Package org.apache.mahout.math

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

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    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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  public double getAverageStd() {
    if (sumWeight == 0.0) {
      return 0.0;
    } else {
      Vector std = getStd();
      return std.zSum() / std.size();
    }
  }

  @Override
  public Vector getVariance() {
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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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      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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    } else {
      Vector p = classify(data);
      if (actual > 0) {
        return Math.max(-100.0, Math.log(p.get(actual - 1)));
      } else {
        return Math.max(-100.0, Math.log(1.0 - p.zSum()));
      }
    }
  }
}
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    } else {
      int i = 0;
      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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    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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    v = lr.classify(new DenseVector(new double[]{0, 1}));
    assertEquals(1 / 3.0, v.get(0), 1.0e-3);
    assertEquals(1 / 3.0, v.get(1), 1.0e-3);

    v = lr.classifyFull(new DenseVector(new double[]{0, 1}));
    assertEquals(1.0, v.zSum(), 1.0e-8);
    assertEquals(1 / 3.0, v.get(0), 1.0e-3);
    assertEquals(1 / 3.0, v.get(1), 1.0e-3);
    assertEquals(1 / 3.0, v.get(2), 1.0e-3);

    // but the weights on the first component are non-zero
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