def _check_weights(weights, n_components):
"""Check the user provided 'weights'.
Parameters
----------
weights : array-like, shape (n_components,)
The proportions of components of each mixture.
n_components : int
Number of components.
Returns
-------
weights : array, shape (n_components,)
"""
weights = check_array(weights,
dtype=[np.float64, np.float32],
ensure_2d=False)
_check_shape(weights, (n_components, ), 'weights')
# check range
if (any(np.less(weights, 0.)) or any(np.greater(weights, 1.))):
raise ValueError("The parameter 'weights' should be in the range "
"[0, 1], but got max value %.5f, min value %.5f" %
(np.min(weights), np.max(weights)))
# check normalization
if not np.allclose(np.abs(1. - np.sum(weights)), 0.):
raise ValueError("The parameter 'weights' should be normalized, "
"but got sum(weights) = %.5f" % np.sum(weights))
return weights
def _check_precisions(precisions, covariance_type, n_components, n_features):
"""Validate user provided precisions.
Parameters
----------
precisions : array-like,
'full' : shape of (n_components, n_features, n_features)
'tied' : shape of (n_features, n_features)
'diag' : shape of (n_components, n_features)
'spherical' : shape of (n_components,)
covariance_type : string
n_components : int
Number of components.
n_features : int
Number of features.
Returns
-------
precisions : array
"""
precisions = check_array(precisions,
dtype=[np.float64, np.float32],
ensure_2d=False,
allow_nd=covariance_type == 'full')
precisions_shape = {
'full': (n_components, n_features, n_features),
'tied': (n_features, n_features),
'diag': (n_components, n_features),
'spherical': (n_components, )
}
_check_shape(precisions, precisions_shape[covariance_type],
'%s precision' % covariance_type)
_check_precisions = {
'full': _check_precisions_full,
'tied': _check_precision_matrix,
'diag': _check_precision_positivity,
'spherical': _check_precision_positivity
}
_check_precisions[covariance_type](precisions, covariance_type)
return precisions
def _check_means(means, n_components, n_features):
"""Validate the provided 'means'.
Parameters
----------
means : array-like, shape (n_components, n_features)
The centers of the current components.
n_components : int
Number of components.
n_features : int
Number of features.
Returns
-------
means : array, (n_components, n_features)
"""
means = check_array(means, dtype=[np.float64, np.float32], ensure_2d=False)
_check_shape(means, (n_components, n_features), 'means')
return means
def _check_reg_weights(reg_weights, n_components, n_features):
"""Validate the user provided 'reg_weights'.
Parameters
----------
means : array-like, shape (n_components, n_features + 1)
The regression weights of the current components.
n_components : int
Number of components.
n_features : int
Number of features.
Returns
-------
means : array, (n_components, n_features)
"""
reg_weights = check_array(reg_weights,
dtype=[np.float64, np.float32],
ensure_2d=False)
_check_shape(reg_weights, (n_components, n_features + 1),
'regression weights')
return reg_weights
