darfix.decomposition.nica.NICA#

class darfix.decomposition.nica.NICA(data, num_components, chunksize=None, lr=0.03, indices=None)[source]#

Bases: Base

Compute the non-negative independent components of the linear generative model x = A * s.

Here, x is a p-dimensional observable random vector and s is the latent random vector of length num_components, whose components are statistically independent and non-negative. The matrix X is assumed to hold n samples of x, stacked in columns (shape(X) = (p, n)). In practice, if shape(X) = (p, n) this function solves X = A * S both for S and A, where A is the so-called mixing matrix, with shape (p, num_components), and S is a (num_components, n) matrix which contains n samples of the latent source vector, stacked in columns.

This function implements the method presented in: Blind Separation of Positive Sources by Globally Convergent Gradient Search (https://core.ac.uk/download/pdf/76988305.pdf)

Parameters:

  • data – array of shape (nsamples, nfeatures).

  • num_components (Union[uint, None]) – Dimension of s. Number of latent random variables.

  • chunksize (int) – Size of every group of samples to apply PCA to. PCA will be fit with arrays of shape (chunksize, nfeatures), where nfeatures is the number of features per sample.

  • lr (float) – Learning rate of gradient descent.

  • max_iter (int) – Maximum number of iterations of gradient descent.

  • tol (float) – Tolerance on update at each iteration.

property V#
property Z#
property data#
fit_transform(max_iter=100, error_step=None)[source]#

Fit to data, then transform it

Parameters:

  • max_iter (int, optional) – Maximum number of iterations, defaults to 100

  • error_step (Union[None,int], optional) – If None, error is not computed, defaults to None Else compute error for every error_step iterations.

  • compute_w (bool, optional) – When False, W is not computed, defaults to True

  • compute_h (bool, optional) – When False, H is not computed, defaults to True

frobenius_norm(chunks=200)#

Frobenius norm (||data - WH||) of a data matrix and a low rank approximation given by WH. Minimizing the Fnorm is the most common optimization criterion for matrix factorization methods. Returns: ——- frobenius norm: F = ||data - WH||

property indices#
property num_components#
property num_features#
property num_samples#
squared_frobenius_norm(chunks=200)#

Frobenius norm (||data - WH||) of a data matrix and a low rank approximation given by WH. Minimizing the Fnorm is the most common optimization criterion for matrix factorization methods. Returns: ——- frobenius norm: F = ||data - WH||