def transform(self, data):
if self.mean is None or self.components is None:
raise NotTrainedException("Train ZCA before use it.")
data_shape = data.shape
data = as_array2d(data)
data_transformed = data - self.mean
data_transformed = np.dot(data_transformed, self.components.T)
return data_transformed.reshape(data_shape)
def train(self, data):
data = as_array2d(data)
self.mean = data.mean(axis=0)
data = data - self.mean
n_features = data.shape[1]
sigma = np.dot(data.T, data) / n_features
U, S, V = np.linalg.svd(sigma)
self.components = (U / np.sqrt(S + self.regularization)).dot(U.T)
def transform(self, data):
""" Apply ZCA transformation on data.
Parameters
----------
data : array-like
Returns
-------
array-like
"""
if self.mean is None or self.components is None:
raise NotTrainedException("Train ZCA before use it.")
data_shape = data.shape
data = as_array2d(data)
data_transformed = data - self.mean
data_transformed = np.dot(data_transformed, self.components.T)
return data_transformed.reshape(data_shape)
def transform(self, data):
"""
Apply ZCA transformation on data.
Parameters
----------
data : array-like
Returns
-------
array-like
"""
if self.mean is None or self.components is None:
raise NotTrainedException("Train ZCA before use it.")
data_shape = data.shape
data = as_array2d(data)
data_transformed = data - self.mean
data_transformed = np.dot(data_transformed, self.components.T)
return data_transformed.reshape(data_shape)
def test_as_array2d(self):
test_input = np.ones(5)
actual_output = as_array2d(test_input)
self.assertEqual((1, 5), actual_output.shape)
def test_as_array2d(self):
test_input = np.ones(5)
actual_output = as_array2d(test_input)
self.assertEqual((1, 5), actual_output.shape)
