peacock-data-public-datasets-idc-cronscript
/
venv
/lib
/python3.10
/site-packages
/scipy
/spatial
/tests
/test__procrustes.py
| import numpy as np | |
| from numpy.testing import assert_allclose, assert_equal, assert_almost_equal | |
| from pytest import raises as assert_raises | |
| from scipy.spatial import procrustes | |
| class TestProcrustes: | |
| def setup_method(self): | |
| """creates inputs""" | |
| # an L | |
| self.data1 = np.array([[1, 3], [1, 2], [1, 1], [2, 1]], 'd') | |
| # a larger, shifted, mirrored L | |
| self.data2 = np.array([[4, -2], [4, -4], [4, -6], [2, -6]], 'd') | |
| # an L shifted up 1, right 1, and with point 4 shifted an extra .5 | |
| # to the right | |
| # pointwise distance disparity with data1: 3*(2) + (1 + 1.5^2) | |
| self.data3 = np.array([[2, 4], [2, 3], [2, 2], [3, 2.5]], 'd') | |
| # data4, data5 are standardized (trace(A*A') = 1). | |
| # procrustes should return an identical copy if they are used | |
| # as the first matrix argument. | |
| shiftangle = np.pi / 8 | |
| self.data4 = np.array([[1, 0], [0, 1], [-1, 0], | |
| [0, -1]], 'd') / np.sqrt(4) | |
| self.data5 = np.array([[np.cos(shiftangle), np.sin(shiftangle)], | |
| [np.cos(np.pi / 2 - shiftangle), | |
| np.sin(np.pi / 2 - shiftangle)], | |
| [-np.cos(shiftangle), | |
| -np.sin(shiftangle)], | |
| [-np.cos(np.pi / 2 - shiftangle), | |
| -np.sin(np.pi / 2 - shiftangle)]], | |
| 'd') / np.sqrt(4) | |
| def test_procrustes(self): | |
| # tests procrustes' ability to match two matrices. | |
| # | |
| # the second matrix is a rotated, shifted, scaled, and mirrored version | |
| # of the first, in two dimensions only | |
| # | |
| # can shift, mirror, and scale an 'L'? | |
| a, b, disparity = procrustes(self.data1, self.data2) | |
| assert_allclose(b, a) | |
| assert_almost_equal(disparity, 0.) | |
| # if first mtx is standardized, leaves first mtx unchanged? | |
| m4, m5, disp45 = procrustes(self.data4, self.data5) | |
| assert_equal(m4, self.data4) | |
| # at worst, data3 is an 'L' with one point off by .5 | |
| m1, m3, disp13 = procrustes(self.data1, self.data3) | |
| #assert_(disp13 < 0.5 ** 2) | |
| def test_procrustes2(self): | |
| # procrustes disparity should not depend on order of matrices | |
| m1, m3, disp13 = procrustes(self.data1, self.data3) | |
| m3_2, m1_2, disp31 = procrustes(self.data3, self.data1) | |
| assert_almost_equal(disp13, disp31) | |
| # try with 3d, 8 pts per | |
| rand1 = np.array([[2.61955202, 0.30522265, 0.55515826], | |
| [0.41124708, -0.03966978, -0.31854548], | |
| [0.91910318, 1.39451809, -0.15295084], | |
| [2.00452023, 0.50150048, 0.29485268], | |
| [0.09453595, 0.67528885, 0.03283872], | |
| [0.07015232, 2.18892599, -1.67266852], | |
| [0.65029688, 1.60551637, 0.80013549], | |
| [-0.6607528, 0.53644208, 0.17033891]]) | |
| rand3 = np.array([[0.0809969, 0.09731461, -0.173442], | |
| [-1.84888465, -0.92589646, -1.29335743], | |
| [0.67031855, -1.35957463, 0.41938621], | |
| [0.73967209, -0.20230757, 0.52418027], | |
| [0.17752796, 0.09065607, 0.29827466], | |
| [0.47999368, -0.88455717, -0.57547934], | |
| [-0.11486344, -0.12608506, -0.3395779], | |
| [-0.86106154, -0.28687488, 0.9644429]]) | |
| res1, res3, disp13 = procrustes(rand1, rand3) | |
| res3_2, res1_2, disp31 = procrustes(rand3, rand1) | |
| assert_almost_equal(disp13, disp31) | |
| def test_procrustes_shape_mismatch(self): | |
| assert_raises(ValueError, procrustes, | |
| np.array([[1, 2], [3, 4]]), | |
| np.array([[5, 6, 7], [8, 9, 10]])) | |
| def test_procrustes_empty_rows_or_cols(self): | |
| empty = np.array([[]]) | |
| assert_raises(ValueError, procrustes, empty, empty) | |
| def test_procrustes_no_variation(self): | |
| assert_raises(ValueError, procrustes, | |
| np.array([[42, 42], [42, 42]]), | |
| np.array([[45, 45], [45, 45]])) | |
| def test_procrustes_bad_number_of_dimensions(self): | |
| # fewer dimensions in one dataset | |
| assert_raises(ValueError, procrustes, | |
| np.array([1, 1, 2, 3, 5, 8]), | |
| np.array([[1, 2], [3, 4]])) | |
| # fewer dimensions in both datasets | |
| assert_raises(ValueError, procrustes, | |
| np.array([1, 1, 2, 3, 5, 8]), | |
| np.array([1, 1, 2, 3, 5, 8])) | |
| # zero dimensions | |
| assert_raises(ValueError, procrustes, np.array(7), np.array(11)) | |
| # extra dimensions | |
| assert_raises(ValueError, procrustes, | |
| np.array([[[11], [7]]]), | |
| np.array([[[5, 13]]])) | |