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# streamlit_simulation/dummy.py
import numpy as np
import torch
class DummyDataset:
def __init__(self, length=100):
self.data = np.zeros((length, 10)) # Dummydaten
self.scaler = DummyScaler()
self.n_channels = 1
self.length = length
def __len__(self):
return self.length
def __getitem__(self, idx):
timeseries = np.zeros((48, 1)) # (SEQ_LEN, Channels)
target = np.zeros((1, 1)) # Forecast target
mask = np.ones((48,)) # Dummy-Maske
return timeseries, target, mask
class DummyScaler:
def inverse_transform(self, x):
return x # keine Skalierung nötig
class DummyOutput:
def __init__(self, forecast_shape):
# gib einen echten Tensor zurück, wie vom echten Modell erwartet
self.forecast = torch.tensor(np.full(forecast_shape, 42.0), dtype=torch.float32)
class DummyTransformerModel:
def __call__(self, x_enc, input_mask):
batch_size, seq_len, channels = x_enc.shape
forecast_shape = (batch_size, 1, channels)
return DummyOutput(forecast_shape)
class DummyLightGBMModel:
def predict(self, X):
return np.zeros(len(X)) # ← gibt jetzt np.ndarray zurück
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