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 package org.apache.mahout.clustering.dirichlet;
 import  org.apache.hadoop.fs.FileStatus;
 import  org.apache.hadoop.fs.FileSystem;
 import  org.apache.hadoop.fs.Path;
 import  org.apache.hadoop.mapred.JobConf;
 import  org.apache.hadoop.mapred.MapReduceBase;
 import  org.apache.hadoop.mapred.Mapper;
 import  org.apache.hadoop.mapred.OutputCollector;
 import  org.apache.hadoop.mapred.OutputLogFilter;
 import  org.apache.hadoop.mapred.Reporter;
 public class DirichletMapper extends MapReduceBase implements
     Mapper<WritableComparable<?>, Vector, Text, Vector> {
   private DirichletState<Vectorstate;
   public void map(WritableComparable<?> keyVector v,
                   OutputCollector<Text, Vectoroutput, Reporter reporterthrows IOException {
     // compute a normalized vector of probabilities that v is described by each model
     Vector pi = normalizedProbabilities(v);
     // then pick one model by sampling a Multinomial distribution based upon them
     // see:
     int k = UncommonDistributions.rMultinom(pi);
     output.collect(new Text(String.valueOf(k)), v);
   public void configure(DirichletState<Vectorstate) {
     this. = state;
   public void configure(JobConf job) {
      = getDirichletState(job);
   public static DirichletState<VectorgetDirichletState(JobConf job) {
     String statePath = job.get(.);
     String modelFactory = job.get(.);
     String numClusters = job.get(.);
     String alpha_0 = job.get(.);
     try {
       DirichletState<Vectorstate = DirichletDriver.createState(modelFactory,
           Integer.parseInt(numClusters), Double.parseDouble(alpha_0));
       Path path = new Path(statePath);
       FileSystem fs = FileSystem.get(path.toUri(), job);
       FileStatus[] status = fs.listStatus(pathnew OutputLogFilter());
       for (FileStatus s : status) {
         SequenceFile.Reader reader = new SequenceFile.Reader(fss.getPath(),
         try {
           Text key = new Text();
           DirichletCluster<Vectorcluster = new DirichletCluster<Vector>();
           while ( {
             int index = Integer.parseInt(key.toString());
             cluster = new DirichletCluster<Vector>();
         } finally {
       // TODO: with more than one mapper, they will all have different mixtures. Will this matter?
       return state;
     } catch (Exception e) {
       throw new IllegalStateException(e);

Compute a normalized vector of probabilities that v is described by each model using the mixture and the model pdfs

state the DirichletState<Vector> of this iteration
v an Vector
the Vector of probabilities
  private static Vector normalizedProbabilities(DirichletState<VectorstateVector v) {
    Vector pi = new DenseVector(state.getNumClusters());
    double max = 0;
    for (int k = 0; k < state.getNumClusters(); k++) {
      double p = state.adjustedProbability(vk);
      if (max < p) {
        max = p;
    // normalize the probabilities by largest observed value
    pi.assign(new TimesFunction(), 1.0 / max);
    return pi;
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