Package org.apache.mahout.matrix

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

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

  /**
   * Compute the centroid by normalizing the pointTotal
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      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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    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.sd * 3);
        if (isSignificant(mm))
          plotEllipse(g2, mm.mean, 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.sd.times(3));
        if (isSignificant(mm))
          plotEllipse(g2, mm.mean, 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.sd * 3);
        if (isSignificant(mm))
          plotEllipse(g2, mm.mean, 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.sd.times(3));
        if (isSignificant(mm))
          plotEllipse(g2, mm.mean, 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.sd * 3);
        if (isSignificant(mm))
          plotEllipse(g2, mm.mean, 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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