public NeuralNetworks(int inputNumber, int outputNumber, int hiddenLayerWidth, int hiddenLayerHeight)
{
HiddenLayerWeights = new Tensor(hiddenLayerWidth, hiddenLayerHeight, hiddenLayerHeight);
InputLayerWeights = new Matrix(hiddenLayerHeight, inputNumber);
OutputLayerWeights = new Matrix(outputNumber, hiddenLayerHeight);
HiddenLayerNeuron = new Neuron[hiddenLayerWidth, hiddenLayerHeight];
OutputLayerNeuron = new Neuron[outputNumber];
InputNumber = inputNumber;
OutputNumber = outputNumber;
HiddenLayerWidth = hiddenLayerWidth;
HiddenLayerHeight = hiddenLayerHeight;
Random random = new Random();
//Initialize weights randomly from -1 to 1
for (int i = 0; i < HiddenLayerWeights.Width; i++)
{
for (int j = 0; j < HiddenLayerWeights.Height; j++)
{
for (int k = 0; k < HiddenLayerWeights.Depth; k++)
{
if (random.Next() % 2 == 0)
HiddenLayerWeights[i, j, k] = random.NextDouble();
else
HiddenLayerWeights[i, j, k] = -random.NextDouble();
}
}
}
for (int i = 0; i < hiddenLayerHeight; i++)
{
for (int j = 0; j < inputNumber; j++)
InputLayerWeights[i, j] = random.NextDouble();
}
for (int i = 0; i < outputNumber; i++)
{
for (int j = 0; j < hiddenLayerHeight; j++)
OutputLayerWeights[i, j] = random.NextDouble();
}
//Initialize neurons
for (int i = 0; i < HiddenLayerWidth; i++)
{
for (int j = 0; j < hiddenLayerHeight; j++)
HiddenLayerNeuron[i, j] = new Neuron();
}
for (int i = 0; i < outputNumber; i++)
OutputLayerNeuron[i] = new Neuron();
//Initizlize input as a 0 vector
LastInput = new Vector(InputNumber);
for (int i = 0; i < inputNumber; i++)
LastInput[i] = 0;
}
/// <summary>
/// Return the fired states the column of hidden neurons at given (width) position as a Vector.
/// 1 if fired, 0 otherwise
/// </summary>
/// <param name="position"></param>
/// <returns></returns>
public Vector GetHiddenColumnFiredStates(int position)
{
Vector vector = new Vector(HiddenLayerHeight);
for (int i = 0; i < HiddenLayerHeight; i++)
vector[i] = HiddenLayerNeuron[position, i].Fired ? 1 : 0;
return vector;
}
public static Vector operator *(Matrix one, Vector other)
{
if (one.Width != other.Dim)
throw new Exception("Dim Error");
Vector newV = new Vector(one.Height);
for (int i = 0; i < newV.Dim; i++)
newV[i] = other * one.Row(i);
return newV;
}
/// <summary>
/// Propagage through the network, a neuron fires if potential >= threshold
/// </summary>
/// <param name="input"></param>
public void ForwardPropagation(Vector input)
{
if (input.Dim != InputNumber)
throw (new Exception("Number of inputs doesn't match, please refer to InputNumber field"));
LastInput = input;
if (input.Dim != InputNumber)
throw new Exception("Input Dimension doesn't match");
for (int i = 0; i < HiddenLayerHeight; i++)
HiddenLayerNeuron[0, i].Fire(InputLayerWeights.Row(i) * HiddenLayerWeights[0, i]);
for (int i = 1; i < HiddenLayerWidth; i++)
{
for (int j = 0; j < HiddenLayerHeight; j++)
HiddenLayerNeuron[i, j].Fire(HiddenLayerWeights[i, j] * GetHiddenColumnFiredStates(i - 1));
}
for (int i = 0; i < OutputNumber; i++)
OutputLayerNeuron[i].Fire(OutputLayerWeights.Row(i) * GetHiddenColumnFiredStates(HiddenLayerWidth - 1));
}
public static Vector operator -(Vector one, Vector other)
{
if (one.Dim != other.Dim)
throw new Exception("Vector lengths aren't the same");
Vector newV = new Vector(one);
for (int i = 0; i < other.Dim; i++)
newV[i] -= other[i];
return newV;
}
public static Vector operator /(Vector one, double param)
{
Vector newV = new Vector(one);
for (int i = 0; i < one.Dim; i++)
newV[i] /= param;
return newV;
}
public Vector this[int x, int y]
{
get
{
Vector vector = new Vector(Depth);
for (int i = 0; i < Depth; i++)
vector[i] = field[x, y, i];
return vector;
}
set
{
//set a vector
}
}
public Vector(Vector other)
{
field = new double[other.Dim];
for (int i = 0; i < other.Dim; i++)
field[i] = other[i];
}
public void SetRow(int position, Vector row)
{
if (Width != row.Dim)
throw new Exception("Dim Error");
for (int i = 0; i < Width; i++)
field[i, position] = row[i];
}
public void SetColumn(int position, Vector column)
{
if (Height != column.Dim)
throw new Exception("Dim Error");
for (int i = 0; i < Height; i++)
field[position, i] = column[i];
}
/// <summary>
/// Returns a row at the given postion as a vector, intial index is 0
/// </summary>
/// <param name="position"></param>
/// <returns></returns>
public Vector Row(int position)
{
Vector newV = new Vector(Width);
for (int i = 0; i < Width; i++)
newV[i] = field[i, position];
return newV;
}
/// <summary>
/// Return a new matrix after GaussianElimination (row reduction) of the original matrix
/// </summary>
/// <returns></returns>
public Matrix GaussianElimination()
{
//if (Height != Width)
// throw new Exception();
Matrix newM = new Matrix(this);
int position = -1;
double param = 0;
int count = 0;
Vector positionV = new Vector(Width);
for (int i = 0; i < Width; i++)
{
if (position != -1)
{
newM.SwitchRow(position, count);
count++;
position = -1;
}
for (int j = count; j < Height; j++)
{
if (newM.field[i, j] != 0)
{
if (position != -1)
newM.SetRow(j, newM.Row(j) - positionV * newM[i, j] / param);
else
{
position = j;
param = newM[i, j];
newM.SetRow(j, newM.Row(j));
positionV = newM.Row(j);
}
}
}
}
return newM;
}
/// <summary>
/// Return a column at the given position as a vector, initial index is 0
/// </summary>
/// <param name="position"></param>
/// <returns></returns>
public Vector Column(int position)
{
Vector newV = new Vector(Height);
for (int i = 0; i < Height; i++)
newV[i] = field[position, i];
return newV;
}
