Package org.apache.mahout.math

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

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    } else if ("--fast".equals(args[0])) {
      FastLineReader in = new FastLineReader(new FileInputStream(args[1]));
      try {
        FastLine line = in.read();
        while (line != null) {
          v.assign(0);
          for (int i = 0; i < FIELDS; i++) {
            double z = line.getDouble(i);
            s[i].add(z);
            encoder[i].addToVector((byte[]) null, z, v);
          }
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    }
  }

  private static Vector randomVector(final Random gen, int n) {
    Vector x = new DenseVector(n);
    x.assign(new DoubleFunction() {
      @Override
      public double apply(double v) {
        return gen.nextGaussian();
      }
    });
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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);
    LanczosSolver solver = new LanczosSolver();
    solver.solve(state, rank, false);
    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);
    LanczosSolver solver = new LanczosSolver();
    solver.solve(state, rank, true);
    //assertOrthonormal(state);
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        randomDistributedMatrix(100, 90, 50, 20, 1.0, false);
    dm.setConf(getConfiguration());

    Vector expected = new DenseVector(50);
    for (int i = 0; i < m.numRows(); i++) {
      expected.assign(m.viewRow(i), Functions.PLUS);
    }
    expected.assign(Functions.DIV, m.numRows());
    Vector actual = dm.columnMeans("DenseVector");
    assertEquals(0.0, expected.getDistanceSquared(actual), EPSILON);
  }
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    Vector expected = new DenseVector(50);
    for (int i = 0; i < m.numRows(); i++) {
      expected.assign(m.viewRow(i), Functions.PLUS);
    }
    expected.assign(Functions.DIV, m.numRows());
    Vector actual = dm.columnMeans("DenseVector");
    assertEquals(0.0, expected.getDistanceSquared(actual), EPSILON);
  }

  @Test
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        randomDistributedMatrix(100, 90, 0, 0, 1.0, false);
    dm.setConf(getConfiguration());

    Vector expected = new DenseVector(0);
    for (int i = 0; i < m.numRows(); i++) {
      expected.assign(m.viewRow(i), Functions.PLUS);
    }
    expected.assign(Functions.DIV, m.numRows());
    Vector actual = dm.columnMeans();
    assertEquals(0.0, expected.getDistanceSquared(actual), EPSILON);
  }
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    Vector expected = new DenseVector(0);
    for (int i = 0; i < m.numRows(); i++) {
      expected.assign(m.viewRow(i), Functions.PLUS);
    }
    expected.assign(Functions.DIV, m.numRows());
    Vector actual = dm.columnMeans();
    assertEquals(0.0, expected.getDistanceSquared(actual), EPSILON);
  }

  @Test
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    sampleData = Lists.newArrayList();
    generateSamples();
    sampleN = 0;
    Vector sum = new DenseVector(2);
    for (VectorWritable v : sampleData) {
      sum.assign(v.get(), Functions.PLUS);
      sampleN++;
    }
    sampleMean = sum.divide(sampleN);

    Vector sampleVar = new DenseVector(2);
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