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MixtureModels

A Julia package for probabilistic mixture models

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MixtureModels.jl

A Julia package for probabilistic mixture models

A mixture model is a probabilistic model that combines multiple components to capture data distribution. Generally, a mixture model is characterized by a collection of components and a discrete distribution over them. From a generative perspective, the procedure to generate a sample from a mixture model consists of two steps: (1) choose a particular component from the discrete distribution, and (2) use the chosen component to generate the sample.

Finite Mixture Model

Finite mixture model is a classical formulation of mixture model, which requires the number of components, often denoted by K, to be fixed before training. The Expectation-Maximization (EM) algorithm is usually used to estimate a mixture model from data.

Basic Usage

The function fit_fmm fits a mixture model to a given data set:

fit_fmm(C, data, K, alg)

Parameters:

  • C: the component type, e.g. one can use MultivariateNormal{PDMat} to indicate Gaussian components with full covariance matrix.
  • data: the input samples
  • K: the number of components to be estimated
  • alg: the algorithm option struct.

Here, alg can be constructed using the fmm_em function, as below

fmm_em(;
    maxiter::Integer=100,    # maximum number of iterations
    tol::Real=1.0e-6,        # tolerable change of objective value upon convergence
    display::Symbol=:none,   # can take value :none, :proc, or :iter
    alpha::Float64=1.0)      # Dirichlet prior coefficient for pi

This function returns a struct of type FiniteMixtureEMResults, which contains the following fields:

  • mixture: an instance of type Mixture, which has two fields: components (a list of components) and pi (component proportions);
  • Q: soft assignment matrix, of size (n, K). In particular, Q[i, k] indicates the probability that the i-th sample belongs to the k-th component;
  • L: likelihood matrix, of size (n, K). In particular, L[i, k] is the logpdf of the i-th sample w.r.t. the k-th component;
  • niters: the number of elapsed iterations;
  • converged: whether the procedure converged;
  • objective: the objective function value of the last iteration.

Examples

# train a Gaussian mixture model with 5 components, showing progress information at each iteration
r = fit_fmm(MultivariateNormal{PDMat}, x, 5, fmm_em(maxiter=50, display=:iter))

# print each component
for comp in r.components
    println(comp)
end

# If you are satisfied with default option values
r = fit_fmm(MultivariateNormal{PDMat}, x, 5, fmm_em())

Note:

In Distributions.jl, the type MultivariateNormal takes a type argument that specifies the form of covariance matrix. Specifically, MultivariateNormal{PDMat} uses full covariance, MultivariateNormal{PDiagMat} uses diagonal covariance, while MultivariateNormal{ScalMat} uses a covariance of the form s * I.

The package provides a demo in demo/gmm.jl to demonstrate the use of this package in fitting Gaussian mixture model. Below is a screenshot of the result:

gmm_demo

Use User-supplied Q matrix

By default, this function initializes the soft assignment matrix Q randomly. The users can also provide their own version of initial Q-matrix, using the function fit_fmm!:

fit_fmm!(C, data, Q, alg)

This function updates Q inplace.

This package provides a convenient function qmatrix to construct Q. For example, if you have a label vector labels, which may be obtained by running a clustering algorithm, you can use this vector to construct Q:

r = fit_fmm!(C, data, qmatrix(labels, K), alg)

Here, qmatrix constructs a matrix Q of size (n, K) such that Q[i, labels[i]] = 1.0 and each row sums to one.

Future Plan

  • Implement variants of Finite Mixture Models
  • Implement Bayesian Nonparametric Mixture Models (e.g. DPMM)

First Commit

07/19/2013

Last Touched

5 days ago

Commits

12 commits

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