Best Java code snippets using de.jungblut.math.DoubleMatrix (Showing top 20 results out of 315)

@Override
public DoubleMatrix gradient(DoubleMatrix matrix) {
  DoubleMatrix newInstance = newInstance(matrix);
  if (matrix.isSparse()) {
    // if we have a sparse matrix, it is more efficient to loop over the
    // sparse column vectors
    int[] columnIndices = matrix.columnIndices();
    for (int col : columnIndices) {
      newInstance.setColumnVector(col, gradient(matrix.getColumnVector(col)));
    }
  } else {
    // on dense matrices we can be faster by directly looping over the items
    for (int i = 0; i < matrix.getRowCount(); i++) {
      for (int j = 0; j < matrix.getColumnCount(); j++) {
        newInstance.set(i, j, gradient(matrix.get(i, j)));
      }
    }
  }
  return newInstance;
}

@Override
public DoubleMatrix apply(DoubleMatrix matrix) {
  DoubleMatrix newInstance = newInstance(matrix);
  if (matrix.isSparse()) {
    // if we have a sparse matrix, it is more efficient to loop over the
    // sparse row vectors
    int[] rows = matrix.rowIndices();
    for (int row : rows) {
      DoubleVector rowVector = matrix.getRowVector(row);
      if (rowVector.getLength() > 0) {
        DoubleVector apply = apply(rowVector);
        newInstance.setRowVector(row, apply);
      }
    }
  } else {
    // on dense matrices we can be faster by directly looping over the items
    for (int i = 0; i < matrix.getRowCount(); i++) {
      for (int j = 0; j < matrix.getColumnCount(); j++) {
        newInstance.set(i, j, apply(matrix.get(i, j)));
      }
    }
  }
  return newInstance;
}

@Override
public double calculateLoss(DoubleMatrix y, DoubleMatrix hypothesis) {
  double sum = 0d;
  for (int col = 0; col < y.getColumnCount(); col++) {
    for (int row = 0; row < y.getRowCount(); row++) {
      double diff = y.get(row, col) - hypothesis.get(row, col);
      sum += (diff * diff);
    }
  }
  return sum / y.getRowCount();
}

public ConditionalLikelihoodCostFunction(DoubleMatrix features,
                     DoubleMatrix outcome) {
  this.features = features;
  this.outcome = outcome;
  this.m = outcome.getRowCount();
  this.classes = outcome.getColumnCount() == 1 ? 2 : outcome.getColumnCount();
}

public static double calculateRegularization(DoubleMatrix[] thetas,
                       final int m, NetworkConfiguration conf) {
  double regularization = 0d;
  // only calculate the regularization term if lambda is not 0
  if (conf.lambda != 0d) {
    for (DoubleMatrix theta : thetas) {
      regularization += (theta.slice(0, theta.getRowCount(), 1,
          theta.getColumnCount())).pow(2).sum();
    }
    regularization = (conf.lambda / (2.0d * m)) * regularization;
  }
  return regularization;
}

static DoubleMatrix binarize(Random r, DoubleMatrix hiddenActivations) {
  for (int i = 0; i < hiddenActivations.getRowCount(); i++) {
    for (int j = 0; j < hiddenActivations.getColumnCount(); j++) {
      hiddenActivations.set(i, j,
          hiddenActivations.get(i, j) > r.nextDouble() ? 1d : 0d);
    }
  }
  return hiddenActivations;
}

@Override
public DoubleMatrix subtract(DoubleMatrix other) {
 DoubleMatrix m = new DenseDoubleMatrix(this.numRows, this.numColumns);
 for (int i = 0; i < numRows; i++) {
  for (int j = 0; j < numColumns; j++) {
   m.set(i, j, this.matrix[translate(i, j, numRows)] - other.get(i, j));
  }
 }
 return m;
}

    .deepCopy();
DoubleMatrix emissionProbabilityMatrix = this.emissionProbabilityMatrix
    .deepCopy();
DoubleVector hiddenPriorProbability = this.hiddenPriorProbability
    .deepCopy();
hiddenPriorProbability = alpha.getRowVector(0).multiply(
    beta.getRowVector(0));
final double modelLikelihood = estimateLikelihood(alpha);
          .iterateNonZero();
      while (iterateNonZero.hasNext()) {
        temp += alpha.get(t, i)
            * emissionProbabilityMatrix.get(j, iterateNonZero.next()
            .getIndex()) * beta.get(t + 1, j);
    transitionProbabilityMatrix.set(i, j,
        transitionProbabilityMatrix.get(i, j) * temp / modelLikelihood);
        DoubleVectorElement next = iterateNonZero.next();
        if (next.getIndex() == j) {
          temp += alpha.get(t, i) * beta.get(t, i);
    emissionProbabilityMatrix.set(i, j, temp / modelLikelihood);
    .subtract(transitionProbabilityMatrix).pow(2).sum()
    + this.emissionProbabilityMatrix.subtract(emissionProbabilityMatrix)
    .pow(2).sum()
    + this.getHiddenPriorProbability().subtract(hiddenPriorProbability)

    unfoldParameters)[0].transpose();
positiveHiddenProbs.setColumnVector(0,
    DenseDoubleVector.ones(positiveHiddenProbs.getRowCount()));
DoubleMatrix positiveAssociations = multiply(data, positiveHiddenProbs,
    true, false);
negativeData.setColumnVector(0,
    DenseDoubleVector.ones(negativeData.getRowCount()));
DoubleMatrix negativeHiddenProbs = activationFunction.apply(multiply(
    negativeData, theta, false, false));
negativeHiddenProbs.setColumnVector(0,
    DenseDoubleVector.ones(negativeHiddenProbs.getRowCount()));
DoubleMatrix negativeAssociations = multiply(negativeData,
    negativeHiddenProbs, true, false);
double j = data.subtract(negativeData).pow(2).sum();
DoubleMatrix thetaGradient = positiveAssociations.subtract(
    negativeAssociations).divide(data.getRowCount());
  DoubleVector bias = thetaGradient.getColumnVector(0);
  thetaGradient = thetaGradient.subtract(thetaGradient.multiply(lambda
      / data.getRowCount()));
  thetaGradient.setColumnVector(0, bias);
        .multiply(-1).transpose()));

                 DoubleMatrix emissionProbabilityMatrix,
                 DoubleVector hiddenPriorProbability, DoubleVector[] features) {
final int numHiddenStates = beta.getColumnCount();
beta.setRowVector(features.length - 1,
    DenseDoubleVector.ones(numHiddenStates));
      while (iterateNonZero.hasNext()) {
        DoubleVectorElement next = iterateNonZero.next();
        sum += beta.get(t + 1, j) * transitionProbabilityMatrix.get(i, j)
            * emissionProbabilityMatrix.get(j, next.getIndex());
    beta.set(t, i, sum);

public static void calculateGradients(DoubleMatrix[] thetas,
                   DoubleMatrix[] thetaGradients, DoubleMatrix[] ax, DoubleMatrix[] deltaX,
                   final int m, NetworkConfiguration conf) {
  // calculate the gradients of the weights
  for (int i = 0; i < thetaGradients.length; i++) {
    DoubleMatrix gradDXA = multiply(deltaX[i + 1], ax[i], true, false, conf);
    if (m != 1) {
      thetaGradients[i] = gradDXA.divide(m);
    } else {
      thetaGradients[i] = gradDXA;
    }
    if (conf.lambda != 0d) {
      thetaGradients[i] = thetaGradients[i].add((thetas[i]
          .multiply(conf.lambda / m)));
      // subtract the regularized bias
      DoubleVector regBias = thetas[i]
          .slice(0, thetas[i].getRowCount(), 0, 1).multiply(conf.lambda / m)
          .getColumnVector(0);
      thetaGradients[i].setColumnVector(0, regBias);
    }
  }
}

                DoubleMatrix emissionProbabilityMatrix,
                DoubleVector hiddenPriorProbability, DoubleVector[] features) {
final int numHiddenStates = alpha.getColumnCount();
for (int i = 0; i < numHiddenStates; i++) {
  double emissionSum = 0d;
  while (firstFeatures.hasNext()) {
    emissionSum += emissionProbabilityMatrix.get(i, firstFeatures.next()
        .getIndex());
  alpha.set(0, i, hiddenPriorProbability.get(i) * emissionSum);
    double sum = 0.0d;
    for (int j = 0; j < numHiddenStates; j++) {
      sum += alpha.get(t - 1, j) * transitionProbabilityMatrix.get(j, i);
      emissionSum += emissionProbabilityMatrix.get(i, featureIterator
          .next().getIndex());
    alpha.set(t, i, sum * emissionSum);

DoubleVector rowVector = probabilityMatrix.getRowVector(row);
  + probabilityMatrix.getColumnCount() - 1);
while (iterateNonZero.hasNext()) {
 DoubleVectorElement next = iterateNonZero.next();
 double currentWordCount = next.getValue();
 double logProbability = FastMath.log(currentWordCount) - normalizer;
 probabilityMatrix.set(row, next.getIndex(), logProbability);

@Override
public DoubleMatrix apply(DoubleMatrix matrix) {
  DoubleMatrix dm = newInstance(matrix);
  for (int row = 0; row < matrix.getRowCount(); row++) {
    DoubleVector apply = apply(matrix.getRowVector(row));
    if (apply.getLength() != 0) {
      dm.setRowVector(row, apply);
    }
  }
  return dm;
}

DenseDoubleMatrix gradient = new DenseDoubleMatrix(theta.getRowCount(),
    theta.getColumnCount());
  DoubleVector rowVector = features.getRowVector(row);
  double[] logProbabilities = new double[classes];
    DoubleVectorElement next = iterateNonZero.next();
    for (int i = 0; i < classes; i++) {
      logProbabilities[i] += theta.get(i, next.getIndex());
      gradient.set(i, next.getIndex(), gradient.get(i, next.getIndex())
          + prob);
      if (correctPrediction(i, outcome.getRowVector(row))) {
        gradient.set(i, next.getIndex(),
            gradient.get(i, next.getIndex()) - 1d);
    if (correctPrediction(i, outcome.getRowVector(row))) {
      cost -= Math.log(prob);

    int count = (int) transitionProbabilities.get(array[i], array[i + 1]);
    transitionProbabilities.set(array[i], array[i + 1], ++count);
final int[] rowEntries = transitionProbabilities.rowIndices();
for (int rowIndex : rowEntries) {
  DoubleVector rowVector = transitionProbabilities.getRowVector(rowIndex);
  double sum = rowVector.sum();
  Iterator<DoubleVectorElement> iterateNonZero = rowVector.iterateNonZero();
    double probability = FastMath.log(columnElement.getValue())
        - FastMath.log(sum);
    transitionProbabilities.set(rowIndex, columnIndex, probability);

/**
 * Scales a matrix into the interval given by min and max.
 *
 * @param input   the input value.
 * @param fromMin the lower bound of the input interval.
 * @param fromMax the upper bound of the input interval.
 * @param toMin   the lower bound of the target interval.
 * @param toMax   the upper bound of the target interval.
 * @return the new matrix with scaled values.
 */
public static DoubleMatrix minMaxScale(DoubleMatrix input, double fromMin,
                    double fromMax, double toMin, double toMax) {
  DoubleMatrix newOne = new DenseDoubleMatrix(input.getRowCount(),
      input.getColumnCount());
  double[][] array = input.toArray();
  for (int row = 0; row < newOne.getRowCount(); row++) {
    for (int col = 0; col < newOne.getColumnCount(); col++) {
      newOne.set(row, col,
          minMaxScale(array[row][col], fromMin, fromMax, toMin, toMax));
    }
  }
  return newOne;
}

/**
 * Sets the weights in the whole matrix uniformly between -eInit and eInit
 * (eInit is the standard deviation) with zero mean.
 */
private void setWeightsUniformly(RandomDataImpl rnd, double eInit) {
  for (int i = 0; i < weights.getColumnCount(); i++) {
    for (int j = 0; j < weights.getRowCount(); j++) {
      weights.set(j, i, rnd.nextUniform(-eInit, eInit));
    }
  }
}

/**
 * Creates a new matrix with the given vector into the first column and the
 * other matrix to the other columns. This is usually used in machine learning
 * algorithms that add a bias on the zero-index column.
 * 
 * @param first the new first column.
 * @param otherMatrix the other matrix to set on from the second column.
 */
public DenseDoubleMatrix(DenseDoubleVector first, DoubleMatrix otherMatrix) {
 this(otherMatrix.getRowCount(), otherMatrix.getColumnCount() + 1);
 // copy the first column
 System.arraycopy(first.toArray(), 0, matrix, 0, first.getDimension());
 int offset = first.getDimension();
 for (int col : otherMatrix.columnIndices()) {
  double[] clv = otherMatrix.getColumnVector(col).toArray();
  System.arraycopy(clv, 0, matrix, offset, clv.length);
  offset += clv.length;
 }
}

/**
 * Row-copies the given matrix to this sparse implementation.
 * 
 * @param mat the matrix to copy.
 */
public SparseDoubleRowMatrix(DoubleMatrix mat) {
 this(mat.getRowCount(), mat.getColumnCount());
 for (int i = 0; i < numColumns; i++) {
  setRowVector(i, mat.getRowVector(i));
 }
}