Package org.apache.mahout.math

Examples of org.apache.mahout.math.Matrix.viewColumn()

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    Matrix u = generateDenseOrthonormalRandom(m, svCnt, rnd);

    // apply singular values
    Matrix mx = m > n ? v : u;
    for (int i = 0; i < svCnt; i++) {
      mx.assignColumn(i, mx.viewColumn(i).times(svalues.getQuick(i)));
    }

    SequenceFile.Writer w =
      SequenceFile.createWriter(dfs,
                                dfs.getConf(),
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    values[2][2] = 0.0;

    Matrix dataset = new DenseMatrix(values);
    trainer.train(labelset, dataset);
    assertTrue(trainer.getModel().classify(dataset.viewColumn(3)));
    assertFalse(trainer.getModel().classify(dataset.viewColumn(0)));
  }

}
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    Matrix u = generateDenseOrthonormalRandom(m, svCnt, rnd);

    // apply singular values
    Matrix mx = m > n ? v : u;
    for (int i = 0; i < svCnt; i++) {
      mx.assignColumn(i, mx.viewColumn(i).times(svalues.getQuick(i)));
    }

    SequenceFile.Writer w =
      SequenceFile.createWriter(dfs,
                                dfs.getConf(),
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    values[1][1] = 0.0;
    values[2][2] = 0.0;

    Matrix dataset = new DenseMatrix(values);
    this.trainer.train(labelset, dataset);
    assertFalse(this.trainer.getModel().classify(dataset.viewColumn(3)));
    assertTrue(this.trainer.getModel().classify(dataset.viewColumn(0)));
  }

}
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    values[2][2] = 0.0;

    Matrix dataset = new DenseMatrix(values);
    this.trainer.train(labelset, dataset);
    assertFalse(this.trainer.getModel().classify(dataset.viewColumn(3)));
    assertTrue(this.trainer.getModel().classify(dataset.viewColumn(0)));
  }

}
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    for (int m = low + 1; m <= high - 1; m++) {

      // Scale column.

      Vector hColumn = hessenBerg.viewColumn(m - 1).viewPart(m, high - m + 1);
      double scale = hColumn.norm(1);

      if (scale != 0.0) {
        // Compute Householder transformation.
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        // Apply Householder similarity transformation
        // H = (I-u*u'/h)*H*(I-u*u')/h)

        Vector ortPiece = ort.viewPart(m, high - m + 1);
        for (int j = m; j < n; j++) {
          double f = ortPiece.dot(hessenBerg.viewColumn(j).viewPart(m, high - m + 1)) / h;
          hessenBerg.viewColumn(j).viewPart(m, high - m + 1).assign(ortPiece, Functions.plusMult(-f));
        }

        for (int i = 0; i <= high; i++) {
          double f = ortPiece.dot(hessenBerg.viewRow(i).viewPart(m, high - m + 1)) / h;
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        // H = (I-u*u'/h)*H*(I-u*u')/h)

        Vector ortPiece = ort.viewPart(m, high - m + 1);
        for (int j = m; j < n; j++) {
          double f = ortPiece.dot(hessenBerg.viewColumn(j).viewPart(m, high - m + 1)) / h;
          hessenBerg.viewColumn(j).viewPart(m, high - m + 1).assign(ortPiece, Functions.plusMult(-f));
        }

        for (int i = 0; i <= high; i++) {
          double f = ortPiece.dot(hessenBerg.viewRow(i).viewPart(m, high - m + 1)) / h;
          hessenBerg.viewRow(i).viewPart(m, high - m + 1).assign(ortPiece, Functions.plusMult(-f));
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    v.assign(0);
    v.viewDiagonal().assign(1);

    for (int m = high - 1; m >= low + 1; m--) {
      if (hessenBerg.getQuick(m, m - 1) != 0.0) {
        ort.viewPart(m + 1, high - m).assign(hessenBerg.viewColumn(m - 1).viewPart(m + 1, high - m));
        for (int j = m; j <= high; j++) {
          double g = ort.viewPart(m, high - m + 1).dot(v.viewColumn(j).viewPart(m, high - m + 1));
          // Double division avoids possible underflow
          g = g / ort.getQuick(m) / hessenBerg.getQuick(m, m - 1);
          v.viewColumn(j).viewPart(m, high - m + 1).assign(ort.viewPart(m, high - m + 1), Functions.plusMult(g));
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    values[1][1] = 0.0;
    values[2][2] = 0.0;

    Matrix dataset = new DenseMatrix(values);
    trainer.train(labelset, dataset);
    assertTrue(trainer.getModel().classify(dataset.viewColumn(3)));
    assertFalse(trainer.getModel().classify(dataset.viewColumn(0)));
  }

}
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