Package org.apache.mahout.math

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

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    }
    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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  public double std() {
    if (s0 > 0) {
      Vector radical = s2.times(s0).minus(s1.times(s1));
      radical = radical.times(radical).assign(new SquareRootFunction());
      Vector stds = radical.assign(new SquareRootFunction()).divide(s0);
      return stds.zSum() / stds.size();
    } else {
      return 0;
    }
  }
 
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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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  public double getAverageStd() {
    if (n == 0) {
      return 0;
    } else {
      Vector std = getStd();
      return std.zSum() / std.size();
    }
  }

  @Override
  public Vector getVariance() {
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      List<VectorWritable> list = redWriter.getValue(key);
      assertEquals("One item in the list", 1, list.size());
      Vector item = list.get(0).get();
     
      // should only be one non-zero item
      assertTrue("One non-zero item in the array", Math.abs(item.zSum() + 0.48) < 0.01);
    }

  }
}
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  @Override
  protected void map(IntWritable key, VectorWritable value, Context context)
      throws IOException, InterruptedException {
    Vector vector = value.get();
    int label = key.get();
    double weight = Math.log((vector.zSum() + alphaI) / (labelSum.get(label) + vocabCount));
    perLabelThetaNormalizer.set(label, perLabelThetaNormalizer.get(label) + weight);
  }
 
  @Override
  protected void setup(Context context) throws IOException, InterruptedException {
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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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      labelSum = new RandomAccessSparseVector(labelMap.size()); 
    }
   
    int label = key.get();
    vector.addTo(featureSum);
    labelSum.set(label, labelSum.get(label) + vector.zSum());
  }
 
  @Override
  protected void setup(Context context) throws IOException, InterruptedException {
    super.setup(context);
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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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