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

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


  public static List<VectorWritable> getPointsWritable(double[][] raw) {
    List<VectorWritable> points = Lists.newArrayList();
    for (double[] fr : raw) {
      Vector vec = new RandomAccessSparseVector(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 = Lists.newArrayList();
    for (double[] fr : raw) {
      Vector vec = new SequentialAccessSparseVector(fr.length);
      vec.assign(fr);
      points.add(vec);
    }
    return points;
  }

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      Vector v = new DenseVector(numCols);
      for(int col = 0; col < numCols; col++) {
        double val = r.nextGaussian();
        v.set(col, val);
      }
      v.assign(Functions.MULT, 1/((row + 1) * v.norm(2)));
      matrix.assignRow(row, v);
    }
    if(symmetric) {
      return matrix.times(matrix.transpose());
    }

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  public void testEigenvalueCheck() throws Exception {
    int size = 100;
    Matrix m = randomHierarchicalSymmetricMatrix(size);


    Vector initialVector = new DenseVector(size);
    initialVector.assign(1.0 / Math.sqrt(size));
    LanczosSolver solver = new LanczosSolver();
    int desiredRank = 80;
    LanczosState state = new LanczosState(m, desiredRank, initialVector);
    // set initial vector?
    solver.solve(state, desiredRank, true);

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  public void testLanczosSolver() throws Exception {
    int numRows = 800;
    int numColumns = 500;
    Matrix corpus = randomHierarchicalMatrix(numRows, numColumns, false);
    Vector initialVector = new DenseVector(numColumns);
    initialVector.assign(1.0 / Math.sqrt(numColumns));
    int rank = 50;
    LanczosState state = new LanczosState(corpus, rank, initialVector);
    long time = timeLanczos(corpus, state, rank, false);
    assertTrue("Lanczos taking too long!  Are you in the debugger? :)", time < 10000);
    assertOrthonormal(state);

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  @Test
  public void testLanczosSolverSymmetric() throws Exception {
    int numCols = 500;
    Matrix corpus = randomHierarchicalSymmetricMatrix(numCols);
    Vector initialVector = new DenseVector(numCols);
    initialVector.assign(1.0 / Math.sqrt(numCols));
    int rank = 30;
    LanczosState state = new LanczosState(corpus, rank, initialVector);
    long time = timeLanczos(corpus, state, rank, true);
    assertTrue("Lanczos taking too long!  Are you in the debugger? :)", time < 10000);
    //assertOrthonormal(state);

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    // then normalize to turn it into a probability distribution
    p1.assign(Functions.div(p1.norm(1)));


    // likewise normalize p2
    p2.assign(Functions.div(p2.norm(1)));


    // sample 100 times from p1
    Multiset<Integer> w1 = HashMultiset.create();
    for (int i = 0; i < 100; i++) {
      w1.add(sample(p1, rand));

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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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    Vector dv = new DenseVector(2).assign(SIZE / 2.0);
    plotRectangle(g2, new DenseVector(2).assign(2), dv);
    plotRectangle(g2, new DenseVector(2).assign(-2), dv);


    // plot the sample data, colored according to the cluster they belong to
    dv.assign(0.03);
    
    Path clusteredPointsPath = new Path(data, "clusteredPoints");
    Path inputPath = new Path(clusteredPointsPath, "part-m-00000");
    HashMap<Integer, Color> colors = new HashMap<Integer, Color>();
    int point = 0;

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          private final Random random = RandomUtils.getRandom();
          @Override
          public Vector apply(Integer dummy) {
            Vector result =
                type == VectorType.SPARSE ? new RandomAccessSparseVector(numItems) : new DenseVector(numItems);
            result.assign(new DoubleFunction(){
              @Override
              public double apply(double ignored) {
                return random.nextDouble();
              }
            });

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