public void normalizedInitializationSigmoid(int fanin, int fanout) {
double max = 4 * Math.sqrt(6.0d / (fanin + fanout));
double[][] featuresMatrixStub = new double[inputSize][outputSize];
RandomUtils.initializeRandomMatrix(featuresMatrixStub, -max, max, 1);
features = Nd4j.create(featuresMatrixStub);
featuresT = features.transpose();
}
public INDArray genGaussianNoise(int id) {
if (!currentNoise.containsKey(id)) {
INDArray zeroMean = Nd4j.zeros(inputSize, outputSize);
currentNoise.put(id, Sampling.normal(RandomUtils.getRandomGenerator(id), zeroMean, noiseVar));
} else {
RealDistribution reals =
new NormalDistribution(
RandomUtils.getRandomGenerator(id),
0,
noiseVarSqrt,
NormalDistribution.DEFAULT_INVERSE_ABSOLUTE_ACCURACY);
INDArrayUtils.shiftLeft(
currentNoise.get(id),
inputSize,
outputSize,
RandomUtils.getRandomGenerator(id).nextInt(inputSize * outputSize),
reals.sample());
}
// currentNoise = Sampling.normal(RandomUtils.getRandomGenerator(id), zeroMean, noiseVar);
return currentNoise.get(id);
}
public void initializeUniform(double min, double max) {
double[][] featuresMatrixStub = new double[inputSize][outputSize];
RandomUtils.initializeRandomMatrix(featuresMatrixStub, min, max, 1);
features = Nd4j.create(featuresMatrixStub);
featuresT = features.transpose();
}