def __init__(self, X, Y, X_data_min, X_data_max, Y_data_mean, Y_data_std):
self.X = X
self.Y = Y
self.X_data_min, self.X_data_scale = P.minmax_scale_params(
X_data_min, X_data_max, feature_range=(0.01, 0.99))
self.Y_data_mean = Y_data_mean
self.Y_data_std = Y_data_std
def test_minmax():
# Pick linguistic features for testing
X, _ = example_file_data_sources_for_acoustic_model()
X = FileSourceDataset(X)
lengths = [len(x) for x in X]
D = X[0].shape[-1]
X_min, X_max = P.minmax(X)
assert np.isfinite(X_min).all()
assert np.isfinite(X_max).all()
x = X[0]
x_scaled = P.minmax_scale(x, X_min, X_max, feature_range=(0, 0.99))
assert np.max(x_scaled) <= 1
assert np.min(x_scaled) >= 0
assert np.isfinite(x_scaled).all()
# Need to specify (min, max) or (scale_, min_)
@raises(ValueError)
def __test_raise1(x, X_min, X_max):
P.minmax_scale(x)
@raises(ValueError)
def __test_raise2(x, X_min, X_max):
P.inv_minmax_scale(x)
__test_raise1(x, X_min, X_max)
__test_raise2(x, X_min, X_max)
# Explicit scale_ and min_
min_, scale_ = P.minmax_scale_params(X_min, X_max, feature_range=(0, 0.99))
x_scaled_hat = P.minmax_scale(x, min_=min_, scale_=scale_)
assert np.allclose(x_scaled, x_scaled_hat)
# For padded dataset
X, _ = example_file_data_sources_for_acoustic_model()
X = PaddedFileSourceDataset(X, 1000)
# Should get same results with padded features
X_min_hat, X_max_hat = P.minmax(X, lengths)
assert np.allclose(X_min, X_min_hat)
assert np.allclose(X_max, X_max_hat)
# Inverse transform
x = X[0]
x_hat = P.inv_minmax_scale(P.minmax_scale(x, X_min, X_max), X_min, X_max)
assert np.allclose(x, x_hat)
x_hat = P.inv_minmax_scale(P.minmax_scale(x, scale_=scale_, min_=min_),
scale_=scale_,
min_=min_)
assert np.allclose(x, x_hat)
