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

org.apache.mahout.matrix.Vector.assign()
Assign the value to all elements of the receiver @param value a double value @return the modified receiver

    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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    plotRectangle(g2, new DenseVector(2).assign(2), dv);
    plotRectangle(g2, new DenseVector(2).assign(-2), dv);


    // plot the sample data
    g2.setColor(Color.DARK_GRAY);
    dv.assign(0.03);
    for (Vector v : sampleData)
      plotRectangle(g2, v, 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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  }




  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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      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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   * @return a Vector which is the new bound centroid
   */
  public Vector computeBoundCentroid() {
    Vector result = new DenseVector(getCenter().size());
    for (Vector v : boundPoints) {
      result.assign(v, new PlusFunction());
    }
    return result.divide(boundPoints.size());
  }


  /**

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      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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