Package org.apache.mahout.matrix

Examples of org.apache.mahout.matrix.DenseVector.assign()

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    super.plotSampleData(g);
    Graphics2D g2 = (Graphics2D) g;
    Vector dv = new DenseVector(2);
    for (Canopy canopy : canopies) {
      if (canopy.getNumPoints() > sampleData.size() * 0.05) {
        dv.assign(t1);
        g2.setColor(colors[0]);
        plotEllipse(g2, canopy.getCenter(), dv);
        dv.assign(t2);
        plotEllipse(g2, canopy.getCenter(), dv);
      }
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    for (Canopy canopy : canopies) {
      if (canopy.getNumPoints() > sampleData.size() * 0.05) {
        dv.assign(t1);
        g2.setColor(colors[0]);
        plotEllipse(g2, canopy.getCenter(), dv);
        dv.assign(t2);
        plotEllipse(g2, canopy.getCenter(), dv);
      }
    }
  }

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    for (List<SoftCluster> cls : clusters) {
      g2.setStroke(new BasicStroke(i == 0 ? 3 : 1));
      g2.setColor(colors[Math.min(colors.length - 1, i--)]);
      for (SoftCluster cluster : cls) {
        //if (true || cluster.getWeightedPointTotal().zSum() > sampleData.size() * 0.05) {
          dv.assign(cluster.std() * 3);
          plotEllipse(g2, cluster.getCenter(), dv);
        //}
      }
    }
  }
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    for (List<Cluster> cls : clusters) {
      g2.setStroke(new BasicStroke(i == 0 ? 3 : 1));
      g2.setColor(colors[Math.min(colors.length - 1, i--)]);
      for (Cluster cluster : cls) {
        //if (true || cluster.getNumPoints() > sampleData.size() * 0.05) {
          dv.assign(cluster.getStd() * 3);
          plotEllipse(g2, cluster.getCenter(), dv);
        //}
      }
    }
  }
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    for (Model<Vector>[] models : result) {
      g2.setStroke(new BasicStroke(i == 0 ? 3 : 1));
      g2.setColor(colors[Math.min(colors.length - 1, i--)]);
      for (Model<Vector> m : models) {
        AsymmetricSampledNormalModel mm = (AsymmetricSampledNormalModel) m;
        dv.assign(mm.getStdDev().times(3));
        if (isSignificant(mm))
          plotEllipse(g2, mm.getMean(), dv);
      }
    }
  }
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    for (Model<Vector>[] models : result) {
      g2.setStroke(new BasicStroke(i == 0 ? 3 : 1));
      g2.setColor(colors[Math.min(colors.length - 1, i--)]);
      for (Model<Vector> m : models) {
        NormalModel mm = (NormalModel) m;
        dv.assign(mm.getStdDev() * 3);
        if (isSignificant(mm))
          plotEllipse(g2, mm.getMean(), dv);
      }
    }
  }
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    for (Model<Vector>[] models : result) {
      g2.setStroke(new BasicStroke(i == 0 ? 3 : 1));
      g2.setColor(colors[Math.min(colors.length - 1, i--)]);
      for (Model<Vector> m : models) {
        NormalModel mm = (NormalModel) m;
        dv.assign(mm.getStdDev() * 3);
        if (isSignificant(mm))
          plotEllipse(g2, mm.getMean(), dv);
      }
    }
  }
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    double docTotal = wordCounts.zSum();
    int docLength = wordCounts.size();

    // initialize variational approximation to p(z|doc)
    Vector gamma = new DenseVector(state.numTopics);
    gamma.assign(state.topicSmoothing + docTotal / state.numTopics);
    Vector nextGamma = new DenseVector(state.numTopics);

    DenseMatrix phi = new DenseMatrix(state.numTopics, docLength);

    // digamma is expensive, precompute
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    final int MAX_ITER = 20;

    boolean converged = false;
    double oldLL = 1;
    while (!converged && iteration < MAX_ITER) {
      nextGamma.assign(state.topicSmoothing); // nG := alpha, for all topics

      int mapping = 0;
      for (Iterator<Vector.Element> iter = wordCounts.iterateNonZero();
          iter.hasNext();) {
      Vector.Element e = iter.next();
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  }


  private static Vector digamma(Vector v) {
    Vector digammaGamma = new DenseVector(v.size());
    digammaGamma.assign(v, new BinaryFunction() {
      @Override
      public double apply(double unused, double g) {
        return digamma(g);
      }
    });
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