ParamMixHMM-class: A Reference Class which contains parameters of a mixture of...

In fchamroukhi/mixHMM: Mixture of HMMs for Curve Clustering and Segmentation

Description

ParamMixHMM contains all the parameters of a mixture of HMM models.

Fields

fData

FData object representing the sample (covariates/inputs X and observed responses/outputs Y).

K

The number of clusters (Number of HMM models).

R

The number of regimes (HMM components) for each cluster.

variance_type

Character indicating if the model is homoskedastic (variance_type = "homoskedastic") or heteroskedastic (variance_type = "heteroskedastic"). By default the model is heteroskedastic.

order_constraint

A logical indicating whether or not a mask of order one should be applied to the transition matrix of the Markov chain to provide ordered states. For the purpose of segmentation, it must be set to TRUE (which is the default value).

alpha

Cluster weights. Matrix of dimension (K, 1).

prior

The prior probabilities of the Markov chains. prior is a matrix of dimension (R, K). The k-th column represents the prior distribution of the Markov chain asociated to the cluster k.

trans_mat

The transition matrices of the Markov chains. trans_mat is an array of dimension (R, R, K).

mask

Mask applied to the transition matrices trans_mat. By default, a mask of order one is applied.

mu

Means. Matrix of dimension (R, K). The k-th column gives represents the k-th cluster and gives the means for the R regimes.

sigma2

The variances for the K clusters. If MixHMM model is heteroskedastic (variance_type = "heteroskedastic") then sigma2 is a matrix of size (R, K) (otherwise MixHMM model is homoskedastic (variance_type = "homoskedastic") and sigma2 is a matrix of size (1, K)).

nu

The degrees of freedom of the MixHMM model representing the complexity of the model.

Methods

initGaussParamHmm(Y, k, R, variance_type, try_algo)

Initialize the means mu and sigma2 for the cluster k.

initParam(init_kmeans = TRUE, try_algo = 1)

Method to initialize parameters alpha, prior, trans_mat, mu and sigma2.

If init_kmeans = TRUE then the curve partition is initialized by the K-means algorithm. Otherwise the curve partition is initialized randomly.

If try_algo = 1 then mu and sigma2 are initialized by segmenting the time series Y uniformly into R contiguous segments. Otherwise, mu and sigma2 are initialized by segmenting randomly the time series Y into R segments.

MStep(statMixHMM)

Method which implements the M-step of the EM algorithm to learn the parameters of the MixHMM model based on statistics provided by the object statMixHMM of class StatMixHMM (which contains the E-step).

fchamroukhi/mixHMM documentation built on Aug. 8, 2019, 3:31 p.m.