/// <summary>
/// Randomizes all weights.
/// </summary>
public void RandomizeWeights()
{
foreach (var layer in Weights)
{
foreach (var neuron in layer)
{
for (var input = 0; input < neuron.Length; input++)
{
neuron[input] = BruteOptimizer.GetRandomNumber(-10, 10);
}
}
}
}
/// <summary>
/// Addes a bias neuron to the specified layer
/// </summary>
/// <param name="layer">The layer to add the bias neuron to, starting with input layer = 0</param>
public void AddBiasNeuron(int layer)
{
layer++; //Adding the bias neuron to layer L means adding extra weights to each neuron in layer L+1
for (var neuron = 0; neuron < Weights[layer].Length; neuron++)
{
var newWeights = new double[Weights[layer][neuron].Length + 1];
Weights[layer][neuron].CopyTo(newWeights, 0);
newWeights[Weights[layer][neuron].Length] = BruteOptimizer.GetRandomNumber(-10, 10);
Weights[layer][neuron] = newWeights;
}
}
/// <summary>
/// Initializes the weights to random values between -10 and 10
/// </summary>
/// <param name="inputs">The number of input neurons</param>
/// <param name="innerLayers">The number of hidden inner layers (not input or output)</param>
/// <param name="neuronsPerLayer">The number of neurons per hidden inner layer</param>
/// <param name="outputNeurons">The number of output neurons</param>
public void InitializeWeights(int inputs, int innerLayers, int neuronsPerLayer, int outputNeurons)
{
Weights = new double[innerLayers + 1][][];
Weights[0] = new double[neuronsPerLayer][];
for (var neuronIndex = 0; neuronIndex < neuronsPerLayer; neuronIndex++)
{
Weights[0][neuronIndex] = new double[inputs];
for (var i = 0; i < inputs; i++)
{
Weights[0][neuronIndex][i] = BruteOptimizer.GetRandomNumber(-10, 10);
}
}
for (var layerIndex = 1; layerIndex < innerLayers; layerIndex++)
{
Weights[layerIndex] = new double[neuronsPerLayer][];
for (var neuronIndex = 0; neuronIndex < neuronsPerLayer; neuronIndex++)
{
Weights[layerIndex][neuronIndex] = new double[neuronsPerLayer];
for (var i = 0; i < neuronsPerLayer; i++)
{
Weights[layerIndex][neuronIndex][i] = BruteOptimizer.GetRandomNumber(-10, 10);
}
}
}
Weights[innerLayers] = new double[outputNeurons][];
for (var neuronIndex = 0; neuronIndex < outputNeurons; neuronIndex++)
{
Weights[innerLayers][neuronIndex] = new double[neuronsPerLayer];
for (var i = 0; i < neuronsPerLayer; i++)
{
Weights[innerLayers][neuronIndex][i] = BruteOptimizer.GetRandomNumber(-10, 10);
}
}
}
