This page describes the GLLiM methods implying getting information on GLLiM’s dimensions, constraints and GLLiMParameters.
Getters
- getDimensions()
Get the dimensions of the GLLiM model.
- Returns:
(string) A string describing the dimensions of the model.
- getConstraints()
Get the constraints of the GLLiM model.
- Returns:
(string) A string describing the constraints of the model.
- getParams()
Get the parameters of the GLLiM model.
- Returns:
(GLLiMParameters) An instance of GLLiMParameters containing the model parameters.
- getParamPi()
Get the mixture coefficients Pi.
- Returns:
(ndarray of shape (K)) A row vector of mixture coefficients.
- getParamA()
Get the parameter matrix A.
- Returns:
(ndarray of shape (D, L, K)) A cube containing the parameter matrix A.
- getParamB()
Get the parameter matrix B.
- Returns:
(ndarray of shape (D, K)) A matrix containing the parameter matrix B.
- getParamC()
Get the parameter matrix C.
- Returns:
(ndarray of shape (L, K)) A matrix containing the parameter matrix C.
- getParamGamma()
Get the gamma parameters.
- Returns:
(ndarray of shape (K, L, L)) 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.
- getParamSigma()
Get the sigma parameters.
- Returns:
(ndarray of shape (K, D, D)) 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.