Package org.ejml.simple

Examples of org.ejml.simple.SimpleMatrix.plus()

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   */
  private SimpleMatrix randomContextMatrix() {
    SimpleMatrix matrix = new SimpleMatrix(numRows, numCols * 2);
    matrix.insertIntoThis(0, 0, identity.scale(op.trainOptions.scalingForInit * 0.1));
    matrix.insertIntoThis(0, numCols, identity.scale(op.trainOptions.scalingForInit * 0.1));
    matrix = matrix.plus(SimpleMatrix.random(numRows,numCols * 2,-1.0/Math.sqrt((double)numCols * 100.0),1.0/Math.sqrt((double)numCols * 100.0),rand));
    return matrix;
  }

  /**
   * Create a random transform matrix based on the initialization
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                // extract the submatrix of A which is being operated on
                SimpleMatrix A_small = QR.extractMatrix(i,END,i,END);

                // A = (I - &gamma;*u*u<sup>T</sup>)A
                A_small = A_small.plus(-gamma,v.mult(v.transpose()).mult(A_small));

                // save the results
                QR.insertIntoThis(i,i,A_small);
                QR.insertIntoThis(i+1,i,v.extractMatrix(1,END,0,1));

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      isClusteringDone = true;
      for (int centroid = 0; centroid < noCentroids; centroid++) {
       
        SimpleMatrix averageVector = new SimpleMatrix(1,noFeatures);
        for (Integer sample : (ArrayList<Integer>) centroids.get(centroid).trainingExamples) {
          averageVector = averageVector.plus(datasetMatrix.extractVector(true, sample));
        }
        averageVector = averageVector.divide(centroids.get(centroid).trainingExamples.size());
       
        if (isClassification) {
          for( int i = 0; i < averageVector.getNumElements(); i++ )
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