def pcd_samples_nowhiten_yescentre_yesbias_test():
output = principal_component_decomposition(large_samples_data_matrix,
bias=True, centre=True,
whiten=False)
eigenvectors, eigenvalues, mean_vector = output
assert_almost_equal(eigenvalues, eigenvalues_centered_biased_s)
assert_almost_equal(eigenvectors, centered_eigenvectors_s)
assert_almost_equal(mean_vector, mean_vector_s)
def pcd_features_yeswhiten_yescentre_nobias_test():
output = principal_component_decomposition(large_samples_data_matrix.T,
whiten=True, centre=True,
bias=False)
eigenvectors, eigenvalues, mean_vector = output
assert_almost_equal(eigenvalues, eigenvalues_centered_no_bias_f)
assert_almost_equal(eigenvectors, centered_eigenvectors_biased_f)
assert_almost_equal(mean_vector, mean_vector_f)
def pcd_samples_nowhiten_nocentre_nobias_test():
output = principal_component_decomposition(large_samples_data_matrix,
center=False, whiten=False,
bias=False)
eigenvectors, eigenvalues, mean_vector = output
assert_almost_equal(eigenvalues, eigenvalues_no_centre_no_bias_s)
assert_almost_equal(eigenvectors, non_centered_eigenvectors_s)
assert_almost_equal(mean_vector, [0.0, 0.0])
def pcd_features_yeswhiten_nocentre_yesbias_test():
output = principal_component_decomposition(large_samples_data_matrix.T,
bias=True, centre=False,
whiten=True)
eigenvectors, eigenvalues, mean_vector = output
assert_almost_equal(eigenvalues, eigenvalues_no_centre_biased_f)
assert_almost_equal(eigenvectors, non_centered_eigenvectors_biased_f)
assert_almost_equal(mean_vector, np.zeros(10))
def pcd_features_yeswhiten_nocentre_yesbias_test():
output = principal_component_decomposition(large_samples_data_matrix.T,
bias=True, center=False,
whiten=True)
eigenvectors, eigenvalues, mean_vector = output
assert_almost_equal(eigenvalues, eigenvalues_no_centre_biased_f)
assert_almost_equal(eigenvectors, non_centered_eigenvectors_biased_f)
assert_almost_equal(mean_vector, np.zeros(10))
def pcd_samples_nowhiten_yescentre_yesbias_test():
output = principal_component_decomposition(large_samples_data_matrix,
bias=True, center=True,
whiten=False)
eigenvectors, eigenvalues, mean_vector = output
assert_almost_equal(eigenvalues, eigenvalues_centered_biased_s)
assert_almost_equal(eigenvectors, centered_eigenvectors_s)
assert_almost_equal(mean_vector, mean_vector_s)
def pcd_features_yeswhiten_yescentre_nobias_test():
output = principal_component_decomposition(large_samples_data_matrix.T,
whiten=True, centre=True,
bias=False)
eigenvectors, eigenvalues, mean_vector = output
assert_almost_equal(eigenvalues, eigenvalues_centered_no_bias_f)
assert_almost_equal(eigenvectors, centered_eigenvectors_biased_f)
assert_almost_equal(mean_vector, mean_vector_f)
def pcd_samples_nowhiten_nocentre_nobias_test():
output = principal_component_decomposition(large_samples_data_matrix,
centre=False, whiten=False,
bias=False)
eigenvectors, eigenvalues, mean_vector = output
assert_almost_equal(eigenvalues, eigenvalues_no_centre_no_bias_s)
assert_almost_equal(eigenvectors, non_centered_eigenvectors_s)
assert_almost_equal(mean_vector, [0.0, 0.0])
def pcd_samples_yeswhiten_nocentre_yesbias_test():
output = principal_component_decomposition(large_samples_data_matrix,
bias=True, centre=False,
whiten=True)
eigenvectors, eigenvalues, mean_vector = output
assert_almost_equal(eigenvalues, eigenvalues_no_centre_biased_s)
assert_almost_equal(eigenvectors.T / np.sqrt(1.0 / eigenvalues),
non_centered_eigenvectors_s.T)
assert_almost_equal(mean_vector, [0.0, 0.0])
def pcd_samples_yeswhiten_yescentre_nobias_test():
output = principal_component_decomposition(large_samples_data_matrix,
whiten=True, centre=True,
bias=False)
eigenvectors, eigenvalues, mean_vector = output
assert_almost_equal(eigenvalues, eigenvalues_centered_no_bias_s)
assert_almost_equal(eigenvectors.T / np.sqrt(1.0 / eigenvalues),
centered_eigenvectors_s.T)
assert_almost_equal(mean_vector, mean_vector_s)
def pcd_samples_yeswhiten_yescentre_nobias_test():
output = principal_component_decomposition(large_samples_data_matrix,
whiten=True, center=True,
bias=False)
eigenvectors, eigenvalues, mean_vector = output
assert_almost_equal(eigenvalues, eigenvalues_centered_no_bias_s)
assert_almost_equal(eigenvectors.T / np.sqrt(1.0 / eigenvalues),
centered_eigenvectors_s.T)
assert_almost_equal(mean_vector, mean_vector_s)
def pcd_features_nowhiten_nocentre_nobias_inplace_test():
# important to copy as this will now destructively effect the input data
# matrix (due to inplace)
output = principal_component_decomposition(large_samples_data_matrix.T.copy(),
centre=False, whiten=False,
bias=False, inplace=True)
eigenvectors, eigenvalues, mean_vector = output
assert_almost_equal(eigenvalues, eigenvalues_no_centre_no_bias_f)
assert_almost_equal(eigenvectors, non_centered_eigenvectors_f)
assert_almost_equal(mean_vector, np.zeros(10))
def pcd_samples_yeswhiten_nocentre_yesbias_test():
output = principal_component_decomposition(large_samples_data_matrix,
bias=True,
center=False,
whiten=True)
eigenvectors, eigenvalues, mean_vector = output
assert_almost_equal(eigenvalues, eigenvalues_no_centre_biased_s)
assert_almost_equal(eigenvectors.T / np.sqrt(1.0 / eigenvalues),
non_centered_eigenvectors_s.T)
assert_almost_equal(mean_vector, [0.0, 0.0])
def pcd_features_nowhiten_nocentre_nobias_inplace_test():
# important to copy as this will now destructively effect the input data
# matrix (due to inplace)
output = principal_component_decomposition(
large_samples_data_matrix.T.copy(),
centre=False,
whiten=False,
bias=False,
inplace=True)
eigenvectors, eigenvalues, mean_vector = output
assert_almost_equal(eigenvalues, eigenvalues_no_centre_no_bias_f)
assert_almost_equal(eigenvectors, non_centered_eigenvectors_f)
assert_almost_equal(mean_vector, np.zeros(10))
def __init__(self,
samples,
centre=True,
bias=False,
verbose=False,
n_samples=None):
# get the first element as the template and use it to configure the
# data matrix
if n_samples is None:
# samples is a list
n_samples = len(samples)
template = samples[0]
samples = samples[1:]
else:
# samples is an iterator
template = next(samples)
n_features = template.n_parameters
template_vector = template.as_vector()
data = np.zeros((n_samples, n_features), dtype=template_vector.dtype)
# now we can fill in the first element from the template
data[0] = template_vector
del template_vector
if verbose:
print('Allocated data matrix {:.2f}'
'GB'.format(data.nbytes / 2**30))
# 1-based as we have the template vector set already
for i, sample in enumerate(samples, 1):
if i >= n_samples:
break
if verbose:
print_dynamic(
'Building data matrix from {} samples - {}'.format(
n_samples,
progress_bar_str(float(i + 1) / n_samples,
show_bar=True)))
data[i] = sample.as_vector()
# compute pca
e_vectors, e_values, mean = principal_component_decomposition(
data, whiten=False, centre=centre, bias=bias, inplace=True)
super(PCAModel, self).__init__(e_vectors, mean, template)
self.centred = centre
self.biased = bias
self._eigenvalues = e_values
# start the active components as all the components
self._n_active_components = int(self.n_components)
self._trimmed_eigenvalues = None
def __init__(self, samples, center=True, bias=False):
# build data matrix
n_samples = len(samples)
n_features = samples[0].n_parameters
data = np.zeros((n_samples, n_features))
for i, sample in enumerate(samples):
data[i] = sample.as_vector()
# compute pca
eigenvectors, eigenvalues, mean_vector = \
principal_component_decomposition(data, whiten=False,
center=center, bias=bias)
super(PCAModel, self).__init__(eigenvectors, mean_vector, samples[0])
self.centered = center
self.biased = bias
self._eigenvalues = eigenvalues
self._n_components = self.n_components
self._trimmed_eigenvalues = None
def __init__(self, samples, center=True, bias=False):
# build data matrix
n_samples = len(samples)
n_features = samples[0].n_parameters
data = np.zeros((n_samples, n_features))
for i, sample in enumerate(samples):
data[i] = sample.as_vector()
# compute pca
eigenvectors, eigenvalues, mean_vector = \
principal_component_decomposition(data, whiten=False,
center=center, bias=bias)
super(PCAModel, self).__init__(eigenvectors, mean_vector, samples[0])
self.centered = center
self.biased = bias
self._eigenvalues = eigenvalues
self._n_components = self.n_components
self._trimmed_eigenvalues = None
def __init__(self, samples, centre=True, bias=False, verbose=False,
n_samples=None):
# get the first element as the template and use it to configure the
# data matrix
if n_samples is None:
# samples is a list
n_samples = len(samples)
template = samples[0]
samples = samples[1:]
else:
# samples is an iterator
template = next(samples)
n_features = template.n_parameters
template_vector = template.as_vector()
data = np.zeros((n_samples, n_features), dtype=template_vector.dtype)
# now we can fill in the first element from the template
data[0] = template_vector
del template_vector
if verbose:
print('Allocated data matrix {:.2f}'
'GB'.format(data.nbytes / 2 ** 30))
# 1-based as we have the template vector set already
for i, sample in enumerate(samples, 1):
if i >= n_samples:
break
if verbose:
print_dynamic(
'Building data matrix from {} samples - {}'.format(
n_samples,
progress_bar_str(float(i + 1) / n_samples, show_bar=True)))
data[i] = sample.as_vector()
# compute pca
e_vectors, e_values, mean = principal_component_decomposition(
data, whiten=False, centre=centre, bias=bias, inplace=True)
super(PCAModel, self).__init__(e_vectors, mean, template)
self.centred = centre
self.biased = bias
self._eigenvalues = e_values
# start the active components as all the components
self._n_active_components = int(self.n_components)
self._trimmed_eigenvalues = None
def __init__(self, samples, centre=True, bias=False):
# build data matrix
n_samples = len(samples)
template = samples[0]
n_features = template.n_parameters
data = np.zeros((n_samples, n_features))
for i, sample in enumerate(samples):
data[i] = sample.as_vector()
# compute pca
eigenvectors, eigenvalues, mean_vector = \
principal_component_decomposition(data, whiten=False,
centre=centre, bias=bias)
super(PCAModel, self).__init__(eigenvectors, mean_vector, template)
self.centred = centre
self.biased = bias
self._eigenvalues = eigenvalues
# start the active components as all the components
self._n_active_components = int(self.n_components)
self._trimmed_eigenvalues = None
def __init__(self, samples, centre=True, bias=False):
# build data matrix
n_samples = len(samples)
template = samples[0]
n_features = template.n_parameters
data = np.zeros((n_samples, n_features))
for i, sample in enumerate(samples):
data[i] = sample.as_vector()
# compute pca
eigenvectors, eigenvalues, mean_vector = \
principal_component_decomposition(data, whiten=False,
centre=centre, bias=bias)
super(PCAModel, self).__init__(eigenvectors, mean_vector, template)
self.centred = centre
self.biased = bias
self._eigenvalues = eigenvalues
# start the active components as all the components
self._n_active_components = int(self.n_components)
self._trimmed_eigenvalues = None
