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

org.ejml.simple.SimpleMatrix.scale()

  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);
    return SimpleMatrix.random(numHid,numHid,-range,range,rand).plus(identity);

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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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        output.set(i, j, Math.exp(output.get(i, j)));
      }
    }
    double sum = output.elementSum();
    // will be safe, since exp should never return 0
    return output.scale(1.0 / sum);
  }


  /**
   * Applies log to each of the entries in the matrix.  Returns a new matrix.
   */

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                            double scale,
                            double regCost) {
    double cost = 0.0; // the regularization cost
    for (TwoDimensionalMap.Entry<String, String, SimpleMatrix> entry : currentMatrices) {
      SimpleMatrix D = derivatives.get(entry.getFirstKey(), entry.getSecondKey());
      D = D.scale(scale).plus(entry.getValue().scale(regCost));
      derivatives.put(entry.getFirstKey(), entry.getSecondKey(), D);
      cost += entry.getValue().elementMult(entry.getValue()).elementSum() * regCost / 2.0;
    }
    return cost;
  }

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                            double scale,
                            double regCost) {
    double cost = 0.0; // the regularization cost
    for (Map.Entry<String, SimpleMatrix> entry : currentMatrices.entrySet()) {
      SimpleMatrix D = derivatives.get(entry.getKey());
      D = D.scale(scale).plus(entry.getValue().scale(regCost));
      derivatives.put(entry.getKey(), D);
      cost += entry.getValue().elementMult(entry.getValue()).elementSum() * regCost / 2.0;
    }
    return cost;
  }

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    int size = deltaFull.getNumElements();
    SimpleTensor Wt_df = new SimpleTensor(size*2, size*2, size);
    // TODO: combine this concatenation with computeTensorDeltaDown?
    SimpleMatrix fullVector = NeuralUtils.concatenate(leftVector, rightVector);
    for (int slice = 0; slice < size; ++slice) {
      Wt_df.setSlice(slice, fullVector.scale(deltaFull.get(slice)).mult(fullVector.transpose()));
    }
    return Wt_df;
  }


  /**

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    ++numUnaryMatrices;


    // scoring matrix
    SimpleMatrix score = SimpleMatrix.random(1, numCols, -1.0/Math.sqrt((double)numCols),1.0/Math.sqrt((double)numCols),rand);
    unaryScore.put(childBasic, score.scale(op.trainOptions.scalingForInit));


    SimpleMatrix transform;
    if (op.trainOptions.useContextWords) {
      transform = new SimpleMatrix(numRows, numCols * 3 + 1);
      // leave room for bias term

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    ++numBinaryMatrices;


    // scoring matrix
    SimpleMatrix score = SimpleMatrix.random(1, numCols, -1.0/Math.sqrt((double)numCols),1.0/Math.sqrt((double)numCols),rand);
    binaryScore.put(leftBasic, rightBasic, score.scale(op.trainOptions.scalingForInit));


    SimpleMatrix binary;
    if (op.trainOptions.useContextWords) {
      binary = new SimpleMatrix(numRows, numCols * 4 + 1);
      // leave room for bias term

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