Package org.apache.mahout.math

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

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      Vector oldEigen = eigenVectors.viewRow(row);
      if (oldEigen == null) {
        break;
      }
      for (int newRow = 0; newRow < eigenVectors2.numRows(); newRow++) {
        Vector newEigen = eigenVectors2.viewRow(newRow);
        if (newEigen != null && oldEigen.dot(newEigen) > 0.9) {
          oldEigensFound.add(row);
          break;
        }
      }
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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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        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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          //assertEquals(10.5 * Math.log(i) - offset0, s0.size(), 10);
        } else if (i > 50) {
          double x = 10.5 * Math.log(i) - s0.size();
          m5.viewRow(k).assign(new double[]{Math.log(s5.size()), Math.log(i), 1});
          m9.viewRow(k).assign(new double[]{Math.log(s9.size()), Math.log(i), 1});

          k++;
          offset0 += (x - offset0) / k;
        }
        if (i > 10000) {
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  public void testOrdering() {
    searcher.clear();
    Matrix queries = new DenseMatrix(100, 20);
    MultiNormal gen = new MultiNormal(20);
    for (int i = 0; i < 100; i++) {
      queries.viewRow(i).assign(gen.sample());
    }
    searcher.addAllMatrixSlices(dataPoints);

    for (MatrixSlice query : queries) {
      List<WeightedThing<Vector>> r = searcher.search(query.vector(), 200);
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   *         last category.
   */
  public Matrix classifyFull(Matrix data) {
    Matrix r = new DenseMatrix(data.numRows(), numCategories());
    for (int row = 0; row < data.numRows(); row++) {
      classifyFull(r.viewRow(row), data.getRow(row));
    }
    return r;
  }

  /**
 
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            .learningRate(5);
    int k = 0;
    int[] ordering = permute(gen, data.numRows());
    for (int epoch = 0; epoch < 100; epoch++) {
      for (int row : ordering) {
        lr.train(row, (int) data.get(row, 9), data.viewRow(row));
        System.out.printf("%d,%d,%.3f\n", epoch, k++, lr.auc());
      }
      assertEquals(1, lr.auc(), 0.2);
    }
    assertEquals(1, lr.auc(), 0.1);
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          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));
        }
        ort.setQuick(m, scale * ort.getQuick(m));
        hessenBerg.setQuick(m, m - 1, scale * g);
      }
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          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));
        }
        ort.setQuick(m, scale * ort.getQuick(m));
        hessenBerg.setQuick(m, m - 1, scale * g);
      }
    }
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  public void testOrdering() {
    searcher.clear();
    Matrix queries = new DenseMatrix(100, 20);
    MultiNormal gen = new MultiNormal(20);
    for (int i = 0; i < 100; i++) {
      queries.viewRow(i).assign(gen.sample());
    }
    searcher.addAllMatrixSlices(dataPoints);

    for (MatrixSlice query : queries) {
      List<WeightedThing<Vector>> r = searcher.search(query.vector(), 200);
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