Estimating Mixture Models

This Java project provides an evaluation interface for estimating Mixture Models (MM) in an online manner. The estimation is done using a version of the Expectation Maximization algorithm. An example implementation is provided for Gaussian Mixture Models, however, the framework allows plugging in other distributions as well.

It receives a mixture model as input, then it generates samples from the MM and tries to estimate the parameters of the given MM. During the simulation in every 1000^th iteration (by default) it takes a snapshot of the current state of the simulation. Finally, it combines these images into an animated .gif file. This is a demo implementation, it is not optimized.

Requirements

• apache-ant, Java SDK
• bash
• gnuplot
• convert (imagemagick)

Getting Started

• downloading: Clone or download.

• building: Use Apache Ant inside the GMMTest directory to compile the sources.

cd GMMTest; ant

• running: Switch to the bin directory and start a simulation by typing:

cd bin java -jar gmmtest.jar 0.5,0.2,0.3 -2.0,1.0,2.0 0.2,1.0,0.5 simulation.gif ./gmmSimulation/ 1000 20000 densityEstimator.SmoothGMM 3 batchSize=1000,alpha=0.2 123456789

This will perform a simulation where you have 3 Gaussian components with the following weights, means and variances, repectively: [0.5, 0.2, 0.3] [-2.0,1.0,2.0] [0.2,1.0,0.5]. The output will be generated in simulation.gif, the simulation will use the ./gmmSimulation/ directory (for intermediate operations), which must not have existed beforehand. It will generate a snapshot in every 1000^th iteration. The simulation will generate 20000 samples. The simulation will use the densityEstimator.SmoothGMM for estimating the mixture model using 3 components. The parameter setting of the estimator classi is given in the next parameter (batchSize=1000,alpha=0.2). The format of the parameter setting argument is slightly intuitive it consists of comma separeted parameter-value pairs in form parameterName=parameterValue. In the above presented sample the estimater class has two parameters. The first one is the batch size which has a value 1000. The second one is the smoothing parameter named alpha which is set to 0.2. The pseudorandom number generator is initialized with seed 123456789. This final parameter is optional.