public (NeuralNetworkVariable, NeuralNetworkVariable, NeuralNetworkVariable) Learn(float[] inputs)
{
for (int i = 0; i < inputCount; i++)
{
inputLayer[0, i] = inputs[i];
}
//make those values in range [-1,1]
//inputLayer = inputLayer.PointwiseTanh();
//compute hidden layers values
hiddenLayers[0] = (inputLayer * weights[0] + bias[0]).PointwiseTanh();
for (int i = 1; i < hiddenLayerCount; i++)
{
hiddenLayers[i] = (hiddenLayers[i - 1] * weights[i] + bias[i]).PointwiseTanh();
}
//compute output layer
outputLayer = (hiddenLayers[hiddenLayerCount - 1] * weights[weights.Count - 1] + bias[bias.Count - 1]).PointwiseTanh();
NeuralNetworkVariable n1 = new NeuralNetworkVariable(outputLayer[0, 0], NeuralNetworkVariable.VariableType.NegativeOneToPositiveOne);
NeuralNetworkVariable n2 = new NeuralNetworkVariable(outputLayer[0, 1], NeuralNetworkVariable.VariableType.NegativeOneToPositiveOne);
NeuralNetworkVariable n3 = new NeuralNetworkVariable(outputLayer[0, 2], NeuralNetworkVariable.VariableType.ZeroToPositiveOne);
return(n1, n2, n3);
}
public (NeuralNetworkVariable, NeuralNetworkVariable, NeuralNetworkVariable) Learn(float inp1, float inp2, float inp3)
{
inputLayer[0, 0] = inp1;
inputLayer[0, 1] = inp2;
inputLayer[0, 2] = inp3;
inputLayer[0, 3] = inp3 - 4;
inputLayer[0, 4] = inp3 * 7;
inputLayer[0, 5] = inp3 / 5;
inputLayer[0, 6] = inp3 - 2;
inputLayer[0, 7] = inp3;
inputLayer[0, 8] = inp3;
//make those values in range [-1,1]
inputLayer = inputLayer.PointwiseTanh();
//compute hidden layers values
hiddenLayers[0] = (inputLayer * weights[0] + bias[0]).PointwiseTanh();
for (int i = 1; i < hiddenLayerCount; i++)
{
hiddenLayers[i] = (hiddenLayers[i - 1] * weights[i] + bias[i]).PointwiseTanh();
}
//compute output layer
outputLayer = (hiddenLayers[hiddenLayerCount - 1] * weights[weights.Count - 1] + bias[bias.Count - 1]).PointwiseTanh();
NeuralNetworkVariable n1 = new NeuralNetworkVariable(outputLayer[0, 0], NeuralNetworkVariable.VariableType.NegativeOneToPositiveOne);
NeuralNetworkVariable n2 = new NeuralNetworkVariable(outputLayer[0, 1], NeuralNetworkVariable.VariableType.NegativeOneToPositiveOne);
//NeuralNetworkVariable n3 = new NeuralNetworkVariable(Sigmoid(outputLayer[0, 2]), NeuralNetworkVariable.VariableType.ZeroToPositiveOne);
NeuralNetworkVariable n3 = new NeuralNetworkVariable(outputLayer[0, 2], NeuralNetworkVariable.VariableType.ZeroToPositiveOne);
return(n1, n2, n3);
}
