LICENSE BLOCK ######################## +# The Original Code is Mozilla Universal charset detector code. +# +# The Initial Developer of the Original Code is +# Netscape Communications Corporation. +# Portions created by the Initial Developer are Copyright (C) 2001 +# the Initial Developer. All Rights Reserved. +# +# Contributor(s): +# Mark Pilgrim - port to Python +# Shy Shalom - original C code +# Proofpoint, Inc. +# +# This library is free software; you can redistribute it and/or +# modify it under the terms of the GNU Lesser General Public +# License as published by the Free Software Foundation; either +# version 2.1 of the License, or (at your option) any later version. +# +# This library is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +# Lesser General Public License for more details. +# +# You should have received a copy of the GNU Lesser General Public +# License along with this library; if not, write to the Free Software +# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA +# 02110-1301 USA +######################### END LICENSE BLOCK ######################### + +from .big5prober import Big5Prober +from .charsetgroupprober import CharSetGroupProber +from .cp949prober import CP949Prober +from .enums import LanguageFilter +from .eucjpprober import EUCJPProber +from .euckrprober import EUCKRProber +from .euctwprober import EUCTWProber +from .gb2312prober import GB2312Prober +from .johabprober import JOHABProber +from .sjisprober import SJISProber +from .utf8prober import UTF8Prober + + +class MBCSGroupProber(CharSetGroupProber): + def __init__(self, lang_filter: LanguageFilter = LanguageFilter.NONE) -> None: + super().__init__(lang_filter=lang_filter) + self.probers = [ + UTF8Prober(), + SJISProber(), + EUCJPProber(), + GB2312Prober(), + EUCKRProber(), + CP949Prober(), + Big5Prober(), + EUCTWProber(), + JOHABProber(), + ] + self.reset() diff --git a/venv/lib/python3.10/site-packages/chardet/mbcssm.py b/venv/lib/python3.10/site-packages/chardet/mbcssm.py new file mode 100644 index 0000000000000000000000000000000000000000..7bbe97e6665356327814e2b797ffcc5724974a46 --- /dev/null +++ b/venv/lib/python3.10/site-packages/chardet/mbcssm.py @@ -0,0 +1,661 @@ +######################## BEGIN LICENSE BLOCK ######################## +# The Original Code is mozilla.org code. +# +# The Initial Developer of the Original Code is +# Netscape Communications Corporation. +# Portions created by the Initial Developer are Copyright (C) 1998 +# the Initial Developer. All Rights Reserved. +# +# Contributor(s): +# Mark Pilgrim - port to Python +# +# This library is free software; you can redistribute it and/or +# modify it under the terms of the GNU Lesser General Public +# License as published by the Free Software Foundation; either +# version 2.1 of the License, or (at your option) any later version. +# +# This library is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +# Lesser General Public License for more details. +# +# You should have received a copy of the GNU Lesser General Public +# License along with this library; if not, write to the Free Software +# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA +# 02110-1301 USA +######################### END LICENSE BLOCK ######################### + +from .codingstatemachinedict import CodingStateMachineDict +from .enums import MachineState + +# BIG5 + +# fmt: off +BIG5_CLS = ( + 1, 1, 1, 1, 1, 1, 1, 1, # 00 - 07 #allow 0x00 as legal value + 1, 1, 1, 1, 1, 1, 0, 0, # 08 - 0f + 1, 1, 1, 1, 1, 1, 1, 1, # 10 - 17 + 1, 1, 1, 0, 1, 1, 1, 1, # 18 - 1f + 1, 1, 1, 1, 1, 1, 1, 1, # 20 - 27 + 1, 1, 1, 1, 1, 1, 1, 1, # 28 - 2f + 1, 1, 1, 1, 1, 1, 1, 1, # 30 - 37 + 1, 1, 1, 1, 1, 1, 1, 1, # 38 - 3f + 2, 2, 2, 2, 2, 2, 2, 2, # 40 - 47 + 2, 2, 2, 2, 2, 2, 2, 2, # 48 - 4f + 2, 2, 2, 2, 2, 2, 2, 2, # 50 - 57 + 2, 2, 2, 2, 2, 2, 2, 2, # 58 - 5f + 2, 2, 2, 2, 2, 2, 2, 2, # 60 - 67 + 2, 2, 2, 2, 2, 2, 2, 2, # 68 - 6f + 2, 2, 2, 2, 2, 2, 2, 2, # 70 - 77 + 2, 2, 2, 2, 2, 2, 2, 1, # 78 - 7f + 4, 4, 4, 4, 4, 4, 4, 4, # 80 - 87 + 4, 4, 4, 4, 4, 4, 4, 4, # 88 - 8f + 4, 4, 4, 4, 4, 4, 4, 4, # 90 - 97 + 4, 4, 4, 4, 4, 4, 4, 4, # 98 - 9f + 4, 3, 3, 3, 3, 3, 3, 3, # a0 - a7 + 3, 3, 3, 3, 3, 3, 3, 3, # a8 - af + 3, 3, 3, 3, 3, 3, 3, 3, # b0 - b7 + 3, 3, 3, 3, 3, 3, 3, 3, # b8 - bf + 3, 3, 3, 3, 3, 3, 3, 3, # c0 - c7 + 3, 3, 3, 3, 3, 3, 3, 3, # c8 - cf + 3, 3, 3, 3, 3, 3, 3, 3, # d0 - d7 + 3, 3, 3, 3, 3, 3, 3, 3, # d8 - df + 3, 3, 3, 3, 3, 3, 3, 3, # e0 - e7 + 3, 3, 3, 3, 3, 3, 3, 3, # e8 - ef + 3, 3, 3, 3, 3, 3, 3, 3, # f0 - f7 + 3, 3, 3, 3, 3, 3, 3, 0 # f8 - ff +) + +BIG5_ST = ( + MachineState.ERROR,MachineState.START,MachineState.START, 3,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,#00-07 + MachineState.ERROR,MachineState.ERROR,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ERROR,#08-0f + MachineState.ERROR,MachineState.START,MachineState.START,MachineState.START,MachineState.START,MachineState.START,MachineState.START,MachineState.START#10-17 +) +# fmt: on + +BIG5_CHAR_LEN_TABLE = (0, 1, 1, 2, 0) + +BIG5_SM_MODEL: CodingStateMachineDict = { + "class_table": BIG5_CLS, + "class_factor": 5, + "state_table": BIG5_ST, + "char_len_table": BIG5_CHAR_LEN_TABLE, + "name": "Big5", +} + +# CP949 +# fmt: off +CP949_CLS = ( + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, # 00 - 0f + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, # 10 - 1f + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, # 20 - 2f + 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, # 30 - 3f + 1, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, # 40 - 4f + 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 1, 1, 1, 1, 1, # 50 - 5f + 1, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, # 60 - 6f + 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 1, 1, 1, 1, 1, # 70 - 7f + 0, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, # 80 - 8f + 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, # 90 - 9f + 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, # a0 - af + 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, # b0 - bf + 7, 7, 7, 7, 7, 7, 9, 2, 2, 3, 2, 2, 2, 2, 2, 2, # c0 - cf + 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, # d0 - df + 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, # e0 - ef + 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, # f0 - ff +) + +CP949_ST = ( +#cls= 0 1 2 3 4 5 6 7 8 9 # previous state = + MachineState.ERROR,MachineState.START, 3,MachineState.ERROR,MachineState.START,MachineState.START, 4, 5,MachineState.ERROR, 6, # MachineState.START + MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR, # MachineState.ERROR + MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME, # MachineState.ITS_ME + MachineState.ERROR,MachineState.ERROR,MachineState.START,MachineState.START,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.START,MachineState.START,MachineState.START, # 3 + MachineState.ERROR,MachineState.ERROR,MachineState.START,MachineState.START,MachineState.START,MachineState.START,MachineState.START,MachineState.START,MachineState.START,MachineState.START, # 4 + MachineState.ERROR,MachineState.START,MachineState.START,MachineState.START,MachineState.START,MachineState.START,MachineState.START,MachineState.START,MachineState.START,MachineState.START, # 5 + MachineState.ERROR,MachineState.START,MachineState.START,MachineState.START,MachineState.START,MachineState.ERROR,MachineState.ERROR,MachineState.START,MachineState.START,MachineState.START, # 6 +) +# fmt: on + +CP949_CHAR_LEN_TABLE = (0, 1, 2, 0, 1, 1, 2, 2, 0, 2) + +CP949_SM_MODEL: CodingStateMachineDict = { + "class_table": CP949_CLS, + "class_factor": 10, + "state_table": CP949_ST, + "char_len_table": CP949_CHAR_LEN_TABLE, + "name": "CP949", +} + +# EUC-JP +# fmt: off +EUCJP_CLS = ( + 4, 4, 4, 4, 4, 4, 4, 4, # 00 - 07 + 4, 4, 4, 4, 4, 4, 5, 5, # 08 - 0f + 4, 4, 4, 4, 4, 4, 4, 4, # 10 - 17 + 4, 4, 4, 5, 4, 4, 4, 4, # 18 - 1f + 4, 4, 4, 4, 4, 4, 4, 4, # 20 - 27 + 4, 4, 4, 4, 4, 4, 4, 4, # 28 - 2f + 4, 4, 4, 4, 4, 4, 4, 4, # 30 - 37 + 4, 4, 4, 4, 4, 4, 4, 4, # 38 - 3f + 4, 4, 4, 4, 4, 4, 4, 4, # 40 - 47 + 4, 4, 4, 4, 4, 4, 4, 4, # 48 - 4f + 4, 4, 4, 4, 4, 4, 4, 4, # 50 - 57 + 4, 4, 4, 4, 4, 4, 4, 4, # 58 - 5f + 4, 4, 4, 4, 4, 4, 4, 4, # 60 - 67 + 4, 4, 4, 4, 4, 4, 4, 4, # 68 - 6f + 4, 4, 4, 4, 4, 4, 4, 4, # 70 - 77 + 4, 4, 4, 4, 4, 4, 4, 4, # 78 - 7f + 5, 5, 5, 5, 5, 5, 5, 5, # 80 - 87 + 5, 5, 5, 5, 5, 5, 1, 3, # 88 - 8f + 5, 5, 5, 5, 5, 5, 5, 5, # 90 - 97 + 5, 5, 5, 5, 5, 5, 5, 5, # 98 - 9f + 5, 2, 2, 2, 2, 2, 2, 2, # a0 - a7 + 2, 2, 2, 2, 2, 2, 2, 2, # a8 - af + 2, 2, 2, 2, 2, 2, 2, 2, # b0 - b7 + 2, 2, 2, 2, 2, 2, 2, 2, # b8 - bf + 2, 2, 2, 2, 2, 2, 2, 2, # c0 - c7 + 2, 2, 2, 2, 2, 2, 2, 2, # c8 - cf + 2, 2, 2, 2, 2, 2, 2, 2, # d0 - d7 + 2, 2, 2, 2, 2, 2, 2, 2, # d8 - df + 0, 0, 0, 0, 0, 0, 0, 0, # e0 - e7 + 0, 0, 0, 0, 0, 0, 0, 0, # e8 - ef + 0, 0, 0, 0, 0, 0, 0, 0, # f0 - f7 + 0, 0, 0, 0, 0, 0, 0, 5 # f8 - ff +) + +EUCJP_ST = ( + 3, 4, 3, 5,MachineState.START,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,#00-07 + MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,#08-0f + MachineState.ITS_ME,MachineState.ITS_ME,MachineState.START,MachineState.ERROR,MachineState.START,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,#10-17 + MachineState.ERROR,MachineState.ERROR,MachineState.START,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR, 3,MachineState.ERROR,#18-1f + 3,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.START,MachineState.START,MachineState.START,MachineState.START#20-27 +) +# fmt: on + +EUCJP_CHAR_LEN_TABLE = (2, 2, 2, 3, 1, 0) + +EUCJP_SM_MODEL: CodingStateMachineDict = { + "class_table": EUCJP_CLS, + "class_factor": 6, + "state_table": EUCJP_ST, + "char_len_table": EUCJP_CHAR_LEN_TABLE, + "name": "EUC-JP", +} + +# EUC-KR +# fmt: off +EUCKR_CLS = ( + 1, 1, 1, 1, 1, 1, 1, 1, # 00 - 07 + 1, 1, 1, 1, 1, 1, 0, 0, # 08 - 0f + 1, 1, 1, 1, 1, 1, 1, 1, # 10 - 17 + 1, 1, 1, 0, 1, 1, 1, 1, # 18 - 1f + 1, 1, 1, 1, 1, 1, 1, 1, # 20 - 27 + 1, 1, 1, 1, 1, 1, 1, 1, # 28 - 2f + 1, 1, 1, 1, 1, 1, 1, 1, # 30 - 37 + 1, 1, 1, 1, 1, 1, 1, 1, # 38 - 3f + 1, 1, 1, 1, 1, 1, 1, 1, # 40 - 47 + 1, 1, 1, 1, 1, 1, 1, 1, # 48 - 4f + 1, 1, 1, 1, 1, 1, 1, 1, # 50 - 57 + 1, 1, 1, 1, 1, 1, 1, 1, # 58 - 5f + 1, 1, 1, 1, 1, 1, 1, 1, # 60 - 67 + 1, 1, 1, 1, 1, 1, 1, 1, # 68 - 6f + 1, 1, 1, 1, 1, 1, 1, 1, # 70 - 77 + 1, 1, 1, 1, 1, 1, 1, 1, # 78 - 7f + 0, 0, 0, 0, 0, 0, 0, 0, # 80 - 87 + 0, 0, 0, 0, 0, 0, 0, 0, # 88 - 8f + 0, 0, 0, 0, 0, 0, 0, 0, # 90 - 97 + 0, 0, 0, 0, 0, 0, 0, 0, # 98 - 9f + 0, 2, 2, 2, 2, 2, 2, 2, # a0 - a7 + 2, 2, 2, 2, 2, 3, 3, 3, # a8 - af + 2, 2, 2, 2, 2, 2, 2, 2, # b0 - b7 + 2, 2, 2, 2, 2, 2, 2, 2, # b8 - bf + 2, 2, 2, 2, 2, 2, 2, 2, # c0 - c7 + 2, 3, 2, 2, 2, 2, 2, 2, # c8 - cf + 2, 2, 2, 2, 2, 2, 2, 2, # d0 - d7 + 2, 2, 2, 2, 2, 2, 2, 2, # d8 - df + 2, 2, 2, 2, 2, 2, 2, 2, # e0 - e7 + 2, 2, 2, 2, 2, 2, 2, 2, # e8 - ef + 2, 2, 2, 2, 2, 2, 2, 2, # f0 - f7 + 2, 2, 2, 2, 2, 2, 2, 0 # f8 - ff +) + +EUCKR_ST = ( + MachineState.ERROR,MachineState.START, 3,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,#00-07 + MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ERROR,MachineState.ERROR,MachineState.START,MachineState.START #08-0f +) +# fmt: on + +EUCKR_CHAR_LEN_TABLE = (0, 1, 2, 0) + +EUCKR_SM_MODEL: CodingStateMachineDict = { + "class_table": EUCKR_CLS, + "class_factor": 4, + "state_table": EUCKR_ST, + "char_len_table": EUCKR_CHAR_LEN_TABLE, + "name": "EUC-KR", +} + +# JOHAB +# fmt: off +JOHAB_CLS = ( + 4,4,4,4,4,4,4,4, # 00 - 07 + 4,4,4,4,4,4,0,0, # 08 - 0f + 4,4,4,4,4,4,4,4, # 10 - 17 + 4,4,4,0,4,4,4,4, # 18 - 1f + 4,4,4,4,4,4,4,4, # 20 - 27 + 4,4,4,4,4,4,4,4, # 28 - 2f + 4,3,3,3,3,3,3,3, # 30 - 37 + 3,3,3,3,3,3,3,3, # 38 - 3f + 3,1,1,1,1,1,1,1, # 40 - 47 + 1,1,1,1,1,1,1,1, # 48 - 4f + 1,1,1,1,1,1,1,1, # 50 - 57 + 1,1,1,1,1,1,1,1, # 58 - 5f + 1,1,1,1,1,1,1,1, # 60 - 67 + 1,1,1,1,1,1,1,1, # 68 - 6f + 1,1,1,1,1,1,1,1, # 70 - 77 + 1,1,1,1,1,1,1,2, # 78 - 7f + 6,6,6,6,8,8,8,8, # 80 - 87 + 8,8,8,8,8,8,8,8, # 88 - 8f + 8,7,7,7,7,7,7,7, # 90 - 97 + 7,7,7,7,7,7,7,7, # 98 - 9f + 7,7,7,7,7,7,7,7, # a0 - a7 + 7,7,7,7,7,7,7,7, # a8 - af + 7,7,7,7,7,7,7,7, # b0 - b7 + 7,7,7,7,7,7,7,7, # b8 - bf + 7,7,7,7,7,7,7,7, # c0 - c7 + 7,7,7,7,7,7,7,7, # c8 - cf + 7,7,7,7,5,5,5,5, # d0 - d7 + 5,9,9,9,9,9,9,5, # d8 - df + 9,9,9,9,9,9,9,9, # e0 - e7 + 9,9,9,9,9,9,9,9, # e8 - ef + 9,9,9,9,9,9,9,9, # f0 - f7 + 9,9,5,5,5,5,5,0 # f8 - ff +) + +JOHAB_ST = ( +# cls = 0 1 2 3 4 5 6 7 8 9 + MachineState.ERROR ,MachineState.START ,MachineState.START ,MachineState.START ,MachineState.START ,MachineState.ERROR ,MachineState.ERROR ,3 ,3 ,4 , # MachineState.START + MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME, # MachineState.ITS_ME + MachineState.ERROR ,MachineState.ERROR ,MachineState.ERROR ,MachineState.ERROR ,MachineState.ERROR ,MachineState.ERROR ,MachineState.ERROR ,MachineState.ERROR ,MachineState.ERROR ,MachineState.ERROR , # MachineState.ERROR + MachineState.ERROR ,MachineState.START ,MachineState.START ,MachineState.ERROR ,MachineState.ERROR ,MachineState.START ,MachineState.START ,MachineState.START ,MachineState.START ,MachineState.START , # 3 + MachineState.ERROR ,MachineState.START ,MachineState.ERROR ,MachineState.START ,MachineState.ERROR ,MachineState.START ,MachineState.ERROR ,MachineState.START ,MachineState.ERROR ,MachineState.START , # 4 +) +# fmt: on + +JOHAB_CHAR_LEN_TABLE = (0, 1, 1, 1, 1, 0, 0, 2, 2, 2) + +JOHAB_SM_MODEL: CodingStateMachineDict = { + "class_table": JOHAB_CLS, + "class_factor": 10, + "state_table": JOHAB_ST, + "char_len_table": JOHAB_CHAR_LEN_TABLE, + "name": "Johab", +} + +# EUC-TW +# fmt: off +EUCTW_CLS = ( + 2, 2, 2, 2, 2, 2, 2, 2, # 00 - 07 + 2, 2, 2, 2, 2, 2, 0, 0, # 08 - 0f + 2, 2, 2, 2, 2, 2, 2, 2, # 10 - 17 + 2, 2, 2, 0, 2, 2, 2, 2, # 18 - 1f + 2, 2, 2, 2, 2, 2, 2, 2, # 20 - 27 + 2, 2, 2, 2, 2, 2, 2, 2, # 28 - 2f + 2, 2, 2, 2, 2, 2, 2, 2, # 30 - 37 + 2, 2, 2, 2, 2, 2, 2, 2, # 38 - 3f + 2, 2, 2, 2, 2, 2, 2, 2, # 40 - 47 + 2, 2, 2, 2, 2, 2, 2, 2, # 48 - 4f + 2, 2, 2, 2, 2, 2, 2, 2, # 50 - 57 + 2, 2, 2, 2, 2, 2, 2, 2, # 58 - 5f + 2, 2, 2, 2, 2, 2, 2, 2, # 60 - 67 + 2, 2, 2, 2, 2, 2, 2, 2, # 68 - 6f + 2, 2, 2, 2, 2, 2, 2, 2, # 70 - 77 + 2, 2, 2, 2, 2, 2, 2, 2, # 78 - 7f + 0, 0, 0, 0, 0, 0, 0, 0, # 80 - 87 + 0, 0, 0, 0, 0, 0, 6, 0, # 88 - 8f + 0, 0, 0, 0, 0, 0, 0, 0, # 90 - 97 + 0, 0, 0, 0, 0, 0, 0, 0, # 98 - 9f + 0, 3, 4, 4, 4, 4, 4, 4, # a0 - a7 + 5, 5, 1, 1, 1, 1, 1, 1, # a8 - af + 1, 1, 1, 1, 1, 1, 1, 1, # b0 - b7 + 1, 1, 1, 1, 1, 1, 1, 1, # b8 - bf + 1, 1, 3, 1, 3, 3, 3, 3, # c0 - c7 + 3, 3, 3, 3, 3, 3, 3, 3, # c8 - cf + 3, 3, 3, 3, 3, 3, 3, 3, # d0 - d7 + 3, 3, 3, 3, 3, 3, 3, 3, # d8 - df + 3, 3, 3, 3, 3, 3, 3, 3, # e0 - e7 + 3, 3, 3, 3, 3, 3, 3, 3, # e8 - ef + 3, 3, 3, 3, 3, 3, 3, 3, # f0 - f7 + 3, 3, 3, 3, 3, 3, 3, 0 # f8 - ff +) + +EUCTW_ST = ( + MachineState.ERROR,MachineState.ERROR,MachineState.START, 3, 3, 3, 4,MachineState.ERROR,#00-07 + MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ITS_ME,MachineState.ITS_ME,#08-0f + MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ERROR,MachineState.START,MachineState.ERROR,#10-17 + MachineState.START,MachineState.START,MachineState.START,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,#18-1f + 5,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.START,MachineState.ERROR,MachineState.START,MachineState.START,#20-27 + MachineState.START,MachineState.ERROR,MachineState.START,MachineState.START,MachineState.START,MachineState.START,MachineState.START,MachineState.START #28-2f +) +# fmt: on + +EUCTW_CHAR_LEN_TABLE = (0, 0, 1, 2, 2, 2, 3) + +EUCTW_SM_MODEL: CodingStateMachineDict = { + "class_table": EUCTW_CLS, + "class_factor": 7, + "state_table": EUCTW_ST, + "char_len_table": EUCTW_CHAR_LEN_TABLE, + "name": "x-euc-tw", +} + +# GB2312 +# fmt: off +GB2312_CLS = ( + 1, 1, 1, 1, 1, 1, 1, 1, # 00 - 07 + 1, 1, 1, 1, 1, 1, 0, 0, # 08 - 0f + 1, 1, 1, 1, 1, 1, 1, 1, # 10 - 17 + 1, 1, 1, 0, 1, 1, 1, 1, # 18 - 1f + 1, 1, 1, 1, 1, 1, 1, 1, # 20 - 27 + 1, 1, 1, 1, 1, 1, 1, 1, # 28 - 2f + 3, 3, 3, 3, 3, 3, 3, 3, # 30 - 37 + 3, 3, 1, 1, 1, 1, 1, 1, # 38 - 3f + 2, 2, 2, 2, 2, 2, 2, 2, # 40 - 47 + 2, 2, 2, 2, 2, 2, 2, 2, # 48 - 4f + 2, 2, 2, 2, 2, 2, 2, 2, # 50 - 57 + 2, 2, 2, 2, 2, 2, 2, 2, # 58 - 5f + 2, 2, 2, 2, 2, 2, 2, 2, # 60 - 67 + 2, 2, 2, 2, 2, 2, 2, 2, # 68 - 6f + 2, 2, 2, 2, 2, 2, 2, 2, # 70 - 77 + 2, 2, 2, 2, 2, 2, 2, 4, # 78 - 7f + 5, 6, 6, 6, 6, 6, 6, 6, # 80 - 87 + 6, 6, 6, 6, 6, 6, 6, 6, # 88 - 8f + 6, 6, 6, 6, 6, 6, 6, 6, # 90 - 97 + 6, 6, 6, 6, 6, 6, 6, 6, # 98 - 9f + 6, 6, 6, 6, 6, 6, 6, 6, # a0 - a7 + 6, 6, 6, 6, 6, 6, 6, 6, # a8 - af + 6, 6, 6, 6, 6, 6, 6, 6, # b0 - b7 + 6, 6, 6, 6, 6, 6, 6, 6, # b8 - bf + 6, 6, 6, 6, 6, 6, 6, 6, # c0 - c7 + 6, 6, 6, 6, 6, 6, 6, 6, # c8 - cf + 6, 6, 6, 6, 6, 6, 6, 6, # d0 - d7 + 6, 6, 6, 6, 6, 6, 6, 6, # d8 - df + 6, 6, 6, 6, 6, 6, 6, 6, # e0 - e7 + 6, 6, 6, 6, 6, 6, 6, 6, # e8 - ef + 6, 6, 6, 6, 6, 6, 6, 6, # f0 - f7 + 6, 6, 6, 6, 6, 6, 6, 0 # f8 - ff +) + +GB2312_ST = ( + MachineState.ERROR,MachineState.START,MachineState.START,MachineState.START,MachineState.START,MachineState.START, 3,MachineState.ERROR,#00-07 + MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ITS_ME,MachineState.ITS_ME,#08-0f + MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ERROR,MachineState.ERROR,MachineState.START,#10-17 + 4,MachineState.ERROR,MachineState.START,MachineState.START,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,#18-1f + MachineState.ERROR,MachineState.ERROR, 5,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ITS_ME,MachineState.ERROR,#20-27 + MachineState.ERROR,MachineState.ERROR,MachineState.START,MachineState.START,MachineState.START,MachineState.START,MachineState.START,MachineState.START #28-2f +) +# fmt: on + +# To be accurate, the length of class 6 can be either 2 or 4. +# But it is not necessary to discriminate between the two since +# it is used for frequency analysis only, and we are validating +# each code range there as well. So it is safe to set it to be +# 2 here. +GB2312_CHAR_LEN_TABLE = (0, 1, 1, 1, 1, 1, 2) + +GB2312_SM_MODEL: CodingStateMachineDict = { + "class_table": GB2312_CLS, + "class_factor": 7, + "state_table": GB2312_ST, + "char_len_table": GB2312_CHAR_LEN_TABLE, + "name": "GB2312", +} + +# Shift_JIS +# fmt: off +SJIS_CLS = ( + 1, 1, 1, 1, 1, 1, 1, 1, # 00 - 07 + 1, 1, 1, 1, 1, 1, 0, 0, # 08 - 0f + 1, 1, 1, 1, 1, 1, 1, 1, # 10 - 17 + 1, 1, 1, 0, 1, 1, 1, 1, # 18 - 1f + 1, 1, 1, 1, 1, 1, 1, 1, # 20 - 27 + 1, 1, 1, 1, 1, 1, 1, 1, # 28 - 2f + 1, 1, 1, 1, 1, 1, 1, 1, # 30 - 37 + 1, 1, 1, 1, 1, 1, 1, 1, # 38 - 3f + 2, 2, 2, 2, 2, 2, 2, 2, # 40 - 47 + 2, 2, 2, 2, 2, 2, 2, 2, # 48 - 4f + 2, 2, 2, 2, 2, 2, 2, 2, # 50 - 57 + 2, 2, 2, 2, 2, 2, 2, 2, # 58 - 5f + 2, 2, 2, 2, 2, 2, 2, 2, # 60 - 67 + 2, 2, 2, 2, 2, 2, 2, 2, # 68 - 6f + 2, 2, 2, 2, 2, 2, 2, 2, # 70 - 77 + 2, 2, 2, 2, 2, 2, 2, 1, # 78 - 7f + 3, 3, 3, 3, 3, 2, 2, 3, # 80 - 87 + 3, 3, 3, 3, 3, 3, 3, 3, # 88 - 8f + 3, 3, 3, 3, 3, 3, 3, 3, # 90 - 97 + 3, 3, 3, 3, 3, 3, 3, 3, # 98 - 9f + #0xa0 is illegal in sjis encoding, but some pages does + #contain such byte. We need to be more error forgiven. + 2, 2, 2, 2, 2, 2, 2, 2, # a0 - a7 + 2, 2, 2, 2, 2, 2, 2, 2, # a8 - af + 2, 2, 2, 2, 2, 2, 2, 2, # b0 - b7 + 2, 2, 2, 2, 2, 2, 2, 2, # b8 - bf + 2, 2, 2, 2, 2, 2, 2, 2, # c0 - c7 + 2, 2, 2, 2, 2, 2, 2, 2, # c8 - cf + 2, 2, 2, 2, 2, 2, 2, 2, # d0 - d7 + 2, 2, 2, 2, 2, 2, 2, 2, # d8 - df + 3, 3, 3, 3, 3, 3, 3, 3, # e0 - e7 + 3, 3, 3, 3, 3, 4, 4, 4, # e8 - ef + 3, 3, 3, 3, 3, 3, 3, 3, # f0 - f7 + 3, 3, 3, 3, 3, 0, 0, 0, # f8 - ff +) + +SJIS_ST = ( + MachineState.ERROR,MachineState.START,MachineState.START, 3,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,#00-07 + MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,#08-0f + MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ERROR,MachineState.ERROR,MachineState.START,MachineState.START,MachineState.START,MachineState.START #10-17 +) +# fmt: on + +SJIS_CHAR_LEN_TABLE = (0, 1, 1, 2, 0, 0) + +SJIS_SM_MODEL: CodingStateMachineDict = { + "class_table": SJIS_CLS, + "class_factor": 6, + "state_table": SJIS_ST, + "char_len_table": SJIS_CHAR_LEN_TABLE, + "name": "Shift_JIS", +} + +# UCS2-BE +# fmt: off +UCS2BE_CLS = ( + 0, 0, 0, 0, 0, 0, 0, 0, # 00 - 07 + 0, 0, 1, 0, 0, 2, 0, 0, # 08 - 0f + 0, 0, 0, 0, 0, 0, 0, 0, # 10 - 17 + 0, 0, 0, 3, 0, 0, 0, 0, # 18 - 1f + 0, 0, 0, 0, 0, 0, 0, 0, # 20 - 27 + 0, 3, 3, 3, 3, 3, 0, 0, # 28 - 2f + 0, 0, 0, 0, 0, 0, 0, 0, # 30 - 37 + 0, 0, 0, 0, 0, 0, 0, 0, # 38 - 3f + 0, 0, 0, 0, 0, 0, 0, 0, # 40 - 47 + 0, 0, 0, 0, 0, 0, 0, 0, # 48 - 4f + 0, 0, 0, 0, 0, 0, 0, 0, # 50 - 57 + 0, 0, 0, 0, 0, 0, 0, 0, # 58 - 5f + 0, 0, 0, 0, 0, 0, 0, 0, # 60 - 67 + 0, 0, 0, 0, 0, 0, 0, 0, # 68 - 6f + 0, 0, 0, 0, 0, 0, 0, 0, # 70 - 77 + 0, 0, 0, 0, 0, 0, 0, 0, # 78 - 7f + 0, 0, 0, 0, 0, 0, 0, 0, # 80 - 87 + 0, 0, 0, 0, 0, 0, 0, 0, # 88 - 8f + 0, 0, 0, 0, 0, 0, 0, 0, # 90 - 97 + 0, 0, 0, 0, 0, 0, 0, 0, # 98 - 9f + 0, 0, 0, 0, 0, 0, 0, 0, # a0 - a7 + 0, 0, 0, 0, 0, 0, 0, 0, # a8 - af + 0, 0, 0, 0, 0, 0, 0, 0, # b0 - b7 + 0, 0, 0, 0, 0, 0, 0, 0, # b8 - bf + 0, 0, 0, 0, 0, 0, 0, 0, # c0 - c7 + 0, 0, 0, 0, 0, 0, 0, 0, # c8 - cf + 0, 0, 0, 0, 0, 0, 0, 0, # d0 - d7 + 0, 0, 0, 0, 0, 0, 0, 0, # d8 - df + 0, 0, 0, 0, 0, 0, 0, 0, # e0 - e7 + 0, 0, 0, 0, 0, 0, 0, 0, # e8 - ef + 0, 0, 0, 0, 0, 0, 0, 0, # f0 - f7 + 0, 0, 0, 0, 0, 0, 4, 5 # f8 - ff +) + +UCS2BE_ST = ( + 5, 7, 7,MachineState.ERROR, 4, 3,MachineState.ERROR,MachineState.ERROR,#00-07 + MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,#08-0f + MachineState.ITS_ME,MachineState.ITS_ME, 6, 6, 6, 6,MachineState.ERROR,MachineState.ERROR,#10-17 + 6, 6, 6, 6, 6,MachineState.ITS_ME, 6, 6,#18-1f + 6, 6, 6, 6, 5, 7, 7,MachineState.ERROR,#20-27 + 5, 8, 6, 6,MachineState.ERROR, 6, 6, 6,#28-2f + 6, 6, 6, 6,MachineState.ERROR,MachineState.ERROR,MachineState.START,MachineState.START #30-37 +) +# fmt: on + +UCS2BE_CHAR_LEN_TABLE = (2, 2, 2, 0, 2, 2) + +UCS2BE_SM_MODEL: CodingStateMachineDict = { + "class_table": UCS2BE_CLS, + "class_factor": 6, + "state_table": UCS2BE_ST, + "char_len_table": UCS2BE_CHAR_LEN_TABLE, + "name": "UTF-16BE", +} + +# UCS2-LE +# fmt: off +UCS2LE_CLS = ( + 0, 0, 0, 0, 0, 0, 0, 0, # 00 - 07 + 0, 0, 1, 0, 0, 2, 0, 0, # 08 - 0f + 0, 0, 0, 0, 0, 0, 0, 0, # 10 - 17 + 0, 0, 0, 3, 0, 0, 0, 0, # 18 - 1f + 0, 0, 0, 0, 0, 0, 0, 0, # 20 - 27 + 0, 3, 3, 3, 3, 3, 0, 0, # 28 - 2f + 0, 0, 0, 0, 0, 0, 0, 0, # 30 - 37 + 0, 0, 0, 0, 0, 0, 0, 0, # 38 - 3f + 0, 0, 0, 0, 0, 0, 0, 0, # 40 - 47 + 0, 0, 0, 0, 0, 0, 0, 0, # 48 - 4f + 0, 0, 0, 0, 0, 0, 0, 0, # 50 - 57 + 0, 0, 0, 0, 0, 0, 0, 0, # 58 - 5f + 0, 0, 0, 0, 0, 0, 0, 0, # 60 - 67 + 0, 0, 0, 0, 0, 0, 0, 0, # 68 - 6f + 0, 0, 0, 0, 0, 0, 0, 0, # 70 - 77 + 0, 0, 0, 0, 0, 0, 0, 0, # 78 - 7f + 0, 0, 0, 0, 0, 0, 0, 0, # 80 - 87 + 0, 0, 0, 0, 0, 0, 0, 0, # 88 - 8f + 0, 0, 0, 0, 0, 0, 0, 0, # 90 - 97 + 0, 0, 0, 0, 0, 0, 0, 0, # 98 - 9f + 0, 0, 0, 0, 0, 0, 0, 0, # a0 - a7 + 0, 0, 0, 0, 0, 0, 0, 0, # a8 - af + 0, 0, 0, 0, 0, 0, 0, 0, # b0 - b7 + 0, 0, 0, 0, 0, 0, 0, 0, # b8 - bf + 0, 0, 0, 0, 0, 0, 0, 0, # c0 - c7 + 0, 0, 0, 0, 0, 0, 0, 0, # c8 - cf + 0, 0, 0, 0, 0, 0, 0, 0, # d0 - d7 + 0, 0, 0, 0, 0, 0, 0, 0, # d8 - df + 0, 0, 0, 0, 0, 0, 0, 0, # e0 - e7 + 0, 0, 0, 0, 0, 0, 0, 0, # e8 - ef + 0, 0, 0, 0, 0, 0, 0, 0, # f0 - f7 + 0, 0, 0, 0, 0, 0, 4, 5 # f8 - ff +) + +UCS2LE_ST = ( + 6, 6, 7, 6, 4, 3,MachineState.ERROR,MachineState.ERROR,#00-07 + MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,#08-0f + MachineState.ITS_ME,MachineState.ITS_ME, 5, 5, 5,MachineState.ERROR,MachineState.ITS_ME,MachineState.ERROR,#10-17 + 5, 5, 5,MachineState.ERROR, 5,MachineState.ERROR, 6, 6,#18-1f + 7, 6, 8, 8, 5, 5, 5,MachineState.ERROR,#20-27 + 5, 5, 5,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR, 5, 5,#28-2f + 5, 5, 5,MachineState.ERROR, 5,MachineState.ERROR,MachineState.START,MachineState.START #30-37 +) +# fmt: on + +UCS2LE_CHAR_LEN_TABLE = (2, 2, 2, 2, 2, 2) + +UCS2LE_SM_MODEL: CodingStateMachineDict = { + "class_table": UCS2LE_CLS, + "class_factor": 6, + "state_table": UCS2LE_ST, + "char_len_table": UCS2LE_CHAR_LEN_TABLE, + "name": "UTF-16LE", +} + +# UTF-8 +# fmt: off +UTF8_CLS = ( + 1, 1, 1, 1, 1, 1, 1, 1, # 00 - 07 #allow 0x00 as a legal value + 1, 1, 1, 1, 1, 1, 0, 0, # 08 - 0f + 1, 1, 1, 1, 1, 1, 1, 1, # 10 - 17 + 1, 1, 1, 0, 1, 1, 1, 1, # 18 - 1f + 1, 1, 1, 1, 1, 1, 1, 1, # 20 - 27 + 1, 1, 1, 1, 1, 1, 1, 1, # 28 - 2f + 1, 1, 1, 1, 1, 1, 1, 1, # 30 - 37 + 1, 1, 1, 1, 1, 1, 1, 1, # 38 - 3f + 1, 1, 1, 1, 1, 1, 1, 1, # 40 - 47 + 1, 1, 1, 1, 1, 1, 1, 1, # 48 - 4f + 1, 1, 1, 1, 1, 1, 1, 1, # 50 - 57 + 1, 1, 1, 1, 1, 1, 1, 1, # 58 - 5f + 1, 1, 1, 1, 1, 1, 1, 1, # 60 - 67 + 1, 1, 1, 1, 1, 1, 1, 1, # 68 - 6f + 1, 1, 1, 1, 1, 1, 1, 1, # 70 - 77 + 1, 1, 1, 1, 1, 1, 1, 1, # 78 - 7f + 2, 2, 2, 2, 3, 3, 3, 3, # 80 - 87 + 4, 4, 4, 4, 4, 4, 4, 4, # 88 - 8f + 4, 4, 4, 4, 4, 4, 4, 4, # 90 - 97 + 4, 4, 4, 4, 4, 4, 4, 4, # 98 - 9f + 5, 5, 5, 5, 5, 5, 5, 5, # a0 - a7 + 5, 5, 5, 5, 5, 5, 5, 5, # a8 - af + 5, 5, 5, 5, 5, 5, 5, 5, # b0 - b7 + 5, 5, 5, 5, 5, 5, 5, 5, # b8 - bf + 0, 0, 6, 6, 6, 6, 6, 6, # c0 - c7 + 6, 6, 6, 6, 6, 6, 6, 6, # c8 - cf + 6, 6, 6, 6, 6, 6, 6, 6, # d0 - d7 + 6, 6, 6, 6, 6, 6, 6, 6, # d8 - df + 7, 8, 8, 8, 8, 8, 8, 8, # e0 - e7 + 8, 8, 8, 8, 8, 9, 8, 8, # e8 - ef + 10, 11, 11, 11, 11, 11, 11, 11, # f0 - f7 + 12, 13, 13, 13, 14, 15, 0, 0 # f8 - ff +) + +UTF8_ST = ( + MachineState.ERROR,MachineState.START,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR, 12, 10,#00-07 + 9, 11, 8, 7, 6, 5, 4, 3,#08-0f + MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,#10-17 + MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,#18-1f + MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,#20-27 + MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,MachineState.ITS_ME,#28-2f + MachineState.ERROR,MachineState.ERROR, 5, 5, 5, 5,MachineState.ERROR,MachineState.ERROR,#30-37 + MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,#38-3f + MachineState.ERROR,MachineState.ERROR,MachineState.ERROR, 5, 5, 5,MachineState.ERROR,MachineState.ERROR,#40-47 + MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,#48-4f + MachineState.ERROR,MachineState.ERROR, 7, 7, 7, 7,MachineState.ERROR,MachineState.ERROR,#50-57 + MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,#58-5f + MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR, 7, 7,MachineState.ERROR,MachineState.ERROR,#60-67 + MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,#68-6f + MachineState.ERROR,MachineState.ERROR, 9, 9, 9, 9,MachineState.ERROR,MachineState.ERROR,#70-77 + MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,#78-7f + MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR, 9,MachineState.ERROR,MachineState.ERROR,#80-87 + MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,#88-8f + MachineState.ERROR,MachineState.ERROR, 12, 12, 12, 12,MachineState.ERROR,MachineState.ERROR,#90-97 + MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,#98-9f + MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR, 12,MachineState.ERROR,MachineState.ERROR,#a0-a7 + MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,#a8-af + MachineState.ERROR,MachineState.ERROR, 12, 12, 12,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,#b0-b7 + MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,#b8-bf + MachineState.ERROR,MachineState.ERROR,MachineState.START,MachineState.START,MachineState.START,MachineState.START,MachineState.ERROR,MachineState.ERROR,#c0-c7 + MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR,MachineState.ERROR #c8-cf +) +# fmt: on + +UTF8_CHAR_LEN_TABLE = (0, 1, 0, 0, 0, 0, 2, 3, 3, 3, 4, 4, 5, 5, 6, 6) + +UTF8_SM_MODEL: CodingStateMachineDict = { + "class_table": UTF8_CLS, + "class_factor": 16, + "state_table": UTF8_ST, + "char_len_table": UTF8_CHAR_LEN_TABLE, + "name": "UTF-8", +} diff --git a/venv/lib/python3.10/site-packages/chardet/py.typed b/venv/lib/python3.10/site-packages/chardet/py.typed new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/venv/lib/python3.10/site-packages/chardet/resultdict.py b/venv/lib/python3.10/site-packages/chardet/resultdict.py new file mode 100644 index 0000000000000000000000000000000000000000..7d36e64c467ca8d9cadc88ab03da71faf1aa8abb --- /dev/null +++ b/venv/lib/python3.10/site-packages/chardet/resultdict.py @@ -0,0 +1,16 @@ +from typing import TYPE_CHECKING, Optional + +if TYPE_CHECKING: + # TypedDict was introduced in Python 3.8. + # + # TODO: Remove the else block and TYPE_CHECKING check when dropping support + # for Python 3.7. + from typing import TypedDict + + class ResultDict(TypedDict): + encoding: Optional[str] + confidence: float + language: Optional[str] + +else: + ResultDict = dict diff --git a/venv/lib/python3.10/site-packages/chardet/sbcharsetprober.py b/venv/lib/python3.10/site-packages/chardet/sbcharsetprober.py new file mode 100644 index 0000000000000000000000000000000000000000..0ffbcdd2c3e21b68566c88a3f05239447489df84 --- /dev/null +++ b/venv/lib/python3.10/site-packages/chardet/sbcharsetprober.py @@ -0,0 +1,162 @@ +######################## BEGIN LICENSE BLOCK ######################## +# The Original Code is Mozilla Universal charset detector code. +# +# The Initial Developer of the Original Code is +# Netscape Communications Corporation. +# Portions created by the Initial Developer are Copyright (C) 2001 +# the Initial Developer. All Rights Reserved. +# +# Contributor(s): +# Mark Pilgrim - port to Python +# Shy Shalom - original C code +# +# This library is free software; you can redistribute it and/or +# modify it under the terms of the GNU Lesser General Public +# License as published by the Free Software Foundation; either +# version 2.1 of the License, or (at your option) any later version. +# +# This library is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +# Lesser General Public License for more details. +# +# You should have received a copy of the GNU Lesser General Public +# License along with this library; if not, write to the Free Software +# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA +# 02110-1301 USA +######################### END LICENSE BLOCK ######################### + +from typing import Dict, List, NamedTuple, Optional, Union + +from .charsetprober import CharSetProber +from .enums import CharacterCategory, ProbingState, SequenceLikelihood + + +class SingleByteCharSetModel(NamedTuple): + charset_name: str + language: str + char_to_order_map: Dict[int, int] + language_model: Dict[int, Dict[int, int]] + typical_positive_ratio: float + keep_ascii_letters: bool + alphabet: str + + +class SingleByteCharSetProber(CharSetProber): + SAMPLE_SIZE = 64 + SB_ENOUGH_REL_THRESHOLD = 1024 # 0.25 * SAMPLE_SIZE^2 + POSITIVE_SHORTCUT_THRESHOLD = 0.95 + NEGATIVE_SHORTCUT_THRESHOLD = 0.05 + + def __init__( + self, + model: SingleByteCharSetModel, + is_reversed: bool = False, + name_prober: Optional[CharSetProber] = None, + ) -> None: + super().__init__() + self._model = model + # TRUE if we need to reverse every pair in the model lookup + self._reversed = is_reversed + # Optional auxiliary prober for name decision + self._name_prober = name_prober + self._last_order = 255 + self._seq_counters: List[int] = [] + self._total_seqs = 0 + self._total_char = 0 + self._control_char = 0 + self._freq_char = 0 + self.reset() + + def reset(self) -> None: + super().reset() + # char order of last character + self._last_order = 255 + self._seq_counters = [0] * SequenceLikelihood.get_num_categories() + self._total_seqs = 0 + self._total_char = 0 + self._control_char = 0 + # characters that fall in our sampling range + self._freq_char = 0 + + @property + def charset_name(self) -> Optional[str]: + if self._name_prober: + return self._name_prober.charset_name + return self._model.charset_name + + @property + def language(self) -> Optional[str]: + if self._name_prober: + return self._name_prober.language + return self._model.language + + def feed(self, byte_str: Union[bytes, bytearray]) -> ProbingState: + # TODO: Make filter_international_words keep things in self.alphabet + if not self._model.keep_ascii_letters: + byte_str = self.filter_international_words(byte_str) + else: + byte_str = self.remove_xml_tags(byte_str) + if not byte_str: + return self.state + char_to_order_map = self._model.char_to_order_map + language_model = self._model.language_model + for char in byte_str: + order = char_to_order_map.get(char, CharacterCategory.UNDEFINED) + # XXX: This was SYMBOL_CAT_ORDER before, with a value of 250, but + # CharacterCategory.SYMBOL is actually 253, so we use CONTROL + # to make it closer to the original intent. The only difference + # is whether or not we count digits and control characters for + # _total_char purposes. + if order < CharacterCategory.CONTROL: + self._total_char += 1 + if order < self.SAMPLE_SIZE: + self._freq_char += 1 + if self._last_order < self.SAMPLE_SIZE: + self._total_seqs += 1 + if not self._reversed: + lm_cat = language_model[self._last_order][order] + else: + lm_cat = language_model[order][self._last_order] + self._seq_counters[lm_cat] += 1 + self._last_order = order + + charset_name = self._model.charset_name + if self.state == ProbingState.DETECTING: + if self._total_seqs > self.SB_ENOUGH_REL_THRESHOLD: + confidence = self.get_confidence() + if confidence > self.POSITIVE_SHORTCUT_THRESHOLD: + self.logger.debug( + "%s confidence = %s, we have a winner", charset_name, confidence + ) + self._state = ProbingState.FOUND_IT + elif confidence < self.NEGATIVE_SHORTCUT_THRESHOLD: + self.logger.debug( + "%s confidence = %s, below negative shortcut threshold %s", + charset_name, + confidence, + self.NEGATIVE_SHORTCUT_THRESHOLD, + ) + self._state = ProbingState.NOT_ME + + return self.state + + def get_confidence(self) -> float: + r = 0.01 + if self._total_seqs > 0: + r = ( + ( + self._seq_counters[SequenceLikelihood.POSITIVE] + + 0.25 * self._seq_counters[SequenceLikelihood.LIKELY] + ) + / self._total_seqs + / self._model.typical_positive_ratio + ) + # The more control characters (proportionnaly to the size + # of the text), the less confident we become in the current + # charset. + r = r * (self._total_char - self._control_char) / self._total_char + r = r * self._freq_char / self._total_char + if r >= 1.0: + r = 0.99 + return r diff --git a/venv/lib/python3.10/site-packages/chardet/utf1632prober.py b/venv/lib/python3.10/site-packages/chardet/utf1632prober.py new file mode 100644 index 0000000000000000000000000000000000000000..6bdec63d6867928bf73a7e513f60cee8f49ca050 --- /dev/null +++ b/venv/lib/python3.10/site-packages/chardet/utf1632prober.py @@ -0,0 +1,225 @@ +######################## BEGIN LICENSE BLOCK ######################## +# +# Contributor(s): +# Jason Zavaglia +# +# This library is free software; you can redistribute it and/or +# modify it under the terms of the GNU Lesser General Public +# License as published by the Free Software Foundation; either +# version 2.1 of the License, or (at your option) any later version. +# +# This library is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +# Lesser General Public License for more details. +# +# You should have received a copy of the GNU Lesser General Public +# License along with this library; if not, write to the Free Software +# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA +# 02110-1301 USA +######################### END LICENSE BLOCK ######################### +from typing import List, Union + +from .charsetprober import CharSetProber +from .enums import ProbingState + + +class UTF1632Prober(CharSetProber): + """ + This class simply looks for occurrences of zero bytes, and infers + whether the file is UTF16 or UTF32 (low-endian or big-endian) + For instance, files looking like ( \0 \0 \0 [nonzero] )+ + have a good probability to be UTF32BE. Files looking like ( \0 [nonzero] )+ + may be guessed to be UTF16BE, and inversely for little-endian varieties. + """ + + # how many logical characters to scan before feeling confident of prediction + MIN_CHARS_FOR_DETECTION = 20 + # a fixed constant ratio of expected zeros or non-zeros in modulo-position. + EXPECTED_RATIO = 0.94 + + def __init__(self) -> None: + super().__init__() + self.position = 0 + self.zeros_at_mod = [0] * 4 + self.nonzeros_at_mod = [0] * 4 + self._state = ProbingState.DETECTING + self.quad = [0, 0, 0, 0] + self.invalid_utf16be = False + self.invalid_utf16le = False + self.invalid_utf32be = False + self.invalid_utf32le = False + self.first_half_surrogate_pair_detected_16be = False + self.first_half_surrogate_pair_detected_16le = False + self.reset() + + def reset(self) -> None: + super().reset() + self.position = 0 + self.zeros_at_mod = [0] * 4 + self.nonzeros_at_mod = [0] * 4 + self._state = ProbingState.DETECTING + self.invalid_utf16be = False + self.invalid_utf16le = False + self.invalid_utf32be = False + self.invalid_utf32le = False + self.first_half_surrogate_pair_detected_16be = False + self.first_half_surrogate_pair_detected_16le = False + self.quad = [0, 0, 0, 0] + + @property + def charset_name(self) -> str: + if self.is_likely_utf32be(): + return "utf-32be" + if self.is_likely_utf32le(): + return "utf-32le" + if self.is_likely_utf16be(): + return "utf-16be" + if self.is_likely_utf16le(): + return "utf-16le" + # default to something valid + return "utf-16" + + @property + def language(self) -> str: + return "" + + def approx_32bit_chars(self) -> float: + return max(1.0, self.position / 4.0) + + def approx_16bit_chars(self) -> float: + return max(1.0, self.position / 2.0) + + def is_likely_utf32be(self) -> bool: + approx_chars = self.approx_32bit_chars() + return approx_chars >= self.MIN_CHARS_FOR_DETECTION and ( + self.zeros_at_mod[0] / approx_chars > self.EXPECTED_RATIO + and self.zeros_at_mod[1] / approx_chars > self.EXPECTED_RATIO + and self.zeros_at_mod[2] / approx_chars > self.EXPECTED_RATIO + and self.nonzeros_at_mod[3] / approx_chars > self.EXPECTED_RATIO + and not self.invalid_utf32be + ) + + def is_likely_utf32le(self) -> bool: + approx_chars = self.approx_32bit_chars() + return approx_chars >= self.MIN_CHARS_FOR_DETECTION and ( + self.nonzeros_at_mod[0] / approx_chars > self.EXPECTED_RATIO + and self.zeros_at_mod[1] / approx_chars > self.EXPECTED_RATIO + and self.zeros_at_mod[2] / approx_chars > self.EXPECTED_RATIO + and self.zeros_at_mod[3] / approx_chars > self.EXPECTED_RATIO + and not self.invalid_utf32le + ) + + def is_likely_utf16be(self) -> bool: + approx_chars = self.approx_16bit_chars() + return approx_chars >= self.MIN_CHARS_FOR_DETECTION and ( + (self.nonzeros_at_mod[1] + self.nonzeros_at_mod[3]) / approx_chars + > self.EXPECTED_RATIO + and (self.zeros_at_mod[0] + self.zeros_at_mod[2]) / approx_chars + > self.EXPECTED_RATIO + and not self.invalid_utf16be + ) + + def is_likely_utf16le(self) -> bool: + approx_chars = self.approx_16bit_chars() + return approx_chars >= self.MIN_CHARS_FOR_DETECTION and ( + (self.nonzeros_at_mod[0] + self.nonzeros_at_mod[2]) / approx_chars + > self.EXPECTED_RATIO + and (self.zeros_at_mod[1] + self.zeros_at_mod[3]) / approx_chars + > self.EXPECTED_RATIO + and not self.invalid_utf16le + ) + + def validate_utf32_characters(self, quad: List[int]) -> None: + """ + Validate if the quad of bytes is valid UTF-32. + + UTF-32 is valid in the range 0x00000000 - 0x0010FFFF + excluding 0x0000D800 - 0x0000DFFF + + https://en.wikipedia.org/wiki/UTF-32 + """ + if ( + quad[0] != 0 + or quad[1] > 0x10 + or (quad[0] == 0 and quad[1] == 0 and 0xD8 <= quad[2] <= 0xDF) + ): + self.invalid_utf32be = True + if ( + quad[3] != 0 + or quad[2] > 0x10 + or (quad[3] == 0 and quad[2] == 0 and 0xD8 <= quad[1] <= 0xDF) + ): + self.invalid_utf32le = True + + def validate_utf16_characters(self, pair: List[int]) -> None: + """ + Validate if the pair of bytes is valid UTF-16. + + UTF-16 is valid in the range 0x0000 - 0xFFFF excluding 0xD800 - 0xFFFF + with an exception for surrogate pairs, which must be in the range + 0xD800-0xDBFF followed by 0xDC00-0xDFFF + + https://en.wikipedia.org/wiki/UTF-16 + """ + if not self.first_half_surrogate_pair_detected_16be: + if 0xD8 <= pair[0] <= 0xDB: + self.first_half_surrogate_pair_detected_16be = True + elif 0xDC <= pair[0] <= 0xDF: + self.invalid_utf16be = True + else: + if 0xDC <= pair[0] <= 0xDF: + self.first_half_surrogate_pair_detected_16be = False + else: + self.invalid_utf16be = True + + if not self.first_half_surrogate_pair_detected_16le: + if 0xD8 <= pair[1] <= 0xDB: + self.first_half_surrogate_pair_detected_16le = True + elif 0xDC <= pair[1] <= 0xDF: + self.invalid_utf16le = True + else: + if 0xDC <= pair[1] <= 0xDF: + self.first_half_surrogate_pair_detected_16le = False + else: + self.invalid_utf16le = True + + def feed(self, byte_str: Union[bytes, bytearray]) -> ProbingState: + for c in byte_str: + mod4 = self.position % 4 + self.quad[mod4] = c + if mod4 == 3: + self.validate_utf32_characters(self.quad) + self.validate_utf16_characters(self.quad[0:2]) + self.validate_utf16_characters(self.quad[2:4]) + if c == 0: + self.zeros_at_mod[mod4] += 1 + else: + self.nonzeros_at_mod[mod4] += 1 + self.position += 1 + return self.state + + @property + def state(self) -> ProbingState: + if self._state in {ProbingState.NOT_ME, ProbingState.FOUND_IT}: + # terminal, decided states + return self._state + if self.get_confidence() > 0.80: + self._state = ProbingState.FOUND_IT + elif self.position > 4 * 1024: + # if we get to 4kb into the file, and we can't conclude it's UTF, + # let's give up + self._state = ProbingState.NOT_ME + return self._state + + def get_confidence(self) -> float: + return ( + 0.85 + if ( + self.is_likely_utf16le() + or self.is_likely_utf16be() + or self.is_likely_utf32le() + or self.is_likely_utf32be() + ) + else 0.00 + ) diff --git a/venv/lib/python3.10/site-packages/chardet/utf8prober.py b/venv/lib/python3.10/site-packages/chardet/utf8prober.py new file mode 100644 index 0000000000000000000000000000000000000000..d96354d97c2195320d0acc1717a5876eafbea2af --- /dev/null +++ b/venv/lib/python3.10/site-packages/chardet/utf8prober.py @@ -0,0 +1,82 @@ +######################## BEGIN LICENSE BLOCK ######################## +# The Original Code is mozilla.org code. +# +# The Initial Developer of the Original Code is +# Netscape Communications Corporation. +# Portions created by the Initial Developer are Copyright (C) 1998 +# the Initial Developer. All Rights Reserved. +# +# Contributor(s): +# Mark Pilgrim - port to Python +# +# This library is free software; you can redistribute it and/or +# modify it under the terms of the GNU Lesser General Public +# License as published by the Free Software Foundation; either +# version 2.1 of the License, or (at your option) any later version. +# +# This library is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU +# Lesser General Public License for more details. +# +# You should have received a copy of the GNU Lesser General Public +# License along with this library; if not, write to the Free Software +# Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA +# 02110-1301 USA +######################### END LICENSE BLOCK ######################### + +from typing import Union + +from .charsetprober import CharSetProber +from .codingstatemachine import CodingStateMachine +from .enums import MachineState, ProbingState +from .mbcssm import UTF8_SM_MODEL + + +class UTF8Prober(CharSetProber): + ONE_CHAR_PROB = 0.5 + + def __init__(self) -> None: + super().__init__() + self.coding_sm = CodingStateMachine(UTF8_SM_MODEL) + self._num_mb_chars = 0 + self.reset() + + def reset(self) -> None: + super().reset() + self.coding_sm.reset() + self._num_mb_chars = 0 + + @property + def charset_name(self) -> str: + return "utf-8" + + @property + def language(self) -> str: + return "" + + def feed(self, byte_str: Union[bytes, bytearray]) -> ProbingState: + for c in byte_str: + coding_state = self.coding_sm.next_state(c) + if coding_state == MachineState.ERROR: + self._state = ProbingState.NOT_ME + break + if coding_state == MachineState.ITS_ME: + self._state = ProbingState.FOUND_IT + break + if coding_state == MachineState.START: + if self.coding_sm.get_current_charlen() >= 2: + self._num_mb_chars += 1 + + if self.state == ProbingState.DETECTING: + if self.get_confidence() > self.SHORTCUT_THRESHOLD: + self._state = ProbingState.FOUND_IT + + return self.state + + def get_confidence(self) -> float: + unlike = 0.99 + if self._num_mb_chars < 6: + unlike *= self.ONE_CHAR_PROB**self._num_mb_chars + return 1.0 - unlike + return unlike diff --git a/venv/lib/python3.10/site-packages/safetensors-0.4.3.dist-info/INSTALLER b/venv/lib/python3.10/site-packages/safetensors-0.4.3.dist-info/INSTALLER new file mode 100644 index 0000000000000000000000000000000000000000..a1b589e38a32041e49332e5e81c2d363dc418d68 --- /dev/null +++ b/venv/lib/python3.10/site-packages/safetensors-0.4.3.dist-info/INSTALLER @@ -0,0 +1 @@ +pip diff --git a/venv/lib/python3.10/site-packages/safetensors-0.4.3.dist-info/METADATA b/venv/lib/python3.10/site-packages/safetensors-0.4.3.dist-info/METADATA new file mode 100644 index 0000000000000000000000000000000000000000..241d3608c6879bda93c8d7a26f3bbf8807e7fcbe --- /dev/null +++ b/venv/lib/python3.10/site-packages/safetensors-0.4.3.dist-info/METADATA @@ -0,0 +1,127 @@ +Metadata-Version: 2.3 +Name: safetensors +Version: 0.4.3 +Classifier: Development Status :: 5 - Production/Stable +Classifier: Intended Audience :: Developers +Classifier: Intended Audience :: Education +Classifier: Intended Audience :: Science/Research +Classifier: License :: OSI Approved :: Apache Software License +Classifier: Operating System :: OS Independent +Classifier: Programming Language :: Python :: 3 +Classifier: Programming Language :: Python :: 3.7 +Classifier: Programming Language :: Python :: 3.8 +Classifier: Programming Language :: Python :: 3.9 +Classifier: Programming Language :: Python :: 3.10 +Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence +Classifier: Typing :: Typed +Requires-Dist: numpy >=1.21.6 ; extra == 'numpy' +Requires-Dist: safetensors[numpy] ; extra == 'torch' +Requires-Dist: torch >=1.10 ; extra == 'torch' +Requires-Dist: safetensors[numpy] ; extra == 'tensorflow' +Requires-Dist: tensorflow >=2.11.0 ; extra == 'tensorflow' +Requires-Dist: safetensors[numpy] ; extra == 'pinned-tf' +Requires-Dist: tensorflow ==2.11.0 ; extra == 'pinned-tf' +Requires-Dist: safetensors[numpy] ; extra == 'jax' +Requires-Dist: flax >=0.6.3 ; extra == 'jax' +Requires-Dist: jax >=0.3.25 ; extra == 'jax' +Requires-Dist: jaxlib >=0.3.25 ; extra == 'jax' +Requires-Dist: mlx >=0.0.9 ; extra == 'mlx' +Requires-Dist: safetensors[numpy] ; extra == 'paddlepaddle' +Requires-Dist: paddlepaddle >=2.4.1 ; extra == 'paddlepaddle' +Requires-Dist: black ==22.3 ; extra == 'quality' +Requires-Dist: click ==8.0.4 ; extra == 'quality' +Requires-Dist: isort >=5.5.4 ; extra == 'quality' +Requires-Dist: flake8 >=3.8.3 ; extra == 'quality' +Requires-Dist: safetensors[numpy] ; extra == 'testing' +Requires-Dist: h5py >=3.7.0 ; extra == 'testing' +Requires-Dist: huggingface-hub >=0.12.1 ; extra == 'testing' +Requires-Dist: setuptools-rust >=1.5.2 ; extra == 'testing' +Requires-Dist: pytest >=7.2.0 ; extra == 'testing' +Requires-Dist: pytest-benchmark >=4.0.0 ; extra == 'testing' +Requires-Dist: hypothesis >=6.70.2 ; extra == 'testing' +Requires-Dist: safetensors[torch] ; extra == 'all' +Requires-Dist: safetensors[numpy] ; extra == 'all' +Requires-Dist: safetensors[pinned-tf] ; extra == 'all' +Requires-Dist: safetensors[jax] ; extra == 'all' +Requires-Dist: safetensors[paddlepaddle] ; extra == 'all' +Requires-Dist: safetensors[quality] ; extra == 'all' +Requires-Dist: safetensors[testing] ; extra == 'all' +Requires-Dist: safetensors[all] ; extra == 'dev' +Provides-Extra: numpy +Provides-Extra: torch +Provides-Extra: tensorflow +Provides-Extra: pinned-tf +Provides-Extra: jax +Provides-Extra: mlx +Provides-Extra: paddlepaddle +Provides-Extra: quality +Provides-Extra: testing +Provides-Extra: all +Provides-Extra: dev +Author-email: Nicolas Patry +Requires-Python: >=3.7 +Description-Content-Type: text/markdown; charset=UTF-8; variant=GFM +Project-URL: Homepage, https://github.com/huggingface/safetensors +Project-URL: Source, https://github.com/huggingface/safetensors + +## Installation + +``` +pip install safetensors +``` + + +## Usage + +### Numpy + +```python +from safetensors.numpy import save_file, load_file +import numpy as np + +tensors = { + "a": np.zeros((2, 2)), + "b": np.zeros((2, 3), dtype=np.uint8) +} + +save_file(tensors, "./model.safetensors") + + +# Now loading +loaded = load_file("./model.safetensors") +``` + +### Torch + +```python +from safetensors.torch import save_file, load_file +import torch + +tensors = { + "a": torch.zeros((2, 2)), + "b": torch.zeros((2, 3), dtype=torch.uint8) +} + +save_file(tensors, "./model.safetensors") + + +# Now loading +loaded = load_file("./model.safetensors") +``` + +### Developing + +``` +# inside ./safetensors/bindings/python +pip install .[dev] +``` +Should be enough to install this library locally. + +### Testing + +``` +# inside ./safetensors/bindings/python +pip install .[dev] +pytest -sv tests/ +``` + diff --git a/venv/lib/python3.10/site-packages/safetensors-0.4.3.dist-info/RECORD b/venv/lib/python3.10/site-packages/safetensors-0.4.3.dist-info/RECORD new file mode 100644 index 0000000000000000000000000000000000000000..19da2aa348a3a5c42585b47067ba0125f30c99dd --- /dev/null +++ b/venv/lib/python3.10/site-packages/safetensors-0.4.3.dist-info/RECORD @@ -0,0 +1,21 @@ +safetensors-0.4.3.dist-info/INSTALLER,sha256=zuuue4knoyJ-UwPPXg8fezS7VCrXJQrAP7zeNuwvFQg,4 +safetensors-0.4.3.dist-info/METADATA,sha256=xyYOydMNNt9ejapL8t76ASs9iTE5oS7IrZOQl8e4wK0,3842 +safetensors-0.4.3.dist-info/RECORD,, +safetensors-0.4.3.dist-info/WHEEL,sha256=JL8sd1C0RQ2f7cmwbAn1Jp257v_vSS2r0VvTBpJeZwA,129 +safetensors/__init__.py,sha256=rFhZV2HBVDIijU2xKjg-0viTLETa-yMLMgFC9-47hdc,171 +safetensors/__init__.pyi,sha256=z6kNUzegHpyQAtFLtHu7ixUYWK9-Kwb6GqvV__6qMew,1970 +safetensors/__pycache__/__init__.cpython-310.pyc,, +safetensors/__pycache__/flax.cpython-310.pyc,, +safetensors/__pycache__/mlx.cpython-310.pyc,, +safetensors/__pycache__/numpy.cpython-310.pyc,, +safetensors/__pycache__/paddle.cpython-310.pyc,, +safetensors/__pycache__/tensorflow.cpython-310.pyc,, +safetensors/__pycache__/torch.cpython-310.pyc,, +safetensors/_safetensors_rust.cpython-310-x86_64-linux-gnu.so,sha256=tjRccWtdZ62y0_JHfAfAsaIUpwqny3EQHZkyeroL-qA,4438576 +safetensors/flax.py,sha256=AuyY2YHxTBy4xeQLxhAyMledHykpz0Qsys13eKiHNYg,3846 +safetensors/mlx.py,sha256=7OTFtbdmGIqLnrjo_36RJRJ7zq1aI6HasvRPLB3d_Gw,3837 +safetensors/numpy.py,sha256=5Z_wSFRxVbsAXpnej2zuqtdYK9K-nqZs1EE_G14C6Ck,4937 +safetensors/paddle.py,sha256=V_RCTXymi2PJAG_jA4-qnfzbYAUB_74VYYXFzhXTLYM,4175 +safetensors/py.typed,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0 +safetensors/tensorflow.py,sha256=6XaUBcxm0xL4ulNcvVsllKZ_qJ_rBSlE50rNnECCNYQ,3890 +safetensors/torch.py,sha256=_Tr2aFS0o5vmauTWKfJFo43N-UR_Ap8crx3O8XpryJI,17582 diff --git a/venv/lib/python3.10/site-packages/safetensors-0.4.3.dist-info/WHEEL b/venv/lib/python3.10/site-packages/safetensors-0.4.3.dist-info/WHEEL new file mode 100644 index 0000000000000000000000000000000000000000..efc3fe1c2443c99179e12648847b89e865477d5f --- /dev/null +++ b/venv/lib/python3.10/site-packages/safetensors-0.4.3.dist-info/WHEEL @@ -0,0 +1,4 @@ +Wheel-Version: 1.0 +Generator: maturin (1.5.1) +Root-Is-Purelib: false +Tag: cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64 diff --git a/venv/lib/python3.10/site-packages/sklearn/covariance/__init__.py b/venv/lib/python3.10/site-packages/sklearn/covariance/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..8fcf8c68444e5e97b45520110b7c9c5ac38a62c7 --- /dev/null +++ b/venv/lib/python3.10/site-packages/sklearn/covariance/__init__.py @@ -0,0 +1,44 @@ +""" +The :mod:`sklearn.covariance` module includes methods and algorithms to +robustly estimate the covariance of features given a set of points. The +precision matrix defined as the inverse of the covariance is also estimated. +Covariance estimation is closely related to the theory of Gaussian Graphical +Models. +""" + +from ._elliptic_envelope import EllipticEnvelope +from ._empirical_covariance import ( + EmpiricalCovariance, + empirical_covariance, + log_likelihood, +) +from ._graph_lasso import GraphicalLasso, GraphicalLassoCV, graphical_lasso +from ._robust_covariance import MinCovDet, fast_mcd +from ._shrunk_covariance import ( + OAS, + LedoitWolf, + ShrunkCovariance, + ledoit_wolf, + ledoit_wolf_shrinkage, + oas, + shrunk_covariance, +) + +__all__ = [ + "EllipticEnvelope", + "EmpiricalCovariance", + "GraphicalLasso", + "GraphicalLassoCV", + "LedoitWolf", + "MinCovDet", + "OAS", + "ShrunkCovariance", + "empirical_covariance", + "fast_mcd", + "graphical_lasso", + "ledoit_wolf", + "ledoit_wolf_shrinkage", + "log_likelihood", + "oas", + "shrunk_covariance", +] diff --git 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0000000000000000000000000000000000000000..ed99a38c0ee56d7fb2222204612dce09529b670d --- /dev/null +++ b/venv/lib/python3.10/site-packages/sklearn/covariance/_elliptic_envelope.py @@ -0,0 +1,267 @@ +# Author: Virgile Fritsch +# +# License: BSD 3 clause + +from numbers import Real + +import numpy as np + +from ..base import OutlierMixin, _fit_context +from ..metrics import accuracy_score +from ..utils._param_validation import Interval +from ..utils.validation import check_is_fitted +from ._robust_covariance import MinCovDet + + +class EllipticEnvelope(OutlierMixin, MinCovDet): + """An object for detecting outliers in a Gaussian distributed dataset. + + Read more in the :ref:`User Guide `. + + Parameters + ---------- + store_precision : bool, default=True + Specify if the estimated precision is stored. + + assume_centered : bool, default=False + If True, the support of robust location and covariance estimates + is computed, and a covariance estimate is recomputed from it, + without centering the data. + Useful to work with data whose mean is significantly equal to + zero but is not exactly zero. + If False, the robust location and covariance are directly computed + with the FastMCD algorithm without additional treatment. + + support_fraction : float, default=None + The proportion of points to be included in the support of the raw + MCD estimate. If None, the minimum value of support_fraction will + be used within the algorithm: `(n_samples + n_features + 1) / 2 * n_samples`. + Range is (0, 1). + + contamination : float, default=0.1 + The amount of contamination of the data set, i.e. the proportion + of outliers in the data set. Range is (0, 0.5]. + + random_state : int, RandomState instance or None, default=None + Determines the pseudo random number generator for shuffling + the data. Pass an int for reproducible results across multiple function + calls. See :term:`Glossary `. + + Attributes + ---------- + location_ : ndarray of shape (n_features,) + Estimated robust location. + + covariance_ : ndarray of shape (n_features, n_features) + Estimated robust covariance matrix. + + precision_ : ndarray of shape (n_features, n_features) + Estimated pseudo inverse matrix. + (stored only if store_precision is True) + + support_ : ndarray of shape (n_samples,) + A mask of the observations that have been used to compute the + robust estimates of location and shape. + + offset_ : float + Offset used to define the decision function from the raw scores. + We have the relation: ``decision_function = score_samples - offset_``. + The offset depends on the contamination parameter and is defined in + such a way we obtain the expected number of outliers (samples with + decision function < 0) in training. + + .. versionadded:: 0.20 + + raw_location_ : ndarray of shape (n_features,) + The raw robust estimated location before correction and re-weighting. + + raw_covariance_ : ndarray of shape (n_features, n_features) + The raw robust estimated covariance before correction and re-weighting. + + raw_support_ : ndarray of shape (n_samples,) + A mask of the observations that have been used to compute + the raw robust estimates of location and shape, before correction + and re-weighting. + + dist_ : ndarray of shape (n_samples,) + Mahalanobis distances of the training set (on which :meth:`fit` is + called) observations. + + n_features_in_ : int + Number of features seen during :term:`fit`. + + .. versionadded:: 0.24 + + feature_names_in_ : ndarray of shape (`n_features_in_`,) + Names of features seen during :term:`fit`. Defined only when `X` + has feature names that are all strings. + + .. versionadded:: 1.0 + + See Also + -------- + EmpiricalCovariance : Maximum likelihood covariance estimator. + GraphicalLasso : Sparse inverse covariance estimation + with an l1-penalized estimator. + LedoitWolf : LedoitWolf Estimator. + MinCovDet : Minimum Covariance Determinant + (robust estimator of covariance). + OAS : Oracle Approximating Shrinkage Estimator. + ShrunkCovariance : Covariance estimator with shrinkage. + + Notes + ----- + Outlier detection from covariance estimation may break or not + perform well in high-dimensional settings. In particular, one will + always take care to work with ``n_samples > n_features ** 2``. + + References + ---------- + .. [1] Rousseeuw, P.J., Van Driessen, K. "A fast algorithm for the + minimum covariance determinant estimator" Technometrics 41(3), 212 + (1999) + + Examples + -------- + >>> import numpy as np + >>> from sklearn.covariance import EllipticEnvelope + >>> true_cov = np.array([[.8, .3], + ... [.3, .4]]) + >>> X = np.random.RandomState(0).multivariate_normal(mean=[0, 0], + ... cov=true_cov, + ... size=500) + >>> cov = EllipticEnvelope(random_state=0).fit(X) + >>> # predict returns 1 for an inlier and -1 for an outlier + >>> cov.predict([[0, 0], + ... [3, 3]]) + array([ 1, -1]) + >>> cov.covariance_ + array([[0.7411..., 0.2535...], + [0.2535..., 0.3053...]]) + >>> cov.location_ + array([0.0813... , 0.0427...]) + """ + + _parameter_constraints: dict = { + **MinCovDet._parameter_constraints, + "contamination": [Interval(Real, 0, 0.5, closed="right")], + } + + def __init__( + self, + *, + store_precision=True, + assume_centered=False, + support_fraction=None, + contamination=0.1, + random_state=None, + ): + super().__init__( + store_precision=store_precision, + assume_centered=assume_centered, + support_fraction=support_fraction, + random_state=random_state, + ) + self.contamination = contamination + + @_fit_context(prefer_skip_nested_validation=True) + def fit(self, X, y=None): + """Fit the EllipticEnvelope model. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + Training data. + + y : Ignored + Not used, present for API consistency by convention. + + Returns + ------- + self : object + Returns the instance itself. + """ + super().fit(X) + self.offset_ = np.percentile(-self.dist_, 100.0 * self.contamination) + return self + + def decision_function(self, X): + """Compute the decision function of the given observations. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The data matrix. + + Returns + ------- + decision : ndarray of shape (n_samples,) + Decision function of the samples. + It is equal to the shifted Mahalanobis distances. + The threshold for being an outlier is 0, which ensures a + compatibility with other outlier detection algorithms. + """ + check_is_fitted(self) + negative_mahal_dist = self.score_samples(X) + return negative_mahal_dist - self.offset_ + + def score_samples(self, X): + """Compute the negative Mahalanobis distances. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The data matrix. + + Returns + ------- + negative_mahal_distances : array-like of shape (n_samples,) + Opposite of the Mahalanobis distances. + """ + check_is_fitted(self) + return -self.mahalanobis(X) + + def predict(self, X): + """ + Predict labels (1 inlier, -1 outlier) of X according to fitted model. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The data matrix. + + Returns + ------- + is_inlier : ndarray of shape (n_samples,) + Returns -1 for anomalies/outliers and +1 for inliers. + """ + values = self.decision_function(X) + is_inlier = np.full(values.shape[0], -1, dtype=int) + is_inlier[values >= 0] = 1 + + return is_inlier + + def score(self, X, y, sample_weight=None): + """Return the mean accuracy on the given test data and labels. + + In multi-label classification, this is the subset accuracy + which is a harsh metric since you require for each sample that + each label set be correctly predicted. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + Test samples. + + y : array-like of shape (n_samples,) or (n_samples, n_outputs) + True labels for X. + + sample_weight : array-like of shape (n_samples,), default=None + Sample weights. + + Returns + ------- + score : float + Mean accuracy of self.predict(X) w.r.t. y. + """ + return accuracy_score(y, self.predict(X), sample_weight=sample_weight) diff --git a/venv/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py b/venv/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py new file mode 100644 index 0000000000000000000000000000000000000000..db52bfa05ded30c7494e0ca3bbda4ddb6a37daa5 --- /dev/null +++ b/venv/lib/python3.10/site-packages/sklearn/covariance/_empirical_covariance.py @@ -0,0 +1,364 @@ +""" +Maximum likelihood covariance estimator. + +""" + +# Author: Alexandre Gramfort +# Gael Varoquaux +# Virgile Fritsch +# +# License: BSD 3 clause + +# avoid division truncation +import warnings + +import numpy as np +from scipy import linalg + +from .. import config_context +from ..base import BaseEstimator, _fit_context +from ..metrics.pairwise import pairwise_distances +from ..utils import check_array +from ..utils._param_validation import validate_params +from ..utils.extmath import fast_logdet + + +@validate_params( + { + "emp_cov": [np.ndarray], + "precision": [np.ndarray], + }, + prefer_skip_nested_validation=True, +) +def log_likelihood(emp_cov, precision): + """Compute the sample mean of the log_likelihood under a covariance model. + + Computes the empirical expected log-likelihood, allowing for universal + comparison (beyond this software package), and accounts for normalization + terms and scaling. + + Parameters + ---------- + emp_cov : ndarray of shape (n_features, n_features) + Maximum Likelihood Estimator of covariance. + + precision : ndarray of shape (n_features, n_features) + The precision matrix of the covariance model to be tested. + + Returns + ------- + log_likelihood_ : float + Sample mean of the log-likelihood. + """ + p = precision.shape[0] + log_likelihood_ = -np.sum(emp_cov * precision) + fast_logdet(precision) + log_likelihood_ -= p * np.log(2 * np.pi) + log_likelihood_ /= 2.0 + return log_likelihood_ + + +@validate_params( + { + "X": ["array-like"], + "assume_centered": ["boolean"], + }, + prefer_skip_nested_validation=True, +) +def empirical_covariance(X, *, assume_centered=False): + """Compute the Maximum likelihood covariance estimator. + + Parameters + ---------- + X : ndarray of shape (n_samples, n_features) + Data from which to compute the covariance estimate. + + assume_centered : bool, default=False + If `True`, data will not be centered before computation. + Useful when working with data whose mean is almost, but not exactly + zero. + If `False`, data will be centered before computation. + + Returns + ------- + covariance : ndarray of shape (n_features, n_features) + Empirical covariance (Maximum Likelihood Estimator). + + Examples + -------- + >>> from sklearn.covariance import empirical_covariance + >>> X = [[1,1,1],[1,1,1],[1,1,1], + ... [0,0,0],[0,0,0],[0,0,0]] + >>> empirical_covariance(X) + array([[0.25, 0.25, 0.25], + [0.25, 0.25, 0.25], + [0.25, 0.25, 0.25]]) + """ + X = check_array(X, ensure_2d=False, force_all_finite=False) + + if X.ndim == 1: + X = np.reshape(X, (1, -1)) + + if X.shape[0] == 1: + warnings.warn( + "Only one sample available. You may want to reshape your data array" + ) + + if assume_centered: + covariance = np.dot(X.T, X) / X.shape[0] + else: + covariance = np.cov(X.T, bias=1) + + if covariance.ndim == 0: + covariance = np.array([[covariance]]) + return covariance + + +class EmpiricalCovariance(BaseEstimator): + """Maximum likelihood covariance estimator. + + Read more in the :ref:`User Guide `. + + Parameters + ---------- + store_precision : bool, default=True + Specifies if the estimated precision is stored. + + assume_centered : bool, default=False + If True, data are not centered before computation. + Useful when working with data whose mean is almost, but not exactly + zero. + If False (default), data are centered before computation. + + Attributes + ---------- + location_ : ndarray of shape (n_features,) + Estimated location, i.e. the estimated mean. + + covariance_ : ndarray of shape (n_features, n_features) + Estimated covariance matrix + + precision_ : ndarray of shape (n_features, n_features) + Estimated pseudo-inverse matrix. + (stored only if store_precision is True) + + n_features_in_ : int + Number of features seen during :term:`fit`. + + .. versionadded:: 0.24 + + feature_names_in_ : ndarray of shape (`n_features_in_`,) + Names of features seen during :term:`fit`. Defined only when `X` + has feature names that are all strings. + + .. versionadded:: 1.0 + + See Also + -------- + EllipticEnvelope : An object for detecting outliers in + a Gaussian distributed dataset. + GraphicalLasso : Sparse inverse covariance estimation + with an l1-penalized estimator. + LedoitWolf : LedoitWolf Estimator. + MinCovDet : Minimum Covariance Determinant + (robust estimator of covariance). + OAS : Oracle Approximating Shrinkage Estimator. + ShrunkCovariance : Covariance estimator with shrinkage. + + Examples + -------- + >>> import numpy as np + >>> from sklearn.covariance import EmpiricalCovariance + >>> from sklearn.datasets import make_gaussian_quantiles + >>> real_cov = np.array([[.8, .3], + ... [.3, .4]]) + >>> rng = np.random.RandomState(0) + >>> X = rng.multivariate_normal(mean=[0, 0], + ... cov=real_cov, + ... size=500) + >>> cov = EmpiricalCovariance().fit(X) + >>> cov.covariance_ + array([[0.7569..., 0.2818...], + [0.2818..., 0.3928...]]) + >>> cov.location_ + array([0.0622..., 0.0193...]) + """ + + _parameter_constraints: dict = { + "store_precision": ["boolean"], + "assume_centered": ["boolean"], + } + + def __init__(self, *, store_precision=True, assume_centered=False): + self.store_precision = store_precision + self.assume_centered = assume_centered + + def _set_covariance(self, covariance): + """Saves the covariance and precision estimates + + Storage is done accordingly to `self.store_precision`. + Precision stored only if invertible. + + Parameters + ---------- + covariance : array-like of shape (n_features, n_features) + Estimated covariance matrix to be stored, and from which precision + is computed. + """ + covariance = check_array(covariance) + # set covariance + self.covariance_ = covariance + # set precision + if self.store_precision: + self.precision_ = linalg.pinvh(covariance, check_finite=False) + else: + self.precision_ = None + + def get_precision(self): + """Getter for the precision matrix. + + Returns + ------- + precision_ : array-like of shape (n_features, n_features) + The precision matrix associated to the current covariance object. + """ + if self.store_precision: + precision = self.precision_ + else: + precision = linalg.pinvh(self.covariance_, check_finite=False) + return precision + + @_fit_context(prefer_skip_nested_validation=True) + def fit(self, X, y=None): + """Fit the maximum likelihood covariance estimator to X. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + Training data, where `n_samples` is the number of samples and + `n_features` is the number of features. + + y : Ignored + Not used, present for API consistency by convention. + + Returns + ------- + self : object + Returns the instance itself. + """ + X = self._validate_data(X) + if self.assume_centered: + self.location_ = np.zeros(X.shape[1]) + else: + self.location_ = X.mean(0) + covariance = empirical_covariance(X, assume_centered=self.assume_centered) + self._set_covariance(covariance) + + return self + + def score(self, X_test, y=None): + """Compute the log-likelihood of `X_test` under the estimated Gaussian model. + + The Gaussian model is defined by its mean and covariance matrix which are + represented respectively by `self.location_` and `self.covariance_`. + + Parameters + ---------- + X_test : array-like of shape (n_samples, n_features) + Test data of which we compute the likelihood, where `n_samples` is + the number of samples and `n_features` is the number of features. + `X_test` is assumed to be drawn from the same distribution than + the data used in fit (including centering). + + y : Ignored + Not used, present for API consistency by convention. + + Returns + ------- + res : float + The log-likelihood of `X_test` with `self.location_` and `self.covariance_` + as estimators of the Gaussian model mean and covariance matrix respectively. + """ + X_test = self._validate_data(X_test, reset=False) + # compute empirical covariance of the test set + test_cov = empirical_covariance(X_test - self.location_, assume_centered=True) + # compute log likelihood + res = log_likelihood(test_cov, self.get_precision()) + + return res + + def error_norm(self, comp_cov, norm="frobenius", scaling=True, squared=True): + """Compute the Mean Squared Error between two covariance estimators. + + Parameters + ---------- + comp_cov : array-like of shape (n_features, n_features) + The covariance to compare with. + + norm : {"frobenius", "spectral"}, default="frobenius" + The type of norm used to compute the error. Available error types: + - 'frobenius' (default): sqrt(tr(A^t.A)) + - 'spectral': sqrt(max(eigenvalues(A^t.A)) + where A is the error ``(comp_cov - self.covariance_)``. + + scaling : bool, default=True + If True (default), the squared error norm is divided by n_features. + If False, the squared error norm is not rescaled. + + squared : bool, default=True + Whether to compute the squared error norm or the error norm. + If True (default), the squared error norm is returned. + If False, the error norm is returned. + + Returns + ------- + result : float + The Mean Squared Error (in the sense of the Frobenius norm) between + `self` and `comp_cov` covariance estimators. + """ + # compute the error + error = comp_cov - self.covariance_ + # compute the error norm + if norm == "frobenius": + squared_norm = np.sum(error**2) + elif norm == "spectral": + squared_norm = np.amax(linalg.svdvals(np.dot(error.T, error))) + else: + raise NotImplementedError( + "Only spectral and frobenius norms are implemented" + ) + # optionally scale the error norm + if scaling: + squared_norm = squared_norm / error.shape[0] + # finally get either the squared norm or the norm + if squared: + result = squared_norm + else: + result = np.sqrt(squared_norm) + + return result + + def mahalanobis(self, X): + """Compute the squared Mahalanobis distances of given observations. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The observations, the Mahalanobis distances of the which we + compute. Observations are assumed to be drawn from the same + distribution than the data used in fit. + + Returns + ------- + dist : ndarray of shape (n_samples,) + Squared Mahalanobis distances of the observations. + """ + X = self._validate_data(X, reset=False) + + precision = self.get_precision() + with config_context(assume_finite=True): + # compute mahalanobis distances + dist = pairwise_distances( + X, self.location_[np.newaxis, :], metric="mahalanobis", VI=precision + ) + + return np.reshape(dist, (len(X),)) ** 2 diff --git a/venv/lib/python3.10/site-packages/sklearn/covariance/_graph_lasso.py b/venv/lib/python3.10/site-packages/sklearn/covariance/_graph_lasso.py new file mode 100644 index 0000000000000000000000000000000000000000..fb40ffda162a4c0f31fc82c9124daff9bb4ecbb2 --- /dev/null +++ b/venv/lib/python3.10/site-packages/sklearn/covariance/_graph_lasso.py @@ -0,0 +1,1110 @@ +"""GraphicalLasso: sparse inverse covariance estimation with an l1-penalized +estimator. +""" + +# Author: Gael Varoquaux +# License: BSD 3 clause +# Copyright: INRIA +import operator +import sys +import time +import warnings +from numbers import Integral, Real + +import numpy as np +from scipy import linalg + +from ..base import _fit_context +from ..exceptions import ConvergenceWarning + +# mypy error: Module 'sklearn.linear_model' has no attribute '_cd_fast' +from ..linear_model import _cd_fast as cd_fast # type: ignore +from ..linear_model import lars_path_gram +from ..model_selection import check_cv, cross_val_score +from ..utils._param_validation import Interval, StrOptions, validate_params +from ..utils.metadata_routing import _RoutingNotSupportedMixin +from ..utils.parallel import Parallel, delayed +from ..utils.validation import ( + _is_arraylike_not_scalar, + check_random_state, + check_scalar, +) +from . import EmpiricalCovariance, empirical_covariance, log_likelihood + + +# Helper functions to compute the objective and dual objective functions +# of the l1-penalized estimator +def _objective(mle, precision_, alpha): + """Evaluation of the graphical-lasso objective function + + the objective function is made of a shifted scaled version of the + normalized log-likelihood (i.e. its empirical mean over the samples) and a + penalisation term to promote sparsity + """ + p = precision_.shape[0] + cost = -2.0 * log_likelihood(mle, precision_) + p * np.log(2 * np.pi) + cost += alpha * (np.abs(precision_).sum() - np.abs(np.diag(precision_)).sum()) + return cost + + +def _dual_gap(emp_cov, precision_, alpha): + """Expression of the dual gap convergence criterion + + The specific definition is given in Duchi "Projected Subgradient Methods + for Learning Sparse Gaussians". + """ + gap = np.sum(emp_cov * precision_) + gap -= precision_.shape[0] + gap += alpha * (np.abs(precision_).sum() - np.abs(np.diag(precision_)).sum()) + return gap + + +# The g-lasso algorithm +def _graphical_lasso( + emp_cov, + alpha, + *, + cov_init=None, + mode="cd", + tol=1e-4, + enet_tol=1e-4, + max_iter=100, + verbose=False, + eps=np.finfo(np.float64).eps, +): + _, n_features = emp_cov.shape + if alpha == 0: + # Early return without regularization + precision_ = linalg.inv(emp_cov) + cost = -2.0 * log_likelihood(emp_cov, precision_) + cost += n_features * np.log(2 * np.pi) + d_gap = np.sum(emp_cov * precision_) - n_features + return emp_cov, precision_, (cost, d_gap), 0 + + if cov_init is None: + covariance_ = emp_cov.copy() + else: + covariance_ = cov_init.copy() + # As a trivial regularization (Tikhonov like), we scale down the + # off-diagonal coefficients of our starting point: This is needed, as + # in the cross-validation the cov_init can easily be + # ill-conditioned, and the CV loop blows. Beside, this takes + # conservative stand-point on the initial conditions, and it tends to + # make the convergence go faster. + covariance_ *= 0.95 + diagonal = emp_cov.flat[:: n_features + 1] + covariance_.flat[:: n_features + 1] = diagonal + precision_ = linalg.pinvh(covariance_) + + indices = np.arange(n_features) + i = 0 # initialize the counter to be robust to `max_iter=0` + costs = list() + # The different l1 regression solver have different numerical errors + if mode == "cd": + errors = dict(over="raise", invalid="ignore") + else: + errors = dict(invalid="raise") + try: + # be robust to the max_iter=0 edge case, see: + # https://github.com/scikit-learn/scikit-learn/issues/4134 + d_gap = np.inf + # set a sub_covariance buffer + sub_covariance = np.copy(covariance_[1:, 1:], order="C") + for i in range(max_iter): + for idx in range(n_features): + # To keep the contiguous matrix `sub_covariance` equal to + # covariance_[indices != idx].T[indices != idx] + # we only need to update 1 column and 1 line when idx changes + if idx > 0: + di = idx - 1 + sub_covariance[di] = covariance_[di][indices != idx] + sub_covariance[:, di] = covariance_[:, di][indices != idx] + else: + sub_covariance[:] = covariance_[1:, 1:] + row = emp_cov[idx, indices != idx] + with np.errstate(**errors): + if mode == "cd": + # Use coordinate descent + coefs = -( + precision_[indices != idx, idx] + / (precision_[idx, idx] + 1000 * eps) + ) + coefs, _, _, _ = cd_fast.enet_coordinate_descent_gram( + coefs, + alpha, + 0, + sub_covariance, + row, + row, + max_iter, + enet_tol, + check_random_state(None), + False, + ) + else: # mode == "lars" + _, _, coefs = lars_path_gram( + Xy=row, + Gram=sub_covariance, + n_samples=row.size, + alpha_min=alpha / (n_features - 1), + copy_Gram=True, + eps=eps, + method="lars", + return_path=False, + ) + # Update the precision matrix + precision_[idx, idx] = 1.0 / ( + covariance_[idx, idx] + - np.dot(covariance_[indices != idx, idx], coefs) + ) + precision_[indices != idx, idx] = -precision_[idx, idx] * coefs + precision_[idx, indices != idx] = -precision_[idx, idx] * coefs + coefs = np.dot(sub_covariance, coefs) + covariance_[idx, indices != idx] = coefs + covariance_[indices != idx, idx] = coefs + if not np.isfinite(precision_.sum()): + raise FloatingPointError( + "The system is too ill-conditioned for this solver" + ) + d_gap = _dual_gap(emp_cov, precision_, alpha) + cost = _objective(emp_cov, precision_, alpha) + if verbose: + print( + "[graphical_lasso] Iteration % 3i, cost % 3.2e, dual gap %.3e" + % (i, cost, d_gap) + ) + costs.append((cost, d_gap)) + if np.abs(d_gap) < tol: + break + if not np.isfinite(cost) and i > 0: + raise FloatingPointError( + "Non SPD result: the system is too ill-conditioned for this solver" + ) + else: + warnings.warn( + "graphical_lasso: did not converge after %i iteration: dual gap: %.3e" + % (max_iter, d_gap), + ConvergenceWarning, + ) + except FloatingPointError as e: + e.args = (e.args[0] + ". The system is too ill-conditioned for this solver",) + raise e + + return covariance_, precision_, costs, i + 1 + + +def alpha_max(emp_cov): + """Find the maximum alpha for which there are some non-zeros off-diagonal. + + Parameters + ---------- + emp_cov : ndarray of shape (n_features, n_features) + The sample covariance matrix. + + Notes + ----- + This results from the bound for the all the Lasso that are solved + in GraphicalLasso: each time, the row of cov corresponds to Xy. As the + bound for alpha is given by `max(abs(Xy))`, the result follows. + """ + A = np.copy(emp_cov) + A.flat[:: A.shape[0] + 1] = 0 + return np.max(np.abs(A)) + + +@validate_params( + { + "emp_cov": ["array-like"], + "cov_init": ["array-like", None], + "return_costs": ["boolean"], + "return_n_iter": ["boolean"], + }, + prefer_skip_nested_validation=False, +) +def graphical_lasso( + emp_cov, + alpha, + *, + cov_init=None, + mode="cd", + tol=1e-4, + enet_tol=1e-4, + max_iter=100, + verbose=False, + return_costs=False, + eps=np.finfo(np.float64).eps, + return_n_iter=False, +): + """L1-penalized covariance estimator. + + Read more in the :ref:`User Guide `. + + .. versionchanged:: v0.20 + graph_lasso has been renamed to graphical_lasso + + Parameters + ---------- + emp_cov : array-like of shape (n_features, n_features) + Empirical covariance from which to compute the covariance estimate. + + alpha : float + The regularization parameter: the higher alpha, the more + regularization, the sparser the inverse covariance. + Range is (0, inf]. + + cov_init : array of shape (n_features, n_features), default=None + The initial guess for the covariance. If None, then the empirical + covariance is used. + + .. deprecated:: 1.3 + `cov_init` is deprecated in 1.3 and will be removed in 1.5. + It currently has no effect. + + mode : {'cd', 'lars'}, default='cd' + The Lasso solver to use: coordinate descent or LARS. Use LARS for + very sparse underlying graphs, where p > n. Elsewhere prefer cd + which is more numerically stable. + + tol : float, default=1e-4 + The tolerance to declare convergence: if the dual gap goes below + this value, iterations are stopped. Range is (0, inf]. + + enet_tol : float, default=1e-4 + The tolerance for the elastic net solver used to calculate the descent + direction. This parameter controls the accuracy of the search direction + for a given column update, not of the overall parameter estimate. Only + used for mode='cd'. Range is (0, inf]. + + max_iter : int, default=100 + The maximum number of iterations. + + verbose : bool, default=False + If verbose is True, the objective function and dual gap are + printed at each iteration. + + return_costs : bool, default=False + If return_costs is True, the objective function and dual gap + at each iteration are returned. + + eps : float, default=eps + The machine-precision regularization in the computation of the + Cholesky diagonal factors. Increase this for very ill-conditioned + systems. Default is `np.finfo(np.float64).eps`. + + return_n_iter : bool, default=False + Whether or not to return the number of iterations. + + Returns + ------- + covariance : ndarray of shape (n_features, n_features) + The estimated covariance matrix. + + precision : ndarray of shape (n_features, n_features) + The estimated (sparse) precision matrix. + + costs : list of (objective, dual_gap) pairs + The list of values of the objective function and the dual gap at + each iteration. Returned only if return_costs is True. + + n_iter : int + Number of iterations. Returned only if `return_n_iter` is set to True. + + See Also + -------- + GraphicalLasso : Sparse inverse covariance estimation + with an l1-penalized estimator. + GraphicalLassoCV : Sparse inverse covariance with + cross-validated choice of the l1 penalty. + + Notes + ----- + The algorithm employed to solve this problem is the GLasso algorithm, + from the Friedman 2008 Biostatistics paper. It is the same algorithm + as in the R `glasso` package. + + One possible difference with the `glasso` R package is that the + diagonal coefficients are not penalized. + + Examples + -------- + >>> import numpy as np + >>> from sklearn.datasets import make_sparse_spd_matrix + >>> from sklearn.covariance import empirical_covariance, graphical_lasso + >>> true_cov = make_sparse_spd_matrix(n_dim=3,random_state=42) + >>> rng = np.random.RandomState(42) + >>> X = rng.multivariate_normal(mean=np.zeros(3), cov=true_cov, size=3) + >>> emp_cov = empirical_covariance(X, assume_centered=True) + >>> emp_cov, _ = graphical_lasso(emp_cov, alpha=0.05) + >>> emp_cov + array([[ 1.68..., 0.21..., -0.20...], + [ 0.21..., 0.22..., -0.08...], + [-0.20..., -0.08..., 0.23...]]) + """ + + if cov_init is not None: + warnings.warn( + ( + "The cov_init parameter is deprecated in 1.3 and will be removed in " + "1.5. It does not have any effect." + ), + FutureWarning, + ) + + model = GraphicalLasso( + alpha=alpha, + mode=mode, + covariance="precomputed", + tol=tol, + enet_tol=enet_tol, + max_iter=max_iter, + verbose=verbose, + eps=eps, + assume_centered=True, + ).fit(emp_cov) + + output = [model.covariance_, model.precision_] + if return_costs: + output.append(model.costs_) + if return_n_iter: + output.append(model.n_iter_) + return tuple(output) + + +class BaseGraphicalLasso(EmpiricalCovariance): + _parameter_constraints: dict = { + **EmpiricalCovariance._parameter_constraints, + "tol": [Interval(Real, 0, None, closed="right")], + "enet_tol": [Interval(Real, 0, None, closed="right")], + "max_iter": [Interval(Integral, 0, None, closed="left")], + "mode": [StrOptions({"cd", "lars"})], + "verbose": ["verbose"], + "eps": [Interval(Real, 0, None, closed="both")], + } + _parameter_constraints.pop("store_precision") + + def __init__( + self, + tol=1e-4, + enet_tol=1e-4, + max_iter=100, + mode="cd", + verbose=False, + eps=np.finfo(np.float64).eps, + assume_centered=False, + ): + super().__init__(assume_centered=assume_centered) + self.tol = tol + self.enet_tol = enet_tol + self.max_iter = max_iter + self.mode = mode + self.verbose = verbose + self.eps = eps + + +class GraphicalLasso(BaseGraphicalLasso): + """Sparse inverse covariance estimation with an l1-penalized estimator. + + Read more in the :ref:`User Guide `. + + .. versionchanged:: v0.20 + GraphLasso has been renamed to GraphicalLasso + + Parameters + ---------- + alpha : float, default=0.01 + The regularization parameter: the higher alpha, the more + regularization, the sparser the inverse covariance. + Range is (0, inf]. + + mode : {'cd', 'lars'}, default='cd' + The Lasso solver to use: coordinate descent or LARS. Use LARS for + very sparse underlying graphs, where p > n. Elsewhere prefer cd + which is more numerically stable. + + covariance : "precomputed", default=None + If covariance is "precomputed", the input data in `fit` is assumed + to be the covariance matrix. If `None`, the empirical covariance + is estimated from the data `X`. + + .. versionadded:: 1.3 + + tol : float, default=1e-4 + The tolerance to declare convergence: if the dual gap goes below + this value, iterations are stopped. Range is (0, inf]. + + enet_tol : float, default=1e-4 + The tolerance for the elastic net solver used to calculate the descent + direction. This parameter controls the accuracy of the search direction + for a given column update, not of the overall parameter estimate. Only + used for mode='cd'. Range is (0, inf]. + + max_iter : int, default=100 + The maximum number of iterations. + + verbose : bool, default=False + If verbose is True, the objective function and dual gap are + plotted at each iteration. + + eps : float, default=eps + The machine-precision regularization in the computation of the + Cholesky diagonal factors. Increase this for very ill-conditioned + systems. Default is `np.finfo(np.float64).eps`. + + .. versionadded:: 1.3 + + assume_centered : bool, default=False + If True, data are not centered before computation. + Useful when working with data whose mean is almost, but not exactly + zero. + If False, data are centered before computation. + + Attributes + ---------- + location_ : ndarray of shape (n_features,) + Estimated location, i.e. the estimated mean. + + covariance_ : ndarray of shape (n_features, n_features) + Estimated covariance matrix + + precision_ : ndarray of shape (n_features, n_features) + Estimated pseudo inverse matrix. + + n_iter_ : int + Number of iterations run. + + costs_ : list of (objective, dual_gap) pairs + The list of values of the objective function and the dual gap at + each iteration. Returned only if return_costs is True. + + .. versionadded:: 1.3 + + n_features_in_ : int + Number of features seen during :term:`fit`. + + .. versionadded:: 0.24 + + feature_names_in_ : ndarray of shape (`n_features_in_`,) + Names of features seen during :term:`fit`. Defined only when `X` + has feature names that are all strings. + + .. versionadded:: 1.0 + + See Also + -------- + graphical_lasso : L1-penalized covariance estimator. + GraphicalLassoCV : Sparse inverse covariance with + cross-validated choice of the l1 penalty. + + Examples + -------- + >>> import numpy as np + >>> from sklearn.covariance import GraphicalLasso + >>> true_cov = np.array([[0.8, 0.0, 0.2, 0.0], + ... [0.0, 0.4, 0.0, 0.0], + ... [0.2, 0.0, 0.3, 0.1], + ... [0.0, 0.0, 0.1, 0.7]]) + >>> np.random.seed(0) + >>> X = np.random.multivariate_normal(mean=[0, 0, 0, 0], + ... cov=true_cov, + ... size=200) + >>> cov = GraphicalLasso().fit(X) + >>> np.around(cov.covariance_, decimals=3) + array([[0.816, 0.049, 0.218, 0.019], + [0.049, 0.364, 0.017, 0.034], + [0.218, 0.017, 0.322, 0.093], + [0.019, 0.034, 0.093, 0.69 ]]) + >>> np.around(cov.location_, decimals=3) + array([0.073, 0.04 , 0.038, 0.143]) + """ + + _parameter_constraints: dict = { + **BaseGraphicalLasso._parameter_constraints, + "alpha": [Interval(Real, 0, None, closed="both")], + "covariance": [StrOptions({"precomputed"}), None], + } + + def __init__( + self, + alpha=0.01, + *, + mode="cd", + covariance=None, + tol=1e-4, + enet_tol=1e-4, + max_iter=100, + verbose=False, + eps=np.finfo(np.float64).eps, + assume_centered=False, + ): + super().__init__( + tol=tol, + enet_tol=enet_tol, + max_iter=max_iter, + mode=mode, + verbose=verbose, + eps=eps, + assume_centered=assume_centered, + ) + self.alpha = alpha + self.covariance = covariance + + @_fit_context(prefer_skip_nested_validation=True) + def fit(self, X, y=None): + """Fit the GraphicalLasso model to X. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + Data from which to compute the covariance estimate. + + y : Ignored + Not used, present for API consistency by convention. + + Returns + ------- + self : object + Returns the instance itself. + """ + # Covariance does not make sense for a single feature + X = self._validate_data(X, ensure_min_features=2, ensure_min_samples=2) + + if self.covariance == "precomputed": + emp_cov = X.copy() + self.location_ = np.zeros(X.shape[1]) + else: + emp_cov = empirical_covariance(X, assume_centered=self.assume_centered) + if self.assume_centered: + self.location_ = np.zeros(X.shape[1]) + else: + self.location_ = X.mean(0) + + self.covariance_, self.precision_, self.costs_, self.n_iter_ = _graphical_lasso( + emp_cov, + alpha=self.alpha, + cov_init=None, + mode=self.mode, + tol=self.tol, + enet_tol=self.enet_tol, + max_iter=self.max_iter, + verbose=self.verbose, + eps=self.eps, + ) + return self + + +# Cross-validation with GraphicalLasso +def graphical_lasso_path( + X, + alphas, + cov_init=None, + X_test=None, + mode="cd", + tol=1e-4, + enet_tol=1e-4, + max_iter=100, + verbose=False, + eps=np.finfo(np.float64).eps, +): + """l1-penalized covariance estimator along a path of decreasing alphas + + Read more in the :ref:`User Guide `. + + Parameters + ---------- + X : ndarray of shape (n_samples, n_features) + Data from which to compute the covariance estimate. + + alphas : array-like of shape (n_alphas,) + The list of regularization parameters, decreasing order. + + cov_init : array of shape (n_features, n_features), default=None + The initial guess for the covariance. + + X_test : array of shape (n_test_samples, n_features), default=None + Optional test matrix to measure generalisation error. + + mode : {'cd', 'lars'}, default='cd' + The Lasso solver to use: coordinate descent or LARS. Use LARS for + very sparse underlying graphs, where p > n. Elsewhere prefer cd + which is more numerically stable. + + tol : float, default=1e-4 + The tolerance to declare convergence: if the dual gap goes below + this value, iterations are stopped. The tolerance must be a positive + number. + + enet_tol : float, default=1e-4 + The tolerance for the elastic net solver used to calculate the descent + direction. This parameter controls the accuracy of the search direction + for a given column update, not of the overall parameter estimate. Only + used for mode='cd'. The tolerance must be a positive number. + + max_iter : int, default=100 + The maximum number of iterations. This parameter should be a strictly + positive integer. + + verbose : int or bool, default=False + The higher the verbosity flag, the more information is printed + during the fitting. + + eps : float, default=eps + The machine-precision regularization in the computation of the + Cholesky diagonal factors. Increase this for very ill-conditioned + systems. Default is `np.finfo(np.float64).eps`. + + .. versionadded:: 1.3 + + Returns + ------- + covariances_ : list of shape (n_alphas,) of ndarray of shape \ + (n_features, n_features) + The estimated covariance matrices. + + precisions_ : list of shape (n_alphas,) of ndarray of shape \ + (n_features, n_features) + The estimated (sparse) precision matrices. + + scores_ : list of shape (n_alphas,), dtype=float + The generalisation error (log-likelihood) on the test data. + Returned only if test data is passed. + """ + inner_verbose = max(0, verbose - 1) + emp_cov = empirical_covariance(X) + if cov_init is None: + covariance_ = emp_cov.copy() + else: + covariance_ = cov_init + covariances_ = list() + precisions_ = list() + scores_ = list() + if X_test is not None: + test_emp_cov = empirical_covariance(X_test) + + for alpha in alphas: + try: + # Capture the errors, and move on + covariance_, precision_, _, _ = _graphical_lasso( + emp_cov, + alpha=alpha, + cov_init=covariance_, + mode=mode, + tol=tol, + enet_tol=enet_tol, + max_iter=max_iter, + verbose=inner_verbose, + eps=eps, + ) + covariances_.append(covariance_) + precisions_.append(precision_) + if X_test is not None: + this_score = log_likelihood(test_emp_cov, precision_) + except FloatingPointError: + this_score = -np.inf + covariances_.append(np.nan) + precisions_.append(np.nan) + if X_test is not None: + if not np.isfinite(this_score): + this_score = -np.inf + scores_.append(this_score) + if verbose == 1: + sys.stderr.write(".") + elif verbose > 1: + if X_test is not None: + print( + "[graphical_lasso_path] alpha: %.2e, score: %.2e" + % (alpha, this_score) + ) + else: + print("[graphical_lasso_path] alpha: %.2e" % alpha) + if X_test is not None: + return covariances_, precisions_, scores_ + return covariances_, precisions_ + + +class GraphicalLassoCV(_RoutingNotSupportedMixin, BaseGraphicalLasso): + """Sparse inverse covariance w/ cross-validated choice of the l1 penalty. + + See glossary entry for :term:`cross-validation estimator`. + + Read more in the :ref:`User Guide `. + + .. versionchanged:: v0.20 + GraphLassoCV has been renamed to GraphicalLassoCV + + Parameters + ---------- + alphas : int or array-like of shape (n_alphas,), dtype=float, default=4 + If an integer is given, it fixes the number of points on the + grids of alpha to be used. If a list is given, it gives the + grid to be used. See the notes in the class docstring for + more details. Range is [1, inf) for an integer. + Range is (0, inf] for an array-like of floats. + + n_refinements : int, default=4 + The number of times the grid is refined. Not used if explicit + values of alphas are passed. Range is [1, inf). + + cv : int, cross-validation generator or iterable, default=None + Determines the cross-validation splitting strategy. + Possible inputs for cv are: + + - None, to use the default 5-fold cross-validation, + - integer, to specify the number of folds. + - :term:`CV splitter`, + - An iterable yielding (train, test) splits as arrays of indices. + + For integer/None inputs :class:`~sklearn.model_selection.KFold` is used. + + Refer :ref:`User Guide ` for the various + cross-validation strategies that can be used here. + + .. versionchanged:: 0.20 + ``cv`` default value if None changed from 3-fold to 5-fold. + + tol : float, default=1e-4 + The tolerance to declare convergence: if the dual gap goes below + this value, iterations are stopped. Range is (0, inf]. + + enet_tol : float, default=1e-4 + The tolerance for the elastic net solver used to calculate the descent + direction. This parameter controls the accuracy of the search direction + for a given column update, not of the overall parameter estimate. Only + used for mode='cd'. Range is (0, inf]. + + max_iter : int, default=100 + Maximum number of iterations. + + mode : {'cd', 'lars'}, default='cd' + The Lasso solver to use: coordinate descent or LARS. Use LARS for + very sparse underlying graphs, where number of features is greater + than number of samples. Elsewhere prefer cd which is more numerically + stable. + + n_jobs : int, default=None + Number of jobs to run in parallel. + ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. + ``-1`` means using all processors. See :term:`Glossary ` + for more details. + + .. versionchanged:: v0.20 + `n_jobs` default changed from 1 to None + + verbose : bool, default=False + If verbose is True, the objective function and duality gap are + printed at each iteration. + + eps : float, default=eps + The machine-precision regularization in the computation of the + Cholesky diagonal factors. Increase this for very ill-conditioned + systems. Default is `np.finfo(np.float64).eps`. + + .. versionadded:: 1.3 + + assume_centered : bool, default=False + If True, data are not centered before computation. + Useful when working with data whose mean is almost, but not exactly + zero. + If False, data are centered before computation. + + Attributes + ---------- + location_ : ndarray of shape (n_features,) + Estimated location, i.e. the estimated mean. + + covariance_ : ndarray of shape (n_features, n_features) + Estimated covariance matrix. + + precision_ : ndarray of shape (n_features, n_features) + Estimated precision matrix (inverse covariance). + + costs_ : list of (objective, dual_gap) pairs + The list of values of the objective function and the dual gap at + each iteration. Returned only if return_costs is True. + + .. versionadded:: 1.3 + + alpha_ : float + Penalization parameter selected. + + cv_results_ : dict of ndarrays + A dict with keys: + + alphas : ndarray of shape (n_alphas,) + All penalization parameters explored. + + split(k)_test_score : ndarray of shape (n_alphas,) + Log-likelihood score on left-out data across (k)th fold. + + .. versionadded:: 1.0 + + mean_test_score : ndarray of shape (n_alphas,) + Mean of scores over the folds. + + .. versionadded:: 1.0 + + std_test_score : ndarray of shape (n_alphas,) + Standard deviation of scores over the folds. + + .. versionadded:: 1.0 + + n_iter_ : int + Number of iterations run for the optimal alpha. + + n_features_in_ : int + Number of features seen during :term:`fit`. + + .. versionadded:: 0.24 + + feature_names_in_ : ndarray of shape (`n_features_in_`,) + Names of features seen during :term:`fit`. Defined only when `X` + has feature names that are all strings. + + .. versionadded:: 1.0 + + See Also + -------- + graphical_lasso : L1-penalized covariance estimator. + GraphicalLasso : Sparse inverse covariance estimation + with an l1-penalized estimator. + + Notes + ----- + The search for the optimal penalization parameter (`alpha`) is done on an + iteratively refined grid: first the cross-validated scores on a grid are + computed, then a new refined grid is centered around the maximum, and so + on. + + One of the challenges which is faced here is that the solvers can + fail to converge to a well-conditioned estimate. The corresponding + values of `alpha` then come out as missing values, but the optimum may + be close to these missing values. + + In `fit`, once the best parameter `alpha` is found through + cross-validation, the model is fit again using the entire training set. + + Examples + -------- + >>> import numpy as np + >>> from sklearn.covariance import GraphicalLassoCV + >>> true_cov = np.array([[0.8, 0.0, 0.2, 0.0], + ... [0.0, 0.4, 0.0, 0.0], + ... [0.2, 0.0, 0.3, 0.1], + ... [0.0, 0.0, 0.1, 0.7]]) + >>> np.random.seed(0) + >>> X = np.random.multivariate_normal(mean=[0, 0, 0, 0], + ... cov=true_cov, + ... size=200) + >>> cov = GraphicalLassoCV().fit(X) + >>> np.around(cov.covariance_, decimals=3) + array([[0.816, 0.051, 0.22 , 0.017], + [0.051, 0.364, 0.018, 0.036], + [0.22 , 0.018, 0.322, 0.094], + [0.017, 0.036, 0.094, 0.69 ]]) + >>> np.around(cov.location_, decimals=3) + array([0.073, 0.04 , 0.038, 0.143]) + """ + + _parameter_constraints: dict = { + **BaseGraphicalLasso._parameter_constraints, + "alphas": [Interval(Integral, 0, None, closed="left"), "array-like"], + "n_refinements": [Interval(Integral, 1, None, closed="left")], + "cv": ["cv_object"], + "n_jobs": [Integral, None], + } + + def __init__( + self, + *, + alphas=4, + n_refinements=4, + cv=None, + tol=1e-4, + enet_tol=1e-4, + max_iter=100, + mode="cd", + n_jobs=None, + verbose=False, + eps=np.finfo(np.float64).eps, + assume_centered=False, + ): + super().__init__( + tol=tol, + enet_tol=enet_tol, + max_iter=max_iter, + mode=mode, + verbose=verbose, + eps=eps, + assume_centered=assume_centered, + ) + self.alphas = alphas + self.n_refinements = n_refinements + self.cv = cv + self.n_jobs = n_jobs + + @_fit_context(prefer_skip_nested_validation=True) + def fit(self, X, y=None): + """Fit the GraphicalLasso covariance model to X. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + Data from which to compute the covariance estimate. + + y : Ignored + Not used, present for API consistency by convention. + + Returns + ------- + self : object + Returns the instance itself. + """ + # Covariance does not make sense for a single feature + X = self._validate_data(X, ensure_min_features=2) + if self.assume_centered: + self.location_ = np.zeros(X.shape[1]) + else: + self.location_ = X.mean(0) + emp_cov = empirical_covariance(X, assume_centered=self.assume_centered) + + cv = check_cv(self.cv, y, classifier=False) + + # List of (alpha, scores, covs) + path = list() + n_alphas = self.alphas + inner_verbose = max(0, self.verbose - 1) + + if _is_arraylike_not_scalar(n_alphas): + for alpha in self.alphas: + check_scalar( + alpha, + "alpha", + Real, + min_val=0, + max_val=np.inf, + include_boundaries="right", + ) + alphas = self.alphas + n_refinements = 1 + else: + n_refinements = self.n_refinements + alpha_1 = alpha_max(emp_cov) + alpha_0 = 1e-2 * alpha_1 + alphas = np.logspace(np.log10(alpha_0), np.log10(alpha_1), n_alphas)[::-1] + + t0 = time.time() + for i in range(n_refinements): + with warnings.catch_warnings(): + # No need to see the convergence warnings on this grid: + # they will always be points that will not converge + # during the cross-validation + warnings.simplefilter("ignore", ConvergenceWarning) + # Compute the cross-validated loss on the current grid + + # NOTE: Warm-restarting graphical_lasso_path has been tried, + # and this did not allow to gain anything + # (same execution time with or without). + this_path = Parallel(n_jobs=self.n_jobs, verbose=self.verbose)( + delayed(graphical_lasso_path)( + X[train], + alphas=alphas, + X_test=X[test], + mode=self.mode, + tol=self.tol, + enet_tol=self.enet_tol, + max_iter=int(0.1 * self.max_iter), + verbose=inner_verbose, + eps=self.eps, + ) + for train, test in cv.split(X, y) + ) + + # Little danse to transform the list in what we need + covs, _, scores = zip(*this_path) + covs = zip(*covs) + scores = zip(*scores) + path.extend(zip(alphas, scores, covs)) + path = sorted(path, key=operator.itemgetter(0), reverse=True) + + # Find the maximum (avoid using built in 'max' function to + # have a fully-reproducible selection of the smallest alpha + # in case of equality) + best_score = -np.inf + last_finite_idx = 0 + for index, (alpha, scores, _) in enumerate(path): + this_score = np.mean(scores) + if this_score >= 0.1 / np.finfo(np.float64).eps: + this_score = np.nan + if np.isfinite(this_score): + last_finite_idx = index + if this_score >= best_score: + best_score = this_score + best_index = index + + # Refine the grid + if best_index == 0: + # We do not need to go back: we have chosen + # the highest value of alpha for which there are + # non-zero coefficients + alpha_1 = path[0][0] + alpha_0 = path[1][0] + elif best_index == last_finite_idx and not best_index == len(path) - 1: + # We have non-converged models on the upper bound of the + # grid, we need to refine the grid there + alpha_1 = path[best_index][0] + alpha_0 = path[best_index + 1][0] + elif best_index == len(path) - 1: + alpha_1 = path[best_index][0] + alpha_0 = 0.01 * path[best_index][0] + else: + alpha_1 = path[best_index - 1][0] + alpha_0 = path[best_index + 1][0] + + if not _is_arraylike_not_scalar(n_alphas): + alphas = np.logspace(np.log10(alpha_1), np.log10(alpha_0), n_alphas + 2) + alphas = alphas[1:-1] + + if self.verbose and n_refinements > 1: + print( + "[GraphicalLassoCV] Done refinement % 2i out of %i: % 3is" + % (i + 1, n_refinements, time.time() - t0) + ) + + path = list(zip(*path)) + grid_scores = list(path[1]) + alphas = list(path[0]) + # Finally, compute the score with alpha = 0 + alphas.append(0) + grid_scores.append( + cross_val_score( + EmpiricalCovariance(), + X, + cv=cv, + n_jobs=self.n_jobs, + verbose=inner_verbose, + ) + ) + grid_scores = np.array(grid_scores) + + self.cv_results_ = {"alphas": np.array(alphas)} + + for i in range(grid_scores.shape[1]): + self.cv_results_[f"split{i}_test_score"] = grid_scores[:, i] + + self.cv_results_["mean_test_score"] = np.mean(grid_scores, axis=1) + self.cv_results_["std_test_score"] = np.std(grid_scores, axis=1) + + best_alpha = alphas[best_index] + self.alpha_ = best_alpha + + # Finally fit the model with the selected alpha + self.covariance_, self.precision_, self.costs_, self.n_iter_ = _graphical_lasso( + emp_cov, + alpha=best_alpha, + mode=self.mode, + tol=self.tol, + enet_tol=self.enet_tol, + max_iter=self.max_iter, + verbose=inner_verbose, + eps=self.eps, + ) + return self diff --git a/venv/lib/python3.10/site-packages/sklearn/covariance/_robust_covariance.py b/venv/lib/python3.10/site-packages/sklearn/covariance/_robust_covariance.py new file mode 100644 index 0000000000000000000000000000000000000000..c90e855ca67681984a6bc4186ca1cb2e7b9fff59 --- /dev/null +++ b/venv/lib/python3.10/site-packages/sklearn/covariance/_robust_covariance.py @@ -0,0 +1,868 @@ +""" +Robust location and covariance estimators. + +Here are implemented estimators that are resistant to outliers. + +""" +# Author: Virgile Fritsch +# +# License: BSD 3 clause + +import warnings +from numbers import Integral, Real + +import numpy as np +from scipy import linalg +from scipy.stats import chi2 + +from ..base import _fit_context +from ..utils import check_array, check_random_state +from ..utils._param_validation import Interval +from ..utils.extmath import fast_logdet +from ._empirical_covariance import EmpiricalCovariance, empirical_covariance + + +# Minimum Covariance Determinant +# Implementing of an algorithm by Rousseeuw & Van Driessen described in +# (A Fast Algorithm for the Minimum Covariance Determinant Estimator, +# 1999, American Statistical Association and the American Society +# for Quality, TECHNOMETRICS) +# XXX Is this really a public function? It's not listed in the docs or +# exported by sklearn.covariance. Deprecate? +def c_step( + X, + n_support, + remaining_iterations=30, + initial_estimates=None, + verbose=False, + cov_computation_method=empirical_covariance, + random_state=None, +): + """C_step procedure described in [Rouseeuw1984]_ aiming at computing MCD. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + Data set in which we look for the n_support observations whose + scatter matrix has minimum determinant. + + n_support : int + Number of observations to compute the robust estimates of location + and covariance from. This parameter must be greater than + `n_samples / 2`. + + remaining_iterations : int, default=30 + Number of iterations to perform. + According to [Rouseeuw1999]_, two iterations are sufficient to get + close to the minimum, and we never need more than 30 to reach + convergence. + + initial_estimates : tuple of shape (2,), default=None + Initial estimates of location and shape from which to run the c_step + procedure: + - initial_estimates[0]: an initial location estimate + - initial_estimates[1]: an initial covariance estimate + + verbose : bool, default=False + Verbose mode. + + cov_computation_method : callable, \ + default=:func:`sklearn.covariance.empirical_covariance` + The function which will be used to compute the covariance. + Must return array of shape (n_features, n_features). + + random_state : int, RandomState instance or None, default=None + Determines the pseudo random number generator for shuffling the data. + Pass an int for reproducible results across multiple function calls. + See :term:`Glossary `. + + Returns + ------- + location : ndarray of shape (n_features,) + Robust location estimates. + + covariance : ndarray of shape (n_features, n_features) + Robust covariance estimates. + + support : ndarray of shape (n_samples,) + A mask for the `n_support` observations whose scatter matrix has + minimum determinant. + + References + ---------- + .. [Rouseeuw1999] A Fast Algorithm for the Minimum Covariance Determinant + Estimator, 1999, American Statistical Association and the American + Society for Quality, TECHNOMETRICS + """ + X = np.asarray(X) + random_state = check_random_state(random_state) + return _c_step( + X, + n_support, + remaining_iterations=remaining_iterations, + initial_estimates=initial_estimates, + verbose=verbose, + cov_computation_method=cov_computation_method, + random_state=random_state, + ) + + +def _c_step( + X, + n_support, + random_state, + remaining_iterations=30, + initial_estimates=None, + verbose=False, + cov_computation_method=empirical_covariance, +): + n_samples, n_features = X.shape + dist = np.inf + + # Initialisation + support = np.zeros(n_samples, dtype=bool) + if initial_estimates is None: + # compute initial robust estimates from a random subset + support[random_state.permutation(n_samples)[:n_support]] = True + else: + # get initial robust estimates from the function parameters + location = initial_estimates[0] + covariance = initial_estimates[1] + # run a special iteration for that case (to get an initial support) + precision = linalg.pinvh(covariance) + X_centered = X - location + dist = (np.dot(X_centered, precision) * X_centered).sum(1) + # compute new estimates + support[np.argsort(dist)[:n_support]] = True + + X_support = X[support] + location = X_support.mean(0) + covariance = cov_computation_method(X_support) + + # Iterative procedure for Minimum Covariance Determinant computation + det = fast_logdet(covariance) + # If the data already has singular covariance, calculate the precision, + # as the loop below will not be entered. + if np.isinf(det): + precision = linalg.pinvh(covariance) + + previous_det = np.inf + while det < previous_det and remaining_iterations > 0 and not np.isinf(det): + # save old estimates values + previous_location = location + previous_covariance = covariance + previous_det = det + previous_support = support + # compute a new support from the full data set mahalanobis distances + precision = linalg.pinvh(covariance) + X_centered = X - location + dist = (np.dot(X_centered, precision) * X_centered).sum(axis=1) + # compute new estimates + support = np.zeros(n_samples, dtype=bool) + support[np.argsort(dist)[:n_support]] = True + X_support = X[support] + location = X_support.mean(axis=0) + covariance = cov_computation_method(X_support) + det = fast_logdet(covariance) + # update remaining iterations for early stopping + remaining_iterations -= 1 + + previous_dist = dist + dist = (np.dot(X - location, precision) * (X - location)).sum(axis=1) + # Check if best fit already found (det => 0, logdet => -inf) + if np.isinf(det): + results = location, covariance, det, support, dist + # Check convergence + if np.allclose(det, previous_det): + # c_step procedure converged + if verbose: + print( + "Optimal couple (location, covariance) found before" + " ending iterations (%d left)" % (remaining_iterations) + ) + results = location, covariance, det, support, dist + elif det > previous_det: + # determinant has increased (should not happen) + warnings.warn( + "Determinant has increased; this should not happen: " + "log(det) > log(previous_det) (%.15f > %.15f). " + "You may want to try with a higher value of " + "support_fraction (current value: %.3f)." + % (det, previous_det, n_support / n_samples), + RuntimeWarning, + ) + results = ( + previous_location, + previous_covariance, + previous_det, + previous_support, + previous_dist, + ) + + # Check early stopping + if remaining_iterations == 0: + if verbose: + print("Maximum number of iterations reached") + results = location, covariance, det, support, dist + + return results + + +def select_candidates( + X, + n_support, + n_trials, + select=1, + n_iter=30, + verbose=False, + cov_computation_method=empirical_covariance, + random_state=None, +): + """Finds the best pure subset of observations to compute MCD from it. + + The purpose of this function is to find the best sets of n_support + observations with respect to a minimization of their covariance + matrix determinant. Equivalently, it removes n_samples-n_support + observations to construct what we call a pure data set (i.e. not + containing outliers). The list of the observations of the pure + data set is referred to as the `support`. + + Starting from a random support, the pure data set is found by the + c_step procedure introduced by Rousseeuw and Van Driessen in + [RV]_. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + Data (sub)set in which we look for the n_support purest observations. + + n_support : int + The number of samples the pure data set must contain. + This parameter must be in the range `[(n + p + 1)/2] < n_support < n`. + + n_trials : int or tuple of shape (2,) + Number of different initial sets of observations from which to + run the algorithm. This parameter should be a strictly positive + integer. + Instead of giving a number of trials to perform, one can provide a + list of initial estimates that will be used to iteratively run + c_step procedures. In this case: + - n_trials[0]: array-like, shape (n_trials, n_features) + is the list of `n_trials` initial location estimates + - n_trials[1]: array-like, shape (n_trials, n_features, n_features) + is the list of `n_trials` initial covariances estimates + + select : int, default=1 + Number of best candidates results to return. This parameter must be + a strictly positive integer. + + n_iter : int, default=30 + Maximum number of iterations for the c_step procedure. + (2 is enough to be close to the final solution. "Never" exceeds 20). + This parameter must be a strictly positive integer. + + verbose : bool, default=False + Control the output verbosity. + + cov_computation_method : callable, \ + default=:func:`sklearn.covariance.empirical_covariance` + The function which will be used to compute the covariance. + Must return an array of shape (n_features, n_features). + + random_state : int, RandomState instance or None, default=None + Determines the pseudo random number generator for shuffling the data. + Pass an int for reproducible results across multiple function calls. + See :term:`Glossary `. + + See Also + --------- + c_step + + Returns + ------- + best_locations : ndarray of shape (select, n_features) + The `select` location estimates computed from the `select` best + supports found in the data set (`X`). + + best_covariances : ndarray of shape (select, n_features, n_features) + The `select` covariance estimates computed from the `select` + best supports found in the data set (`X`). + + best_supports : ndarray of shape (select, n_samples) + The `select` best supports found in the data set (`X`). + + References + ---------- + .. [RV] A Fast Algorithm for the Minimum Covariance Determinant + Estimator, 1999, American Statistical Association and the American + Society for Quality, TECHNOMETRICS + """ + random_state = check_random_state(random_state) + + if isinstance(n_trials, Integral): + run_from_estimates = False + elif isinstance(n_trials, tuple): + run_from_estimates = True + estimates_list = n_trials + n_trials = estimates_list[0].shape[0] + else: + raise TypeError( + "Invalid 'n_trials' parameter, expected tuple or integer, got %s (%s)" + % (n_trials, type(n_trials)) + ) + + # compute `n_trials` location and shape estimates candidates in the subset + all_estimates = [] + if not run_from_estimates: + # perform `n_trials` computations from random initial supports + for j in range(n_trials): + all_estimates.append( + _c_step( + X, + n_support, + remaining_iterations=n_iter, + verbose=verbose, + cov_computation_method=cov_computation_method, + random_state=random_state, + ) + ) + else: + # perform computations from every given initial estimates + for j in range(n_trials): + initial_estimates = (estimates_list[0][j], estimates_list[1][j]) + all_estimates.append( + _c_step( + X, + n_support, + remaining_iterations=n_iter, + initial_estimates=initial_estimates, + verbose=verbose, + cov_computation_method=cov_computation_method, + random_state=random_state, + ) + ) + all_locs_sub, all_covs_sub, all_dets_sub, all_supports_sub, all_ds_sub = zip( + *all_estimates + ) + # find the `n_best` best results among the `n_trials` ones + index_best = np.argsort(all_dets_sub)[:select] + best_locations = np.asarray(all_locs_sub)[index_best] + best_covariances = np.asarray(all_covs_sub)[index_best] + best_supports = np.asarray(all_supports_sub)[index_best] + best_ds = np.asarray(all_ds_sub)[index_best] + + return best_locations, best_covariances, best_supports, best_ds + + +def fast_mcd( + X, + support_fraction=None, + cov_computation_method=empirical_covariance, + random_state=None, +): + """Estimate the Minimum Covariance Determinant matrix. + + Read more in the :ref:`User Guide `. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + The data matrix, with p features and n samples. + + support_fraction : float, default=None + The proportion of points to be included in the support of the raw + MCD estimate. Default is `None`, which implies that the minimum + value of `support_fraction` will be used within the algorithm: + `(n_samples + n_features + 1) / 2 * n_samples`. This parameter must be + in the range (0, 1). + + cov_computation_method : callable, \ + default=:func:`sklearn.covariance.empirical_covariance` + The function which will be used to compute the covariance. + Must return an array of shape (n_features, n_features). + + random_state : int, RandomState instance or None, default=None + Determines the pseudo random number generator for shuffling the data. + Pass an int for reproducible results across multiple function calls. + See :term:`Glossary `. + + Returns + ------- + location : ndarray of shape (n_features,) + Robust location of the data. + + covariance : ndarray of shape (n_features, n_features) + Robust covariance of the features. + + support : ndarray of shape (n_samples,), dtype=bool + A mask of the observations that have been used to compute + the robust location and covariance estimates of the data set. + + Notes + ----- + The FastMCD algorithm has been introduced by Rousseuw and Van Driessen + in "A Fast Algorithm for the Minimum Covariance Determinant Estimator, + 1999, American Statistical Association and the American Society + for Quality, TECHNOMETRICS". + The principle is to compute robust estimates and random subsets before + pooling them into a larger subsets, and finally into the full data set. + Depending on the size of the initial sample, we have one, two or three + such computation levels. + + Note that only raw estimates are returned. If one is interested in + the correction and reweighting steps described in [RouseeuwVan]_, + see the MinCovDet object. + + References + ---------- + + .. [RouseeuwVan] A Fast Algorithm for the Minimum Covariance + Determinant Estimator, 1999, American Statistical Association + and the American Society for Quality, TECHNOMETRICS + + .. [Butler1993] R. W. Butler, P. L. Davies and M. Jhun, + Asymptotics For The Minimum Covariance Determinant Estimator, + The Annals of Statistics, 1993, Vol. 21, No. 3, 1385-1400 + """ + random_state = check_random_state(random_state) + + X = check_array(X, ensure_min_samples=2, estimator="fast_mcd") + n_samples, n_features = X.shape + + # minimum breakdown value + if support_fraction is None: + n_support = int(np.ceil(0.5 * (n_samples + n_features + 1))) + else: + n_support = int(support_fraction * n_samples) + + # 1-dimensional case quick computation + # (Rousseeuw, P. J. and Leroy, A. M. (2005) References, in Robust + # Regression and Outlier Detection, John Wiley & Sons, chapter 4) + if n_features == 1: + if n_support < n_samples: + # find the sample shortest halves + X_sorted = np.sort(np.ravel(X)) + diff = X_sorted[n_support:] - X_sorted[: (n_samples - n_support)] + halves_start = np.where(diff == np.min(diff))[0] + # take the middle points' mean to get the robust location estimate + location = ( + 0.5 + * (X_sorted[n_support + halves_start] + X_sorted[halves_start]).mean() + ) + support = np.zeros(n_samples, dtype=bool) + X_centered = X - location + support[np.argsort(np.abs(X_centered), 0)[:n_support]] = True + covariance = np.asarray([[np.var(X[support])]]) + location = np.array([location]) + # get precision matrix in an optimized way + precision = linalg.pinvh(covariance) + dist = (np.dot(X_centered, precision) * (X_centered)).sum(axis=1) + else: + support = np.ones(n_samples, dtype=bool) + covariance = np.asarray([[np.var(X)]]) + location = np.asarray([np.mean(X)]) + X_centered = X - location + # get precision matrix in an optimized way + precision = linalg.pinvh(covariance) + dist = (np.dot(X_centered, precision) * (X_centered)).sum(axis=1) + # Starting FastMCD algorithm for p-dimensional case + if (n_samples > 500) and (n_features > 1): + # 1. Find candidate supports on subsets + # a. split the set in subsets of size ~ 300 + n_subsets = n_samples // 300 + n_samples_subsets = n_samples // n_subsets + samples_shuffle = random_state.permutation(n_samples) + h_subset = int(np.ceil(n_samples_subsets * (n_support / float(n_samples)))) + # b. perform a total of 500 trials + n_trials_tot = 500 + # c. select 10 best (location, covariance) for each subset + n_best_sub = 10 + n_trials = max(10, n_trials_tot // n_subsets) + n_best_tot = n_subsets * n_best_sub + all_best_locations = np.zeros((n_best_tot, n_features)) + try: + all_best_covariances = np.zeros((n_best_tot, n_features, n_features)) + except MemoryError: + # The above is too big. Let's try with something much small + # (and less optimal) + n_best_tot = 10 + all_best_covariances = np.zeros((n_best_tot, n_features, n_features)) + n_best_sub = 2 + for i in range(n_subsets): + low_bound = i * n_samples_subsets + high_bound = low_bound + n_samples_subsets + current_subset = X[samples_shuffle[low_bound:high_bound]] + best_locations_sub, best_covariances_sub, _, _ = select_candidates( + current_subset, + h_subset, + n_trials, + select=n_best_sub, + n_iter=2, + cov_computation_method=cov_computation_method, + random_state=random_state, + ) + subset_slice = np.arange(i * n_best_sub, (i + 1) * n_best_sub) + all_best_locations[subset_slice] = best_locations_sub + all_best_covariances[subset_slice] = best_covariances_sub + # 2. Pool the candidate supports into a merged set + # (possibly the full dataset) + n_samples_merged = min(1500, n_samples) + h_merged = int(np.ceil(n_samples_merged * (n_support / float(n_samples)))) + if n_samples > 1500: + n_best_merged = 10 + else: + n_best_merged = 1 + # find the best couples (location, covariance) on the merged set + selection = random_state.permutation(n_samples)[:n_samples_merged] + locations_merged, covariances_merged, supports_merged, d = select_candidates( + X[selection], + h_merged, + n_trials=(all_best_locations, all_best_covariances), + select=n_best_merged, + cov_computation_method=cov_computation_method, + random_state=random_state, + ) + # 3. Finally get the overall best (locations, covariance) couple + if n_samples < 1500: + # directly get the best couple (location, covariance) + location = locations_merged[0] + covariance = covariances_merged[0] + support = np.zeros(n_samples, dtype=bool) + dist = np.zeros(n_samples) + support[selection] = supports_merged[0] + dist[selection] = d[0] + else: + # select the best couple on the full dataset + locations_full, covariances_full, supports_full, d = select_candidates( + X, + n_support, + n_trials=(locations_merged, covariances_merged), + select=1, + cov_computation_method=cov_computation_method, + random_state=random_state, + ) + location = locations_full[0] + covariance = covariances_full[0] + support = supports_full[0] + dist = d[0] + elif n_features > 1: + # 1. Find the 10 best couples (location, covariance) + # considering two iterations + n_trials = 30 + n_best = 10 + locations_best, covariances_best, _, _ = select_candidates( + X, + n_support, + n_trials=n_trials, + select=n_best, + n_iter=2, + cov_computation_method=cov_computation_method, + random_state=random_state, + ) + # 2. Select the best couple on the full dataset amongst the 10 + locations_full, covariances_full, supports_full, d = select_candidates( + X, + n_support, + n_trials=(locations_best, covariances_best), + select=1, + cov_computation_method=cov_computation_method, + random_state=random_state, + ) + location = locations_full[0] + covariance = covariances_full[0] + support = supports_full[0] + dist = d[0] + + return location, covariance, support, dist + + +class MinCovDet(EmpiricalCovariance): + """Minimum Covariance Determinant (MCD): robust estimator of covariance. + + The Minimum Covariance Determinant covariance estimator is to be applied + on Gaussian-distributed data, but could still be relevant on data + drawn from a unimodal, symmetric distribution. It is not meant to be used + with multi-modal data (the algorithm used to fit a MinCovDet object is + likely to fail in such a case). + One should consider projection pursuit methods to deal with multi-modal + datasets. + + Read more in the :ref:`User Guide `. + + Parameters + ---------- + store_precision : bool, default=True + Specify if the estimated precision is stored. + + assume_centered : bool, default=False + If True, the support of the robust location and the covariance + estimates is computed, and a covariance estimate is recomputed from + it, without centering the data. + Useful to work with data whose mean is significantly equal to + zero but is not exactly zero. + If False, the robust location and covariance are directly computed + with the FastMCD algorithm without additional treatment. + + support_fraction : float, default=None + The proportion of points to be included in the support of the raw + MCD estimate. Default is None, which implies that the minimum + value of support_fraction will be used within the algorithm: + `(n_samples + n_features + 1) / 2 * n_samples`. The parameter must be + in the range (0, 1]. + + random_state : int, RandomState instance or None, default=None + Determines the pseudo random number generator for shuffling the data. + Pass an int for reproducible results across multiple function calls. + See :term:`Glossary `. + + Attributes + ---------- + raw_location_ : ndarray of shape (n_features,) + The raw robust estimated location before correction and re-weighting. + + raw_covariance_ : ndarray of shape (n_features, n_features) + The raw robust estimated covariance before correction and re-weighting. + + raw_support_ : ndarray of shape (n_samples,) + A mask of the observations that have been used to compute + the raw robust estimates of location and shape, before correction + and re-weighting. + + location_ : ndarray of shape (n_features,) + Estimated robust location. + + covariance_ : ndarray of shape (n_features, n_features) + Estimated robust covariance matrix. + + precision_ : ndarray of shape (n_features, n_features) + Estimated pseudo inverse matrix. + (stored only if store_precision is True) + + support_ : ndarray of shape (n_samples,) + A mask of the observations that have been used to compute + the robust estimates of location and shape. + + dist_ : ndarray of shape (n_samples,) + Mahalanobis distances of the training set (on which :meth:`fit` is + called) observations. + + n_features_in_ : int + Number of features seen during :term:`fit`. + + .. versionadded:: 0.24 + + feature_names_in_ : ndarray of shape (`n_features_in_`,) + Names of features seen during :term:`fit`. Defined only when `X` + has feature names that are all strings. + + .. versionadded:: 1.0 + + See Also + -------- + EllipticEnvelope : An object for detecting outliers in + a Gaussian distributed dataset. + EmpiricalCovariance : Maximum likelihood covariance estimator. + GraphicalLasso : Sparse inverse covariance estimation + with an l1-penalized estimator. + GraphicalLassoCV : Sparse inverse covariance with cross-validated + choice of the l1 penalty. + LedoitWolf : LedoitWolf Estimator. + OAS : Oracle Approximating Shrinkage Estimator. + ShrunkCovariance : Covariance estimator with shrinkage. + + References + ---------- + + .. [Rouseeuw1984] P. J. Rousseeuw. Least median of squares regression. + J. Am Stat Ass, 79:871, 1984. + .. [Rousseeuw] A Fast Algorithm for the Minimum Covariance Determinant + Estimator, 1999, American Statistical Association and the American + Society for Quality, TECHNOMETRICS + .. [ButlerDavies] R. W. Butler, P. L. Davies and M. Jhun, + Asymptotics For The Minimum Covariance Determinant Estimator, + The Annals of Statistics, 1993, Vol. 21, No. 3, 1385-1400 + + Examples + -------- + >>> import numpy as np + >>> from sklearn.covariance import MinCovDet + >>> from sklearn.datasets import make_gaussian_quantiles + >>> real_cov = np.array([[.8, .3], + ... [.3, .4]]) + >>> rng = np.random.RandomState(0) + >>> X = rng.multivariate_normal(mean=[0, 0], + ... cov=real_cov, + ... size=500) + >>> cov = MinCovDet(random_state=0).fit(X) + >>> cov.covariance_ + array([[0.7411..., 0.2535...], + [0.2535..., 0.3053...]]) + >>> cov.location_ + array([0.0813... , 0.0427...]) + """ + + _parameter_constraints: dict = { + **EmpiricalCovariance._parameter_constraints, + "support_fraction": [Interval(Real, 0, 1, closed="right"), None], + "random_state": ["random_state"], + } + _nonrobust_covariance = staticmethod(empirical_covariance) + + def __init__( + self, + *, + store_precision=True, + assume_centered=False, + support_fraction=None, + random_state=None, + ): + self.store_precision = store_precision + self.assume_centered = assume_centered + self.support_fraction = support_fraction + self.random_state = random_state + + @_fit_context(prefer_skip_nested_validation=True) + def fit(self, X, y=None): + """Fit a Minimum Covariance Determinant with the FastMCD algorithm. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + Training data, where `n_samples` is the number of samples + and `n_features` is the number of features. + + y : Ignored + Not used, present for API consistency by convention. + + Returns + ------- + self : object + Returns the instance itself. + """ + X = self._validate_data(X, ensure_min_samples=2, estimator="MinCovDet") + random_state = check_random_state(self.random_state) + n_samples, n_features = X.shape + # check that the empirical covariance is full rank + if (linalg.svdvals(np.dot(X.T, X)) > 1e-8).sum() != n_features: + warnings.warn( + "The covariance matrix associated to your dataset is not full rank" + ) + # compute and store raw estimates + raw_location, raw_covariance, raw_support, raw_dist = fast_mcd( + X, + support_fraction=self.support_fraction, + cov_computation_method=self._nonrobust_covariance, + random_state=random_state, + ) + if self.assume_centered: + raw_location = np.zeros(n_features) + raw_covariance = self._nonrobust_covariance( + X[raw_support], assume_centered=True + ) + # get precision matrix in an optimized way + precision = linalg.pinvh(raw_covariance) + raw_dist = np.sum(np.dot(X, precision) * X, 1) + self.raw_location_ = raw_location + self.raw_covariance_ = raw_covariance + self.raw_support_ = raw_support + self.location_ = raw_location + self.support_ = raw_support + self.dist_ = raw_dist + # obtain consistency at normal models + self.correct_covariance(X) + # re-weight estimator + self.reweight_covariance(X) + + return self + + def correct_covariance(self, data): + """Apply a correction to raw Minimum Covariance Determinant estimates. + + Correction using the empirical correction factor suggested + by Rousseeuw and Van Driessen in [RVD]_. + + Parameters + ---------- + data : array-like of shape (n_samples, n_features) + The data matrix, with p features and n samples. + The data set must be the one which was used to compute + the raw estimates. + + Returns + ------- + covariance_corrected : ndarray of shape (n_features, n_features) + Corrected robust covariance estimate. + + References + ---------- + + .. [RVD] A Fast Algorithm for the Minimum Covariance + Determinant Estimator, 1999, American Statistical Association + and the American Society for Quality, TECHNOMETRICS + """ + + # Check that the covariance of the support data is not equal to 0. + # Otherwise self.dist_ = 0 and thus correction = 0. + n_samples = len(self.dist_) + n_support = np.sum(self.support_) + if n_support < n_samples and np.allclose(self.raw_covariance_, 0): + raise ValueError( + "The covariance matrix of the support data " + "is equal to 0, try to increase support_fraction" + ) + correction = np.median(self.dist_) / chi2(data.shape[1]).isf(0.5) + covariance_corrected = self.raw_covariance_ * correction + self.dist_ /= correction + return covariance_corrected + + def reweight_covariance(self, data): + """Re-weight raw Minimum Covariance Determinant estimates. + + Re-weight observations using Rousseeuw's method (equivalent to + deleting outlying observations from the data set before + computing location and covariance estimates) described + in [RVDriessen]_. + + Parameters + ---------- + data : array-like of shape (n_samples, n_features) + The data matrix, with p features and n samples. + The data set must be the one which was used to compute + the raw estimates. + + Returns + ------- + location_reweighted : ndarray of shape (n_features,) + Re-weighted robust location estimate. + + covariance_reweighted : ndarray of shape (n_features, n_features) + Re-weighted robust covariance estimate. + + support_reweighted : ndarray of shape (n_samples,), dtype=bool + A mask of the observations that have been used to compute + the re-weighted robust location and covariance estimates. + + References + ---------- + + .. [RVDriessen] A Fast Algorithm for the Minimum Covariance + Determinant Estimator, 1999, American Statistical Association + and the American Society for Quality, TECHNOMETRICS + """ + n_samples, n_features = data.shape + mask = self.dist_ < chi2(n_features).isf(0.025) + if self.assume_centered: + location_reweighted = np.zeros(n_features) + else: + location_reweighted = data[mask].mean(0) + covariance_reweighted = self._nonrobust_covariance( + data[mask], assume_centered=self.assume_centered + ) + support_reweighted = np.zeros(n_samples, dtype=bool) + support_reweighted[mask] = True + self._set_covariance(covariance_reweighted) + self.location_ = location_reweighted + self.support_ = support_reweighted + X_centered = data - self.location_ + self.dist_ = np.sum(np.dot(X_centered, self.get_precision()) * X_centered, 1) + return location_reweighted, covariance_reweighted, support_reweighted diff --git a/venv/lib/python3.10/site-packages/sklearn/covariance/_shrunk_covariance.py b/venv/lib/python3.10/site-packages/sklearn/covariance/_shrunk_covariance.py new file mode 100644 index 0000000000000000000000000000000000000000..2c8248d0f65025b3cd5f1e4e2c969c4b4fa9bf91 --- /dev/null +++ b/venv/lib/python3.10/site-packages/sklearn/covariance/_shrunk_covariance.py @@ -0,0 +1,816 @@ +""" +Covariance estimators using shrinkage. + +Shrinkage corresponds to regularising `cov` using a convex combination: +shrunk_cov = (1-shrinkage)*cov + shrinkage*structured_estimate. + +""" + +# Author: Alexandre Gramfort +# Gael Varoquaux +# Virgile Fritsch +# +# License: BSD 3 clause + +# avoid division truncation +import warnings +from numbers import Integral, Real + +import numpy as np + +from ..base import _fit_context +from ..utils import check_array +from ..utils._param_validation import Interval, validate_params +from . import EmpiricalCovariance, empirical_covariance + + +def _ledoit_wolf(X, *, assume_centered, block_size): + """Estimate the shrunk Ledoit-Wolf covariance matrix.""" + # for only one feature, the result is the same whatever the shrinkage + if len(X.shape) == 2 and X.shape[1] == 1: + if not assume_centered: + X = X - X.mean() + return np.atleast_2d((X**2).mean()), 0.0 + n_features = X.shape[1] + + # get Ledoit-Wolf shrinkage + shrinkage = ledoit_wolf_shrinkage( + X, assume_centered=assume_centered, block_size=block_size + ) + emp_cov = empirical_covariance(X, assume_centered=assume_centered) + mu = np.sum(np.trace(emp_cov)) / n_features + shrunk_cov = (1.0 - shrinkage) * emp_cov + shrunk_cov.flat[:: n_features + 1] += shrinkage * mu + + return shrunk_cov, shrinkage + + +def _oas(X, *, assume_centered=False): + """Estimate covariance with the Oracle Approximating Shrinkage algorithm. + + The formulation is based on [1]_. + [1] "Shrinkage algorithms for MMSE covariance estimation.", + Chen, Y., Wiesel, A., Eldar, Y. C., & Hero, A. O. + IEEE Transactions on Signal Processing, 58(10), 5016-5029, 2010. + https://arxiv.org/pdf/0907.4698.pdf + """ + if len(X.shape) == 2 and X.shape[1] == 1: + # for only one feature, the result is the same whatever the shrinkage + if not assume_centered: + X = X - X.mean() + return np.atleast_2d((X**2).mean()), 0.0 + + n_samples, n_features = X.shape + + emp_cov = empirical_covariance(X, assume_centered=assume_centered) + + # The shrinkage is defined as: + # shrinkage = min( + # trace(S @ S.T) + trace(S)**2) / ((n + 1) (trace(S @ S.T) - trace(S)**2 / p), 1 + # ) + # where n and p are n_samples and n_features, respectively (cf. Eq. 23 in [1]). + # The factor 2 / p is omitted since it does not impact the value of the estimator + # for large p. + + # Instead of computing trace(S)**2, we can compute the average of the squared + # elements of S that is equal to trace(S)**2 / p**2. + # See the definition of the Frobenius norm: + # https://en.wikipedia.org/wiki/Matrix_norm#Frobenius_norm + alpha = np.mean(emp_cov**2) + mu = np.trace(emp_cov) / n_features + mu_squared = mu**2 + + # The factor 1 / p**2 will cancel out since it is in both the numerator and + # denominator + num = alpha + mu_squared + den = (n_samples + 1) * (alpha - mu_squared / n_features) + shrinkage = 1.0 if den == 0 else min(num / den, 1.0) + + # The shrunk covariance is defined as: + # (1 - shrinkage) * S + shrinkage * F (cf. Eq. 4 in [1]) + # where S is the empirical covariance and F is the shrinkage target defined as + # F = trace(S) / n_features * np.identity(n_features) (cf. Eq. 3 in [1]) + shrunk_cov = (1.0 - shrinkage) * emp_cov + shrunk_cov.flat[:: n_features + 1] += shrinkage * mu + + return shrunk_cov, shrinkage + + +############################################################################### +# Public API +# ShrunkCovariance estimator + + +@validate_params( + { + "emp_cov": ["array-like"], + "shrinkage": [Interval(Real, 0, 1, closed="both")], + }, + prefer_skip_nested_validation=True, +) +def shrunk_covariance(emp_cov, shrinkage=0.1): + """Calculate covariance matrices shrunk on the diagonal. + + Read more in the :ref:`User Guide `. + + Parameters + ---------- + emp_cov : array-like of shape (..., n_features, n_features) + Covariance matrices to be shrunk, at least 2D ndarray. + + shrinkage : float, default=0.1 + Coefficient in the convex combination used for the computation + of the shrunk estimate. Range is [0, 1]. + + Returns + ------- + shrunk_cov : ndarray of shape (..., n_features, n_features) + Shrunk covariance matrices. + + Notes + ----- + The regularized (shrunk) covariance is given by:: + + (1 - shrinkage) * cov + shrinkage * mu * np.identity(n_features) + + where `mu = trace(cov) / n_features`. + + Examples + -------- + >>> import numpy as np + >>> from sklearn.datasets import make_gaussian_quantiles + >>> from sklearn.covariance import empirical_covariance, shrunk_covariance + >>> real_cov = np.array([[.8, .3], [.3, .4]]) + >>> rng = np.random.RandomState(0) + >>> X = rng.multivariate_normal(mean=[0, 0], cov=real_cov, size=500) + >>> shrunk_covariance(empirical_covariance(X)) + array([[0.73..., 0.25...], + [0.25..., 0.41...]]) + """ + emp_cov = check_array(emp_cov, allow_nd=True) + n_features = emp_cov.shape[-1] + + shrunk_cov = (1.0 - shrinkage) * emp_cov + mu = np.trace(emp_cov, axis1=-2, axis2=-1) / n_features + mu = np.expand_dims(mu, axis=tuple(range(mu.ndim, emp_cov.ndim))) + shrunk_cov += shrinkage * mu * np.eye(n_features) + + return shrunk_cov + + +class ShrunkCovariance(EmpiricalCovariance): + """Covariance estimator with shrinkage. + + Read more in the :ref:`User Guide `. + + Parameters + ---------- + store_precision : bool, default=True + Specify if the estimated precision is stored. + + assume_centered : bool, default=False + If True, data will not be centered before computation. + Useful when working with data whose mean is almost, but not exactly + zero. + If False, data will be centered before computation. + + shrinkage : float, default=0.1 + Coefficient in the convex combination used for the computation + of the shrunk estimate. Range is [0, 1]. + + Attributes + ---------- + covariance_ : ndarray of shape (n_features, n_features) + Estimated covariance matrix + + location_ : ndarray of shape (n_features,) + Estimated location, i.e. the estimated mean. + + precision_ : ndarray of shape (n_features, n_features) + Estimated pseudo inverse matrix. + (stored only if store_precision is True) + + n_features_in_ : int + Number of features seen during :term:`fit`. + + .. versionadded:: 0.24 + + feature_names_in_ : ndarray of shape (`n_features_in_`,) + Names of features seen during :term:`fit`. Defined only when `X` + has feature names that are all strings. + + .. versionadded:: 1.0 + + See Also + -------- + EllipticEnvelope : An object for detecting outliers in + a Gaussian distributed dataset. + EmpiricalCovariance : Maximum likelihood covariance estimator. + GraphicalLasso : Sparse inverse covariance estimation + with an l1-penalized estimator. + GraphicalLassoCV : Sparse inverse covariance with cross-validated + choice of the l1 penalty. + LedoitWolf : LedoitWolf Estimator. + MinCovDet : Minimum Covariance Determinant + (robust estimator of covariance). + OAS : Oracle Approximating Shrinkage Estimator. + + Notes + ----- + The regularized covariance is given by: + + (1 - shrinkage) * cov + shrinkage * mu * np.identity(n_features) + + where mu = trace(cov) / n_features + + Examples + -------- + >>> import numpy as np + >>> from sklearn.covariance import ShrunkCovariance + >>> from sklearn.datasets import make_gaussian_quantiles + >>> real_cov = np.array([[.8, .3], + ... [.3, .4]]) + >>> rng = np.random.RandomState(0) + >>> X = rng.multivariate_normal(mean=[0, 0], + ... cov=real_cov, + ... size=500) + >>> cov = ShrunkCovariance().fit(X) + >>> cov.covariance_ + array([[0.7387..., 0.2536...], + [0.2536..., 0.4110...]]) + >>> cov.location_ + array([0.0622..., 0.0193...]) + """ + + _parameter_constraints: dict = { + **EmpiricalCovariance._parameter_constraints, + "shrinkage": [Interval(Real, 0, 1, closed="both")], + } + + def __init__(self, *, store_precision=True, assume_centered=False, shrinkage=0.1): + super().__init__( + store_precision=store_precision, assume_centered=assume_centered + ) + self.shrinkage = shrinkage + + @_fit_context(prefer_skip_nested_validation=True) + def fit(self, X, y=None): + """Fit the shrunk covariance model to X. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + Training data, where `n_samples` is the number of samples + and `n_features` is the number of features. + + y : Ignored + Not used, present for API consistency by convention. + + Returns + ------- + self : object + Returns the instance itself. + """ + X = self._validate_data(X) + # Not calling the parent object to fit, to avoid a potential + # matrix inversion when setting the precision + if self.assume_centered: + self.location_ = np.zeros(X.shape[1]) + else: + self.location_ = X.mean(0) + covariance = empirical_covariance(X, assume_centered=self.assume_centered) + covariance = shrunk_covariance(covariance, self.shrinkage) + self._set_covariance(covariance) + + return self + + +# Ledoit-Wolf estimator + + +@validate_params( + { + "X": ["array-like"], + "assume_centered": ["boolean"], + "block_size": [Interval(Integral, 1, None, closed="left")], + }, + prefer_skip_nested_validation=True, +) +def ledoit_wolf_shrinkage(X, assume_centered=False, block_size=1000): + """Estimate the shrunk Ledoit-Wolf covariance matrix. + + Read more in the :ref:`User Guide `. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + Data from which to compute the Ledoit-Wolf shrunk covariance shrinkage. + + assume_centered : bool, default=False + If True, data will not be centered before computation. + Useful to work with data whose mean is significantly equal to + zero but is not exactly zero. + If False, data will be centered before computation. + + block_size : int, default=1000 + Size of blocks into which the covariance matrix will be split. + + Returns + ------- + shrinkage : float + Coefficient in the convex combination used for the computation + of the shrunk estimate. + + Notes + ----- + The regularized (shrunk) covariance is: + + (1 - shrinkage) * cov + shrinkage * mu * np.identity(n_features) + + where mu = trace(cov) / n_features + + Examples + -------- + >>> import numpy as np + >>> from sklearn.covariance import ledoit_wolf_shrinkage + >>> real_cov = np.array([[.4, .2], [.2, .8]]) + >>> rng = np.random.RandomState(0) + >>> X = rng.multivariate_normal(mean=[0, 0], cov=real_cov, size=50) + >>> shrinkage_coefficient = ledoit_wolf_shrinkage(X) + >>> shrinkage_coefficient + 0.23... + """ + X = check_array(X) + # for only one feature, the result is the same whatever the shrinkage + if len(X.shape) == 2 and X.shape[1] == 1: + return 0.0 + if X.ndim == 1: + X = np.reshape(X, (1, -1)) + + if X.shape[0] == 1: + warnings.warn( + "Only one sample available. You may want to reshape your data array" + ) + n_samples, n_features = X.shape + + # optionally center data + if not assume_centered: + X = X - X.mean(0) + + # A non-blocked version of the computation is present in the tests + # in tests/test_covariance.py + + # number of blocks to split the covariance matrix into + n_splits = int(n_features / block_size) + X2 = X**2 + emp_cov_trace = np.sum(X2, axis=0) / n_samples + mu = np.sum(emp_cov_trace) / n_features + beta_ = 0.0 # sum of the coefficients of + delta_ = 0.0 # sum of the *squared* coefficients of + # starting block computation + for i in range(n_splits): + for j in range(n_splits): + rows = slice(block_size * i, block_size * (i + 1)) + cols = slice(block_size * j, block_size * (j + 1)) + beta_ += np.sum(np.dot(X2.T[rows], X2[:, cols])) + delta_ += np.sum(np.dot(X.T[rows], X[:, cols]) ** 2) + rows = slice(block_size * i, block_size * (i + 1)) + beta_ += np.sum(np.dot(X2.T[rows], X2[:, block_size * n_splits :])) + delta_ += np.sum(np.dot(X.T[rows], X[:, block_size * n_splits :]) ** 2) + for j in range(n_splits): + cols = slice(block_size * j, block_size * (j + 1)) + beta_ += np.sum(np.dot(X2.T[block_size * n_splits :], X2[:, cols])) + delta_ += np.sum(np.dot(X.T[block_size * n_splits :], X[:, cols]) ** 2) + delta_ += np.sum( + np.dot(X.T[block_size * n_splits :], X[:, block_size * n_splits :]) ** 2 + ) + delta_ /= n_samples**2 + beta_ += np.sum( + np.dot(X2.T[block_size * n_splits :], X2[:, block_size * n_splits :]) + ) + # use delta_ to compute beta + beta = 1.0 / (n_features * n_samples) * (beta_ / n_samples - delta_) + # delta is the sum of the squared coefficients of ( - mu*Id) / p + delta = delta_ - 2.0 * mu * emp_cov_trace.sum() + n_features * mu**2 + delta /= n_features + # get final beta as the min between beta and delta + # We do this to prevent shrinking more than "1", which would invert + # the value of covariances + beta = min(beta, delta) + # finally get shrinkage + shrinkage = 0 if beta == 0 else beta / delta + return shrinkage + + +@validate_params( + {"X": ["array-like"]}, + prefer_skip_nested_validation=False, +) +def ledoit_wolf(X, *, assume_centered=False, block_size=1000): + """Estimate the shrunk Ledoit-Wolf covariance matrix. + + Read more in the :ref:`User Guide `. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + Data from which to compute the covariance estimate. + + assume_centered : bool, default=False + If True, data will not be centered before computation. + Useful to work with data whose mean is significantly equal to + zero but is not exactly zero. + If False, data will be centered before computation. + + block_size : int, default=1000 + Size of blocks into which the covariance matrix will be split. + This is purely a memory optimization and does not affect results. + + Returns + ------- + shrunk_cov : ndarray of shape (n_features, n_features) + Shrunk covariance. + + shrinkage : float + Coefficient in the convex combination used for the computation + of the shrunk estimate. + + Notes + ----- + The regularized (shrunk) covariance is: + + (1 - shrinkage) * cov + shrinkage * mu * np.identity(n_features) + + where mu = trace(cov) / n_features + + Examples + -------- + >>> import numpy as np + >>> from sklearn.covariance import empirical_covariance, ledoit_wolf + >>> real_cov = np.array([[.4, .2], [.2, .8]]) + >>> rng = np.random.RandomState(0) + >>> X = rng.multivariate_normal(mean=[0, 0], cov=real_cov, size=50) + >>> covariance, shrinkage = ledoit_wolf(X) + >>> covariance + array([[0.44..., 0.16...], + [0.16..., 0.80...]]) + >>> shrinkage + 0.23... + """ + estimator = LedoitWolf( + assume_centered=assume_centered, + block_size=block_size, + store_precision=False, + ).fit(X) + + return estimator.covariance_, estimator.shrinkage_ + + +class LedoitWolf(EmpiricalCovariance): + """LedoitWolf Estimator. + + Ledoit-Wolf is a particular form of shrinkage, where the shrinkage + coefficient is computed using O. Ledoit and M. Wolf's formula as + described in "A Well-Conditioned Estimator for Large-Dimensional + Covariance Matrices", Ledoit and Wolf, Journal of Multivariate + Analysis, Volume 88, Issue 2, February 2004, pages 365-411. + + Read more in the :ref:`User Guide `. + + Parameters + ---------- + store_precision : bool, default=True + Specify if the estimated precision is stored. + + assume_centered : bool, default=False + If True, data will not be centered before computation. + Useful when working with data whose mean is almost, but not exactly + zero. + If False (default), data will be centered before computation. + + block_size : int, default=1000 + Size of blocks into which the covariance matrix will be split + during its Ledoit-Wolf estimation. This is purely a memory + optimization and does not affect results. + + Attributes + ---------- + covariance_ : ndarray of shape (n_features, n_features) + Estimated covariance matrix. + + location_ : ndarray of shape (n_features,) + Estimated location, i.e. the estimated mean. + + precision_ : ndarray of shape (n_features, n_features) + Estimated pseudo inverse matrix. + (stored only if store_precision is True) + + shrinkage_ : float + Coefficient in the convex combination used for the computation + of the shrunk estimate. Range is [0, 1]. + + n_features_in_ : int + Number of features seen during :term:`fit`. + + .. versionadded:: 0.24 + + feature_names_in_ : ndarray of shape (`n_features_in_`,) + Names of features seen during :term:`fit`. Defined only when `X` + has feature names that are all strings. + + .. versionadded:: 1.0 + + See Also + -------- + EllipticEnvelope : An object for detecting outliers in + a Gaussian distributed dataset. + EmpiricalCovariance : Maximum likelihood covariance estimator. + GraphicalLasso : Sparse inverse covariance estimation + with an l1-penalized estimator. + GraphicalLassoCV : Sparse inverse covariance with cross-validated + choice of the l1 penalty. + MinCovDet : Minimum Covariance Determinant + (robust estimator of covariance). + OAS : Oracle Approximating Shrinkage Estimator. + ShrunkCovariance : Covariance estimator with shrinkage. + + Notes + ----- + The regularised covariance is: + + (1 - shrinkage) * cov + shrinkage * mu * np.identity(n_features) + + where mu = trace(cov) / n_features + and shrinkage is given by the Ledoit and Wolf formula (see References) + + References + ---------- + "A Well-Conditioned Estimator for Large-Dimensional Covariance Matrices", + Ledoit and Wolf, Journal of Multivariate Analysis, Volume 88, Issue 2, + February 2004, pages 365-411. + + Examples + -------- + >>> import numpy as np + >>> from sklearn.covariance import LedoitWolf + >>> real_cov = np.array([[.4, .2], + ... [.2, .8]]) + >>> np.random.seed(0) + >>> X = np.random.multivariate_normal(mean=[0, 0], + ... cov=real_cov, + ... size=50) + >>> cov = LedoitWolf().fit(X) + >>> cov.covariance_ + array([[0.4406..., 0.1616...], + [0.1616..., 0.8022...]]) + >>> cov.location_ + array([ 0.0595... , -0.0075...]) + """ + + _parameter_constraints: dict = { + **EmpiricalCovariance._parameter_constraints, + "block_size": [Interval(Integral, 1, None, closed="left")], + } + + def __init__(self, *, store_precision=True, assume_centered=False, block_size=1000): + super().__init__( + store_precision=store_precision, assume_centered=assume_centered + ) + self.block_size = block_size + + @_fit_context(prefer_skip_nested_validation=True) + def fit(self, X, y=None): + """Fit the Ledoit-Wolf shrunk covariance model to X. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + Training data, where `n_samples` is the number of samples + and `n_features` is the number of features. + y : Ignored + Not used, present for API consistency by convention. + + Returns + ------- + self : object + Returns the instance itself. + """ + # Not calling the parent object to fit, to avoid computing the + # covariance matrix (and potentially the precision) + X = self._validate_data(X) + if self.assume_centered: + self.location_ = np.zeros(X.shape[1]) + else: + self.location_ = X.mean(0) + covariance, shrinkage = _ledoit_wolf( + X - self.location_, assume_centered=True, block_size=self.block_size + ) + self.shrinkage_ = shrinkage + self._set_covariance(covariance) + + return self + + +# OAS estimator +@validate_params( + {"X": ["array-like"]}, + prefer_skip_nested_validation=False, +) +def oas(X, *, assume_centered=False): + """Estimate covariance with the Oracle Approximating Shrinkage as proposed in [1]_. + + Read more in the :ref:`User Guide `. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + Data from which to compute the covariance estimate. + + assume_centered : bool, default=False + If True, data will not be centered before computation. + Useful to work with data whose mean is significantly equal to + zero but is not exactly zero. + If False, data will be centered before computation. + + Returns + ------- + shrunk_cov : array-like of shape (n_features, n_features) + Shrunk covariance. + + shrinkage : float + Coefficient in the convex combination used for the computation + of the shrunk estimate. + + Notes + ----- + The regularised covariance is: + + (1 - shrinkage) * cov + shrinkage * mu * np.identity(n_features), + + where mu = trace(cov) / n_features and shrinkage is given by the OAS formula + (see [1]_). + + The shrinkage formulation implemented here differs from Eq. 23 in [1]_. In + the original article, formula (23) states that 2/p (p being the number of + features) is multiplied by Trace(cov*cov) in both the numerator and + denominator, but this operation is omitted because for a large p, the value + of 2/p is so small that it doesn't affect the value of the estimator. + + References + ---------- + .. [1] :arxiv:`"Shrinkage algorithms for MMSE covariance estimation.", + Chen, Y., Wiesel, A., Eldar, Y. C., & Hero, A. O. + IEEE Transactions on Signal Processing, 58(10), 5016-5029, 2010. + <0907.4698>` + + Examples + -------- + >>> import numpy as np + >>> from sklearn.covariance import oas + >>> rng = np.random.RandomState(0) + >>> real_cov = [[.8, .3], [.3, .4]] + >>> X = rng.multivariate_normal(mean=[0, 0], cov=real_cov, size=500) + >>> shrunk_cov, shrinkage = oas(X) + >>> shrunk_cov + array([[0.7533..., 0.2763...], + [0.2763..., 0.3964...]]) + >>> shrinkage + 0.0195... + """ + estimator = OAS( + assume_centered=assume_centered, + ).fit(X) + return estimator.covariance_, estimator.shrinkage_ + + +class OAS(EmpiricalCovariance): + """Oracle Approximating Shrinkage Estimator as proposed in [1]_. + + Read more in the :ref:`User Guide `. + + Parameters + ---------- + store_precision : bool, default=True + Specify if the estimated precision is stored. + + assume_centered : bool, default=False + If True, data will not be centered before computation. + Useful when working with data whose mean is almost, but not exactly + zero. + If False (default), data will be centered before computation. + + Attributes + ---------- + covariance_ : ndarray of shape (n_features, n_features) + Estimated covariance matrix. + + location_ : ndarray of shape (n_features,) + Estimated location, i.e. the estimated mean. + + precision_ : ndarray of shape (n_features, n_features) + Estimated pseudo inverse matrix. + (stored only if store_precision is True) + + shrinkage_ : float + coefficient in the convex combination used for the computation + of the shrunk estimate. Range is [0, 1]. + + n_features_in_ : int + Number of features seen during :term:`fit`. + + .. versionadded:: 0.24 + + feature_names_in_ : ndarray of shape (`n_features_in_`,) + Names of features seen during :term:`fit`. Defined only when `X` + has feature names that are all strings. + + .. versionadded:: 1.0 + + See Also + -------- + EllipticEnvelope : An object for detecting outliers in + a Gaussian distributed dataset. + EmpiricalCovariance : Maximum likelihood covariance estimator. + GraphicalLasso : Sparse inverse covariance estimation + with an l1-penalized estimator. + GraphicalLassoCV : Sparse inverse covariance with cross-validated + choice of the l1 penalty. + LedoitWolf : LedoitWolf Estimator. + MinCovDet : Minimum Covariance Determinant + (robust estimator of covariance). + ShrunkCovariance : Covariance estimator with shrinkage. + + Notes + ----- + The regularised covariance is: + + (1 - shrinkage) * cov + shrinkage * mu * np.identity(n_features), + + where mu = trace(cov) / n_features and shrinkage is given by the OAS formula + (see [1]_). + + The shrinkage formulation implemented here differs from Eq. 23 in [1]_. In + the original article, formula (23) states that 2/p (p being the number of + features) is multiplied by Trace(cov*cov) in both the numerator and + denominator, but this operation is omitted because for a large p, the value + of 2/p is so small that it doesn't affect the value of the estimator. + + References + ---------- + .. [1] :arxiv:`"Shrinkage algorithms for MMSE covariance estimation.", + Chen, Y., Wiesel, A., Eldar, Y. C., & Hero, A. O. + IEEE Transactions on Signal Processing, 58(10), 5016-5029, 2010. + <0907.4698>` + + Examples + -------- + >>> import numpy as np + >>> from sklearn.covariance import OAS + >>> from sklearn.datasets import make_gaussian_quantiles + >>> real_cov = np.array([[.8, .3], + ... [.3, .4]]) + >>> rng = np.random.RandomState(0) + >>> X = rng.multivariate_normal(mean=[0, 0], + ... cov=real_cov, + ... size=500) + >>> oas = OAS().fit(X) + >>> oas.covariance_ + array([[0.7533..., 0.2763...], + [0.2763..., 0.3964...]]) + >>> oas.precision_ + array([[ 1.7833..., -1.2431... ], + [-1.2431..., 3.3889...]]) + >>> oas.shrinkage_ + 0.0195... + """ + + @_fit_context(prefer_skip_nested_validation=True) + def fit(self, X, y=None): + """Fit the Oracle Approximating Shrinkage covariance model to X. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + Training data, where `n_samples` is the number of samples + and `n_features` is the number of features. + y : Ignored + Not used, present for API consistency by convention. + + Returns + ------- + self : object + Returns the instance itself. + """ + X = self._validate_data(X) + # Not calling the parent object to fit, to avoid computing the + # covariance matrix (and potentially the precision) + if self.assume_centered: + self.location_ = np.zeros(X.shape[1]) + else: + self.location_ = X.mean(0) + + covariance, shrinkage = _oas(X - self.location_, assume_centered=True) + self.shrinkage_ = shrinkage + self._set_covariance(covariance) + + return self diff --git a/venv/lib/python3.10/site-packages/sklearn/covariance/tests/__init__.py b/venv/lib/python3.10/site-packages/sklearn/covariance/tests/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/venv/lib/python3.10/site-packages/sklearn/covariance/tests/__pycache__/__init__.cpython-310.pyc b/venv/lib/python3.10/site-packages/sklearn/covariance/tests/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..eb59f6547af0ec6f216134486c54c52bcfe23705 Binary files /dev/null and b/venv/lib/python3.10/site-packages/sklearn/covariance/tests/__pycache__/__init__.cpython-310.pyc differ diff --git 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a/venv/lib/python3.10/site-packages/sklearn/covariance/tests/test_covariance.py b/venv/lib/python3.10/site-packages/sklearn/covariance/tests/test_covariance.py new file mode 100644 index 0000000000000000000000000000000000000000..ef4bd63149d60624d113a5659248a904fa4679e1 --- /dev/null +++ b/venv/lib/python3.10/site-packages/sklearn/covariance/tests/test_covariance.py @@ -0,0 +1,377 @@ +# Author: Alexandre Gramfort +# Gael Varoquaux +# Virgile Fritsch +# +# License: BSD 3 clause + +import numpy as np +import pytest + +from sklearn import datasets +from sklearn.covariance import ( + OAS, + EmpiricalCovariance, + LedoitWolf, + ShrunkCovariance, + empirical_covariance, + ledoit_wolf, + ledoit_wolf_shrinkage, + oas, + shrunk_covariance, +) +from sklearn.covariance._shrunk_covariance import _ledoit_wolf +from sklearn.utils._testing import ( + assert_allclose, + assert_almost_equal, + assert_array_almost_equal, + assert_array_equal, +) + +from .._shrunk_covariance import _oas + +X, _ = datasets.load_diabetes(return_X_y=True) +X_1d = X[:, 0] +n_samples, n_features = X.shape + + +def test_covariance(): + # Tests Covariance module on a simple dataset. + # test covariance fit from data + cov = EmpiricalCovariance() + cov.fit(X) + emp_cov = empirical_covariance(X) + assert_array_almost_equal(emp_cov, cov.covariance_, 4) + assert_almost_equal(cov.error_norm(emp_cov), 0) + assert_almost_equal(cov.error_norm(emp_cov, norm="spectral"), 0) + assert_almost_equal(cov.error_norm(emp_cov, norm="frobenius"), 0) + assert_almost_equal(cov.error_norm(emp_cov, scaling=False), 0) + assert_almost_equal(cov.error_norm(emp_cov, squared=False), 0) + with pytest.raises(NotImplementedError): + cov.error_norm(emp_cov, norm="foo") + # Mahalanobis distances computation test + mahal_dist = cov.mahalanobis(X) + assert np.amin(mahal_dist) > 0 + + # test with n_features = 1 + X_1d = X[:, 0].reshape((-1, 1)) + cov = EmpiricalCovariance() + cov.fit(X_1d) + assert_array_almost_equal(empirical_covariance(X_1d), cov.covariance_, 4) + assert_almost_equal(cov.error_norm(empirical_covariance(X_1d)), 0) + assert_almost_equal(cov.error_norm(empirical_covariance(X_1d), norm="spectral"), 0) + + # test with one sample + # Create X with 1 sample and 5 features + X_1sample = np.arange(5).reshape(1, 5) + cov = EmpiricalCovariance() + warn_msg = "Only one sample available. You may want to reshape your data array" + with pytest.warns(UserWarning, match=warn_msg): + cov.fit(X_1sample) + + assert_array_almost_equal(cov.covariance_, np.zeros(shape=(5, 5), dtype=np.float64)) + + # test integer type + X_integer = np.asarray([[0, 1], [1, 0]]) + result = np.asarray([[0.25, -0.25], [-0.25, 0.25]]) + assert_array_almost_equal(empirical_covariance(X_integer), result) + + # test centered case + cov = EmpiricalCovariance(assume_centered=True) + cov.fit(X) + assert_array_equal(cov.location_, np.zeros(X.shape[1])) + + +@pytest.mark.parametrize("n_matrices", [1, 3]) +def test_shrunk_covariance_func(n_matrices): + """Check `shrunk_covariance` function.""" + + n_features = 2 + cov = np.ones((n_features, n_features)) + cov_target = np.array([[1, 0.5], [0.5, 1]]) + + if n_matrices > 1: + cov = np.repeat(cov[np.newaxis, ...], n_matrices, axis=0) + cov_target = np.repeat(cov_target[np.newaxis, ...], n_matrices, axis=0) + + cov_shrunk = shrunk_covariance(cov, 0.5) + assert_allclose(cov_shrunk, cov_target) + + +def test_shrunk_covariance(): + """Check consistency between `ShrunkCovariance` and `shrunk_covariance`.""" + + # Tests ShrunkCovariance module on a simple dataset. + # compare shrunk covariance obtained from data and from MLE estimate + cov = ShrunkCovariance(shrinkage=0.5) + cov.fit(X) + assert_array_almost_equal( + shrunk_covariance(empirical_covariance(X), shrinkage=0.5), cov.covariance_, 4 + ) + + # same test with shrinkage not provided + cov = ShrunkCovariance() + cov.fit(X) + assert_array_almost_equal( + shrunk_covariance(empirical_covariance(X)), cov.covariance_, 4 + ) + + # same test with shrinkage = 0 (<==> empirical_covariance) + cov = ShrunkCovariance(shrinkage=0.0) + cov.fit(X) + assert_array_almost_equal(empirical_covariance(X), cov.covariance_, 4) + + # test with n_features = 1 + X_1d = X[:, 0].reshape((-1, 1)) + cov = ShrunkCovariance(shrinkage=0.3) + cov.fit(X_1d) + assert_array_almost_equal(empirical_covariance(X_1d), cov.covariance_, 4) + + # test shrinkage coeff on a simple data set (without saving precision) + cov = ShrunkCovariance(shrinkage=0.5, store_precision=False) + cov.fit(X) + assert cov.precision_ is None + + +def test_ledoit_wolf(): + # Tests LedoitWolf module on a simple dataset. + # test shrinkage coeff on a simple data set + X_centered = X - X.mean(axis=0) + lw = LedoitWolf(assume_centered=True) + lw.fit(X_centered) + shrinkage_ = lw.shrinkage_ + + score_ = lw.score(X_centered) + assert_almost_equal( + ledoit_wolf_shrinkage(X_centered, assume_centered=True), shrinkage_ + ) + assert_almost_equal( + ledoit_wolf_shrinkage(X_centered, assume_centered=True, block_size=6), + shrinkage_, + ) + # compare shrunk covariance obtained from data and from MLE estimate + lw_cov_from_mle, lw_shrinkage_from_mle = ledoit_wolf( + X_centered, assume_centered=True + ) + assert_array_almost_equal(lw_cov_from_mle, lw.covariance_, 4) + assert_almost_equal(lw_shrinkage_from_mle, lw.shrinkage_) + # compare estimates given by LW and ShrunkCovariance + scov = ShrunkCovariance(shrinkage=lw.shrinkage_, assume_centered=True) + scov.fit(X_centered) + assert_array_almost_equal(scov.covariance_, lw.covariance_, 4) + + # test with n_features = 1 + X_1d = X[:, 0].reshape((-1, 1)) + lw = LedoitWolf(assume_centered=True) + lw.fit(X_1d) + lw_cov_from_mle, lw_shrinkage_from_mle = ledoit_wolf(X_1d, assume_centered=True) + assert_array_almost_equal(lw_cov_from_mle, lw.covariance_, 4) + assert_almost_equal(lw_shrinkage_from_mle, lw.shrinkage_) + assert_array_almost_equal((X_1d**2).sum() / n_samples, lw.covariance_, 4) + + # test shrinkage coeff on a simple data set (without saving precision) + lw = LedoitWolf(store_precision=False, assume_centered=True) + lw.fit(X_centered) + assert_almost_equal(lw.score(X_centered), score_, 4) + assert lw.precision_ is None + + # Same tests without assuming centered data + # test shrinkage coeff on a simple data set + lw = LedoitWolf() + lw.fit(X) + assert_almost_equal(lw.shrinkage_, shrinkage_, 4) + assert_almost_equal(lw.shrinkage_, ledoit_wolf_shrinkage(X)) + assert_almost_equal(lw.shrinkage_, ledoit_wolf(X)[1]) + assert_almost_equal( + lw.shrinkage_, _ledoit_wolf(X=X, assume_centered=False, block_size=10000)[1] + ) + assert_almost_equal(lw.score(X), score_, 4) + # compare shrunk covariance obtained from data and from MLE estimate + lw_cov_from_mle, lw_shrinkage_from_mle = ledoit_wolf(X) + assert_array_almost_equal(lw_cov_from_mle, lw.covariance_, 4) + assert_almost_equal(lw_shrinkage_from_mle, lw.shrinkage_) + # compare estimates given by LW and ShrunkCovariance + scov = ShrunkCovariance(shrinkage=lw.shrinkage_) + scov.fit(X) + assert_array_almost_equal(scov.covariance_, lw.covariance_, 4) + + # test with n_features = 1 + X_1d = X[:, 0].reshape((-1, 1)) + lw = LedoitWolf() + lw.fit(X_1d) + assert_allclose( + X_1d.var(ddof=0), + _ledoit_wolf(X=X_1d, assume_centered=False, block_size=10000)[0], + ) + lw_cov_from_mle, lw_shrinkage_from_mle = ledoit_wolf(X_1d) + assert_array_almost_equal(lw_cov_from_mle, lw.covariance_, 4) + assert_almost_equal(lw_shrinkage_from_mle, lw.shrinkage_) + assert_array_almost_equal(empirical_covariance(X_1d), lw.covariance_, 4) + + # test with one sample + # warning should be raised when using only 1 sample + X_1sample = np.arange(5).reshape(1, 5) + lw = LedoitWolf() + + warn_msg = "Only one sample available. You may want to reshape your data array" + with pytest.warns(UserWarning, match=warn_msg): + lw.fit(X_1sample) + + assert_array_almost_equal(lw.covariance_, np.zeros(shape=(5, 5), dtype=np.float64)) + + # test shrinkage coeff on a simple data set (without saving precision) + lw = LedoitWolf(store_precision=False) + lw.fit(X) + assert_almost_equal(lw.score(X), score_, 4) + assert lw.precision_ is None + + +def _naive_ledoit_wolf_shrinkage(X): + # A simple implementation of the formulas from Ledoit & Wolf + + # The computation below achieves the following computations of the + # "O. Ledoit and M. Wolf, A Well-Conditioned Estimator for + # Large-Dimensional Covariance Matrices" + # beta and delta are given in the beginning of section 3.2 + n_samples, n_features = X.shape + emp_cov = empirical_covariance(X, assume_centered=False) + mu = np.trace(emp_cov) / n_features + delta_ = emp_cov.copy() + delta_.flat[:: n_features + 1] -= mu + delta = (delta_**2).sum() / n_features + X2 = X**2 + beta_ = ( + 1.0 + / (n_features * n_samples) + * np.sum(np.dot(X2.T, X2) / n_samples - emp_cov**2) + ) + + beta = min(beta_, delta) + shrinkage = beta / delta + return shrinkage + + +def test_ledoit_wolf_small(): + # Compare our blocked implementation to the naive implementation + X_small = X[:, :4] + lw = LedoitWolf() + lw.fit(X_small) + shrinkage_ = lw.shrinkage_ + + assert_almost_equal(shrinkage_, _naive_ledoit_wolf_shrinkage(X_small)) + + +def test_ledoit_wolf_large(): + # test that ledoit_wolf doesn't error on data that is wider than block_size + rng = np.random.RandomState(0) + # use a number of features that is larger than the block-size + X = rng.normal(size=(10, 20)) + lw = LedoitWolf(block_size=10).fit(X) + # check that covariance is about diagonal (random normal noise) + assert_almost_equal(lw.covariance_, np.eye(20), 0) + cov = lw.covariance_ + + # check that the result is consistent with not splitting data into blocks. + lw = LedoitWolf(block_size=25).fit(X) + assert_almost_equal(lw.covariance_, cov) + + +@pytest.mark.parametrize( + "ledoit_wolf_fitting_function", [LedoitWolf().fit, ledoit_wolf_shrinkage] +) +def test_ledoit_wolf_empty_array(ledoit_wolf_fitting_function): + """Check that we validate X and raise proper error with 0-sample array.""" + X_empty = np.zeros((0, 2)) + with pytest.raises(ValueError, match="Found array with 0 sample"): + ledoit_wolf_fitting_function(X_empty) + + +def test_oas(): + # Tests OAS module on a simple dataset. + # test shrinkage coeff on a simple data set + X_centered = X - X.mean(axis=0) + oa = OAS(assume_centered=True) + oa.fit(X_centered) + shrinkage_ = oa.shrinkage_ + score_ = oa.score(X_centered) + # compare shrunk covariance obtained from data and from MLE estimate + oa_cov_from_mle, oa_shrinkage_from_mle = oas(X_centered, assume_centered=True) + assert_array_almost_equal(oa_cov_from_mle, oa.covariance_, 4) + assert_almost_equal(oa_shrinkage_from_mle, oa.shrinkage_) + # compare estimates given by OAS and ShrunkCovariance + scov = ShrunkCovariance(shrinkage=oa.shrinkage_, assume_centered=True) + scov.fit(X_centered) + assert_array_almost_equal(scov.covariance_, oa.covariance_, 4) + + # test with n_features = 1 + X_1d = X[:, 0:1] + oa = OAS(assume_centered=True) + oa.fit(X_1d) + oa_cov_from_mle, oa_shrinkage_from_mle = oas(X_1d, assume_centered=True) + assert_array_almost_equal(oa_cov_from_mle, oa.covariance_, 4) + assert_almost_equal(oa_shrinkage_from_mle, oa.shrinkage_) + assert_array_almost_equal((X_1d**2).sum() / n_samples, oa.covariance_, 4) + + # test shrinkage coeff on a simple data set (without saving precision) + oa = OAS(store_precision=False, assume_centered=True) + oa.fit(X_centered) + assert_almost_equal(oa.score(X_centered), score_, 4) + assert oa.precision_ is None + + # Same tests without assuming centered data-------------------------------- + # test shrinkage coeff on a simple data set + oa = OAS() + oa.fit(X) + assert_almost_equal(oa.shrinkage_, shrinkage_, 4) + assert_almost_equal(oa.score(X), score_, 4) + # compare shrunk covariance obtained from data and from MLE estimate + oa_cov_from_mle, oa_shrinkage_from_mle = oas(X) + assert_array_almost_equal(oa_cov_from_mle, oa.covariance_, 4) + assert_almost_equal(oa_shrinkage_from_mle, oa.shrinkage_) + # compare estimates given by OAS and ShrunkCovariance + scov = ShrunkCovariance(shrinkage=oa.shrinkage_) + scov.fit(X) + assert_array_almost_equal(scov.covariance_, oa.covariance_, 4) + + # test with n_features = 1 + X_1d = X[:, 0].reshape((-1, 1)) + oa = OAS() + oa.fit(X_1d) + oa_cov_from_mle, oa_shrinkage_from_mle = oas(X_1d) + assert_array_almost_equal(oa_cov_from_mle, oa.covariance_, 4) + assert_almost_equal(oa_shrinkage_from_mle, oa.shrinkage_) + assert_array_almost_equal(empirical_covariance(X_1d), oa.covariance_, 4) + + # test with one sample + # warning should be raised when using only 1 sample + X_1sample = np.arange(5).reshape(1, 5) + oa = OAS() + warn_msg = "Only one sample available. You may want to reshape your data array" + with pytest.warns(UserWarning, match=warn_msg): + oa.fit(X_1sample) + + assert_array_almost_equal(oa.covariance_, np.zeros(shape=(5, 5), dtype=np.float64)) + + # test shrinkage coeff on a simple data set (without saving precision) + oa = OAS(store_precision=False) + oa.fit(X) + assert_almost_equal(oa.score(X), score_, 4) + assert oa.precision_ is None + + # test function _oas without assuming centered data + X_1f = X[:, 0:1] + oa = OAS() + oa.fit(X_1f) + # compare shrunk covariance obtained from data and from MLE estimate + _oa_cov_from_mle, _oa_shrinkage_from_mle = _oas(X_1f) + assert_array_almost_equal(_oa_cov_from_mle, oa.covariance_, 4) + assert_almost_equal(_oa_shrinkage_from_mle, oa.shrinkage_) + assert_array_almost_equal((X_1f**2).sum() / n_samples, oa.covariance_, 4) + + +def test_EmpiricalCovariance_validates_mahalanobis(): + """Checks that EmpiricalCovariance validates data with mahalanobis.""" + cov = EmpiricalCovariance().fit(X) + + msg = f"X has 2 features, but \\w+ is expecting {X.shape[1]} features as input" + with pytest.raises(ValueError, match=msg): + cov.mahalanobis(X[:, :2]) diff --git a/venv/lib/python3.10/site-packages/sklearn/covariance/tests/test_elliptic_envelope.py b/venv/lib/python3.10/site-packages/sklearn/covariance/tests/test_elliptic_envelope.py new file mode 100644 index 0000000000000000000000000000000000000000..ca85717fb378243ff8dcb75db1adade9a6c50c18 --- /dev/null +++ b/venv/lib/python3.10/site-packages/sklearn/covariance/tests/test_elliptic_envelope.py @@ -0,0 +1,52 @@ +""" +Testing for Elliptic Envelope algorithm (sklearn.covariance.elliptic_envelope). +""" + +import numpy as np +import pytest + +from sklearn.covariance import EllipticEnvelope +from sklearn.exceptions import NotFittedError +from sklearn.utils._testing import ( + assert_almost_equal, + assert_array_almost_equal, + assert_array_equal, +) + + +def test_elliptic_envelope(global_random_seed): + rnd = np.random.RandomState(global_random_seed) + X = rnd.randn(100, 10) + clf = EllipticEnvelope(contamination=0.1) + with pytest.raises(NotFittedError): + clf.predict(X) + with pytest.raises(NotFittedError): + clf.decision_function(X) + clf.fit(X) + y_pred = clf.predict(X) + scores = clf.score_samples(X) + decisions = clf.decision_function(X) + + assert_array_almost_equal(scores, -clf.mahalanobis(X)) + assert_array_almost_equal(clf.mahalanobis(X), clf.dist_) + assert_almost_equal( + clf.score(X, np.ones(100)), (100 - y_pred[y_pred == -1].size) / 100.0 + ) + assert sum(y_pred == -1) == sum(decisions < 0) + + +def test_score_samples(): + X_train = [[1, 1], [1, 2], [2, 1]] + clf1 = EllipticEnvelope(contamination=0.2).fit(X_train) + clf2 = EllipticEnvelope().fit(X_train) + assert_array_equal( + clf1.score_samples([[2.0, 2.0]]), + clf1.decision_function([[2.0, 2.0]]) + clf1.offset_, + ) + assert_array_equal( + clf2.score_samples([[2.0, 2.0]]), + clf2.decision_function([[2.0, 2.0]]) + clf2.offset_, + ) + assert_array_equal( + clf1.score_samples([[2.0, 2.0]]), clf2.score_samples([[2.0, 2.0]]) + ) diff --git a/venv/lib/python3.10/site-packages/sklearn/covariance/tests/test_graphical_lasso.py b/venv/lib/python3.10/site-packages/sklearn/covariance/tests/test_graphical_lasso.py new file mode 100644 index 0000000000000000000000000000000000000000..a7d251a5bbdfe40e5a422111ab3e1c187e0efbed --- /dev/null +++ b/venv/lib/python3.10/site-packages/sklearn/covariance/tests/test_graphical_lasso.py @@ -0,0 +1,286 @@ +""" Test the graphical_lasso module. +""" +import sys +from io import StringIO + +import numpy as np +import pytest +from numpy.testing import assert_allclose +from scipy import linalg + +from sklearn import datasets +from sklearn.covariance import ( + GraphicalLasso, + GraphicalLassoCV, + empirical_covariance, + graphical_lasso, +) +from sklearn.datasets import make_sparse_spd_matrix +from sklearn.utils import check_random_state +from sklearn.utils._testing import ( + _convert_container, + assert_array_almost_equal, + assert_array_less, +) + + +def test_graphical_lassos(random_state=1): + """Test the graphical lasso solvers. + + This checks is unstable for some random seeds where the covariance found with "cd" + and "lars" solvers are different (4 cases / 100 tries). + """ + # Sample data from a sparse multivariate normal + dim = 20 + n_samples = 100 + random_state = check_random_state(random_state) + prec = make_sparse_spd_matrix(dim, alpha=0.95, random_state=random_state) + cov = linalg.inv(prec) + X = random_state.multivariate_normal(np.zeros(dim), cov, size=n_samples) + emp_cov = empirical_covariance(X) + + for alpha in (0.0, 0.1, 0.25): + covs = dict() + icovs = dict() + for method in ("cd", "lars"): + cov_, icov_, costs = graphical_lasso( + emp_cov, return_costs=True, alpha=alpha, mode=method + ) + covs[method] = cov_ + icovs[method] = icov_ + costs, dual_gap = np.array(costs).T + # Check that the costs always decrease (doesn't hold if alpha == 0) + if not alpha == 0: + # use 1e-12 since the cost can be exactly 0 + assert_array_less(np.diff(costs), 1e-12) + # Check that the 2 approaches give similar results + assert_allclose(covs["cd"], covs["lars"], atol=5e-4) + assert_allclose(icovs["cd"], icovs["lars"], atol=5e-4) + + # Smoke test the estimator + model = GraphicalLasso(alpha=0.25).fit(X) + model.score(X) + assert_array_almost_equal(model.covariance_, covs["cd"], decimal=4) + assert_array_almost_equal(model.covariance_, covs["lars"], decimal=4) + + # For a centered matrix, assume_centered could be chosen True or False + # Check that this returns indeed the same result for centered data + Z = X - X.mean(0) + precs = list() + for assume_centered in (False, True): + prec_ = GraphicalLasso(assume_centered=assume_centered).fit(Z).precision_ + precs.append(prec_) + assert_array_almost_equal(precs[0], precs[1]) + + +def test_graphical_lasso_when_alpha_equals_0(): + """Test graphical_lasso's early return condition when alpha=0.""" + X = np.random.randn(100, 10) + emp_cov = empirical_covariance(X, assume_centered=True) + + model = GraphicalLasso(alpha=0, covariance="precomputed").fit(emp_cov) + assert_allclose(model.precision_, np.linalg.inv(emp_cov)) + + _, precision = graphical_lasso(emp_cov, alpha=0) + assert_allclose(precision, np.linalg.inv(emp_cov)) + + +@pytest.mark.parametrize("mode", ["cd", "lars"]) +def test_graphical_lasso_n_iter(mode): + X, _ = datasets.make_classification(n_samples=5_000, n_features=20, random_state=0) + emp_cov = empirical_covariance(X) + + _, _, n_iter = graphical_lasso( + emp_cov, 0.2, mode=mode, max_iter=2, return_n_iter=True + ) + assert n_iter == 2 + + +def test_graphical_lasso_iris(): + # Hard-coded solution from R glasso package for alpha=1.0 + # (need to set penalize.diagonal to FALSE) + cov_R = np.array( + [ + [0.68112222, 0.0000000, 0.265820, 0.02464314], + [0.00000000, 0.1887129, 0.000000, 0.00000000], + [0.26582000, 0.0000000, 3.095503, 0.28697200], + [0.02464314, 0.0000000, 0.286972, 0.57713289], + ] + ) + icov_R = np.array( + [ + [1.5190747, 0.000000, -0.1304475, 0.0000000], + [0.0000000, 5.299055, 0.0000000, 0.0000000], + [-0.1304475, 0.000000, 0.3498624, -0.1683946], + [0.0000000, 0.000000, -0.1683946, 1.8164353], + ] + ) + X = datasets.load_iris().data + emp_cov = empirical_covariance(X) + for method in ("cd", "lars"): + cov, icov = graphical_lasso(emp_cov, alpha=1.0, return_costs=False, mode=method) + assert_array_almost_equal(cov, cov_R) + assert_array_almost_equal(icov, icov_R) + + +def test_graph_lasso_2D(): + # Hard-coded solution from Python skggm package + # obtained by calling `quic(emp_cov, lam=.1, tol=1e-8)` + cov_skggm = np.array([[3.09550269, 1.186972], [1.186972, 0.57713289]]) + + icov_skggm = np.array([[1.52836773, -3.14334831], [-3.14334831, 8.19753385]]) + X = datasets.load_iris().data[:, 2:] + emp_cov = empirical_covariance(X) + for method in ("cd", "lars"): + cov, icov = graphical_lasso(emp_cov, alpha=0.1, return_costs=False, mode=method) + assert_array_almost_equal(cov, cov_skggm) + assert_array_almost_equal(icov, icov_skggm) + + +def test_graphical_lasso_iris_singular(): + # Small subset of rows to test the rank-deficient case + # Need to choose samples such that none of the variances are zero + indices = np.arange(10, 13) + + # Hard-coded solution from R glasso package for alpha=0.01 + cov_R = np.array( + [ + [0.08, 0.056666662595, 0.00229729713223, 0.00153153142149], + [0.056666662595, 0.082222222222, 0.00333333333333, 0.00222222222222], + [0.002297297132, 0.003333333333, 0.00666666666667, 0.00009009009009], + [0.001531531421, 0.002222222222, 0.00009009009009, 0.00222222222222], + ] + ) + icov_R = np.array( + [ + [24.42244057, -16.831679593, 0.0, 0.0], + [-16.83168201, 24.351841681, -6.206896552, -12.5], + [0.0, -6.206896171, 153.103448276, 0.0], + [0.0, -12.499999143, 0.0, 462.5], + ] + ) + X = datasets.load_iris().data[indices, :] + emp_cov = empirical_covariance(X) + for method in ("cd", "lars"): + cov, icov = graphical_lasso( + emp_cov, alpha=0.01, return_costs=False, mode=method + ) + assert_array_almost_equal(cov, cov_R, decimal=5) + assert_array_almost_equal(icov, icov_R, decimal=5) + + +def test_graphical_lasso_cv(random_state=1): + # Sample data from a sparse multivariate normal + dim = 5 + n_samples = 6 + random_state = check_random_state(random_state) + prec = make_sparse_spd_matrix(dim, alpha=0.96, random_state=random_state) + cov = linalg.inv(prec) + X = random_state.multivariate_normal(np.zeros(dim), cov, size=n_samples) + # Capture stdout, to smoke test the verbose mode + orig_stdout = sys.stdout + try: + sys.stdout = StringIO() + # We need verbose very high so that Parallel prints on stdout + GraphicalLassoCV(verbose=100, alphas=5, tol=1e-1).fit(X) + finally: + sys.stdout = orig_stdout + + +@pytest.mark.parametrize("alphas_container_type", ["list", "tuple", "array"]) +def test_graphical_lasso_cv_alphas_iterable(alphas_container_type): + """Check that we can pass an array-like to `alphas`. + + Non-regression test for: + https://github.com/scikit-learn/scikit-learn/issues/22489 + """ + true_cov = np.array( + [ + [0.8, 0.0, 0.2, 0.0], + [0.0, 0.4, 0.0, 0.0], + [0.2, 0.0, 0.3, 0.1], + [0.0, 0.0, 0.1, 0.7], + ] + ) + rng = np.random.RandomState(0) + X = rng.multivariate_normal(mean=[0, 0, 0, 0], cov=true_cov, size=200) + alphas = _convert_container([0.02, 0.03], alphas_container_type) + GraphicalLassoCV(alphas=alphas, tol=1e-1, n_jobs=1).fit(X) + + +@pytest.mark.parametrize( + "alphas,err_type,err_msg", + [ + ([-0.02, 0.03], ValueError, "must be > 0"), + ([0, 0.03], ValueError, "must be > 0"), + (["not_number", 0.03], TypeError, "must be an instance of float"), + ], +) +def test_graphical_lasso_cv_alphas_invalid_array(alphas, err_type, err_msg): + """Check that if an array-like containing a value + outside of (0, inf] is passed to `alphas`, a ValueError is raised. + Check if a string is passed, a TypeError is raised. + """ + true_cov = np.array( + [ + [0.8, 0.0, 0.2, 0.0], + [0.0, 0.4, 0.0, 0.0], + [0.2, 0.0, 0.3, 0.1], + [0.0, 0.0, 0.1, 0.7], + ] + ) + rng = np.random.RandomState(0) + X = rng.multivariate_normal(mean=[0, 0, 0, 0], cov=true_cov, size=200) + + with pytest.raises(err_type, match=err_msg): + GraphicalLassoCV(alphas=alphas, tol=1e-1, n_jobs=1).fit(X) + + +def test_graphical_lasso_cv_scores(): + splits = 4 + n_alphas = 5 + n_refinements = 3 + true_cov = np.array( + [ + [0.8, 0.0, 0.2, 0.0], + [0.0, 0.4, 0.0, 0.0], + [0.2, 0.0, 0.3, 0.1], + [0.0, 0.0, 0.1, 0.7], + ] + ) + rng = np.random.RandomState(0) + X = rng.multivariate_normal(mean=[0, 0, 0, 0], cov=true_cov, size=200) + cov = GraphicalLassoCV(cv=splits, alphas=n_alphas, n_refinements=n_refinements).fit( + X + ) + + cv_results = cov.cv_results_ + # alpha and one for each split + + total_alphas = n_refinements * n_alphas + 1 + keys = ["alphas"] + split_keys = [f"split{i}_test_score" for i in range(splits)] + for key in keys + split_keys: + assert key in cv_results + assert len(cv_results[key]) == total_alphas + + cv_scores = np.asarray([cov.cv_results_[key] for key in split_keys]) + expected_mean = cv_scores.mean(axis=0) + expected_std = cv_scores.std(axis=0) + + assert_allclose(cov.cv_results_["mean_test_score"], expected_mean) + assert_allclose(cov.cv_results_["std_test_score"], expected_std) + + +# TODO(1.5): remove in 1.5 +def test_graphical_lasso_cov_init_deprecation(): + """Check that we raise a deprecation warning if providing `cov_init` in + `graphical_lasso`.""" + rng, dim, n_samples = np.random.RandomState(0), 20, 100 + prec = make_sparse_spd_matrix(dim, alpha=0.95, random_state=0) + cov = linalg.inv(prec) + X = rng.multivariate_normal(np.zeros(dim), cov, size=n_samples) + + emp_cov = empirical_covariance(X) + with pytest.warns(FutureWarning, match="cov_init parameter is deprecated"): + graphical_lasso(emp_cov, alpha=0.1, cov_init=emp_cov) diff --git a/venv/lib/python3.10/site-packages/sklearn/covariance/tests/test_robust_covariance.py b/venv/lib/python3.10/site-packages/sklearn/covariance/tests/test_robust_covariance.py new file mode 100644 index 0000000000000000000000000000000000000000..44dcdbbbf824934a8f31bc832a389d90f396c6d6 --- /dev/null +++ b/venv/lib/python3.10/site-packages/sklearn/covariance/tests/test_robust_covariance.py @@ -0,0 +1,171 @@ +# Author: Alexandre Gramfort +# Gael Varoquaux +# Virgile Fritsch +# +# License: BSD 3 clause + +import itertools + +import numpy as np +import pytest + +from sklearn import datasets +from sklearn.covariance import MinCovDet, empirical_covariance, fast_mcd +from sklearn.utils._testing import assert_array_almost_equal + +X = datasets.load_iris().data +X_1d = X[:, 0] +n_samples, n_features = X.shape + + +def test_mcd(global_random_seed): + # Tests the FastMCD algorithm implementation + # Small data set + # test without outliers (random independent normal data) + launch_mcd_on_dataset(100, 5, 0, 0.02, 0.1, 75, global_random_seed) + # test with a contaminated data set (medium contamination) + launch_mcd_on_dataset(100, 5, 20, 0.3, 0.3, 65, global_random_seed) + # test with a contaminated data set (strong contamination) + launch_mcd_on_dataset(100, 5, 40, 0.1, 0.1, 50, global_random_seed) + + # Medium data set + launch_mcd_on_dataset(1000, 5, 450, 0.1, 0.1, 540, global_random_seed) + + # Large data set + launch_mcd_on_dataset(1700, 5, 800, 0.1, 0.1, 870, global_random_seed) + + # 1D data set + launch_mcd_on_dataset(500, 1, 100, 0.02, 0.02, 350, global_random_seed) + + +def test_fast_mcd_on_invalid_input(): + X = np.arange(100) + msg = "Expected 2D array, got 1D array instead" + with pytest.raises(ValueError, match=msg): + fast_mcd(X) + + +def test_mcd_class_on_invalid_input(): + X = np.arange(100) + mcd = MinCovDet() + msg = "Expected 2D array, got 1D array instead" + with pytest.raises(ValueError, match=msg): + mcd.fit(X) + + +def launch_mcd_on_dataset( + n_samples, n_features, n_outliers, tol_loc, tol_cov, tol_support, seed +): + rand_gen = np.random.RandomState(seed) + data = rand_gen.randn(n_samples, n_features) + # add some outliers + outliers_index = rand_gen.permutation(n_samples)[:n_outliers] + outliers_offset = 10.0 * (rand_gen.randint(2, size=(n_outliers, n_features)) - 0.5) + data[outliers_index] += outliers_offset + inliers_mask = np.ones(n_samples).astype(bool) + inliers_mask[outliers_index] = False + + pure_data = data[inliers_mask] + # compute MCD by fitting an object + mcd_fit = MinCovDet(random_state=seed).fit(data) + T = mcd_fit.location_ + S = mcd_fit.covariance_ + H = mcd_fit.support_ + # compare with the estimates learnt from the inliers + error_location = np.mean((pure_data.mean(0) - T) ** 2) + assert error_location < tol_loc + error_cov = np.mean((empirical_covariance(pure_data) - S) ** 2) + assert error_cov < tol_cov + assert np.sum(H) >= tol_support + assert_array_almost_equal(mcd_fit.mahalanobis(data), mcd_fit.dist_) + + +def test_mcd_issue1127(): + # Check that the code does not break with X.shape = (3, 1) + # (i.e. n_support = n_samples) + rnd = np.random.RandomState(0) + X = rnd.normal(size=(3, 1)) + mcd = MinCovDet() + mcd.fit(X) + + +def test_mcd_issue3367(global_random_seed): + # Check that MCD completes when the covariance matrix is singular + # i.e. one of the rows and columns are all zeros + rand_gen = np.random.RandomState(global_random_seed) + + # Think of these as the values for X and Y -> 10 values between -5 and 5 + data_values = np.linspace(-5, 5, 10).tolist() + # Get the cartesian product of all possible coordinate pairs from above set + data = np.array(list(itertools.product(data_values, data_values))) + + # Add a third column that's all zeros to make our data a set of point + # within a plane, which means that the covariance matrix will be singular + data = np.hstack((data, np.zeros((data.shape[0], 1)))) + + # The below line of code should raise an exception if the covariance matrix + # is singular. As a further test, since we have points in XYZ, the + # principle components (Eigenvectors) of these directly relate to the + # geometry of the points. Since it's a plane, we should be able to test + # that the Eigenvector that corresponds to the smallest Eigenvalue is the + # plane normal, specifically [0, 0, 1], since everything is in the XY plane + # (as I've set it up above). To do this one would start by: + # + # evals, evecs = np.linalg.eigh(mcd_fit.covariance_) + # normal = evecs[:, np.argmin(evals)] + # + # After which we need to assert that our `normal` is equal to [0, 0, 1]. + # Do note that there is floating point error associated with this, so it's + # best to subtract the two and then compare some small tolerance (e.g. + # 1e-12). + MinCovDet(random_state=rand_gen).fit(data) + + +def test_mcd_support_covariance_is_zero(): + # Check that MCD returns a ValueError with informative message when the + # covariance of the support data is equal to 0. + X_1 = np.array([0.5, 0.1, 0.1, 0.1, 0.957, 0.1, 0.1, 0.1, 0.4285, 0.1]) + X_1 = X_1.reshape(-1, 1) + X_2 = np.array([0.5, 0.3, 0.3, 0.3, 0.957, 0.3, 0.3, 0.3, 0.4285, 0.3]) + X_2 = X_2.reshape(-1, 1) + msg = ( + "The covariance matrix of the support data is equal to 0, try to " + "increase support_fraction" + ) + for X in [X_1, X_2]: + with pytest.raises(ValueError, match=msg): + MinCovDet().fit(X) + + +def test_mcd_increasing_det_warning(global_random_seed): + # Check that a warning is raised if we observe increasing determinants + # during the c_step. In theory the sequence of determinants should be + # decreasing. Increasing determinants are likely due to ill-conditioned + # covariance matrices that result in poor precision matrices. + + X = [ + [5.1, 3.5, 1.4, 0.2], + [4.9, 3.0, 1.4, 0.2], + [4.7, 3.2, 1.3, 0.2], + [4.6, 3.1, 1.5, 0.2], + [5.0, 3.6, 1.4, 0.2], + [4.6, 3.4, 1.4, 0.3], + [5.0, 3.4, 1.5, 0.2], + [4.4, 2.9, 1.4, 0.2], + [4.9, 3.1, 1.5, 0.1], + [5.4, 3.7, 1.5, 0.2], + [4.8, 3.4, 1.6, 0.2], + [4.8, 3.0, 1.4, 0.1], + [4.3, 3.0, 1.1, 0.1], + [5.1, 3.5, 1.4, 0.3], + [5.7, 3.8, 1.7, 0.3], + [5.4, 3.4, 1.7, 0.2], + [4.6, 3.6, 1.0, 0.2], + [5.0, 3.0, 1.6, 0.2], + [5.2, 3.5, 1.5, 0.2], + ] + + mcd = MinCovDet(support_fraction=0.5, random_state=global_random_seed) + warn_msg = "Determinant has increased" + with pytest.warns(RuntimeWarning, match=warn_msg): + mcd.fit(X) diff --git a/venv/lib/python3.10/site-packages/sklearn/manifold/__init__.py b/venv/lib/python3.10/site-packages/sklearn/manifold/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..1e8d96c7cf94bbcba0adf28ea3138823468d2691 --- /dev/null +++ b/venv/lib/python3.10/site-packages/sklearn/manifold/__init__.py @@ -0,0 +1,21 @@ +""" +The :mod:`sklearn.manifold` module implements data embedding techniques. +""" + +from ._isomap import Isomap +from ._locally_linear import LocallyLinearEmbedding, locally_linear_embedding +from ._mds import MDS, smacof +from ._spectral_embedding import SpectralEmbedding, spectral_embedding +from ._t_sne import TSNE, trustworthiness + +__all__ = [ + "locally_linear_embedding", + "LocallyLinearEmbedding", + "Isomap", + "MDS", + "smacof", + "SpectralEmbedding", + "spectral_embedding", + "TSNE", + "trustworthiness", +] diff --git a/venv/lib/python3.10/site-packages/sklearn/manifold/__pycache__/__init__.cpython-310.pyc b/venv/lib/python3.10/site-packages/sklearn/manifold/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 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+from ..isotonic import IsotonicRegression +from ..metrics import euclidean_distances +from ..utils import check_array, check_random_state, check_symmetric +from ..utils._param_validation import Interval, StrOptions, validate_params +from ..utils.parallel import Parallel, delayed + + +def _smacof_single( + dissimilarities, + metric=True, + n_components=2, + init=None, + max_iter=300, + verbose=0, + eps=1e-3, + random_state=None, + normalized_stress=False, +): + """Computes multidimensional scaling using SMACOF algorithm. + + Parameters + ---------- + dissimilarities : ndarray of shape (n_samples, n_samples) + Pairwise dissimilarities between the points. Must be symmetric. + + metric : bool, default=True + Compute metric or nonmetric SMACOF algorithm. + When ``False`` (i.e. non-metric MDS), dissimilarities with 0 are considered as + missing values. + + n_components : int, default=2 + Number of dimensions in which to immerse the dissimilarities. If an + ``init`` array is provided, this option is overridden and the shape of + ``init`` is used to determine the dimensionality of the embedding + space. + + init : ndarray of shape (n_samples, n_components), default=None + Starting configuration of the embedding to initialize the algorithm. By + default, the algorithm is initialized with a randomly chosen array. + + max_iter : int, default=300 + Maximum number of iterations of the SMACOF algorithm for a single run. + + verbose : int, default=0 + Level of verbosity. + + eps : float, default=1e-3 + Relative tolerance with respect to stress at which to declare + convergence. The value of `eps` should be tuned separately depending + on whether or not `normalized_stress` is being used. + + random_state : int, RandomState instance or None, default=None + Determines the random number generator used to initialize the centers. + Pass an int for reproducible results across multiple function calls. + See :term:`Glossary `. + + normalized_stress : bool, default=False + Whether use and return normed stress value (Stress-1) instead of raw + stress calculated by default. Only supported in non-metric MDS. The + caller must ensure that if `normalized_stress=True` then `metric=False` + + .. versionadded:: 1.2 + + Returns + ------- + X : ndarray of shape (n_samples, n_components) + Coordinates of the points in a ``n_components``-space. + + stress : float + The final value of the stress (sum of squared distance of the + disparities and the distances for all constrained points). + If `normalized_stress=True`, and `metric=False` returns Stress-1. + A value of 0 indicates "perfect" fit, 0.025 excellent, 0.05 good, + 0.1 fair, and 0.2 poor [1]_. + + n_iter : int + The number of iterations corresponding to the best stress. + + References + ---------- + .. [1] "Nonmetric multidimensional scaling: a numerical method" Kruskal, J. + Psychometrika, 29 (1964) + + .. [2] "Multidimensional scaling by optimizing goodness of fit to a nonmetric + hypothesis" Kruskal, J. Psychometrika, 29, (1964) + + .. [3] "Modern Multidimensional Scaling - Theory and Applications" Borg, I.; + Groenen P. Springer Series in Statistics (1997) + """ + dissimilarities = check_symmetric(dissimilarities, raise_exception=True) + + n_samples = dissimilarities.shape[0] + random_state = check_random_state(random_state) + + sim_flat = ((1 - np.tri(n_samples)) * dissimilarities).ravel() + sim_flat_w = sim_flat[sim_flat != 0] + if init is None: + # Randomly choose initial configuration + X = random_state.uniform(size=n_samples * n_components) + X = X.reshape((n_samples, n_components)) + else: + # overrides the parameter p + n_components = init.shape[1] + if n_samples != init.shape[0]: + raise ValueError( + "init matrix should be of shape (%d, %d)" % (n_samples, n_components) + ) + X = init + + old_stress = None + ir = IsotonicRegression() + for it in range(max_iter): + # Compute distance and monotonic regression + dis = euclidean_distances(X) + + if metric: + disparities = dissimilarities + else: + dis_flat = dis.ravel() + # dissimilarities with 0 are considered as missing values + dis_flat_w = dis_flat[sim_flat != 0] + + # Compute the disparities using a monotonic regression + disparities_flat = ir.fit_transform(sim_flat_w, dis_flat_w) + disparities = dis_flat.copy() + disparities[sim_flat != 0] = disparities_flat + disparities = disparities.reshape((n_samples, n_samples)) + disparities *= np.sqrt( + (n_samples * (n_samples - 1) / 2) / (disparities**2).sum() + ) + + # Compute stress + stress = ((dis.ravel() - disparities.ravel()) ** 2).sum() / 2 + if normalized_stress: + stress = np.sqrt(stress / ((disparities.ravel() ** 2).sum() / 2)) + # Update X using the Guttman transform + dis[dis == 0] = 1e-5 + ratio = disparities / dis + B = -ratio + B[np.arange(len(B)), np.arange(len(B))] += ratio.sum(axis=1) + X = 1.0 / n_samples * np.dot(B, X) + + dis = np.sqrt((X**2).sum(axis=1)).sum() + if verbose >= 2: + print("it: %d, stress %s" % (it, stress)) + if old_stress is not None: + if (old_stress - stress / dis) < eps: + if verbose: + print("breaking at iteration %d with stress %s" % (it, stress)) + break + old_stress = stress / dis + + return X, stress, it + 1 + + +@validate_params( + { + "dissimilarities": ["array-like"], + "metric": ["boolean"], + "n_components": [Interval(Integral, 1, None, closed="left")], + "init": ["array-like", None], + "n_init": [Interval(Integral, 1, None, closed="left")], + "n_jobs": [Integral, None], + "max_iter": [Interval(Integral, 1, None, closed="left")], + "verbose": ["verbose"], + "eps": [Interval(Real, 0, None, closed="left")], + "random_state": ["random_state"], + "return_n_iter": ["boolean"], + "normalized_stress": ["boolean", StrOptions({"auto"})], + }, + prefer_skip_nested_validation=True, +) +def smacof( + dissimilarities, + *, + metric=True, + n_components=2, + init=None, + n_init=8, + n_jobs=None, + max_iter=300, + verbose=0, + eps=1e-3, + random_state=None, + return_n_iter=False, + normalized_stress="auto", +): + """Compute multidimensional scaling using the SMACOF algorithm. + + The SMACOF (Scaling by MAjorizing a COmplicated Function) algorithm is a + multidimensional scaling algorithm which minimizes an objective function + (the *stress*) using a majorization technique. Stress majorization, also + known as the Guttman Transform, guarantees a monotone convergence of + stress, and is more powerful than traditional techniques such as gradient + descent. + + The SMACOF algorithm for metric MDS can be summarized by the following + steps: + + 1. Set an initial start configuration, randomly or not. + 2. Compute the stress + 3. Compute the Guttman Transform + 4. Iterate 2 and 3 until convergence. + + The nonmetric algorithm adds a monotonic regression step before computing + the stress. + + Parameters + ---------- + dissimilarities : array-like of shape (n_samples, n_samples) + Pairwise dissimilarities between the points. Must be symmetric. + + metric : bool, default=True + Compute metric or nonmetric SMACOF algorithm. + When ``False`` (i.e. non-metric MDS), dissimilarities with 0 are considered as + missing values. + + n_components : int, default=2 + Number of dimensions in which to immerse the dissimilarities. If an + ``init`` array is provided, this option is overridden and the shape of + ``init`` is used to determine the dimensionality of the embedding + space. + + init : array-like of shape (n_samples, n_components), default=None + Starting configuration of the embedding to initialize the algorithm. By + default, the algorithm is initialized with a randomly chosen array. + + n_init : int, default=8 + Number of times the SMACOF algorithm will be run with different + initializations. The final results will be the best output of the runs, + determined by the run with the smallest final stress. If ``init`` is + provided, this option is overridden and a single run is performed. + + n_jobs : int, default=None + The number of jobs to use for the computation. If multiple + initializations are used (``n_init``), each run of the algorithm is + computed in parallel. + + ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. + ``-1`` means using all processors. See :term:`Glossary ` + for more details. + + max_iter : int, default=300 + Maximum number of iterations of the SMACOF algorithm for a single run. + + verbose : int, default=0 + Level of verbosity. + + eps : float, default=1e-3 + Relative tolerance with respect to stress at which to declare + convergence. The value of `eps` should be tuned separately depending + on whether or not `normalized_stress` is being used. + + random_state : int, RandomState instance or None, default=None + Determines the random number generator used to initialize the centers. + Pass an int for reproducible results across multiple function calls. + See :term:`Glossary `. + + return_n_iter : bool, default=False + Whether or not to return the number of iterations. + + normalized_stress : bool or "auto" default="auto" + Whether use and return normed stress value (Stress-1) instead of raw + stress calculated by default. Only supported in non-metric MDS. + + .. versionadded:: 1.2 + + .. versionchanged:: 1.4 + The default value changed from `False` to `"auto"` in version 1.4. + + Returns + ------- + X : ndarray of shape (n_samples, n_components) + Coordinates of the points in a ``n_components``-space. + + stress : float + The final value of the stress (sum of squared distance of the + disparities and the distances for all constrained points). + If `normalized_stress=True`, and `metric=False` returns Stress-1. + A value of 0 indicates "perfect" fit, 0.025 excellent, 0.05 good, + 0.1 fair, and 0.2 poor [1]_. + + n_iter : int + The number of iterations corresponding to the best stress. Returned + only if ``return_n_iter`` is set to ``True``. + + References + ---------- + .. [1] "Nonmetric multidimensional scaling: a numerical method" Kruskal, J. + Psychometrika, 29 (1964) + + .. [2] "Multidimensional scaling by optimizing goodness of fit to a nonmetric + hypothesis" Kruskal, J. Psychometrika, 29, (1964) + + .. [3] "Modern Multidimensional Scaling - Theory and Applications" Borg, I.; + Groenen P. Springer Series in Statistics (1997) + + Examples + -------- + >>> import numpy as np + >>> from sklearn.manifold import smacof + >>> from sklearn.metrics import euclidean_distances + >>> X = np.array([[0, 1, 2], [1, 0, 3],[2, 3, 0]]) + >>> dissimilarities = euclidean_distances(X) + >>> mds_result, stress = smacof(dissimilarities, n_components=2, random_state=42) + >>> mds_result + array([[ 0.05... -1.07... ], + [ 1.74..., -0.75...], + [-1.79..., 1.83...]]) + >>> stress + 0.0012... + """ + + dissimilarities = check_array(dissimilarities) + random_state = check_random_state(random_state) + + if normalized_stress == "auto": + normalized_stress = not metric + + if normalized_stress and metric: + raise ValueError( + "Normalized stress is not supported for metric MDS. Either set" + " `normalized_stress=False` or use `metric=False`." + ) + if hasattr(init, "__array__"): + init = np.asarray(init).copy() + if not n_init == 1: + warnings.warn( + "Explicit initial positions passed: " + "performing only one init of the MDS instead of %d" % n_init + ) + n_init = 1 + + best_pos, best_stress = None, None + + if effective_n_jobs(n_jobs) == 1: + for it in range(n_init): + pos, stress, n_iter_ = _smacof_single( + dissimilarities, + metric=metric, + n_components=n_components, + init=init, + max_iter=max_iter, + verbose=verbose, + eps=eps, + random_state=random_state, + normalized_stress=normalized_stress, + ) + if best_stress is None or stress < best_stress: + best_stress = stress + best_pos = pos.copy() + best_iter = n_iter_ + else: + seeds = random_state.randint(np.iinfo(np.int32).max, size=n_init) + results = Parallel(n_jobs=n_jobs, verbose=max(verbose - 1, 0))( + delayed(_smacof_single)( + dissimilarities, + metric=metric, + n_components=n_components, + init=init, + max_iter=max_iter, + verbose=verbose, + eps=eps, + random_state=seed, + normalized_stress=normalized_stress, + ) + for seed in seeds + ) + positions, stress, n_iters = zip(*results) + best = np.argmin(stress) + best_stress = stress[best] + best_pos = positions[best] + best_iter = n_iters[best] + + if return_n_iter: + return best_pos, best_stress, best_iter + else: + return best_pos, best_stress + + +class MDS(BaseEstimator): + """Multidimensional scaling. + + Read more in the :ref:`User Guide `. + + Parameters + ---------- + n_components : int, default=2 + Number of dimensions in which to immerse the dissimilarities. + + metric : bool, default=True + If ``True``, perform metric MDS; otherwise, perform nonmetric MDS. + When ``False`` (i.e. non-metric MDS), dissimilarities with 0 are considered as + missing values. + + n_init : int, default=4 + Number of times the SMACOF algorithm will be run with different + initializations. The final results will be the best output of the runs, + determined by the run with the smallest final stress. + + max_iter : int, default=300 + Maximum number of iterations of the SMACOF algorithm for a single run. + + verbose : int, default=0 + Level of verbosity. + + eps : float, default=1e-3 + Relative tolerance with respect to stress at which to declare + convergence. The value of `eps` should be tuned separately depending + on whether or not `normalized_stress` is being used. + + n_jobs : int, default=None + The number of jobs to use for the computation. If multiple + initializations are used (``n_init``), each run of the algorithm is + computed in parallel. + + ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. + ``-1`` means using all processors. See :term:`Glossary ` + for more details. + + random_state : int, RandomState instance or None, default=None + Determines the random number generator used to initialize the centers. + Pass an int for reproducible results across multiple function calls. + See :term:`Glossary `. + + dissimilarity : {'euclidean', 'precomputed'}, default='euclidean' + Dissimilarity measure to use: + + - 'euclidean': + Pairwise Euclidean distances between points in the dataset. + + - 'precomputed': + Pre-computed dissimilarities are passed directly to ``fit`` and + ``fit_transform``. + + normalized_stress : bool or "auto" default="auto" + Whether use and return normed stress value (Stress-1) instead of raw + stress calculated by default. Only supported in non-metric MDS. + + .. versionadded:: 1.2 + + .. versionchanged:: 1.4 + The default value changed from `False` to `"auto"` in version 1.4. + + Attributes + ---------- + embedding_ : ndarray of shape (n_samples, n_components) + Stores the position of the dataset in the embedding space. + + stress_ : float + The final value of the stress (sum of squared distance of the + disparities and the distances for all constrained points). + If `normalized_stress=True`, and `metric=False` returns Stress-1. + A value of 0 indicates "perfect" fit, 0.025 excellent, 0.05 good, + 0.1 fair, and 0.2 poor [1]_. + + dissimilarity_matrix_ : ndarray of shape (n_samples, n_samples) + Pairwise dissimilarities between the points. Symmetric matrix that: + + - either uses a custom dissimilarity matrix by setting `dissimilarity` + to 'precomputed'; + - or constructs a dissimilarity matrix from data using + Euclidean distances. + + n_features_in_ : int + Number of features seen during :term:`fit`. + + .. versionadded:: 0.24 + + feature_names_in_ : ndarray of shape (`n_features_in_`,) + Names of features seen during :term:`fit`. Defined only when `X` + has feature names that are all strings. + + .. versionadded:: 1.0 + + n_iter_ : int + The number of iterations corresponding to the best stress. + + See Also + -------- + sklearn.decomposition.PCA : Principal component analysis that is a linear + dimensionality reduction method. + sklearn.decomposition.KernelPCA : Non-linear dimensionality reduction using + kernels and PCA. + TSNE : T-distributed Stochastic Neighbor Embedding. + Isomap : Manifold learning based on Isometric Mapping. + LocallyLinearEmbedding : Manifold learning using Locally Linear Embedding. + SpectralEmbedding : Spectral embedding for non-linear dimensionality. + + References + ---------- + .. [1] "Nonmetric multidimensional scaling: a numerical method" Kruskal, J. + Psychometrika, 29 (1964) + + .. [2] "Multidimensional scaling by optimizing goodness of fit to a nonmetric + hypothesis" Kruskal, J. Psychometrika, 29, (1964) + + .. [3] "Modern Multidimensional Scaling - Theory and Applications" Borg, I.; + Groenen P. Springer Series in Statistics (1997) + + Examples + -------- + >>> from sklearn.datasets import load_digits + >>> from sklearn.manifold import MDS + >>> X, _ = load_digits(return_X_y=True) + >>> X.shape + (1797, 64) + >>> embedding = MDS(n_components=2, normalized_stress='auto') + >>> X_transformed = embedding.fit_transform(X[:100]) + >>> X_transformed.shape + (100, 2) + + For a more detailed example of usage, see: + :ref:`sphx_glr_auto_examples_manifold_plot_mds.py` + """ + + _parameter_constraints: dict = { + "n_components": [Interval(Integral, 1, None, closed="left")], + "metric": ["boolean"], + "n_init": [Interval(Integral, 1, None, closed="left")], + "max_iter": [Interval(Integral, 1, None, closed="left")], + "verbose": ["verbose"], + "eps": [Interval(Real, 0.0, None, closed="left")], + "n_jobs": [None, Integral], + "random_state": ["random_state"], + "dissimilarity": [StrOptions({"euclidean", "precomputed"})], + "normalized_stress": ["boolean", StrOptions({"auto"})], + } + + def __init__( + self, + n_components=2, + *, + metric=True, + n_init=4, + max_iter=300, + verbose=0, + eps=1e-3, + n_jobs=None, + random_state=None, + dissimilarity="euclidean", + normalized_stress="auto", + ): + self.n_components = n_components + self.dissimilarity = dissimilarity + self.metric = metric + self.n_init = n_init + self.max_iter = max_iter + self.eps = eps + self.verbose = verbose + self.n_jobs = n_jobs + self.random_state = random_state + self.normalized_stress = normalized_stress + + def _more_tags(self): + return {"pairwise": self.dissimilarity == "precomputed"} + + def fit(self, X, y=None, init=None): + """ + Compute the position of the points in the embedding space. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) or \ + (n_samples, n_samples) + Input data. If ``dissimilarity=='precomputed'``, the input should + be the dissimilarity matrix. + + y : Ignored + Not used, present for API consistency by convention. + + init : ndarray of shape (n_samples, n_components), default=None + Starting configuration of the embedding to initialize the SMACOF + algorithm. By default, the algorithm is initialized with a randomly + chosen array. + + Returns + ------- + self : object + Fitted estimator. + """ + self.fit_transform(X, init=init) + return self + + @_fit_context(prefer_skip_nested_validation=True) + def fit_transform(self, X, y=None, init=None): + """ + Fit the data from `X`, and returns the embedded coordinates. + + Parameters + ---------- + X : array-like of shape (n_samples, n_features) or \ + (n_samples, n_samples) + Input data. If ``dissimilarity=='precomputed'``, the input should + be the dissimilarity matrix. + + y : Ignored + Not used, present for API consistency by convention. + + init : ndarray of shape (n_samples, n_components), default=None + Starting configuration of the embedding to initialize the SMACOF + algorithm. By default, the algorithm is initialized with a randomly + chosen array. + + Returns + ------- + X_new : ndarray of shape (n_samples, n_components) + X transformed in the new space. + """ + X = self._validate_data(X) + if X.shape[0] == X.shape[1] and self.dissimilarity != "precomputed": + warnings.warn( + "The MDS API has changed. ``fit`` now constructs an" + " dissimilarity matrix from data. To use a custom " + "dissimilarity matrix, set " + "``dissimilarity='precomputed'``." + ) + + if self.dissimilarity == "precomputed": + self.dissimilarity_matrix_ = X + elif self.dissimilarity == "euclidean": + self.dissimilarity_matrix_ = euclidean_distances(X) + + self.embedding_, self.stress_, self.n_iter_ = smacof( + self.dissimilarity_matrix_, + metric=self.metric, + n_components=self.n_components, + init=init, + n_init=self.n_init, + n_jobs=self.n_jobs, + max_iter=self.max_iter, + verbose=self.verbose, + eps=self.eps, + random_state=self.random_state, + return_n_iter=True, + normalized_stress=self.normalized_stress, + ) + + return self.embedding_ diff --git a/venv/lib/python3.10/site-packages/sklearn/manifold/_spectral_embedding.py b/venv/lib/python3.10/site-packages/sklearn/manifold/_spectral_embedding.py new file mode 100644 index 0000000000000000000000000000000000000000..a2839954c117ad283c125bacc8f3b5e1f6483969 --- /dev/null +++ b/venv/lib/python3.10/site-packages/sklearn/manifold/_spectral_embedding.py @@ -0,0 +1,749 @@ +"""Spectral Embedding.""" + +# Author: Gael Varoquaux +# Wei LI +# License: BSD 3 clause + + +import warnings +from numbers import Integral, Real + +import numpy as np +from scipy import sparse +from scipy.linalg import eigh +from scipy.sparse.csgraph import connected_components +from scipy.sparse.linalg import eigsh, lobpcg + +from ..base import BaseEstimator, _fit_context +from ..metrics.pairwise import rbf_kernel +from ..neighbors import NearestNeighbors, kneighbors_graph +from ..utils import ( + check_array, + check_random_state, + check_symmetric, +) +from ..utils._arpack import _init_arpack_v0 +from ..utils._param_validation import Interval, StrOptions +from ..utils.extmath import _deterministic_vector_sign_flip +from ..utils.fixes import laplacian as csgraph_laplacian +from ..utils.fixes import parse_version, sp_version + + +def _graph_connected_component(graph, node_id): + """Find the largest graph connected components that contains one + given node. + + Parameters + ---------- + graph : array-like of shape (n_samples, n_samples) + Adjacency matrix of the graph, non-zero weight means an edge + between the nodes. + + node_id : int + The index of the query node of the graph. + + Returns + ------- + connected_components_matrix : array-like of shape (n_samples,) + An array of bool value indicating the indexes of the nodes + belonging to the largest connected components of the given query + node. + """ + n_node = graph.shape[0] + if sparse.issparse(graph): + # speed up row-wise access to boolean connection mask + graph = graph.tocsr() + connected_nodes = np.zeros(n_node, dtype=bool) + nodes_to_explore = np.zeros(n_node, dtype=bool) + nodes_to_explore[node_id] = True + for _ in range(n_node): + last_num_component = connected_nodes.sum() + np.logical_or(connected_nodes, nodes_to_explore, out=connected_nodes) + if last_num_component >= connected_nodes.sum(): + break + indices = np.where(nodes_to_explore)[0] + nodes_to_explore.fill(False) + for i in indices: + if sparse.issparse(graph): + # scipy not yet implemented 1D sparse slices; can be changed back to + # `neighbors = graph[i].toarray().ravel()` once implemented + neighbors = graph[[i], :].toarray().ravel() + else: + neighbors = graph[i] + np.logical_or(nodes_to_explore, neighbors, out=nodes_to_explore) + return connected_nodes + + +def _graph_is_connected(graph): + """Return whether the graph is connected (True) or Not (False). + + Parameters + ---------- + graph : {array-like, sparse matrix} of shape (n_samples, n_samples) + Adjacency matrix of the graph, non-zero weight means an edge + between the nodes. + + Returns + ------- + is_connected : bool + True means the graph is fully connected and False means not. + """ + if sparse.issparse(graph): + # Before Scipy 1.11.3, `connected_components` only supports 32-bit indices. + # PR: https://github.com/scipy/scipy/pull/18913 + # First integration in 1.11.3: https://github.com/scipy/scipy/pull/19279 + # TODO(jjerphan): Once SciPy 1.11.3 is the minimum supported version, use + # `accept_large_sparse=True`. + accept_large_sparse = sp_version >= parse_version("1.11.3") + graph = check_array( + graph, accept_sparse=True, accept_large_sparse=accept_large_sparse + ) + # sparse graph, find all the connected components + n_connected_components, _ = connected_components(graph) + return n_connected_components == 1 + else: + # dense graph, find all connected components start from node 0 + return _graph_connected_component(graph, 0).sum() == graph.shape[0] + + +def _set_diag(laplacian, value, norm_laplacian): + """Set the diagonal of the laplacian matrix and convert it to a + sparse format well suited for eigenvalue decomposition. + + Parameters + ---------- + laplacian : {ndarray, sparse matrix} + The graph laplacian. + + value : float + The value of the diagonal. + + norm_laplacian : bool + Whether the value of the diagonal should be changed or not. + + Returns + ------- + laplacian : {array, sparse matrix} + An array of matrix in a form that is well suited to fast + eigenvalue decomposition, depending on the band width of the + matrix. + """ + n_nodes = laplacian.shape[0] + # We need all entries in the diagonal to values + if not sparse.issparse(laplacian): + if norm_laplacian: + laplacian.flat[:: n_nodes + 1] = value + else: + laplacian = laplacian.tocoo() + if norm_laplacian: + diag_idx = laplacian.row == laplacian.col + laplacian.data[diag_idx] = value + # If the matrix has a small number of diagonals (as in the + # case of structured matrices coming from images), the + # dia format might be best suited for matvec products: + n_diags = np.unique(laplacian.row - laplacian.col).size + if n_diags <= 7: + # 3 or less outer diagonals on each side + laplacian = laplacian.todia() + else: + # csr has the fastest matvec and is thus best suited to + # arpack + laplacian = laplacian.tocsr() + return laplacian + + +def spectral_embedding( + adjacency, + *, + n_components=8, + eigen_solver=None, + random_state=None, + eigen_tol="auto", + norm_laplacian=True, + drop_first=True, +): + """Project the sample on the first eigenvectors of the graph Laplacian. + + The adjacency matrix is used to compute a normalized graph Laplacian + whose spectrum (especially the eigenvectors associated to the + smallest eigenvalues) has an interpretation in terms of minimal + number of cuts necessary to split the graph into comparably sized + components. + + This embedding can also 'work' even if the ``adjacency`` variable is + not strictly the adjacency matrix of a graph but more generally + an affinity or similarity matrix between samples (for instance the + heat kernel of a euclidean distance matrix or a k-NN matrix). + + However care must taken to always make the affinity matrix symmetric + so that the eigenvector decomposition works as expected. + + Note : Laplacian Eigenmaps is the actual algorithm implemented here. + + Read more in the :ref:`User Guide `. + + Parameters + ---------- + adjacency : {array-like, sparse graph} of shape (n_samples, n_samples) + The adjacency matrix of the graph to embed. + + n_components : int, default=8 + The dimension of the projection subspace. + + eigen_solver : {'arpack', 'lobpcg', 'amg'}, default=None + The eigenvalue decomposition strategy to use. AMG requires pyamg + to be installed. It can be faster on very large, sparse problems, + but may also lead to instabilities. If None, then ``'arpack'`` is + used. + + random_state : int, RandomState instance or None, default=None + A pseudo random number generator used for the initialization + of the lobpcg eigen vectors decomposition when `eigen_solver == + 'amg'`, and for the K-Means initialization. Use an int to make + the results deterministic across calls (See + :term:`Glossary `). + + .. note:: + When using `eigen_solver == 'amg'`, + it is necessary to also fix the global numpy seed with + `np.random.seed(int)` to get deterministic results. See + https://github.com/pyamg/pyamg/issues/139 for further + information. + + eigen_tol : float, default="auto" + Stopping criterion for eigendecomposition of the Laplacian matrix. + If `eigen_tol="auto"` then the passed tolerance will depend on the + `eigen_solver`: + + - If `eigen_solver="arpack"`, then `eigen_tol=0.0`; + - If `eigen_solver="lobpcg"` or `eigen_solver="amg"`, then + `eigen_tol=None` which configures the underlying `lobpcg` solver to + automatically resolve the value according to their heuristics. See, + :func:`scipy.sparse.linalg.lobpcg` for details. + + Note that when using `eigen_solver="amg"` values of `tol<1e-5` may lead + to convergence issues and should be avoided. + + .. versionadded:: 1.2 + Added 'auto' option. + + norm_laplacian : bool, default=True + If True, then compute symmetric normalized Laplacian. + + drop_first : bool, default=True + Whether to drop the first eigenvector. For spectral embedding, this + should be True as the first eigenvector should be constant vector for + connected graph, but for spectral clustering, this should be kept as + False to retain the first eigenvector. + + Returns + ------- + embedding : ndarray of shape (n_samples, n_components) + The reduced samples. + + Notes + ----- + Spectral Embedding (Laplacian Eigenmaps) is most useful when the graph + has one connected component. If there graph has many components, the first + few eigenvectors will simply uncover the connected components of the graph. + + References + ---------- + * https://en.wikipedia.org/wiki/LOBPCG + + * :doi:`"Toward the Optimal Preconditioned Eigensolver: Locally Optimal + Block Preconditioned Conjugate Gradient Method", + Andrew V. Knyazev + <10.1137/S1064827500366124>` + + Examples + -------- + >>> from sklearn.datasets import load_digits + >>> from sklearn.neighbors import kneighbors_graph + >>> from sklearn.manifold import spectral_embedding + >>> X, _ = load_digits(return_X_y=True) + >>> X = X[:100] + >>> affinity_matrix = kneighbors_graph( + ... X, n_neighbors=int(X.shape[0] / 10), include_self=True + ... ) + >>> # make the matrix symmetric + >>> affinity_matrix = 0.5 * (affinity_matrix + affinity_matrix.T) + >>> embedding = spectral_embedding(affinity_matrix, n_components=2, random_state=42) + >>> embedding.shape + (100, 2) + """ + adjacency = check_symmetric(adjacency) + + if eigen_solver == "amg": + try: + from pyamg import smoothed_aggregation_solver + except ImportError as e: + raise ValueError( + "The eigen_solver was set to 'amg', but pyamg is not available." + ) from e + + if eigen_solver is None: + eigen_solver = "arpack" + elif eigen_solver not in ("arpack", "lobpcg", "amg"): + raise ValueError( + "Unknown value for eigen_solver: '%s'." + "Should be 'amg', 'arpack', or 'lobpcg'" % eigen_solver + ) + + random_state = check_random_state(random_state) + + n_nodes = adjacency.shape[0] + # Whether to drop the first eigenvector + if drop_first: + n_components = n_components + 1 + + if not _graph_is_connected(adjacency): + warnings.warn( + "Graph is not fully connected, spectral embedding may not work as expected." + ) + + laplacian, dd = csgraph_laplacian( + adjacency, normed=norm_laplacian, return_diag=True + ) + if ( + eigen_solver == "arpack" + or eigen_solver != "lobpcg" + and (not sparse.issparse(laplacian) or n_nodes < 5 * n_components) + ): + # lobpcg used with eigen_solver='amg' has bugs for low number of nodes + # for details see the source code in scipy: + # https://github.com/scipy/scipy/blob/v0.11.0/scipy/sparse/linalg/eigen + # /lobpcg/lobpcg.py#L237 + # or matlab: + # https://www.mathworks.com/matlabcentral/fileexchange/48-lobpcg-m + laplacian = _set_diag(laplacian, 1, norm_laplacian) + + # Here we'll use shift-invert mode for fast eigenvalues + # (see https://docs.scipy.org/doc/scipy/reference/tutorial/arpack.html + # for a short explanation of what this means) + # Because the normalized Laplacian has eigenvalues between 0 and 2, + # I - L has eigenvalues between -1 and 1. ARPACK is most efficient + # when finding eigenvalues of largest magnitude (keyword which='LM') + # and when these eigenvalues are very large compared to the rest. + # For very large, very sparse graphs, I - L can have many, many + # eigenvalues very near 1.0. This leads to slow convergence. So + # instead, we'll use ARPACK's shift-invert mode, asking for the + # eigenvalues near 1.0. This effectively spreads-out the spectrum + # near 1.0 and leads to much faster convergence: potentially an + # orders-of-magnitude speedup over simply using keyword which='LA' + # in standard mode. + try: + # We are computing the opposite of the laplacian inplace so as + # to spare a memory allocation of a possibly very large array + tol = 0 if eigen_tol == "auto" else eigen_tol + laplacian *= -1 + v0 = _init_arpack_v0(laplacian.shape[0], random_state) + laplacian = check_array( + laplacian, accept_sparse="csr", accept_large_sparse=False + ) + _, diffusion_map = eigsh( + laplacian, k=n_components, sigma=1.0, which="LM", tol=tol, v0=v0 + ) + embedding = diffusion_map.T[n_components::-1] + if norm_laplacian: + # recover u = D^-1/2 x from the eigenvector output x + embedding = embedding / dd + except RuntimeError: + # When submatrices are exactly singular, an LU decomposition + # in arpack fails. We fallback to lobpcg + eigen_solver = "lobpcg" + # Revert the laplacian to its opposite to have lobpcg work + laplacian *= -1 + + elif eigen_solver == "amg": + # Use AMG to get a preconditioner and speed up the eigenvalue + # problem. + if not sparse.issparse(laplacian): + warnings.warn("AMG works better for sparse matrices") + laplacian = check_array( + laplacian, dtype=[np.float64, np.float32], accept_sparse=True + ) + laplacian = _set_diag(laplacian, 1, norm_laplacian) + + # The Laplacian matrix is always singular, having at least one zero + # eigenvalue, corresponding to the trivial eigenvector, which is a + # constant. Using a singular matrix for preconditioning may result in + # random failures in LOBPCG and is not supported by the existing + # theory: + # see https://doi.org/10.1007/s10208-015-9297-1 + # Shift the Laplacian so its diagononal is not all ones. The shift + # does change the eigenpairs however, so we'll feed the shifted + # matrix to the solver and afterward set it back to the original. + diag_shift = 1e-5 * sparse.eye(laplacian.shape[0]) + laplacian += diag_shift + if hasattr(sparse, "csr_array") and isinstance(laplacian, sparse.csr_array): + # `pyamg` does not work with `csr_array` and we need to convert it to a + # `csr_matrix` object. + laplacian = sparse.csr_matrix(laplacian) + ml = smoothed_aggregation_solver(check_array(laplacian, accept_sparse="csr")) + laplacian -= diag_shift + + M = ml.aspreconditioner() + # Create initial approximation X to eigenvectors + X = random_state.standard_normal(size=(laplacian.shape[0], n_components + 1)) + X[:, 0] = dd.ravel() + X = X.astype(laplacian.dtype) + + tol = None if eigen_tol == "auto" else eigen_tol + _, diffusion_map = lobpcg(laplacian, X, M=M, tol=tol, largest=False) + embedding = diffusion_map.T + if norm_laplacian: + # recover u = D^-1/2 x from the eigenvector output x + embedding = embedding / dd + if embedding.shape[0] == 1: + raise ValueError + + if eigen_solver == "lobpcg": + laplacian = check_array( + laplacian, dtype=[np.float64, np.float32], accept_sparse=True + ) + if n_nodes < 5 * n_components + 1: + # see note above under arpack why lobpcg has problems with small + # number of nodes + # lobpcg will fallback to eigh, so we short circuit it + if sparse.issparse(laplacian): + laplacian = laplacian.toarray() + _, diffusion_map = eigh(laplacian, check_finite=False) + embedding = diffusion_map.T[:n_components] + if norm_laplacian: + # recover u = D^-1/2 x from the eigenvector output x + embedding = embedding / dd + else: + laplacian = _set_diag(laplacian, 1, norm_laplacian) + # We increase the number of eigenvectors requested, as lobpcg + # doesn't behave well in low dimension and create initial + # approximation X to eigenvectors + X = random_state.standard_normal( + size=(laplacian.shape[0], n_components + 1) + ) + X[:, 0] = dd.ravel() + X = X.astype(laplacian.dtype) + tol = None if eigen_tol == "auto" else eigen_tol + _, diffusion_map = lobpcg( + laplacian, X, tol=tol, largest=False, maxiter=2000 + ) + embedding = diffusion_map.T[:n_components] + if norm_laplacian: + # recover u = D^-1/2 x from the eigenvector output x + embedding = embedding / dd + if embedding.shape[0] == 1: + raise ValueError + + embedding = _deterministic_vector_sign_flip(embedding) + if drop_first: + return embedding[1:n_components].T + else: + return embedding[:n_components].T + + +class SpectralEmbedding(BaseEstimator): + """Spectral embedding for non-linear dimensionality reduction. + + Forms an affinity matrix given by the specified function and + applies spectral decomposition to the corresponding graph laplacian. + The resulting transformation is given by the value of the + eigenvectors for each data point. + + Note : Laplacian Eigenmaps is the actual algorithm implemented here. + + Read more in the :ref:`User Guide `. + + Parameters + ---------- + n_components : int, default=2 + The dimension of the projected subspace. + + affinity : {'nearest_neighbors', 'rbf', 'precomputed', \ + 'precomputed_nearest_neighbors'} or callable, \ + default='nearest_neighbors' + How to construct the affinity matrix. + - 'nearest_neighbors' : construct the affinity matrix by computing a + graph of nearest neighbors. + - 'rbf' : construct the affinity matrix by computing a radial basis + function (RBF) kernel. + - 'precomputed' : interpret ``X`` as a precomputed affinity matrix. + - 'precomputed_nearest_neighbors' : interpret ``X`` as a sparse graph + of precomputed nearest neighbors, and constructs the affinity matrix + by selecting the ``n_neighbors`` nearest neighbors. + - callable : use passed in function as affinity + the function takes in data matrix (n_samples, n_features) + and return affinity matrix (n_samples, n_samples). + + gamma : float, default=None + Kernel coefficient for rbf kernel. If None, gamma will be set to + 1/n_features. + + random_state : int, RandomState instance or None, default=None + A pseudo random number generator used for the initialization + of the lobpcg eigen vectors decomposition when `eigen_solver == + 'amg'`, and for the K-Means initialization. Use an int to make + the results deterministic across calls (See + :term:`Glossary `). + + .. note:: + When using `eigen_solver == 'amg'`, + it is necessary to also fix the global numpy seed with + `np.random.seed(int)` to get deterministic results. See + https://github.com/pyamg/pyamg/issues/139 for further + information. + + eigen_solver : {'arpack', 'lobpcg', 'amg'}, default=None + The eigenvalue decomposition strategy to use. AMG requires pyamg + to be installed. It can be faster on very large, sparse problems. + If None, then ``'arpack'`` is used. + + eigen_tol : float, default="auto" + Stopping criterion for eigendecomposition of the Laplacian matrix. + If `eigen_tol="auto"` then the passed tolerance will depend on the + `eigen_solver`: + + - If `eigen_solver="arpack"`, then `eigen_tol=0.0`; + - If `eigen_solver="lobpcg"` or `eigen_solver="amg"`, then + `eigen_tol=None` which configures the underlying `lobpcg` solver to + automatically resolve the value according to their heuristics. See, + :func:`scipy.sparse.linalg.lobpcg` for details. + + Note that when using `eigen_solver="lobpcg"` or `eigen_solver="amg"` + values of `tol<1e-5` may lead to convergence issues and should be + avoided. + + .. versionadded:: 1.2 + + n_neighbors : int, default=None + Number of nearest neighbors for nearest_neighbors graph building. + If None, n_neighbors will be set to max(n_samples/10, 1). + + n_jobs : int, default=None + The number of parallel jobs to run. + ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. + ``-1`` means using all processors. See :term:`Glossary ` + for more details. + + Attributes + ---------- + embedding_ : ndarray of shape (n_samples, n_components) + Spectral embedding of the training matrix. + + affinity_matrix_ : ndarray of shape (n_samples, n_samples) + Affinity_matrix constructed from samples or precomputed. + + n_features_in_ : int + Number of features seen during :term:`fit`. + + .. versionadded:: 0.24 + + feature_names_in_ : ndarray of shape (`n_features_in_`,) + Names of features seen during :term:`fit`. Defined only when `X` + has feature names that are all strings. + + .. versionadded:: 1.0 + + n_neighbors_ : int + Number of nearest neighbors effectively used. + + See Also + -------- + Isomap : Non-linear dimensionality reduction through Isometric Mapping. + + References + ---------- + + - :doi:`A Tutorial on Spectral Clustering, 2007 + Ulrike von Luxburg + <10.1007/s11222-007-9033-z>` + + - `On Spectral Clustering: Analysis and an algorithm, 2001 + Andrew Y. Ng, Michael I. Jordan, Yair Weiss + `_ + + - :doi:`Normalized cuts and image segmentation, 2000 + Jianbo Shi, Jitendra Malik + <10.1109/34.868688>` + + Examples + -------- + >>> from sklearn.datasets import load_digits + >>> from sklearn.manifold import SpectralEmbedding + >>> X, _ = load_digits(return_X_y=True) + >>> X.shape + (1797, 64) + >>> embedding = SpectralEmbedding(n_components=2) + >>> X_transformed = embedding.fit_transform(X[:100]) + >>> X_transformed.shape + (100, 2) + """ + + _parameter_constraints: dict = { + "n_components": [Interval(Integral, 1, None, closed="left")], + "affinity": [ + StrOptions( + { + "nearest_neighbors", + "rbf", + "precomputed", + "precomputed_nearest_neighbors", + }, + ), + callable, + ], + "gamma": [Interval(Real, 0, None, closed="left"), None], + "random_state": ["random_state"], + "eigen_solver": [StrOptions({"arpack", "lobpcg", "amg"}), None], + "eigen_tol": [Interval(Real, 0, None, closed="left"), StrOptions({"auto"})], + "n_neighbors": [Interval(Integral, 1, None, closed="left"), None], + "n_jobs": [None, Integral], + } + + def __init__( + self, + n_components=2, + *, + affinity="nearest_neighbors", + gamma=None, + random_state=None, + eigen_solver=None, + eigen_tol="auto", + n_neighbors=None, + n_jobs=None, + ): + self.n_components = n_components + self.affinity = affinity + self.gamma = gamma + self.random_state = random_state + self.eigen_solver = eigen_solver + self.eigen_tol = eigen_tol + self.n_neighbors = n_neighbors + self.n_jobs = n_jobs + + def _more_tags(self): + return { + "pairwise": self.affinity in [ + "precomputed", + "precomputed_nearest_neighbors", + ] + } + + def _get_affinity_matrix(self, X, Y=None): + """Calculate the affinity matrix from data + Parameters + ---------- + X : array-like of shape (n_samples, n_features) + Training vector, where `n_samples` is the number of samples + and `n_features` is the number of features. + + If affinity is "precomputed" + X : array-like of shape (n_samples, n_samples), + Interpret X as precomputed adjacency graph computed from + samples. + + Y: Ignored + + Returns + ------- + affinity_matrix of shape (n_samples, n_samples) + """ + if self.affinity == "precomputed": + self.affinity_matrix_ = X + return self.affinity_matrix_ + if self.affinity == "precomputed_nearest_neighbors": + estimator = NearestNeighbors( + n_neighbors=self.n_neighbors, n_jobs=self.n_jobs, metric="precomputed" + ).fit(X) + connectivity = estimator.kneighbors_graph(X=X, mode="connectivity") + self.affinity_matrix_ = 0.5 * (connectivity + connectivity.T) + return self.affinity_matrix_ + if self.affinity == "nearest_neighbors": + if sparse.issparse(X): + warnings.warn( + "Nearest neighbors affinity currently does " + "not support sparse input, falling back to " + "rbf affinity" + ) + self.affinity = "rbf" + else: + self.n_neighbors_ = ( + self.n_neighbors + if self.n_neighbors is not None + else max(int(X.shape[0] / 10), 1) + ) + self.affinity_matrix_ = kneighbors_graph( + X, self.n_neighbors_, include_self=True, n_jobs=self.n_jobs + ) + # currently only symmetric affinity_matrix supported + self.affinity_matrix_ = 0.5 * ( + self.affinity_matrix_ + self.affinity_matrix_.T + ) + return self.affinity_matrix_ + if self.affinity == "rbf": + self.gamma_ = self.gamma if self.gamma is not None else 1.0 / X.shape[1] + self.affinity_matrix_ = rbf_kernel(X, gamma=self.gamma_) + return self.affinity_matrix_ + self.affinity_matrix_ = self.affinity(X) + return self.affinity_matrix_ + + @_fit_context(prefer_skip_nested_validation=True) + def fit(self, X, y=None): + """Fit the model from data in X. + + Parameters + ---------- + X : {array-like, sparse matrix} of shape (n_samples, n_features) + Training vector, where `n_samples` is the number of samples + and `n_features` is the number of features. + + If affinity is "precomputed" + X : {array-like, sparse matrix}, shape (n_samples, n_samples), + Interpret X as precomputed adjacency graph computed from + samples. + + y : Ignored + Not used, present for API consistency by convention. + + Returns + ------- + self : object + Returns the instance itself. + """ + X = self._validate_data(X, accept_sparse="csr", ensure_min_samples=2) + + random_state = check_random_state(self.random_state) + + affinity_matrix = self._get_affinity_matrix(X) + self.embedding_ = spectral_embedding( + affinity_matrix, + n_components=self.n_components, + eigen_solver=self.eigen_solver, + eigen_tol=self.eigen_tol, + random_state=random_state, + ) + return self + + def fit_transform(self, X, y=None): + """Fit the model from data in X and transform X. + + Parameters + ---------- + X : {array-like, sparse matrix} of shape (n_samples, n_features) + Training vector, where `n_samples` is the number of samples + and `n_features` is the number of features. + + If affinity is "precomputed" + X : {array-like, sparse matrix} of shape (n_samples, n_samples), + Interpret X as precomputed adjacency graph computed from + samples. + + y : Ignored + Not used, present for API consistency by convention. + + Returns + ------- + X_new : array-like of shape (n_samples, n_components) + Spectral embedding of the training matrix. + """ + self.fit(X) + return self.embedding_ diff --git a/venv/lib/python3.10/site-packages/sklearn/manifold/_t_sne.py b/venv/lib/python3.10/site-packages/sklearn/manifold/_t_sne.py new file mode 100644 index 0000000000000000000000000000000000000000..2233bea3a768197684f820f9b92841e0670a1338 --- /dev/null +++ b/venv/lib/python3.10/site-packages/sklearn/manifold/_t_sne.py @@ -0,0 +1,1174 @@ +# Author: Alexander Fabisch -- +# Author: Christopher Moody +# Author: Nick Travers +# License: BSD 3 clause (C) 2014 + +# This is the exact and Barnes-Hut t-SNE implementation. There are other +# modifications of the algorithm: +# * Fast Optimization for t-SNE: +# https://cseweb.ucsd.edu/~lvdmaaten/workshops/nips2010/papers/vandermaaten.pdf + +from numbers import Integral, Real +from time import time + +import numpy as np +from scipy import linalg +from scipy.sparse import csr_matrix, issparse +from scipy.spatial.distance import pdist, squareform + +from ..base import ( + BaseEstimator, + ClassNamePrefixFeaturesOutMixin, + TransformerMixin, + _fit_context, +) +from ..decomposition import PCA +from ..metrics.pairwise import _VALID_METRICS, pairwise_distances +from ..neighbors import NearestNeighbors +from ..utils import check_random_state +from ..utils._openmp_helpers import _openmp_effective_n_threads +from ..utils._param_validation import Interval, StrOptions, validate_params +from ..utils.validation import _num_samples, check_non_negative + +# mypy error: Module 'sklearn.manifold' has no attribute '_utils' +# mypy error: Module 'sklearn.manifold' has no attribute '_barnes_hut_tsne' +from . import _barnes_hut_tsne, _utils # type: ignore + +MACHINE_EPSILON = np.finfo(np.double).eps + + +def _joint_probabilities(distances, desired_perplexity, verbose): + """Compute joint probabilities p_ij from distances. + + Parameters + ---------- + distances : ndarray of shape (n_samples * (n_samples-1) / 2,) + Distances of samples are stored as condensed matrices, i.e. + we omit the diagonal and duplicate entries and store everything + in a one-dimensional array. + + desired_perplexity : float + Desired perplexity of the joint probability distributions. + + verbose : int + Verbosity level. + + Returns + ------- + P : ndarray of shape (n_samples * (n_samples-1) / 2,) + Condensed joint probability matrix. + """ + # Compute conditional probabilities such that they approximately match + # the desired perplexity + distances = distances.astype(np.float32, copy=False) + conditional_P = _utils._binary_search_perplexity( + distances, desired_perplexity, verbose + ) + P = conditional_P + conditional_P.T + sum_P = np.maximum(np.sum(P), MACHINE_EPSILON) + P = np.maximum(squareform(P) / sum_P, MACHINE_EPSILON) + return P + + +def _joint_probabilities_nn(distances, desired_perplexity, verbose): + """Compute joint probabilities p_ij from distances using just nearest + neighbors. + + This method is approximately equal to _joint_probabilities. The latter + is O(N), but limiting the joint probability to nearest neighbors improves + this substantially to O(uN). + + Parameters + ---------- + distances : sparse matrix of shape (n_samples, n_samples) + Distances of samples to its n_neighbors nearest neighbors. All other + distances are left to zero (and are not materialized in memory). + Matrix should be of CSR format. + + desired_perplexity : float + Desired perplexity of the joint probability distributions. + + verbose : int + Verbosity level. + + Returns + ------- + P : sparse matrix of shape (n_samples, n_samples) + Condensed joint probability matrix with only nearest neighbors. Matrix + will be of CSR format. + """ + t0 = time() + # Compute conditional probabilities such that they approximately match + # the desired perplexity + distances.sort_indices() + n_samples = distances.shape[0] + distances_data = distances.data.reshape(n_samples, -1) + distances_data = distances_data.astype(np.float32, copy=False) + conditional_P = _utils._binary_search_perplexity( + distances_data, desired_perplexity, verbose + ) + assert np.all(np.isfinite(conditional_P)), "All probabilities should be finite" + + # Symmetrize the joint probability distribution using sparse operations + P = csr_matrix( + (conditional_P.ravel(), distances.indices, distances.indptr), + shape=(n_samples, n_samples), + ) + P = P + P.T + + # Normalize the joint probability distribution + sum_P = np.maximum(P.sum(), MACHINE_EPSILON) + P /= sum_P + + assert np.all(np.abs(P.data) <= 1.0) + if verbose >= 2: + duration = time() - t0 + print("[t-SNE] Computed conditional probabilities in {:.3f}s".format(duration)) + return P + + +def _kl_divergence( + params, + P, + degrees_of_freedom, + n_samples, + n_components, + skip_num_points=0, + compute_error=True, +): + """t-SNE objective function: gradient of the KL divergence + of p_ijs and q_ijs and the absolute error. + + Parameters + ---------- + params : ndarray of shape (n_params,) + Unraveled embedding. + + P : ndarray of shape (n_samples * (n_samples-1) / 2,) + Condensed joint probability matrix. + + degrees_of_freedom : int + Degrees of freedom of the Student's-t distribution. + + n_samples : int + Number of samples. + + n_components : int + Dimension of the embedded space. + + skip_num_points : int, default=0 + This does not compute the gradient for points with indices below + `skip_num_points`. This is useful when computing transforms of new + data where you'd like to keep the old data fixed. + + compute_error: bool, default=True + If False, the kl_divergence is not computed and returns NaN. + + Returns + ------- + kl_divergence : float + Kullback-Leibler divergence of p_ij and q_ij. + + grad : ndarray of shape (n_params,) + Unraveled gradient of the Kullback-Leibler divergence with respect to + the embedding. + """ + X_embedded = params.reshape(n_samples, n_components) + + # Q is a heavy-tailed distribution: Student's t-distribution + dist = pdist(X_embedded, "sqeuclidean") + dist /= degrees_of_freedom + dist += 1.0 + dist **= (degrees_of_freedom + 1.0) / -2.0 + Q = np.maximum(dist / (2.0 * np.sum(dist)), MACHINE_EPSILON) + + # Optimization trick below: np.dot(x, y) is faster than + # np.sum(x * y) because it calls BLAS + + # Objective: C (Kullback-Leibler divergence of P and Q) + if compute_error: + kl_divergence = 2.0 * np.dot(P, np.log(np.maximum(P, MACHINE_EPSILON) / Q)) + else: + kl_divergence = np.nan + + # Gradient: dC/dY + # pdist always returns double precision distances. Thus we need to take + grad = np.ndarray((n_samples, n_components), dtype=params.dtype) + PQd = squareform((P - Q) * dist) + for i in range(skip_num_points, n_samples): + grad[i] = np.dot(np.ravel(PQd[i], order="K"), X_embedded[i] - X_embedded) + grad = grad.ravel() + c = 2.0 * (degrees_of_freedom + 1.0) / degrees_of_freedom + grad *= c + + return kl_divergence, grad + + +def _kl_divergence_bh( + params, + P, + degrees_of_freedom, + n_samples, + n_components, + angle=0.5, + skip_num_points=0, + verbose=False, + compute_error=True, + num_threads=1, +): + """t-SNE objective function: KL divergence of p_ijs and q_ijs. + + Uses Barnes-Hut tree methods to calculate the gradient that + runs in O(NlogN) instead of O(N^2). + + Parameters + ---------- + params : ndarray of shape (n_params,) + Unraveled embedding. + + P : sparse matrix of shape (n_samples, n_sample) + Sparse approximate joint probability matrix, computed only for the + k nearest-neighbors and symmetrized. Matrix should be of CSR format. + + degrees_of_freedom : int + Degrees of freedom of the Student's-t distribution. + + n_samples : int + Number of samples. + + n_components : int + Dimension of the embedded space. + + angle : float, default=0.5 + This is the trade-off between speed and accuracy for Barnes-Hut T-SNE. + 'angle' is the angular size (referred to as theta in [3]) of a distant + node as measured from a point. If this size is below 'angle' then it is + used as a summary node of all points contained within it. + This method is not very sensitive to changes in this parameter + in the range of 0.2 - 0.8. Angle less than 0.2 has quickly increasing + computation time and angle greater 0.8 has quickly increasing error. + + skip_num_points : int, default=0 + This does not compute the gradient for points with indices below + `skip_num_points`. This is useful when computing transforms of new + data where you'd like to keep the old data fixed. + + verbose : int, default=False + Verbosity level. + + compute_error: bool, default=True + If False, the kl_divergence is not computed and returns NaN. + + num_threads : int, default=1 + Number of threads used to compute the gradient. This is set here to + avoid calling _openmp_effective_n_threads for each gradient step. + + Returns + ------- + kl_divergence : float + Kullback-Leibler divergence of p_ij and q_ij. + + grad : ndarray of shape (n_params,) + Unraveled gradient of the Kullback-Leibler divergence with respect to + the embedding. + """ + params = params.astype(np.float32, copy=False) + X_embedded = params.reshape(n_samples, n_components) + + val_P = P.data.astype(np.float32, copy=False) + neighbors = P.indices.astype(np.int64, copy=False) + indptr = P.indptr.astype(np.int64, copy=False) + + grad = np.zeros(X_embedded.shape, dtype=np.float32) + error = _barnes_hut_tsne.gradient( + val_P, + X_embedded, + neighbors, + indptr, + grad, + angle, + n_components, + verbose, + dof=degrees_of_freedom, + compute_error=compute_error, + num_threads=num_threads, + ) + c = 2.0 * (degrees_of_freedom + 1.0) / degrees_of_freedom + grad = grad.ravel() + grad *= c + + return error, grad + + +def _gradient_descent( + objective, + p0, + it, + n_iter, + n_iter_check=1, + n_iter_without_progress=300, + momentum=0.8, + learning_rate=200.0, + min_gain=0.01, + min_grad_norm=1e-7, + verbose=0, + args=None, + kwargs=None, +): + """Batch gradient descent with momentum and individual gains. + + Parameters + ---------- + objective : callable + Should return a tuple of cost and gradient for a given parameter + vector. When expensive to compute, the cost can optionally + be None and can be computed every n_iter_check steps using + the objective_error function. + + p0 : array-like of shape (n_params,) + Initial parameter vector. + + it : int + Current number of iterations (this function will be called more than + once during the optimization). + + n_iter : int + Maximum number of gradient descent iterations. + + n_iter_check : int, default=1 + Number of iterations before evaluating the global error. If the error + is sufficiently low, we abort the optimization. + + n_iter_without_progress : int, default=300 + Maximum number of iterations without progress before we abort the + optimization. + + momentum : float within (0.0, 1.0), default=0.8 + The momentum generates a weight for previous gradients that decays + exponentially. + + learning_rate : float, default=200.0 + The learning rate for t-SNE is usually in the range [10.0, 1000.0]. If + the learning rate is too high, the data may look like a 'ball' with any + point approximately equidistant from its nearest neighbours. If the + learning rate is too low, most points may look compressed in a dense + cloud with few outliers. + + min_gain : float, default=0.01 + Minimum individual gain for each parameter. + + min_grad_norm : float, default=1e-7 + If the gradient norm is below this threshold, the optimization will + be aborted. + + verbose : int, default=0 + Verbosity level. + + args : sequence, default=None + Arguments to pass to objective function. + + kwargs : dict, default=None + Keyword arguments to pass to objective function. + + Returns + ------- + p : ndarray of shape (n_params,) + Optimum parameters. + + error : float + Optimum. + + i : int + Last iteration. + """ + if args is None: + args = [] + if kwargs is None: + kwargs = {} + + p = p0.copy().ravel() + update = np.zeros_like(p) + gains = np.ones_like(p) + error = np.finfo(float).max + best_error = np.finfo(float).max + best_iter = i = it + + tic = time() + for i in range(it, n_iter): + check_convergence = (i + 1) % n_iter_check == 0 + # only compute the error when needed + kwargs["compute_error"] = check_convergence or i == n_iter - 1 + + error, grad = objective(p, *args, **kwargs) + + inc = update * grad < 0.0 + dec = np.invert(inc) + gains[inc] += 0.2 + gains[dec] *= 0.8 + np.clip(gains, min_gain, np.inf, out=gains) + grad *= gains + update = momentum * update - learning_rate * grad + p += update + + if check_convergence: + toc = time() + duration = toc - tic + tic = toc + grad_norm = linalg.norm(grad) + + if verbose >= 2: + print( + "[t-SNE] Iteration %d: error = %.7f," + " gradient norm = %.7f" + " (%s iterations in %0.3fs)" + % (i + 1, error, grad_norm, n_iter_check, duration) + ) + + if error < best_error: + best_error = error + best_iter = i + elif i - best_iter > n_iter_without_progress: + if verbose >= 2: + print( + "[t-SNE] Iteration %d: did not make any progress " + "during the last %d episodes. Finished." + % (i + 1, n_iter_without_progress) + ) + break + if grad_norm <= min_grad_norm: + if verbose >= 2: + print( + "[t-SNE] Iteration %d: gradient norm %f. Finished." + % (i + 1, grad_norm) + ) + break + + return p, error, i + + +@validate_params( + { + "X": ["array-like", "sparse matrix"], + "X_embedded": ["array-like", "sparse matrix"], + "n_neighbors": [Interval(Integral, 1, None, closed="left")], + "metric": [StrOptions(set(_VALID_METRICS) | {"precomputed"}), callable], + }, + prefer_skip_nested_validation=True, +) +def trustworthiness(X, X_embedded, *, n_neighbors=5, metric="euclidean"): + r"""Indicate to what extent the local structure is retained. + + The trustworthiness is within [0, 1]. It is defined as + + .. math:: + + T(k) = 1 - \frac{2}{nk (2n - 3k - 1)} \sum^n_{i=1} + \sum_{j \in \mathcal{N}_{i}^{k}} \max(0, (r(i, j) - k)) + + where for each sample i, :math:`\mathcal{N}_{i}^{k}` are its k nearest + neighbors in the output space, and every sample j is its :math:`r(i, j)`-th + nearest neighbor in the input space. In other words, any unexpected nearest + neighbors in the output space are penalised in proportion to their rank in + the input space. + + Parameters + ---------- + X : {array-like, sparse matrix} of shape (n_samples, n_features) or \ + (n_samples, n_samples) + If the metric is 'precomputed' X must be a square distance + matrix. Otherwise it contains a sample per row. + + X_embedded : {array-like, sparse matrix} of shape (n_samples, n_components) + Embedding of the training data in low-dimensional space. + + n_neighbors : int, default=5 + The number of neighbors that will be considered. Should be fewer than + `n_samples / 2` to ensure the trustworthiness to lies within [0, 1], as + mentioned in [1]_. An error will be raised otherwise. + + metric : str or callable, default='euclidean' + Which metric to use for computing pairwise distances between samples + from the original input space. If metric is 'precomputed', X must be a + matrix of pairwise distances or squared distances. Otherwise, for a list + of available metrics, see the documentation of argument metric in + `sklearn.pairwise.pairwise_distances` and metrics listed in + `sklearn.metrics.pairwise.PAIRWISE_DISTANCE_FUNCTIONS`. Note that the + "cosine" metric uses :func:`~sklearn.metrics.pairwise.cosine_distances`. + + .. versionadded:: 0.20 + + Returns + ------- + trustworthiness : float + Trustworthiness of the low-dimensional embedding. + + References + ---------- + .. [1] Jarkko Venna and Samuel Kaski. 2001. Neighborhood + Preservation in Nonlinear Projection Methods: An Experimental Study. + In Proceedings of the International Conference on Artificial Neural Networks + (ICANN '01). Springer-Verlag, Berlin, Heidelberg, 485-491. + + .. [2] Laurens van der Maaten. Learning a Parametric Embedding by Preserving + Local Structure. Proceedings of the Twelfth International Conference on + Artificial Intelligence and Statistics, PMLR 5:384-391, 2009. + + Examples + -------- + >>> from sklearn.datasets import make_blobs + >>> from sklearn.decomposition import PCA + >>> from sklearn.manifold import trustworthiness + >>> X, _ = make_blobs(n_samples=100, n_features=10, centers=3, random_state=42) + >>> X_embedded = PCA(n_components=2).fit_transform(X) + >>> print(f"{trustworthiness(X, X_embedded, n_neighbors=5):.2f}") + 0.92 + """ + n_samples = _num_samples(X) + if n_neighbors >= n_samples / 2: + raise ValueError( + f"n_neighbors ({n_neighbors}) should be less than n_samples / 2" + f" ({n_samples / 2})" + ) + dist_X = pairwise_distances(X, metric=metric) + if metric == "precomputed": + dist_X = dist_X.copy() + # we set the diagonal to np.inf to exclude the points themselves from + # their own neighborhood + np.fill_diagonal(dist_X, np.inf) + ind_X = np.argsort(dist_X, axis=1) + # `ind_X[i]` is the index of sorted distances between i and other samples + ind_X_embedded = ( + NearestNeighbors(n_neighbors=n_neighbors) + .fit(X_embedded) + .kneighbors(return_distance=False) + ) + + # We build an inverted index of neighbors in the input space: For sample i, + # we define `inverted_index[i]` as the inverted index of sorted distances: + # inverted_index[i][ind_X[i]] = np.arange(1, n_sample + 1) + inverted_index = np.zeros((n_samples, n_samples), dtype=int) + ordered_indices = np.arange(n_samples + 1) + inverted_index[ordered_indices[:-1, np.newaxis], ind_X] = ordered_indices[1:] + ranks = ( + inverted_index[ordered_indices[:-1, np.newaxis], ind_X_embedded] - n_neighbors + ) + t = np.sum(ranks[ranks > 0]) + t = 1.0 - t * ( + 2.0 / (n_samples * n_neighbors * (2.0 * n_samples - 3.0 * n_neighbors - 1.0)) + ) + return t + + +class TSNE(ClassNamePrefixFeaturesOutMixin, TransformerMixin, BaseEstimator): + """T-distributed Stochastic Neighbor Embedding. + + t-SNE [1] is a tool to visualize high-dimensional data. It converts + similarities between data points to joint probabilities and tries + to minimize the Kullback-Leibler divergence between the joint + probabilities of the low-dimensional embedding and the + high-dimensional data. t-SNE has a cost function that is not convex, + i.e. with different initializations we can get different results. + + It is highly recommended to use another dimensionality reduction + method (e.g. PCA for dense data or TruncatedSVD for sparse data) + to reduce the number of dimensions to a reasonable amount (e.g. 50) + if the number of features is very high. This will suppress some + noise and speed up the computation of pairwise distances between + samples. For more tips see Laurens van der Maaten's FAQ [2]. + + Read more in the :ref:`User Guide `. + + Parameters + ---------- + n_components : int, default=2 + Dimension of the embedded space. + + perplexity : float, default=30.0 + The perplexity is related to the number of nearest neighbors that + is used in other manifold learning algorithms. Larger datasets + usually require a larger perplexity. Consider selecting a value + between 5 and 50. Different values can result in significantly + different results. The perplexity must be less than the number + of samples. + + early_exaggeration : float, default=12.0 + Controls how tight natural clusters in the original space are in + the embedded space and how much space will be between them. For + larger values, the space between natural clusters will be larger + in the embedded space. Again, the choice of this parameter is not + very critical. If the cost function increases during initial + optimization, the early exaggeration factor or the learning rate + might be too high. + + learning_rate : float or "auto", default="auto" + The learning rate for t-SNE is usually in the range [10.0, 1000.0]. If + the learning rate is too high, the data may look like a 'ball' with any + point approximately equidistant from its nearest neighbours. If the + learning rate is too low, most points may look compressed in a dense + cloud with few outliers. If the cost function gets stuck in a bad local + minimum increasing the learning rate may help. + Note that many other t-SNE implementations (bhtsne, FIt-SNE, openTSNE, + etc.) use a definition of learning_rate that is 4 times smaller than + ours. So our learning_rate=200 corresponds to learning_rate=800 in + those other implementations. The 'auto' option sets the learning_rate + to `max(N / early_exaggeration / 4, 50)` where N is the sample size, + following [4] and [5]. + + .. versionchanged:: 1.2 + The default value changed to `"auto"`. + + n_iter : int, default=1000 + Maximum number of iterations for the optimization. Should be at + least 250. + + n_iter_without_progress : int, default=300 + Maximum number of iterations without progress before we abort the + optimization, used after 250 initial iterations with early + exaggeration. Note that progress is only checked every 50 iterations so + this value is rounded to the next multiple of 50. + + .. versionadded:: 0.17 + parameter *n_iter_without_progress* to control stopping criteria. + + min_grad_norm : float, default=1e-7 + If the gradient norm is below this threshold, the optimization will + be stopped. + + metric : str or callable, default='euclidean' + The metric to use when calculating distance between instances in a + feature array. If metric is a string, it must be one of the options + allowed by scipy.spatial.distance.pdist for its metric parameter, or + a metric listed in pairwise.PAIRWISE_DISTANCE_FUNCTIONS. + If metric is "precomputed", X is assumed to be a distance matrix. + Alternatively, if metric is a callable function, it is called on each + pair of instances (rows) and the resulting value recorded. The callable + should take two arrays from X as input and return a value indicating + the distance between them. The default is "euclidean" which is + interpreted as squared euclidean distance. + + metric_params : dict, default=None + Additional keyword arguments for the metric function. + + .. versionadded:: 1.1 + + init : {"random", "pca"} or ndarray of shape (n_samples, n_components), \ + default="pca" + Initialization of embedding. + PCA initialization cannot be used with precomputed distances and is + usually more globally stable than random initialization. + + .. versionchanged:: 1.2 + The default value changed to `"pca"`. + + verbose : int, default=0 + Verbosity level. + + random_state : int, RandomState instance or None, default=None + Determines the random number generator. Pass an int for reproducible + results across multiple function calls. Note that different + initializations might result in different local minima of the cost + function. See :term:`Glossary `. + + method : {'barnes_hut', 'exact'}, default='barnes_hut' + By default the gradient calculation algorithm uses Barnes-Hut + approximation running in O(NlogN) time. method='exact' + will run on the slower, but exact, algorithm in O(N^2) time. The + exact algorithm should be used when nearest-neighbor errors need + to be better than 3%. However, the exact method cannot scale to + millions of examples. + + .. versionadded:: 0.17 + Approximate optimization *method* via the Barnes-Hut. + + angle : float, default=0.5 + Only used if method='barnes_hut' + This is the trade-off between speed and accuracy for Barnes-Hut T-SNE. + 'angle' is the angular size (referred to as theta in [3]) of a distant + node as measured from a point. If this size is below 'angle' then it is + used as a summary node of all points contained within it. + This method is not very sensitive to changes in this parameter + in the range of 0.2 - 0.8. Angle less than 0.2 has quickly increasing + computation time and angle greater 0.8 has quickly increasing error. + + n_jobs : int, default=None + The number of parallel jobs to run for neighbors search. This parameter + has no impact when ``metric="precomputed"`` or + (``metric="euclidean"`` and ``method="exact"``). + ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context. + ``-1`` means using all processors. See :term:`Glossary ` + for more details. + + .. versionadded:: 0.22 + + Attributes + ---------- + embedding_ : array-like of shape (n_samples, n_components) + Stores the embedding vectors. + + kl_divergence_ : float + Kullback-Leibler divergence after optimization. + + n_features_in_ : int + Number of features seen during :term:`fit`. + + .. versionadded:: 0.24 + + feature_names_in_ : ndarray of shape (`n_features_in_`,) + Names of features seen during :term:`fit`. Defined only when `X` + has feature names that are all strings. + + .. versionadded:: 1.0 + + learning_rate_ : float + Effective learning rate. + + .. versionadded:: 1.2 + + n_iter_ : int + Number of iterations run. + + See Also + -------- + sklearn.decomposition.PCA : Principal component analysis that is a linear + dimensionality reduction method. + sklearn.decomposition.KernelPCA : Non-linear dimensionality reduction using + kernels and PCA. + MDS : Manifold learning using multidimensional scaling. + Isomap : Manifold learning based on Isometric Mapping. + LocallyLinearEmbedding : Manifold learning using Locally Linear Embedding. + SpectralEmbedding : Spectral embedding for non-linear dimensionality. + + Notes + ----- + For an example of using :class:`~sklearn.manifold.TSNE` in combination with + :class:`~sklearn.neighbors.KNeighborsTransformer` see + :ref:`sphx_glr_auto_examples_neighbors_approximate_nearest_neighbors.py`. + + References + ---------- + + [1] van der Maaten, L.J.P.; Hinton, G.E. Visualizing High-Dimensional Data + Using t-SNE. Journal of Machine Learning Research 9:2579-2605, 2008. + + [2] van der Maaten, L.J.P. t-Distributed Stochastic Neighbor Embedding + https://lvdmaaten.github.io/tsne/ + + [3] L.J.P. van der Maaten. Accelerating t-SNE using Tree-Based Algorithms. + Journal of Machine Learning Research 15(Oct):3221-3245, 2014. + https://lvdmaaten.github.io/publications/papers/JMLR_2014.pdf + + [4] Belkina, A. C., Ciccolella, C. O., Anno, R., Halpert, R., Spidlen, J., + & Snyder-Cappione, J. E. (2019). Automated optimized parameters for + T-distributed stochastic neighbor embedding improve visualization + and analysis of large datasets. Nature Communications, 10(1), 1-12. + + [5] Kobak, D., & Berens, P. (2019). The art of using t-SNE for single-cell + transcriptomics. Nature Communications, 10(1), 1-14. + + Examples + -------- + >>> import numpy as np + >>> from sklearn.manifold import TSNE + >>> X = np.array([[0, 0, 0], [0, 1, 1], [1, 0, 1], [1, 1, 1]]) + >>> X_embedded = TSNE(n_components=2, learning_rate='auto', + ... init='random', perplexity=3).fit_transform(X) + >>> X_embedded.shape + (4, 2) + """ + + _parameter_constraints: dict = { + "n_components": [Interval(Integral, 1, None, closed="left")], + "perplexity": [Interval(Real, 0, None, closed="neither")], + "early_exaggeration": [Interval(Real, 1, None, closed="left")], + "learning_rate": [ + StrOptions({"auto"}), + Interval(Real, 0, None, closed="neither"), + ], + "n_iter": [Interval(Integral, 250, None, closed="left")], + "n_iter_without_progress": [Interval(Integral, -1, None, closed="left")], + "min_grad_norm": [Interval(Real, 0, None, closed="left")], + "metric": [StrOptions(set(_VALID_METRICS) | {"precomputed"}), callable], + "metric_params": [dict, None], + "init": [ + StrOptions({"pca", "random"}), + np.ndarray, + ], + "verbose": ["verbose"], + "random_state": ["random_state"], + "method": [StrOptions({"barnes_hut", "exact"})], + "angle": [Interval(Real, 0, 1, closed="both")], + "n_jobs": [None, Integral], + } + + # Control the number of exploration iterations with early_exaggeration on + _EXPLORATION_N_ITER = 250 + + # Control the number of iterations between progress checks + _N_ITER_CHECK = 50 + + def __init__( + self, + n_components=2, + *, + perplexity=30.0, + early_exaggeration=12.0, + learning_rate="auto", + n_iter=1000, + n_iter_without_progress=300, + min_grad_norm=1e-7, + metric="euclidean", + metric_params=None, + init="pca", + verbose=0, + random_state=None, + method="barnes_hut", + angle=0.5, + n_jobs=None, + ): + self.n_components = n_components + self.perplexity = perplexity + self.early_exaggeration = early_exaggeration + self.learning_rate = learning_rate + self.n_iter = n_iter + self.n_iter_without_progress = n_iter_without_progress + self.min_grad_norm = min_grad_norm + self.metric = metric + self.metric_params = metric_params + self.init = init + self.verbose = verbose + self.random_state = random_state + self.method = method + self.angle = angle + self.n_jobs = n_jobs + + def _check_params_vs_input(self, X): + if self.perplexity >= X.shape[0]: + raise ValueError("perplexity must be less than n_samples") + + def _fit(self, X, skip_num_points=0): + """Private function to fit the model using X as training data.""" + + if isinstance(self.init, str) and self.init == "pca" and issparse(X): + raise TypeError( + "PCA initialization is currently not supported " + "with the sparse input matrix. Use " + 'init="random" instead.' + ) + + if self.learning_rate == "auto": + # See issue #18018 + self.learning_rate_ = X.shape[0] / self.early_exaggeration / 4 + self.learning_rate_ = np.maximum(self.learning_rate_, 50) + else: + self.learning_rate_ = self.learning_rate + + if self.method == "barnes_hut": + X = self._validate_data( + X, + accept_sparse=["csr"], + ensure_min_samples=2, + dtype=[np.float32, np.float64], + ) + else: + X = self._validate_data( + X, accept_sparse=["csr", "csc", "coo"], dtype=[np.float32, np.float64] + ) + if self.metric == "precomputed": + if isinstance(self.init, str) and self.init == "pca": + raise ValueError( + 'The parameter init="pca" cannot be used with metric="precomputed".' + ) + if X.shape[0] != X.shape[1]: + raise ValueError("X should be a square distance matrix") + + check_non_negative( + X, + ( + "TSNE.fit(). With metric='precomputed', X " + "should contain positive distances." + ), + ) + + if self.method == "exact" and issparse(X): + raise TypeError( + 'TSNE with method="exact" does not accept sparse ' + 'precomputed distance matrix. Use method="barnes_hut" ' + "or provide the dense distance matrix." + ) + + if self.method == "barnes_hut" and self.n_components > 3: + raise ValueError( + "'n_components' should be inferior to 4 for the " + "barnes_hut algorithm as it relies on " + "quad-tree or oct-tree." + ) + random_state = check_random_state(self.random_state) + + n_samples = X.shape[0] + + neighbors_nn = None + if self.method == "exact": + # Retrieve the distance matrix, either using the precomputed one or + # computing it. + if self.metric == "precomputed": + distances = X + else: + if self.verbose: + print("[t-SNE] Computing pairwise distances...") + + if self.metric == "euclidean": + # Euclidean is squared here, rather than using **= 2, + # because euclidean_distances already calculates + # squared distances, and returns np.sqrt(dist) for + # squared=False. + # Also, Euclidean is slower for n_jobs>1, so don't set here + distances = pairwise_distances(X, metric=self.metric, squared=True) + else: + metric_params_ = self.metric_params or {} + distances = pairwise_distances( + X, metric=self.metric, n_jobs=self.n_jobs, **metric_params_ + ) + + if np.any(distances < 0): + raise ValueError( + "All distances should be positive, the metric given is not correct" + ) + + if self.metric != "euclidean": + distances **= 2 + + # compute the joint probability distribution for the input space + P = _joint_probabilities(distances, self.perplexity, self.verbose) + assert np.all(np.isfinite(P)), "All probabilities should be finite" + assert np.all(P >= 0), "All probabilities should be non-negative" + assert np.all( + P <= 1 + ), "All probabilities should be less or then equal to one" + + else: + # Compute the number of nearest neighbors to find. + # LvdM uses 3 * perplexity as the number of neighbors. + # In the event that we have very small # of points + # set the neighbors to n - 1. + n_neighbors = min(n_samples - 1, int(3.0 * self.perplexity + 1)) + + if self.verbose: + print("[t-SNE] Computing {} nearest neighbors...".format(n_neighbors)) + + # Find the nearest neighbors for every point + knn = NearestNeighbors( + algorithm="auto", + n_jobs=self.n_jobs, + n_neighbors=n_neighbors, + metric=self.metric, + metric_params=self.metric_params, + ) + t0 = time() + knn.fit(X) + duration = time() - t0 + if self.verbose: + print( + "[t-SNE] Indexed {} samples in {:.3f}s...".format( + n_samples, duration + ) + ) + + t0 = time() + distances_nn = knn.kneighbors_graph(mode="distance") + duration = time() - t0 + if self.verbose: + print( + "[t-SNE] Computed neighbors for {} samples in {:.3f}s...".format( + n_samples, duration + ) + ) + + # Free the memory used by the ball_tree + del knn + + # knn return the euclidean distance but we need it squared + # to be consistent with the 'exact' method. Note that the + # the method was derived using the euclidean method as in the + # input space. Not sure of the implication of using a different + # metric. + distances_nn.data **= 2 + + # compute the joint probability distribution for the input space + P = _joint_probabilities_nn(distances_nn, self.perplexity, self.verbose) + + if isinstance(self.init, np.ndarray): + X_embedded = self.init + elif self.init == "pca": + pca = PCA( + n_components=self.n_components, + svd_solver="randomized", + random_state=random_state, + ) + # Always output a numpy array, no matter what is configured globally + pca.set_output(transform="default") + X_embedded = pca.fit_transform(X).astype(np.float32, copy=False) + # PCA is rescaled so that PC1 has standard deviation 1e-4 which is + # the default value for random initialization. See issue #18018. + X_embedded = X_embedded / np.std(X_embedded[:, 0]) * 1e-4 + elif self.init == "random": + # The embedding is initialized with iid samples from Gaussians with + # standard deviation 1e-4. + X_embedded = 1e-4 * random_state.standard_normal( + size=(n_samples, self.n_components) + ).astype(np.float32) + + # Degrees of freedom of the Student's t-distribution. The suggestion + # degrees_of_freedom = n_components - 1 comes from + # "Learning a Parametric Embedding by Preserving Local Structure" + # Laurens van der Maaten, 2009. + degrees_of_freedom = max(self.n_components - 1, 1) + + return self._tsne( + P, + degrees_of_freedom, + n_samples, + X_embedded=X_embedded, + neighbors=neighbors_nn, + skip_num_points=skip_num_points, + ) + + def _tsne( + self, + P, + degrees_of_freedom, + n_samples, + X_embedded, + neighbors=None, + skip_num_points=0, + ): + """Runs t-SNE.""" + # t-SNE minimizes the Kullback-Leiber divergence of the Gaussians P + # and the Student's t-distributions Q. The optimization algorithm that + # we use is batch gradient descent with two stages: + # * initial optimization with early exaggeration and momentum at 0.5 + # * final optimization with momentum at 0.8 + params = X_embedded.ravel() + + opt_args = { + "it": 0, + "n_iter_check": self._N_ITER_CHECK, + "min_grad_norm": self.min_grad_norm, + "learning_rate": self.learning_rate_, + "verbose": self.verbose, + "kwargs": dict(skip_num_points=skip_num_points), + "args": [P, degrees_of_freedom, n_samples, self.n_components], + "n_iter_without_progress": self._EXPLORATION_N_ITER, + "n_iter": self._EXPLORATION_N_ITER, + "momentum": 0.5, + } + if self.method == "barnes_hut": + obj_func = _kl_divergence_bh + opt_args["kwargs"]["angle"] = self.angle + # Repeat verbose argument for _kl_divergence_bh + opt_args["kwargs"]["verbose"] = self.verbose + # Get the number of threads for gradient computation here to + # avoid recomputing it at each iteration. + opt_args["kwargs"]["num_threads"] = _openmp_effective_n_threads() + else: + obj_func = _kl_divergence + + # Learning schedule (part 1): do 250 iteration with lower momentum but + # higher learning rate controlled via the early exaggeration parameter + P *= self.early_exaggeration + params, kl_divergence, it = _gradient_descent(obj_func, params, **opt_args) + if self.verbose: + print( + "[t-SNE] KL divergence after %d iterations with early exaggeration: %f" + % (it + 1, kl_divergence) + ) + + # Learning schedule (part 2): disable early exaggeration and finish + # optimization with a higher momentum at 0.8 + P /= self.early_exaggeration + remaining = self.n_iter - self._EXPLORATION_N_ITER + if it < self._EXPLORATION_N_ITER or remaining > 0: + opt_args["n_iter"] = self.n_iter + opt_args["it"] = it + 1 + opt_args["momentum"] = 0.8 + opt_args["n_iter_without_progress"] = self.n_iter_without_progress + params, kl_divergence, it = _gradient_descent(obj_func, params, **opt_args) + + # Save the final number of iterations + self.n_iter_ = it + + if self.verbose: + print( + "[t-SNE] KL divergence after %d iterations: %f" + % (it + 1, kl_divergence) + ) + + X_embedded = params.reshape(n_samples, self.n_components) + self.kl_divergence_ = kl_divergence + + return X_embedded + + @_fit_context( + # TSNE.metric is not validated yet + prefer_skip_nested_validation=False + ) + def fit_transform(self, X, y=None): + """Fit X into an embedded space and return that transformed output. + + Parameters + ---------- + X : {array-like, sparse matrix} of shape (n_samples, n_features) or \ + (n_samples, n_samples) + If the metric is 'precomputed' X must be a square distance + matrix. Otherwise it contains a sample per row. If the method + is 'exact', X may be a sparse matrix of type 'csr', 'csc' + or 'coo'. If the method is 'barnes_hut' and the metric is + 'precomputed', X may be a precomputed sparse graph. + + y : None + Ignored. + + Returns + ------- + X_new : ndarray of shape (n_samples, n_components) + Embedding of the training data in low-dimensional space. + """ + self._check_params_vs_input(X) + embedding = self._fit(X) + self.embedding_ = embedding + return self.embedding_ + + @_fit_context( + # TSNE.metric is not validated yet + prefer_skip_nested_validation=False + ) + def fit(self, X, y=None): + """Fit X into an embedded space. + + Parameters + ---------- + X : {array-like, sparse matrix} of shape (n_samples, n_features) or \ + (n_samples, n_samples) + If the metric is 'precomputed' X must be a square distance + matrix. Otherwise it contains a sample per row. If the method + is 'exact', X may be a sparse matrix of type 'csr', 'csc' + or 'coo'. If the method is 'barnes_hut' and the metric is + 'precomputed', X may be a precomputed sparse graph. + + y : None + Ignored. + + Returns + ------- + self : object + Fitted estimator. + """ + self.fit_transform(X) + return self + + @property + def _n_features_out(self): + """Number of transformed output features.""" + return self.embedding_.shape[1] + + def _more_tags(self): + return {"pairwise": self.metric == "precomputed"} diff --git a/venv/lib/python3.10/site-packages/sklearn/manifold/tests/__pycache__/__init__.cpython-310.pyc b/venv/lib/python3.10/site-packages/sklearn/manifold/tests/__pycache__/__init__.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..89e28a04b24926f58ac3d020a684524bf9cf38aa Binary files /dev/null and b/venv/lib/python3.10/site-packages/sklearn/manifold/tests/__pycache__/__init__.cpython-310.pyc differ diff --git a/venv/lib/python3.10/site-packages/sklearn/manifold/tests/__pycache__/test_isomap.cpython-310.pyc b/venv/lib/python3.10/site-packages/sklearn/manifold/tests/__pycache__/test_isomap.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..1a3611696ba5b4ca99324575a3b20bbbea1121fa Binary files /dev/null and b/venv/lib/python3.10/site-packages/sklearn/manifold/tests/__pycache__/test_isomap.cpython-310.pyc differ diff --git a/venv/lib/python3.10/site-packages/sklearn/manifold/tests/__pycache__/test_mds.cpython-310.pyc b/venv/lib/python3.10/site-packages/sklearn/manifold/tests/__pycache__/test_mds.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a94ecb5a27144c356dbbccadf1260498872188ea Binary files /dev/null and b/venv/lib/python3.10/site-packages/sklearn/manifold/tests/__pycache__/test_mds.cpython-310.pyc differ diff --git a/venv/lib/python3.10/site-packages/sklearn/manifold/tests/__pycache__/test_spectral_embedding.cpython-310.pyc b/venv/lib/python3.10/site-packages/sklearn/manifold/tests/__pycache__/test_spectral_embedding.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..3aa72f8f2251b26aea005667f2298c7cb3f952e8 Binary files /dev/null and b/venv/lib/python3.10/site-packages/sklearn/manifold/tests/__pycache__/test_spectral_embedding.cpython-310.pyc differ diff --git a/venv/lib/python3.10/site-packages/sklearn/manifold/tests/__pycache__/test_t_sne.cpython-310.pyc b/venv/lib/python3.10/site-packages/sklearn/manifold/tests/__pycache__/test_t_sne.cpython-310.pyc new file mode 100644 index 0000000000000000000000000000000000000000..cc8bd261a1e33563480b2f9470ca6d347a5091f6 Binary files /dev/null and b/venv/lib/python3.10/site-packages/sklearn/manifold/tests/__pycache__/test_t_sne.cpython-310.pyc differ