Package org.apache.mahout.math

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

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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
      double 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
      double alpha = currentVector.dot(nextVector);
      nextVector.assign(currentVector, new PlusMult(-alpha));
      endTime(TimingSection.ITERATE);
      startTime(TimingSection.ORTHOGANLIZE);
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      calculateScaleFactor(nextVector);
      nextVector.assign(new Scale(1 / scaleFactor));
      nextVector.assign(previousVector, new PlusMult(-beta));
      // now orthogonalize
      double alpha = currentVector.dot(nextVector);
      nextVector.assign(currentVector, new PlusMult(-alpha));
      endTime(TimingSection.ITERATE);
      startTime(TimingSection.ORTHOGANLIZE);
      orthoganalizeAgainstAllButLast(nextVector, basis);
      endTime(TimingSection.ORTHOGANLIZE);
      // and normalize
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      beta = nextVector.norm(2);
      if (outOfRange(beta) || outOfRange(alpha)) {
        log.warn("Lanczos parameters out of range: alpha = {}, beta = {}.  Bailing out early!", alpha, beta);
        break;
      }
      nextVector.assign(new Scale(1 / beta));
      basis.assignRow(i, nextVector);
      previousVector = currentVector;
      currentVector = nextVector;
      // save the projections and norms!
      triDiag.set(i - 1, i - 1, alpha);
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      Vector realEigen = new DenseVector(corpus.numCols());
      // the eigenvectors live as columns of V, in reverse order.  Weird but true.
      DoubleMatrix1D ejCol = eigenVects.viewColumn(basis.numRows() - i - 1);
      for (int j = 0; j < ejCol.size(); j++) {
        double d = ejCol.getQuick(j);
        realEigen.assign(basis.getRow(j), new PlusMult(d));
      }
      realEigen = realEigen.normalize();
      eigenVectors.assignRow(i, realEigen);
      log.info("Eigenvector {} found with eigenvalue {}", i, eigenVals.get(i));
      eigenValues.add(eigenVals.get(i));
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  private static List<VectorWritable> getPointsWritable() {
    List<VectorWritable> points = new ArrayList<VectorWritable>();
    for (double[] fr : RAW) {
      Vector vec = new RandomAccessSparseVector(fr.length);
      vec.assign(fr);
      points.add(new VectorWritable(vec));
    }
    return points;
  }
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  private static List<Vector> getPoints() {
    List<Vector> points = new ArrayList<Vector>();
    for (double[] fr : RAW) {
      Vector vec = new RandomAccessSparseVector(fr.length);
      vec.assign(fr);
      points.add(vec);
    }
    return points;
  }
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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 (VectorWritable v : SAMPLE_DATA) {
      plotRectangle(g2, v.get(), dv);
    }
  }
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      numerators = numerators == null
          ? prefValue == BOOLEAN_PREF_VALUE ? simColumn.clone() : simColumn.times(prefValue)
          : numerators.plus(prefValue == BOOLEAN_PREF_VALUE ? simColumn : simColumn.times(prefValue));

      simColumn.assign(ABSOLUTE_VALUES);
      denominators = denominators == null ? simColumn : denominators.plus(simColumn);
    }

    if (numerators == null) {
      return;
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   * uniform over all input dimensions, L_2 normalized.
   */
  @Override
  protected Vector getInitialVector(VectorIterable corpus) {
    Vector initialVector = new DenseVector(corpus.numCols());
    initialVector.assign(1.0 / Math.sqrt(corpus.numCols()));
    return initialVector;
  }
 
  /**
   * Factored-out LanczosSolver for the purpose of invoking it programmatically
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