CPC¶
Multiview Common Principal Components¶
CPC computes Common principal components of a set of matrices.
This file uses a variation of Trendafilov (2010) method to compute the k first common principal components of a set of matrices in an efficient way
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class
multiview.cpcmv.
MVCPC
(k=0)¶ Compute common principal components of x.
Parameters: k (int, default 0) – Number of components to extract (0 means all p components). -
eigenvalues_
¶ ndarray – Stores the eigenvalues computed in the algorithm.
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eigenvectors_
¶ ndarray – Stores the eigenvectors computed in the algorithm.
References
Trendafilov, N. (2010). Stepwise estimation of common principal components. Computational Statistics and Data Analysis, 54, 3446–3457.
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fit
(x)¶ Compute k common principal components of x.
Parameters: x (array_like or ndarray) – A set of n matrices of dimension pxp given as a n x p x p matrix.
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fit_transform
(x)¶ Compute k common principal components of x, and return those components.
Parameters: x (array_like or ndarray) – A set of n matrices of dimension pxp given as a n x p x p matrix.
Returns: values – Tuple with two elements:
the eigenvalues
the common eigenvectors
Return type: tuple
Raises: - ValueError: Matrices are not square matrices or k value is
negative.
Examples
>>> import numpy as np >>> x = np.array([[[2, 1, 8], [4, 5, 6], [3, 7, 9]], [[1, 4, 7], [2, 5, 8], [3, 6, 9]]]) >>> mv_cpc = MVCPC(k=3) >>> mv_cpc.fit_transform(x) (array([[ 16.09601677, 16.21849616], [ -0.11903382, -0.85516505], [ 0.02301705, -0.3633311 ]]), array([[ 0.45139369, -0.88875921, 0.07969196], [ 0.55811719, 0.35088538, 0.75192065], [ 0.69623914, 0.29493478, -0.65441923]])) >>>
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