Package org.apache.mahout.math

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

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  }

  public void testMSCanopyAsFormatStringSparseWithBindings() {
    double[] d = { 1.1, 0.0, 3.3 };
    Vector m = new SequentialAccessSparseVector(3);
    m.assign(d);
    Printable cluster = new MeanShiftCanopy(m, 123);
    String[] bindings = { "fee", null, "foo" };
    String formatString = cluster.asFormatString(bindings);
    System.out.println(formatString);
    assertEquals("format", "C123: [fee:1.100, foo:3.300]", formatString);
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  public static List<VectorWritable> getPointsWritable(double[][] raw) {
    List<VectorWritable> points = new ArrayList<VectorWritable>();
    int i = 0;
    for (double[] fr : raw) {
      Vector vec = new RandomAccessSparseVector(String.valueOf(i++), fr.length);
      vec.assign(fr);
      points.add(new VectorWritable(vec));
    }
    return points;
  }
 
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  public static List<Vector> getPoints(double[][] raw) {
    List<Vector> points = new ArrayList<Vector>();
    for (int i = 0; i < raw.length; i++) {
      double[] fr = raw[i];
      Vector vec = new SequentialAccessSparseVector(String.valueOf(i), fr.length);
      vec.assign(fr);
      points.add(vec);
    }
    return points;
  }
 
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  private static List<VectorWritable> getPointsWritable(double[][] raw) {
    List<VectorWritable> points = new ArrayList<VectorWritable>();
    int i = 0;
    for (double[] fr : raw) {
      Vector vec = new RandomAccessSparseVector(String.valueOf(i++), fr.length);
      vec.assign(fr);
      points.add(new VectorWritable(vec));
    }
    return points;
  }
 
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  private static List<Vector> getPoints(double[][] raw) {
    List<Vector> points = new ArrayList<Vector>();
    int i = 0;
    for (double[] fr : raw) {
      Vector vec = new RandomAccessSparseVector(String.valueOf(i++), fr.length);
      vec.assign(fr);
      points.add(vec);
    }
    return points;
  }
 
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  private static List<VectorWritable> getPoints(double[][] raw) {
    List<VectorWritable> points = new ArrayList<VectorWritable>();
    int i = 0;
    for (double[] fr : raw) {
      Vector vec = new RandomAccessSparseVector(String.valueOf(i++), fr.length);
      vec.assign(fr);
      points.add(new VectorWritable(vec));
    }
    return points;
  }
 
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   * @return
   */
  @Override
  protected Vector getInitialVector(VectorIterable corpus) {
    Vector initialVector = new DenseVector(corpus.numCols());
    initialVector.assign(1/Math.sqrt(corpus.numCols()));
    return initialVector;
  }

  @Override
  public int run(String[] strings) throws Exception {
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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;
  }
 
  /**
   * Create a new instance on the sample data with the given additional parameters
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    for (int i = 1; i < desiredRank; i++) {
      startTime(TimingSection.ITERATE);
      Vector nextVector = isSymmetric ? corpus.times(currentVector) : corpus.timesSquared(currentVector);
      log.info("{} passes through the corpus so far...", i);
      calculateScaleFactor(nextVector);
      nextVector.assign(new Scale(1 / scaleFactor));
      nextVector.assign(previousVector, new PlusMult(-beta));
      // now orthogonalize
      alpha = currentVector.dot(nextVector);
      nextVector.assign(currentVector, new PlusMult(-alpha));
      endTime(TimingSection.ITERATE);
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      startTime(TimingSection.ITERATE);
      Vector nextVector = isSymmetric ? corpus.times(currentVector) : corpus.timesSquared(currentVector);
      log.info("{} passes through the corpus so far...", i);
      calculateScaleFactor(nextVector);
      nextVector.assign(new Scale(1 / scaleFactor));
      nextVector.assign(previousVector, new PlusMult(-beta));
      // now orthogonalize
      alpha = currentVector.dot(nextVector);
      nextVector.assign(currentVector, new PlusMult(-alpha));
      endTime(TimingSection.ITERATE);
      startTime(TimingSection.ORTHOGANLIZE);
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