/// <summary>
/// Train the neural network for the specified pattern. The neural network
/// can be trained for more than one pattern. To do this simply call the
/// train method more than once.
/// </summary>
/// <param name="pattern">The pattern to train on.</param>
public void Train(bool[] pattern)
{
if (pattern.Length != this.weightMatrix.Rows)
{
throw new NeuralNetworkError("Can't train a pattern of size "
+ pattern.Length + " on a hopfield network of size "
+ this.weightMatrix.Rows);
}
// Create a row matrix from the input, convert boolean to bipolar
Matrix.Matrix m2 = Matrix.Matrix.CreateRowMatrix(BiPolarUtil
.Bipolar2double(pattern));
// Transpose the matrix and multiply by the original input matrix
Matrix.Matrix m1 = MatrixMath.Transpose(m2);
Matrix.Matrix m3 = MatrixMath.Multiply(m1, m2);
// matrix 3 should be square by now, so create an identity
// matrix of the same size.
Matrix.Matrix identity = MatrixMath.Identity(m3.Rows);
// subtract the identity matrix
Matrix.Matrix m4 = MatrixMath.Subtract(m3, identity);
// now add the calculated matrix, for this pattern, to the
// existing weight matrix.
this.weightMatrix = MatrixMath.Add(this.weightMatrix, m4);
}
/// <summary>
/// Present a pattern to the neural network and receive the result.
/// </summary>
/// <param name="pattern">The pattern to be presented to the neural network.</param>
/// <returns>The output from the neural network.</returns>
public bool[] Present(bool[] pattern)
{
bool[] output = new bool[pattern.Length];
// convert the input pattern into a matrix with a single row.
// also convert the boolean values to bipolar(-1=false, 1=true)
Matrix.Matrix inputMatrix = Matrix.Matrix.CreateRowMatrix(BiPolarUtil
.Bipolar2double(pattern));
// Process each value in the pattern
for (int col = 0; col < pattern.Length; col++)
{
Matrix.Matrix columnMatrix = this.weightMatrix.GetCol(col);
columnMatrix = MatrixMath.Transpose(columnMatrix);
// The output for this input element is the dot product of the
// input matrix and one column from the weight matrix.
double dotProduct = MatrixMath.DotProduct(inputMatrix,
columnMatrix);
// Convert the dot product to either true or false.
if (dotProduct > 0)
{
output[col] = true;
}
else
{
output[col] = false;
}
}
return(output);
}
public bool[] Present(bool[] pattern)
{
var output = new bool[pattern.Length];
Matrix inputMatrix = Matrix.CreateRowMatrix(BiPolarUtil.Bipolar2Double(pattern));
for (int col = 0; col < pattern.Length; col++)
{
Matrix columnMatrix = _weightMatrix.GetCol(col);
columnMatrix = MatrixMath.Transpose(columnMatrix);
double dotProduct = MatrixMath.FindMult(inputMatrix, columnMatrix);
if (dotProduct > 0)
{
output[col] = true;
}
else
{
output[col] = false;
}
}
return(output);
}
/// <summary>
/// Allowes indexed access to the data.
/// </summary>
/// <param name="x">The index.</param>
/// <returns>The value at the specified index.</returns>
public double this[int x]
{
get
{
return(BiPolarUtil.Bipolar2double(this.data[x]));
}
set
{
this.data[x] = BiPolarUtil.Double2bipolar(value);
}
}
public void Train(bool[] pattern)
{
Matrix m2 = Matrix.CreateRowMatrix(BiPolarUtil.Bipolar2Double(pattern));
Matrix m1 = MatrixMath.Transpose(m2);
Matrix m3 = MatrixMath.Multiply(m1, m2);
Matrix identity = MatrixMath.CreateIdentityMatrix(m3.Rows);
Matrix m4 = MatrixMath.Subtract(m3, identity);
_weightMatrix = MatrixMath.Add(_weightMatrix, m4);
}
/// <summary>
/// Note: for Hopfield networks, you will usually want to call the "run"
/// method to compute the output.
/// This method can be used to copy the input data to the current state. A
/// single iteration is then run, and the new current state is returned.
/// </summary>
///
/// <param name="input">The input pattern.</param>
/// <returns>The new current state.</returns>
public override sealed IMLData Compute(IMLData input)
{
var result = new BiPolarMLData(input.Count);
input.CopyTo(CurrentState.Data, 0, input.Count);
Run();
for (int i = 0; i < CurrentState.Count; i++)
{
result.SetBoolean(i,
BiPolarUtil.Double2bipolar(CurrentState[i]));
}
EngineArray.ArrayCopy(CurrentState.Data, result.Data);
return(result);
}
