Package de.jungblut.math.dense

Examples of de.jungblut.math.dense.DenseDoubleVector

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  public VectorWritable(double x) {
    this.vector = new DenseDoubleVector(new double[] { x });
  }

  public VectorWritable(double x, double y) {
    this.vector = new DenseDoubleVector(new double[] { x, y });
  }
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  public VectorWritable(double x, double y) {
    this.vector = new DenseDoubleVector(new double[] { x, y });
  }

  public VectorWritable(double[] arr) {
    this.vector = new DenseDoubleVector(arr);
  }
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    }
  }

  public static DoubleVector readVector(DataInput in) throws IOException {
    final int length = in.readInt();
    DoubleVector vector = new DenseDoubleVector(length);
    for (int i = 0; i < length; i++) {
      vector.set(i, in.readDouble());
    }
    return vector;
  }
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    this.numHiddenStates = numHiddenStates;
    this.transitionProbabilityMatrix = new DenseDoubleMatrix(numHiddenStates,
        numHiddenStates);
    this.emissionProbabilityMatrix = new DenseDoubleMatrix(numHiddenStates,
        numVisibleStates);
    this.hiddenPriorProbability = new DenseDoubleVector(numHiddenStates);
  }
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    Random random = new Random(seed);
    transitionProbabilityMatrix = new DenseDoubleMatrix(numHiddenStates,
        numHiddenStates, random);
    emissionProbabilityMatrix = new DenseDoubleMatrix(numHiddenStates,
        numVisibleStates, random);
    hiddenPriorProbability = new DenseDoubleVector(numHiddenStates);
    for (int i = 0; i < numHiddenStates; i++) {
      hiddenPriorProbability.set(i, random.nextDouble());
    }
    normalizeProbabilities();
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    assertTrue(Arrays.equals(filterRelevantItems, relevantItems));
  }

  @Test
  public void testGetTopRankedItems() {
    DenseDoubleVector rankedTokens = new DenseDoubleVector(new double[] { 0.9,
        0.8, 0.2, 0.3, 0.75 });
    int[] topRankedItems = IterativeSimilarityAggregation.getTopRankedItems(
        rankedTokens, 0.7);
    assertEquals(3, topRankedItems.length);
    assertTrue(Arrays.equals(new int[] { 0, 1, 4 }, topRankedItems));
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    DoubleMatrix outcome = new DenseDoubleMatrix(features.getRowCount(),
        classes == 2 ? 1 : classes);
    // follow the backpointers
    for (position = m - 1; position >= 0; position--) {
      DenseDoubleVector vec = null;
      if (classes != 2) {
        vec = new DenseDoubleVector(classes);
        vec.set(bestLabel, 1);
      } else {
        vec = new DenseDoubleVector(1);
        vec.set(0, bestLabel);
      }
      outcome.setRowVector(position, vec);
      bestLabel = backpointers[position][bestLabel];
    }
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        int index = 0;
        for (int i = 0; i < line.length; i++) {
          if (i != outcomeIndex)
            fArray[index++] = Double.parseDouble(line[i]);
        }
        DoubleVector f = new DenseDoubleVector(fArray);
        DenseDoubleVector o = new DenseDoubleVector(1);
        o.set(0, Double.parseDouble(line[outcomeIndex]));
        featureList.add(f);
        outcomeList.add(o);
      }

    } catch (IOException e) {
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   */
  public static Tuple<DoubleMatrix, DoubleVector> meanNormalizeRows(
      DoubleMatrix pMatrix) {
    DoubleMatrix matrix = new DenseDoubleMatrix(pMatrix.getRowCount(),
        pMatrix.getColumnCount());
    DoubleVector meanVector = new DenseDoubleVector(matrix.getRowCount());
    for (int row = 0; row < matrix.getRowCount(); row++) {
      double mean = 0.0d;
      int nonZeroElements = 0;
      for (int column = 0; column < matrix.getColumnCount(); column++) {
        double val = pMatrix.get(row, column);
        if (val != DoubleMatrix.NOT_FLAGGED) {
          mean += val;
          nonZeroElements++;
        }
      }
      // prevent division by zero
      if (nonZeroElements != 0.0d) {
        mean = mean / nonZeroElements;
      }
      meanVector.set(row, mean);
      for (int column = 0; column < matrix.getColumnCount(); column++) {
        double val = pMatrix.get(row, column);
        if (val != DoubleMatrix.NOT_FLAGGED) {
          matrix.set(row, column, val - mean);
        }
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  public static Tuple3<DoubleMatrix, DoubleVector, DoubleVector> meanNormalizeColumns(
      DoubleMatrix x) {
    DenseDoubleMatrix toReturn = new DenseDoubleMatrix(x.getRowCount(),
        x.getColumnCount());
    final int length = x.getColumnCount();
    DoubleVector meanVector = new DenseDoubleVector(length);
    DoubleVector stddevVector = new DenseDoubleVector(length);
    for (int col = 0; col < length; col++) {
      DoubleVector column = x.getColumnVector(col);
      double mean = column.sum() / column.getLength();
      meanVector.set(col, mean);
      double var = column.subtract(mean).pow(2).sum() / column.getLength();
      stddevVector.set(col, Math.sqrt(var));
    }

    for (int col = 0; col < length; col++) {
      DoubleVector column = x.getColumnVector(col)
          .subtract(meanVector.get(col)).divide(stddevVector.get(col));
      toReturn.setColumn(col, column.toArray());
    }

    return new Tuple3<>(toReturn, meanVector, stddevVector);
  }
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