Package org.apache.mahout.math

Examples of org.apache.mahout.math.DenseVector

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    trainer = new PerceptronTrainer(3, 0.5, 0.1, 1.0, 1.0);
  }

  public void testUpdate() throws TrainingException {
    double[] labels = { 1.0, 1.0, 1.0, 0.0 };
    Vector labelset = new DenseVector(labels);
    double[][] values = new double[3][4];
    for (int i = 0; i < 3; i++) {
      values[i][0] = 1.0;
      values[i][1] = 1.0;
      values[i][2] = 1.0;
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   * @param x
   *          an Observation
   * @return the Vector of probabilities
   */
  private Vector normalizedProbabilities(DirichletState<O> state, O x) {
    Vector pi = new DenseVector(numClusters);
    double max = 0;
    for (int k = 0; k < numClusters; k++) {
      double p = state.adjustedProbability(x, k);
      pi.set(k, p);
      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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  @Override
  public void paint(Graphics g) {
    super.plotSampleData(g);
    Graphics2D g2 = (Graphics2D) g;
   
    Vector dv = new DenseVector(2);
    int i = DisplayDirichlet.result.size() - 1;
    for (Model<VectorWritable>[] models : result) {
      g2.setStroke(new BasicStroke(i == 0 ? 3 : 1));
      g2.setColor(colors[Math.min(DisplayDirichlet.colors.length - 1, i--)]);
      for (Model<VectorWritable> m : models) {
        AsymmetricSampledNormalModel mm = (AsymmetricSampledNormalModel) m;
        dv.set(0, mm.getStdDev().get(0) * 3);
        dv.set(1, mm.getStdDev().get(1) * 3);
        if (DisplayDirichlet.isSignificant(mm)) {
          DisplayDirichlet.plotEllipse(g2, mm.getMean(), dv);
        }
      }
    }
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    getResults();
    new DisplayASNOutputState();
  }
 
  static void generateResults() {
    DisplayDirichlet.generateResults(new NormalModelDistribution(new VectorWritable(new DenseVector(2))));
  }
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  @Override
  public Model<VectorWritable>[] sampleFromPrior(int howMany) {
    Model<VectorWritable>[] result = new NormalModel[howMany];
    for (int i = 0; i < howMany; i++) {
      DenseVector mean = new DenseVector(60);
      for (int j = 0; j < 60; j++) {
        mean.set(j, UncommonDistributions.rNorm(30, 0.5));
      }
      result[i] = new NormalModel(mean, 1);
    }
    return result;
  }
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   *          int number of words in the vocabulary
   * @param numWords
   *          E[count] for each word
   */
  private Vector generateRandomDoc(int numWords, double sparsity) throws MathException {
    Vector v = new DenseVector(numWords);
    PoissonDistribution dist = new PoissonDistributionImpl(sparsity);
    for (int i = 0; i < numWords; i++) {
      // random integer
      v.setQuick(i, dist.inverseCumulativeProbability(random.nextDouble()) + 1);
    }
    return v;
  }
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  @Override
  public void paint(Graphics g) {
    plotSampleData(g);
    Graphics2D g2 = (Graphics2D) g;
    Vector dv = new DenseVector(2);
    int i = DisplayFuzzyKMeans.clusters.size() - 1;
    for (List<SoftCluster> cls : clusters) {
      g2.setStroke(new BasicStroke(i == 0 ? 3 : 1));
      g2.setColor(colors[Math.min(DisplayDirichlet.colors.length - 1, i--)]);
      for (SoftCluster cluster : cls) {
        // if (true || cluster.getWeightedPointTotal().zSum() > sampleData.size() * 0.05) {
        dv.assign(Math.max(cluster.std(), 0.3) * 3);
        DisplayDirichlet.plotEllipse(g2, cluster.getCenter(), dv);
        // }
      }
    }
  }
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  @Override
  public void paint(Graphics g) {
    super.plotSampleData(g);
    Graphics2D g2 = (Graphics2D) g;
   
    Vector dv = new DenseVector(2);
    int i = DisplayDirichlet.result.size() - 1;
    for (Model<VectorWritable>[] models : result) {
      g2.setStroke(new BasicStroke(i == 0 ? 3 : 1));
      g2.setColor(colors[Math.min(DisplayDirichlet.colors.length - 1, i--)]);
      for (Model<VectorWritable> m : models) {
        AsymmetricSampledNormalModel mm = (AsymmetricSampledNormalModel) m;
        dv.assign(mm.getStdDev().times(3));
        if (DisplayDirichlet.isSignificant(mm)) {
          DisplayDirichlet.plotEllipse(g2, mm.getMean(), dv);
        }
      }
    }
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    for (String value : numbers) {
      if (value.length() > 0) {
        doubles.add(Double.valueOf(value));
      }
    }
    Vector point = new DenseVector(doubles.size());
    int index = 0;
    for (Double d : doubles) {
      point.set(index++, d);
    }
    MeanShiftCanopy canopy = new MeanShiftCanopy(point, nextCanopyId++);
    output.collect(new Text(), canopy);
  }
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    generateResults();
    new DisplayASNDirichlet();
  }
 
  static void generateResults() {
    DisplayDirichlet.generateResults(new AsymmetricSampledNormalDistribution(new VectorWritable(new DenseVector(2))));
  }
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