def init_from_covariance_matrix(cls,
C,
mean,
n_samples,
centred=True,
max_n_components=None):
r"""
Build the Principal Component Analysis (PCA) by eigenvalue
decomposition of the provided covariance/scatter matrix. For details
of the implementation of PCA, see :map:`pcacov`.
Parameters
----------
C : ``(n_features, n_features)`` `ndarray`
The Covariance/Scatter matrix, where `N` is the number of features.
mean : :map:`Vectorizable`
The mean instance. It must be a :map:`Vectorizable` and *not* an
`ndarray`.
n_samples : `int`
The number of samples used to generate the covariance matrix.
centred : `bool`, optional
When ``True`` we assume that the data were centered before
computing the covariance matrix.
max_n_components : `int`, optional
The maximum number of components to keep in the model. Any
components above and beyond this one are discarded.
"""
# Create new pca instance
self_model = PCAVectorModel.__new__(cls)
self_model.n_samples = n_samples
# Compute pca on covariance
e_vectors, e_values = pcacov(C)
# The call to __init__ of MeanLinearModel is done in here
self_model._constructor_helper(eigenvalues=e_values,
eigenvectors=e_vectors,
mean=mean.as_vector(),
centred=centred,
max_n_components=max_n_components)
VectorizableBackedModel.__init__(self_model, mean)
return self_model
def init_from_components(cls,
components,
eigenvalues,
mean,
n_samples,
centred,
max_n_components=None):
r"""
Build the Principal Component Analysis (PCA) using the provided
components (eigenvectors) and eigenvalues.
Parameters
----------
components : ``(n_components, n_features)`` `ndarray`
The eigenvectors to be used.
eigenvalues : ``(n_components, )`` `ndarray`
The corresponding eigenvalues.
mean : :map:`Vectorizable`
The mean instance. It must be a :map:`Vectorizable` and *not* an
`ndarray`.
n_samples : `int`
The number of samples used to generate the eigenvectors.
centred : `bool`, optional
When ``True`` we assume that the data were centered before
computing the eigenvectors.
max_n_components : `int`, optional
The maximum number of components to keep in the model. Any
components above and beyond this one are discarded.
"""
# Create new pca instance
self_model = PCAVectorModel.__new__(cls)
self_model.n_samples = n_samples
# The call to __init__ of MeanLinearModel is done in here
self_model._constructor_helper(eigenvalues=eigenvalues,
eigenvectors=components,
mean=mean.as_vector(),
centred=centred,
max_n_components=max_n_components)
VectorizableBackedModel.__init__(self_model, mean)
return self_model
