a.i. neural framework / J.A.R.V.I.S. / Just A Rather Very Intelligent System, C#

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//Rextester.Program.Main is the entry point for your code. Don't change it.
//Compiler version 4.0.30319.17929 for Microsoft (R) .NET Framework 4.5

using System;
using System.Collections.Generic;
using System.Linq;
using System.Text.RegularExpressions;

namespace NeuralFactor{

public interface INeuronSignal  
{  
    double Output { get; set; }  
} 
public interface INeuronReceptor  
{  
    Dictionary<INeuronSignal, NeuralFactor> Input { get; }  
} 
public class NeuralFactor  
{  
    #region Constructors  
    public NeuralFactor(double weight)  
    {  
        m_weight = weight;  
        m_delta = 0;  
    }  
    #endregion  
 
    #region Member Variables  
    private double m_weight;  
    private double m_delta;  
    #endregion  
 
    #region Properties  
    public double Weight  
    {  
        get { return m_weight; }  
        set { m_weight = value; }  
    }  
    public double Delta  
    {  
        get { return m_delta; }  
        set { m_delta = value; }  
    }  
    #endregion  
 
    #region Methods  
    public void ApplyDelta()  
    {  
        m_weight += m_delta;  
        m_delta = 0;  
    }  
    #endregion

public interface INeuron : INeuronSignal, INeuronReceptor  
{  
    void Pulse(INeuralLayer layer);  
    void ApplyLearning(INeuralLayer layer);  
    NeuralFactor Bias { get; set; }  
    double BiasWeight { get; set; }  
    double Error { get; set; }  
} 
      public interface INeuralLayer : IList<INeuron>  
{  
    void Pulse(INeuralNet net);  
    void ApplyLearning(INeuralNet net);  
} 
private static double Sigmoid(double value)  
{  
      return 1 / (1 + Math.Exp(-value));  
}  
public void Pulse(INeuralLayer layer)  
{  
    lock (this)  
    {  
        m_output = 0;  
        foreach (KeyValuePair<INeuronSignal, NeuralFactor> item in m_input)  
            m_output += item.Key.Output * item.Value.Weight;  
        m_output += m_bias.Weight * BiasWeight;  
        m_output = Sigmoid(m_output);  
    }  
} 
public void Pulse(INeuralNet net)  
{  
    foreach (INeuron n in m_neurons)  
        n.Pulse(this);  
}  
public void ApplyLearning(INeuralNet net)  
{  
    foreach (INeuron n in m_neurons)  
        n.ApplyLearning(this);  
}  
public void Initialize(int randomSeed, int inputNeuronCount, int hiddenNeuronCount, int outputNeuronCount)  
{  
    int i, j, k, layerCount;  
    Random rand;  
    INeuralLayer layer;  
    // initializations  
    rand = new Random(randomSeed);  
    m_inputLayer = new NeuralLayer();  
    m_outputLayer = new NeuralLayer();  
    m_hiddenLayer = new NeuralLayer();  
    for (i = 0; i < inputNeuronCount; i++)  
        m_inputLayer.Add(new Neuron());  
    for (i = 0; i < outputNeuronCount; i++)  
        m_outputLayer.Add(new Neuron());  
    for (i = 0; i < hiddenNeuronCount; i++)  
        m_hiddenLayer.Add(new Neuron());  
    // wire-up input layer to hidden layer  
    for (i = 0; i < m_hiddenLayer.Count; i++)  
        for (j = 0; j < m_inputLayer.Count; j++)  
            m_hiddenLayer[i].Input.Add(m_inputLayer[j],  
                 new NeuralFactor( rand.NextDouble()));  
    // wire-up output layer to hidden layer  
    for (i = 0; i < m_outputLayer.Count; i++)  
        for (j = 0; j < m_hiddenLayer.Count; j++)  
            m_outputLayer[i].Input.Add(HiddenLayer[j],  
                 new NeuralFactor(rand.NextDouble()));  
}  
public void Pulse()  
{  
    lock (this)  
    {  
        m_hiddenLayer.Pulse(this);  
        m_outputLayer.Pulse(this);  
    }  
}  
public void ApplyLearning()  
{  
    lock (this)  
    {  
        m_hiddenLayer.ApplyLearning(this);  
        m_outputLayer.ApplyLearning(this);  
    }  
}  
private void BackPropogation(double[] desiredResults)  
{  
    int i, j;  
    double temp, error;  
    INeuron outputNode, inputNode, hiddenNode, node, node2;  
    // Calcualte output error values  
    for (i = 0; i < m_outputLayer.Count; i++)  
    {  
        temp = m_outputLayer[i].Output;  
        m_outputLayer[i].Error = (desiredResults[i] - temp) * temp * (1.0F - temp);  
    }  
    // calculate hidden layer error values  
    for (i = 0; i < m_hiddenLayer.Count; i++)  
    {  
        node = m_hiddenLayer[i];  
        error = 0;  
        for (j = 0; j < m_outputLayer.Count; j++)  
        {  
            outputNode = m_outputLayer[j];  
            error += outputNode.Error * outputNode.Input[node].Weight * node.Output * (1.0 - node.Output);  
        }  
        node.Error = error;  
    }  
    // adjust output layer weight change  
    for (i = 0; i < m_hiddenLayer.Count; i++)  
    {  
        node = m_hiddenLayer[i];  
        for (j = 0; j < m_outputLayer.Count; j++)  
        {  
            outputNode = m_outputLayer[j];  
            outputNode.Input[node].Weight += m_learningRate * m_outputLayer[j].Error * node.Output;  
            outputNode.Bias.Delta += m_learningRate * m_outputLayer[j].Error * outputNode.Bias.Weight;  
        }  
    }  
    // adjust hidden layer weight change  
    for (i = 0; i < m_inputLayer.Count; i++)  
    {  
        inputNode = m_inputLayer[i];  
        for (j = 0; j < m_hiddenLayer.Count; j++)  
        {  
            hiddenNode = m_hiddenLayer[j];  
            hiddenNode.Input[inputNode].Weight += m_learningRate * hiddenNode.Error * inputNode.Output;  
            hiddenNode.Bias.Delta += m_learningRate * hiddenNode.Error * inputNode.Bias.Weight;  
        }  
    }  
}  
public void Train(double[] input, double[] desiredResult)  
{  
    int i;  
    if (input.Length != m_inputLayer.Count)  
        throw new ArgumentException(string.Format("Expecting {0} inputs for this net", m_inputLayer.Count));  
    // initialize data  
    for (i = 0; i < m_inputLayer.Count; i++)  
    {  
        Neuron n = m_inputLayer[i] as Neuron;  
        if (null != n) // maybe make interface get;set;  
            n.Output = input[i];  
    }  
    Pulse();  
    BackPropogation(desiredResult);   
}  
public void Train(double[][] inputs, double[][] expected)  
{  
    for (int i = 0; i < inputs.Length; i++)  
        Train(inputs[i], expected[i]);  
}  
private void button1_Click(object sender, EventArgs e)  
{  
    net = new NeuralNet();  
    double high, mid, low;  
    high = .9;  
    low = .1;  
    mid = .5;  
    // initialize with  
    //   2 perception neurons  
    //   2 hidden layer neurons  
    //   1 output neuron  
    net.Initialize(1, 2, 2, 1);  
    double[][] input = new double[4][];  
    input[0] = new double[] {high, high};  
    input[1] = new double[] {low, high};  
    input[2] = new double[] {high, low};  
    input[3] = new double[] {low, low};  
    double[][] output = new double[4][];  
    output[0] = new double[] { low };  
    output[1] = new double[] { high };  
    output[2] = new double[] { high };  
    output[3] = new double[] { low };  
    double ll, lh, hl, hh;  
    int count;  
    count = 0;  
    do  
    {  
        count++;  
        for (int i = 0; i < 100; i++)  
            net.Train(input, output);  
        net.ApplyLearning();  
        net.PerceptionLayer[0].Output = low;  
        net.PerceptionLayer[1].Output = low;  
        net.Pulse();  
        ll = net.OutputLayer[0].Output;  
        net.PerceptionLayer[0].Output = high;  
        net.PerceptionLayer[1].Output = low;  
        net.Pulse();  
        hl = net.OutputLayer[0].Output;  
        net.PerceptionLayer[0].Output = low;  
        net.PerceptionLayer[1].Output = high;  
        net.Pulse();  
        lh = net.OutputLayer[0].Output;  
        net.PerceptionLayer[0].Output = high;  
        net.PerceptionLayer[1].Output = high;  
        net.Pulse();  
        hh = net.OutputLayer[0].Output;  
    }  
    while (hh > mid || lh < mid || hl < mid || ll > mid);  
    MessageBox.Show((count*100).ToString() + " iterations required for training");  
}

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