Examples of DoubleMatrix

de.jungblut.math.DoubleMatrix
fr.soleil.data.container.matrix.DoubleMatrix
org.apache.hama.commons.math.DoubleMatrix
Standard matrix interface for double elements. Every implementation should return a fresh new Matrix when operating with other elements.
org.apache.hama.ml.math.DoubleMatrix
Standard matrix interface for double elements. Every implementation should return a fresh new Matrix when operating with other elements.

org.jblas.DoubleMatrix

utes x = y + z. Even in-place methods return the result, such that you can easily chain in-place methods, for example: x.addi(y).addi(z) // computes x += y; x += z

Methods which operate element-wise only make sure that the length of the matrices is correct. Therefore, you can add a 3 * 3 matrix to a 1 * 9 matrix, for example.

Finally, there exist versions which take doubles instead of DoubleMatrix Objects as arguments. These then compute the operation with the same value as the right-hand-side. The same effect can be achieved by passing a DoubleMatrix with exactly one element.

Operation	Method	Comment
x + y	x.add(y)
x - y	x.sub(y), y.rsub(x)	rsub subtracts left from right hand side
x * y	x.mul(y)	element-wise multiplication
	x.mmul(y)	matrix-matrix multiplication
	x.dot(y)	scalar-product
x / y	x.div(y), y.rdiv(x)	rdiv divides right hand side by left hand side.
- x	x.neg()

There also exist operations which work on whole columns or rows.

Method	Description
x.addRowVector	adds a vector to each row (addiRowVector works in-place)
x.addColumnVector	adds a vector to each column
x.subRowVector	subtracts a vector from each row
x.subColumnVector	subtracts a vector from each column
x.mulRow	Multiplies a row by a scalar
x.mulColumn	multiplies a row by a column

In principle, you could achieve the same result by first calling getColumn(), adding, and then calling putColumn, but these methods are much faster.

The following comparison operations are available

Operation	Method
x < y	x.lt(y)
x <= y	x.le(y)
x > y	x.gt(y)
x >= y	x.ge(y)
x == y	x.eq(y)
x != y	x.ne(y)

Logical operations are also supported. For these operations, a value different from zero is treated as "true" and zero is treated as "false". All operations are carried out elementwise.

Operation	Method
x & y	x.and(y)
x \| y	x.or(y)
x ^ y	x.xor(y)
! x	x.not()

Finally, there are a few more methods to compute various things:

Method	Description
x.max()	Return maximal element
x.argmax()	Return index of largest element
x.min()	Return minimal element
x.argmin()	Return index of largest element
x.columnMins()	Return column-wise minima
x.columnArgmins()	Return column-wise index of minima
x.columnMaxs()	Return column-wise maxima
x.columnArgmaxs()	Return column-wise index of maxima

@author Mikio Braun, Johannes Schaback

org.neo4j.graphalgo.impl.util.MatrixUtil.DoubleMatrix

Examples of org.apache.hama.commons.math.DoubleMatrix


    // get layer related information
    DoubleFunction squashingFunction = this.squashingFunctionList
        .get(curLayerIdx);
    DoubleVector curLayerOutput = outputCache.get(curLayerIdx);
    DoubleMatrix weightMatrix = this.weightMatrixList.get(curLayerIdx);
    DoubleMatrix prevWeightMatrix = this.prevWeightUpdatesList.get(curLayerIdx);


    // next layer is not output layer, remove the delta of bias neuron
    if (curLayerIdx != this.layerSizeList.size() - 2) {
      nextLayerDelta = nextLayerDelta.slice(1,
          nextLayerDelta.getDimension() - 1);
    }


    DoubleVector delta = weightMatrix.transpose()
        .multiplyVector(nextLayerDelta);
    for (int i = 0; i < delta.getDimension(); ++i) {
      delta.set(
          i,
          delta.get(i)
              * squashingFunction.applyDerivative(curLayerOutput.get(i)));
    }


    // update weights
    for (int i = 0; i < weightUpdateMatrix.getRowCount(); ++i) {
      for (int j = 0; j < weightUpdateMatrix.getColumnCount(); ++j) {
        weightUpdateMatrix.set(i, j,
            -learningRate * nextLayerDelta.get(i) * curLayerOutput.get(j)
                + this.momentumWeight * prevWeightMatrix.get(i, j));
      }
    }


    return delta;
  }

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Examples of org.apache.hama.commons.math.DoubleMatrix

    int offset = 1;
    while (offset < matrix_size) {
      slices.add(vc.slice(offset, matrix_rank));
      offset += matrix_rank;
    }
    DoubleMatrix res = new DenseDoubleMatrix((DoubleVector[])slices.toArray());
    return res;
  }

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Examples of org.apache.hama.commons.math.DoubleMatrix

    // 
    // ν_ld, μ_lc (V) is type of matrix, 
    // but 2τ * y_bd (α_al + μ_l: * x_a:)(r − R) (M later) will be type of vector
    // in order to add vector values to matrix
    // we create matrix with MMMMM and then transpose it.
    DoubleMatrix tmp = null;
    if (isAvailableItemFeature) {
      DoubleVector[] Mtransposed = new DenseDoubleVector[rank];
      for (int i = 0; i<rank; i++) {
        Mtransposed[i] = e.itemFeatureFactorized.getRowVector(i).multiply(aal_ml_xa.get(i));
      }
      tmp = new DenseDoubleMatrix(Mtransposed);
      tmp = tmp.multiply(2*TETTA*scoreDifference);
      res.itemFeatureFactorized = e.itemFeatureFactorized.add(tmp);
    }


    if (isAvailableUserFeature) {
      DoubleVector[] Mtransposed = new DenseDoubleVector[rank];
      for (int i = 0; i<rank; i++) {
        Mtransposed[i] = e.userFeatureFactorized.getRowVector(i).multiply(bbl_vl_yb.get(i));
      }
      tmp = new DenseDoubleMatrix(Mtransposed);
      tmp = tmp.multiply(2*TETTA*scoreDifference);
      res.userFeatureFactorized = e.userFeatureFactorized.add(tmp);
    }
    return res;
  }

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Examples of org.apache.hama.commons.math.DoubleMatrix

    String master = peer.getPeerName(peer.getNumPeers()/2);
    peer.send(master, msg);
    peer.sync();


    // normalize
    DoubleMatrix res = null;
    if (peer.getPeerName().equals(master)) {
      res = new DenseDoubleMatrix(featureMatrix.getRowCount(),
                                  featureMatrix.getColumnCount(), 0);
      int incomingMsgCount = 0;
      while ( (msg = peer.getCurrentMessage()) != null) {
        MatrixWritable tmp = (MatrixWritable) msg.get(msgFeatureMatrix);
        res.add(tmp.getMatrix());
        incomingMsgCount++;
      }
      res.divide(incomingMsgCount);
    }


    if (broadcast) {
      if (peer.getPeerName().equals(master)) {
        // broadcast to all

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Examples of org.apache.hama.commons.math.DoubleMatrix

      // save item features
      for (Map.Entry<String, VectorWritable> feature : inpItemsFeatures.entrySet()) {
        peer.write(new Text(OnlineCF.Settings.DFLT_MODEL_ITEM_FEATURES_DELIM+feature.getKey()), feature.getValue());
      }
      // item feature factorized values should be normalized
      DoubleMatrix res = normalizeMatrix(peer, itemFeatureMatrix, 
          OnlineCF.Settings.MSG_ITEM_FEATURE_MATRIX, false);
      
      if (res != null) {
        Text key = new Text(OnlineCF.Settings.DFLT_MODEL_ITEM_MTX_FEATURES_DELIM + 
            OnlineCF.Settings.MSG_ITEM_FEATURE_MATRIX.toString());
        peer.write(key, convertMatrixToVector(res));
      }
    }


    if (userFeatureMatrix != null) {
      // save user features
   // save item features
      for (Map.Entry<String, VectorWritable> feature : inpUsersFeatures.entrySet()) {
        peer.write(new Text(OnlineCF.Settings.DFLT_MODEL_USER_FEATURES_DELIM+feature.getKey()), feature.getValue());
      }
      // user feature factorized values should be normalized
      DoubleMatrix res = normalizeMatrix(peer, userFeatureMatrix, 
          OnlineCF.Settings.MSG_USER_FEATURE_MATRIX, false);
      
      if (res != null) {
        Text key = new Text(OnlineCF.Settings.DFLT_MODEL_USER_MTX_FEATURES_DELIM + 
            OnlineCF.Settings.MSG_USER_FEATURE_MATRIX.toString());

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Examples of org.apache.hama.commons.math.DoubleMatrix

      }
    }
  }


  public static DoubleMatrix read(DataInput in) throws IOException {
    DoubleMatrix mat = new DenseDoubleMatrix(in.readInt(), in.readInt());
    for (int row = 0; row < mat.getRowCount(); row++) {
      for (int col = 0; col < mat.getColumnCount(); col++) {
        mat.set(row, col, in.readDouble());
      }
    }
    return mat;
  }

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Examples of org.apache.hama.commons.math.DoubleMatrix

    assertEquals(LearningStyle.UNSUPERVISED, annCopy.getLearningStyle());


    // compare weights
    DoubleMatrix[] weightsMatrices = annCopy.getWeightMatrices();
    for (int i = 0; i < weightsMatrices.length; ++i) {
      DoubleMatrix expectMat = matrices[i];
      DoubleMatrix actualMat = weightsMatrices[i];
      for (int j = 0; j < expectMat.getRowCount(); ++j) {
        for (int k = 0; k < expectMat.getColumnCount(); ++k) {
          assertEquals(expectMat.get(j, k), actualMat.get(j, k), 0.000001);
        }
      }
    }
    
    FeatureTransformer copyTransformer = annCopy.getFeatureTransformer();

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Examples of org.apache.hama.ml.math.DoubleMatrix

      }
    }
  }


  public static DoubleMatrix read(DataInput in) throws IOException {
    DoubleMatrix mat = new DenseDoubleMatrix(in.readInt(), in.readInt());
    for (int row = 0; row < mat.getRowCount(); row++) {
      for (int col = 0; col < mat.getColumnCount(); col++) {
        mat.set(row, col, in.readDouble());
      }
    }
    return mat;
  }

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Examples of org.jblas.DoubleMatrix


public class JBLASNativeJavaInterface {


    //  Computes the eigenvalues of a general matrix.
public static ComplexDoubleMatrix jblas_eigenvalues(double [][]dM) {
    return org.jblas.Eigen.eigenvalues(new DoubleMatrix(dM));
}

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Examples of org.jblas.DoubleMatrix

   //   Computes the eigenvalues and eigenvectors of a general matrix.
   //   returns an array of ComplexDoubleMatrix objects containing the eigenvectors
   //          stored as the columns of the first matrix, and the eigenvalues as the
   //         diagonal elements of the second matrix.
public static ComplexDoubleMatrix[]  jblas_eigenvectors(double [][]dM) {
    return org.jblas.Eigen.eigenvectors(new DoubleMatrix(dM));
}

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