gmm

swordfish.function.gmm()

Train the Gaussian Mixture Model (GMM) with the given data set.

参数:

• X (Constant) -- The training data set. For univariate data, X is a vector; For multivariate data, X is a matrix/table where each column is a sample.

• k (Constant) -- An integer indicating the number of independent Gaussians in a mixture model.

• maxlter (Constant, optional) -- A positive integer indicating the maximum EM iterations to perform. The default value is 300.

• tolerance (Constant, optional) -- A floating-point number indicating the convergence tolerance. EM iterations will stop when the lower bound average gain is below this threshold. The default value is 1e-4.

• randomSeed (Constant, optional) -- The random seed given to the method.

• mean (Constant, optional) --

  A vector or matrix indicating the initial means.

  • For univariate data, it is a vector of length k;

  • For multivariate data, it is a matrix whose number of columns is k and number of rows is the same as the number of variables in X;

  • If mean is unspecified, k values are randomly selected from X as the initial means.

• sigma (Constant, optional) --

  Can be:

  • a vector, indicating the initialized variance of each submodel if X is univariate data;

  • a tuple of length k, indicating the covariance matrix of each submodel if X is multivariate data;

  • a vector with element values of 1 or an identity matrix if sigma is unspecified.

返回:

A dictionary with the following keys:

• modelName: a string "Gaussian Mixture Model"

• prior: the prior probability of each submodel

• mean: the expectation of each submodel

• sigma: If X is univariate data, it represents the variance of each submodel; If X is multivariate data, it represents the covariance matrix of each submodel.

返回类型:

Constant