Package org.ejml.simple

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

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  }

  SimpleMatrix randomTransformMatrix() {
    SimpleMatrix binary = new SimpleMatrix(numHid, numHid * 2 + 1);
    // bias column values are initialized zero
    binary.insertIntoThis(0, 0, randomTransformBlock());
    binary.insertIntoThis(0, numHid, randomTransformBlock());
    return binary.scale(op.trainOptions.scalingForInit);
  }

  SimpleMatrix randomTransformBlock() {
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  SimpleMatrix randomTransformMatrix() {
    SimpleMatrix binary = new SimpleMatrix(numHid, numHid * 2 + 1);
    // bias column values are initialized zero
    binary.insertIntoThis(0, 0, randomTransformBlock());
    binary.insertIntoThis(0, numHid, randomTransformBlock());
    return binary.scale(op.trainOptions.scalingForInit);
  }

  SimpleMatrix randomTransformBlock() {
    double range = 1.0 / (Math.sqrt((double) numHid) * 2.0);
 
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   */
  SimpleMatrix randomClassificationMatrix() {
    SimpleMatrix score = new SimpleMatrix(numClasses, numHid + 1);
    // Leave the bias column with 0 values
    double range = 1.0 / (Math.sqrt((double) numHid));
    score.insertIntoThis(0, 0, SimpleMatrix.random(numClasses, numHid, -range, range, rand));
    return score.scale(op.trainOptions.scalingForInit);
  }

  SimpleMatrix randomWordVector() {
    return randomWordVector(op.numHid, rand);
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    size++;

    SimpleMatrix result = new SimpleMatrix(size, 1);
    int index = 0;
    for (SimpleMatrix vector : vectors) {
      result.insertIntoThis(index, 0, vector);
      index += vector.numRows();
    }
    result.set(index, 0, 1.0);
    return result;
  }
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    }

    SimpleMatrix result = new SimpleMatrix(size, 1);
    int index = 0;
    for (SimpleMatrix vector : vectors) {
      result.insertIntoThis(index, 0, vector);
      index += vector.numRows();
    }
    return result;
  }
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    SimpleMatrix transform;
    if (op.trainOptions.useContextWords) {
      transform = new SimpleMatrix(numRows, numCols * 3 + 1);
      // leave room for bias term
      transform.insertIntoThis(0,numCols + 1, randomContextMatrix());
    } else {
      transform = new SimpleMatrix(numRows, numCols + 1);
    }
    SimpleMatrix unary = randomTransformMatrix();
    transform.insertIntoThis(0, 0, unary);
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    SimpleMatrix binary;
    if (op.trainOptions.useContextWords) {
      binary = new SimpleMatrix(numRows, numCols * 4 + 1);
      // leave room for bias term
      binary.insertIntoThis(0,numCols*2+1, randomContextMatrix());
    } else {
      binary = new SimpleMatrix(numRows, numCols * 2 + 1);
    }
    SimpleMatrix left = randomTransformMatrix();
    SimpleMatrix right = randomTransformMatrix();
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   * unary or binary transform matrix to get the transform matrix
   * which uses context words.
   */
  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;
  }

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   * which uses context words.
   */
  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;
  }

  /**
 
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        // compute Q by first extracting the householder vectors from the
        // columns of QR and then applying it to Q
        for( int j = N-1; j>= 0; j-- ) {
            SimpleMatrix u = new SimpleMatrix(QR.numRows(),1);
            u.insertIntoThis(j,0,QR.extractMatrix(j, END,j,j+1));
            u.set(j,1.0);

            // A = (I - &gamma;*u*u<sup>T</sup>)*A<br>
            Q = Q.plus(-gammas[j],u.mult(u.transpose()).mult(Q));
        }
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