Setters

setParams(theta)

Set the parameters of the GLLiM model.

Parameters:

theta (GLLiMParameters)

setParamPi(Pi)

Set the mixture coefficients Pi.

Parameters:

Pi (ndarray of shape (K))

setParamA(A)

Set the parameter matrix A.

Parameters:

A (ndarray of shape (D, L, K))

setParamB(B)

Set the parameter matrix B.

Parameters:

B (ndarray of shape (D, K))

setParamC(C)

Set the parameter matrix C.

Parameters:

C (ndarray of shape (L, K))

setParamGamma(Gamma)

Set the gamma parameters. Shape depends on Gamma constraints. Gamma is a ndarray containing the K covariance matrices of the mixture of Gaussian distributions that define the low-dimensional data.

In the case of Full covariance matrix (gamma_type = ‘full’), Gamma is of shape (K, L, L).

In the case of Diagonal covariance matrix (gamma_type = ‘diag’), Gamma is of shape (K, L) with Gamma[k] representing the variances vector of the k^{th} gaussian.

In the case of Isotropic covariance matrix (gamma_type = ‘iso’), Gamma is of shape (K) with Gamma[k] representing the unique variance of the k^{th} gaussian.

Parameters:

Gamma (ndarray of shape (K, L*, L*))

setParamSigma(Sigma)

Set the sigma parameters. Shape depends on Gamma constraints. Sigma is a ndarray containing the K covariance matrices of the mixture of Gaussian distributions that define the high-dimensional data.

In the case of Full covariance matrix (gamma_type = ‘full’), Sigma is of shape (K, D, D).

In the case of Diagonal covariance matrix (gamma_type = ‘diag’), Sigma is of shape (K, D) with Sigma[k] representing the variances vector of the k^{th} gaussian.

In the case of Isotropic covariance matrix (gamma_type = ‘iso’), Sigma is of shape (K) with Sigma[k] representing the unique variance of the k^{th} gaussian.

Parameters:

Sigma (ndarray of shape (K, D*, D*))