public void Have_bias_and_have_inputs_with_weights()
{
var perceptron = new ThresholdNeuron();
//
perceptron.bias.ShouldCompile();
perceptron.Inputs.ShouldCompile();
}
/// <summary>
/// This method implements weight update procedure for the whole network for
/// this learning rule
/// </summary>
/// <param name="patternError">
/// single pattern error vector
///
/// if the output is 0 and required value is 1, increase rthe weights
/// if the output is 1 and required value is 0, decrease the weights
/// otherwice leave weights unchanged
/// </param>
protected internal override void updateNetworkWeights(double[] patternError)
{
int i = 0;
List <Neuron> outputNeurons = neuralNetwork.OutputNeurons;
foreach (Neuron outputNeuron in outputNeurons)
{
ThresholdNeuron neuron = (ThresholdNeuron)outputNeuron;
double outErr = patternError[i];
double thresh = neuron.Thresh;
double netInput = neuron.NetInput;
double threshError = thresh - netInput; // distance from zero
// use output error to decide weathet to inrease, decrase or leave unchanged weights
// add errorCorrection to threshError to move above or below zero
double neuronError = outErr * (Math.Abs(threshError) + errorCorrection);
// use same adjustment principle as PerceptronLearning,
// just with different neuronError
neuron.Error = neuronError;
updateNeuronWeights(neuron);
i++;
} // for
}
