public void vectorToParams(double[] theta) { NeuralUtils.vectorToParams(theta, binaryTransform.valueIterator(), binaryClassification.valueIterator(), SimpleTensor.iteratorSimpleMatrix(binaryTensors.valueIterator()), unaryClassification.values().iterator(), wordVectors.values().iterator()); }
@SuppressWarnings("unchecked") public void vectorToParams(double[] theta) { if (op.trainOptions.trainWordVectors) { NeuralUtils.vectorToParams(theta, binaryTransform.valueIterator(), unaryTransform.values().iterator(), binaryScore.valueIterator(), unaryScore.values().iterator(), wordVectors.values().iterator()); } else { NeuralUtils.vectorToParams(theta, binaryTransform.valueIterator(), unaryTransform.values().iterator(), binaryScore.valueIterator(), unaryScore.values().iterator()); } }
@SuppressWarnings("unchecked") public double[] paramsToVector(double scale) { int totalSize = totalParamSize(); if (op.trainOptions.trainWordVectors) { return NeuralUtils.paramsToVector(scale, totalSize, binaryTransform.valueIterator(), unaryTransform.values().iterator(), binaryScore.valueIterator(), unaryScore.values().iterator(), wordVectors.values().iterator()); } else { return NeuralUtils.paramsToVector(scale, totalSize, binaryTransform.valueIterator(), unaryTransform.values().iterator(), binaryScore.valueIterator(), unaryScore.values().iterator()); } }
@SuppressWarnings("unchecked") public double[] paramsToVector() { int totalSize = totalParamSize(); if (op.trainOptions.trainWordVectors) { return NeuralUtils.paramsToVector(totalSize, binaryTransform.valueIterator(), unaryTransform.values().iterator(), binaryScore.valueIterator(), unaryScore.values().iterator(), wordVectors.values().iterator()); } else { return NeuralUtils.paramsToVector(totalSize, binaryTransform.valueIterator(), unaryTransform.values().iterator(), binaryScore.valueIterator(), unaryScore.values().iterator()); } }
public double[] paramsToVector() { int totalSize = totalParamSize(); return NeuralUtils.paramsToVector(totalSize, binaryTransform.valueIterator(), binaryClassification.valueIterator(), SimpleTensor.iteratorSimpleMatrix(binaryTensors.valueIterator()), unaryClassification.values().iterator(), wordVectors.values().iterator()); }
value += scaleAndRegularize(derivatives.wordVectorD, model.wordVectors, scale, model.op.trainOptions.regWordVector, true, false); derivative = NeuralUtils.paramsToVector(theta.length, derivatives.binaryTD.valueIterator(), derivatives.binaryCD.valueIterator(), SimpleTensor.iteratorSimpleMatrix(derivatives.binaryTensorTD.valueIterator()), derivatives.unaryCD.values().iterator(), derivatives.wordVectorD.values().iterator());
if (op.trainOptions.trainWordVectors) { localDerivativeGood = NeuralUtils.paramsToVector(theta.length, binaryW_dfsG.valueIterator(), unaryW_dfsG.values().iterator(), binaryScoreDerivativesG.valueIterator(), unaryScoreDerivativesG.values().iterator(), wordVectorDerivativesG.values().iterator()); binaryW_dfsB.valueIterator(), unaryW_dfsB.values().iterator(), binaryScoreDerivativesB.valueIterator(), unaryScoreDerivativesB.values().iterator(), wordVectorDerivativesB.values().iterator()); } else { localDerivativeGood = NeuralUtils.paramsToVector(theta.length, binaryW_dfsG.valueIterator(), unaryW_dfsG.values().iterator(), binaryScoreDerivativesG.valueIterator(), unaryScoreDerivativesG.values().iterator()); binaryW_dfsB.valueIterator(), unaryW_dfsB.values().iterator(), binaryScoreDerivativesB.valueIterator(), unaryScoreDerivativesB.values().iterator());
@SuppressWarnings("unchecked") public void vectorToParams(double[] theta) { if (op.trainOptions.trainWordVectors) { NeuralUtils.vectorToParams(theta, binaryTransform.valueIterator(), unaryTransform.values().iterator(), binaryScore.valueIterator(), unaryScore.values().iterator(), wordVectors.values().iterator()); } else { NeuralUtils.vectorToParams(theta, binaryTransform.valueIterator(), unaryTransform.values().iterator(), binaryScore.valueIterator(), unaryScore.values().iterator()); } }
@SuppressWarnings("unchecked") public void vectorToParams(double[] theta) { if (op.trainOptions.trainWordVectors) { NeuralUtils.vectorToParams(theta, binaryTransform.valueIterator(), unaryTransform.values().iterator(), binaryScore.valueIterator(), unaryScore.values().iterator(), wordVectors.values().iterator()); } else { NeuralUtils.vectorToParams(theta, binaryTransform.valueIterator(), unaryTransform.values().iterator(), binaryScore.valueIterator(), unaryScore.values().iterator()); } }
@SuppressWarnings("unchecked") public void vectorToParams(double[] theta) { if (op.trainOptions.trainWordVectors) { NeuralUtils.vectorToParams(theta, binaryTransform.valueIterator(), unaryTransform.values().iterator(), binaryScore.valueIterator(), unaryScore.values().iterator(), wordVectors.values().iterator()); } else { NeuralUtils.vectorToParams(theta, binaryTransform.valueIterator(), unaryTransform.values().iterator(), binaryScore.valueIterator(), unaryScore.values().iterator()); } }
@SuppressWarnings("unchecked") public double[] paramsToVector() { int totalSize = totalParamSize(); if (op.trainOptions.trainWordVectors) { return NeuralUtils.paramsToVector(totalSize, binaryTransform.valueIterator(), unaryTransform.values().iterator(), binaryScore.valueIterator(), unaryScore.values().iterator(), wordVectors.values().iterator()); } else { return NeuralUtils.paramsToVector(totalSize, binaryTransform.valueIterator(), unaryTransform.values().iterator(), binaryScore.valueIterator(), unaryScore.values().iterator()); } }
public void vectorToParams(double[] theta) { NeuralUtils.vectorToParams(theta, binaryTransform.valueIterator(), binaryClassification.valueIterator(), SimpleTensor.iteratorSimpleMatrix(binaryTensors.valueIterator()), unaryClassification.values().iterator(), wordVectors.values().iterator()); }
@SuppressWarnings("unchecked") public double[] paramsToVector(double scale) { int totalSize = totalParamSize(); if (op.trainOptions.trainWordVectors) { return NeuralUtils.paramsToVector(scale, totalSize, binaryTransform.valueIterator(), unaryTransform.values().iterator(), binaryScore.valueIterator(), unaryScore.values().iterator(), wordVectors.values().iterator()); } else { return NeuralUtils.paramsToVector(scale, totalSize, binaryTransform.valueIterator(), unaryTransform.values().iterator(), binaryScore.valueIterator(), unaryScore.values().iterator()); } }
public double[] paramsToVector() { int totalSize = totalParamSize(); return NeuralUtils.paramsToVector(totalSize, binaryTransform.valueIterator(), binaryClassification.valueIterator(), SimpleTensor.iteratorSimpleMatrix(binaryTensors.valueIterator()), unaryClassification.values().iterator(), wordVectors.values().iterator()); }
@SuppressWarnings("unchecked") public double[] paramsToVector(double scale) { int totalSize = totalParamSize(); if (op.trainOptions.trainWordVectors) { return NeuralUtils.paramsToVector(scale, totalSize, binaryTransform.valueIterator(), unaryTransform.values().iterator(), binaryScore.valueIterator(), unaryScore.values().iterator(), wordVectors.values().iterator()); } else { return NeuralUtils.paramsToVector(scale, totalSize, binaryTransform.valueIterator(), unaryTransform.values().iterator(), binaryScore.valueIterator(), unaryScore.values().iterator()); } }
public void vectorToParams(double[] theta) { NeuralUtils.vectorToParams(theta, binaryTransform.valueIterator(), binaryClassification.valueIterator(), SimpleTensor.iteratorSimpleMatrix(binaryTensors.valueIterator()), unaryClassification.values().iterator(), wordVectors.values().iterator()); }
@SuppressWarnings("unchecked") public double[] paramsToVector(double scale) { int totalSize = totalParamSize(); if (op.trainOptions.trainWordVectors) { return NeuralUtils.paramsToVector(scale, totalSize, binaryTransform.valueIterator(), unaryTransform.values().iterator(), binaryScore.valueIterator(), unaryScore.values().iterator(), wordVectors.values().iterator()); } else { return NeuralUtils.paramsToVector(scale, totalSize, binaryTransform.valueIterator(), unaryTransform.values().iterator(), binaryScore.valueIterator(), unaryScore.values().iterator()); } }
@SuppressWarnings("unchecked") public double[] paramsToVector() { int totalSize = totalParamSize(); if (op.trainOptions.trainWordVectors) { return NeuralUtils.paramsToVector(totalSize, binaryTransform.valueIterator(), unaryTransform.values().iterator(), binaryScore.valueIterator(), unaryScore.values().iterator(), wordVectors.values().iterator()); } else { return NeuralUtils.paramsToVector(totalSize, binaryTransform.valueIterator(), unaryTransform.values().iterator(), binaryScore.valueIterator(), unaryScore.values().iterator()); } }
@SuppressWarnings("unchecked") public double[] paramsToVector() { int totalSize = totalParamSize(); if (op.trainOptions.trainWordVectors) { return NeuralUtils.paramsToVector(totalSize, binaryTransform.valueIterator(), unaryTransform.values().iterator(), binaryScore.valueIterator(), unaryScore.values().iterator(), wordVectors.values().iterator()); } else { return NeuralUtils.paramsToVector(totalSize, binaryTransform.valueIterator(), unaryTransform.values().iterator(), binaryScore.valueIterator(), unaryScore.values().iterator()); } }
public double[] paramsToVector() { int totalSize = totalParamSize(); return NeuralUtils.paramsToVector(totalSize, binaryTransform.valueIterator(), binaryClassification.valueIterator(), SimpleTensor.iteratorSimpleMatrix(binaryTensors.valueIterator()), unaryClassification.values().iterator(), wordVectors.values().iterator()); }