Examples of edu.stanford.nlp.neural.SimpleTensor.scale()

  }


  SimpleTensor randomBinaryTensor() {
    double range = 1.0 / (4.0 * numHid);
    SimpleTensor tensor = SimpleTensor.random(numHid * 2, numHid * 2, numHid, -range, range, rand);
    return tensor.scale(op.trainOptions.scalingForInit);
  }


  SimpleMatrix randomTransformMatrix() {
    SimpleMatrix binary = new SimpleMatrix(numHid, numHid * 2 + 1);
    // bias column values are initialized zero

                                  double scale,
                                  double regCost) {
    double cost = 0.0; // the regularization cost
    for (TwoDimensionalMap.Entry<String, String, SimpleTensor> entry : currentMatrices) {
      SimpleTensor 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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