Package org.apache.mahout.math

Examples of org.apache.mahout.math.DenseVector.zSum()

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    } else {
      int i = 0;
      for (Model<VectorWritable> model : models) {
        pdfs.set(i++, model.pdf(new VectorWritable(instance)));
      }
      return pdfs.assign(new TimesFunction(), 1.0 / pdfs.zSum());
    }
  }

  @Override
  public double classifyScalar(Vector instance) {
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    w.addToVector("and", v3);
    w.addToVector("more", v3);
    assertEquals(0, v3.minus(v2).norm(1), 0);

    // moreover, the locations set in the unweighted case should be the same as in the weighted case
    assertEquals(v3.zSum(), v3.dot(v1), 0);
  }

  @Test
  public void testAsString() {
    Locale.setDefault(Locale.ENGLISH);
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      int i = 0;
      Vector pdfs = new DenseVector(models.size());
      for (Cluster model : models) {
        pdfs.set(i++, model.pdf(new VectorWritable(instance)));
      }
      return pdfs.assign(new TimesFunction(), 1.0 / pdfs.zSum());
    }
  }
 
  @Override
  public double classifyScalar(Vector instance) {
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    throws IOException, InterruptedException {
    Vector pi = new DenseVector(clusters.size());
    for (int i = 0; i < clusters.size(); i++) {
      pi.set(i, clusters.get(i).getModel().pdf(vector));
    }
    pi = pi.divide(pi.zSum());
    if (emitMostLikely) {
      emitMostLikelyCluster(vector, clusters, pi, context);
    } else {
      emitAllClusters(vector, clusters, pi, context);
    }
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    Vector pi = new DenseVector(clusters.size());
    for (int i = 0; i < clusters.size(); i++) {
      double pdf = clusters.get(i).getModel().pdf(vector);
      pi.set(i, pdf);
    }
    pi = pi.divide(pi.zSum());
    if (emitMostLikely) {
      emitMostLikelyCluster(vector, clusters, pi, writer);
    } else {
      emitAllClusters(vector, clusters, pi, writer);
    }
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    w.addToVector("and", v3);
    w.addToVector("more", v3);
    assertEquals(0, v3.minus(v2).norm(1), 0);

    // moreover, the locations set in the unweighted case should be the same as in the weighted case
    assertEquals(v3.zSum(), v3.dot(v1), 0);
  }

  @Test
  public void testAsString() {
    Locale.setDefault(Locale.ENGLISH);
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    throws IOException, InterruptedException {
    Vector pi = new DenseVector(clusters.size());
    for (int i = 0; i < clusters.size(); i++) {
      pi.set(i, clusters.get(i).getModel().pdf(vector));
    }
    pi = pi.divide(pi.zSum());
    if (emitMostLikely) {
      emitMostLikelyCluster(vector, clusters, pi, context);
    } else {
      emitAllClusters(vector, clusters, pi, context);
    }
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    Vector pi = new DenseVector(clusters.size());
    for (int i = 0; i < clusters.size(); i++) {
      double pdf = clusters.get(i).getModel().pdf(vector);
      pi.set(i, pdf);
    }
    pi = pi.divide(pi.zSum());
    if (emitMostLikely) {
      emitMostLikelyCluster(vector, clusters, pi, writer);
    } else {
      emitAllClusters(vector, clusters, pi, writer);
    }
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    int i = 0;
    Vector pdfs = new DenseVector(models.size());
    for (Cluster model : models) {
      pdfs.set(i++, model.pdf(new VectorWritable(data)));
    }
    return pdfs.assign(new TimesFunction(), 1.0 / pdfs.zSum());
  }
 
  @Override
  public void close(ClusterClassifier posterior) {
    for (Cluster cluster : posterior.getModels()) {
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    w.addToVector("and", v3);
    w.addToVector("more", v3);
    assertEquals(0, v3.minus(v2).norm(1), 0);

    // moreover, the locations set in the unweighted case should be the same as in the weighted case
    assertEquals(v3.zSum(), v3.dot(v1), 0);
  }

  @Test
  public void testAsString() {
    Locale.setDefault(Locale.ENGLISH);
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