List of zSum() Examples

Examples of zSum()

org.apache.mahout.math.DenseVector.zSum()
org.apache.mahout.math.Vector.zSum()
Return the sum of all the elements of the receiver @return a double
org.apache.mahout.matrix.Vector.zSum()
Return the sum of all the elements of the receiver @return a double

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

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

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

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

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

    }


    int label = index.get();
//    instance.addTo(weightsPerFeature);
    weightsPerFeature.assign(instance, Functions.PLUS);
    weightsPerLabel.set(label, weightsPerLabel.get(label) + instance.zSum());
  }


  @Override
  protected void cleanup(Context ctx) throws IOException, InterruptedException {
    if (weightsPerFeature != null) {

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

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

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

    }
    mean = s1.divide(s0);
    // compute the average of the component stds
    if (s0 > 1) {
      Vector std = s2.times(s0).minus(s1.times(s1)).assign(new SquareRootFunction()).divide(s0);
      stdDev = std.zSum() / std.size();
    } else {
      stdDev = Double.MIN_VALUE;
    }
  }

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

    } else {
      Vector p = classify(data);
      if (actual > 0) {
        return Math.max(-100, Math.log(p.get(actual - 1)));
      } else {
        return Math.max(-100, Math.log(1 - p.zSum()));
      }
    }
  }
}

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

    Vector v = lr.classify(new DenseVector(new double[]{0, 0}));
    assertEquals(1 / 3.0, v.get(0), 1.0e-8);
    assertEquals(1 / 3.0, v.get(1), 1.0e-8);


    v = lr.classifyFull(new DenseVector(new double[]{0, 0}));
    assertEquals(1.0, v.zSum(), 1.0e-8);
    assertEquals(1 / 3.0, v.get(0), 1.0e-8);
    assertEquals(1 / 3.0, v.get(1), 1.0e-8);
    assertEquals(1 / 3.0, v.get(2), 1.0e-8);


    // weights for second vector component are still zero so all classifications are equally likely

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