NeuralNetwork.py File Reference

Namespaces
	NeuralNetwork

Functions
def	NeuralNetwork.RandomSamplesUniform (minCoordinate, maxCoordinate, dim, numSamples)
def	NeuralNetwork.RandomSamplesGauss (mean, sigma, dim, numSamples)
def	NeuralNetwork.ExactFunction (dimOutput, coordinates)

Variables
int	NeuralNetwork.randomSeed = 1234567
int	NeuralNetwork.minCoordinate = -3
int	NeuralNetwork.maxCoordinate = 3
int	NeuralNetwork.meanNoise = 0
int	NeuralNetwork.sigmaNoise = 1
int	NeuralNetwork.dimInput = 2
int	NeuralNetwork.dimOutput = 2
int	NeuralNetwork.numSamples = 10
int	NeuralNetwork.numNeuronsHiddenLayer = 3
int	NeuralNetwork.verboseLevel = 0
	NeuralNetwork.SupportPointsInput = RandomSamplesUniform(minCoordinate, maxCoordinate, dimInput, numSamples)
	NeuralNetwork.SupportPointsOutputExact = ExactFunction(dimOutput, SupportPointsInput)
	NeuralNetwork.SupportPointsOutputNoise = RandomSamplesGauss(meanNoise, sigmaNoise, dimOutput, numSamples)
	NeuralNetwork.SupportPointsOutputWithNoise = SupportPointsOutputExact+SupportPointsOutputNoise
	NeuralNetwork.myNetwork = nuto.NeuralNetwork([numNeuronsHiddenLayer])
int	NeuralNetwork.lowerBound = -1
int	NeuralNetwork.upperBound = 1
	NeuralNetwork.SupportPointsApproximation = np.zeros((dimOutput, numSamples))
	NeuralNetwork.SupportPointsApproximationMin = np.zeros((dimOutput, numSamples))
	NeuralNetwork.SupportPointsApproximationMax = np.zeros((dimOutput, numSamples))
	NeuralNetwork.NumParameters = myNetwork.GetNumParameters()
	NeuralNetwork.gradient = np.zeros((NumParameters, 1))
	NeuralNetwork.hessian = np.zeros((NumParameters, NumParameters))