/*
* Calculate outputs from given inputs using calculated weights
*/
public double[] calculateOutput(double[] inputDimValues)
{
//Only accepts the same number of input values as the RBF will take
if (inputDimValues.Length != m_numInputDimensions)
{
throw new ArgumentOutOfRangeException();
}
double[] outputValues = new double[m_numOutputDimensions];
Matrix A = new Matrix(1, m_trainingPoints.Count);
for (int outputIndex = 0; outputIndex < m_numOutputDimensions; outputIndex++)
{
for (int trainingIndex = 0; trainingIndex < m_trainingPoints.Count; trainingIndex++)
{
double[] distPoints = new double[m_numInputDimensions];
for (int inputIndex = 0; inputIndex < m_numInputDimensions; inputIndex++)
{
distPoints[inputIndex] = inputDimValues[inputIndex] - m_trainingPoints[trainingIndex].inputDim[inputIndex];
}
A[0, trainingIndex] = RBFFunctions.gaussKernel(summedDistance(distPoints), m_sigma);
}
Matrix dot = A.Multiply(m_weights[outputIndex]);
outputValues[outputIndex] = dot[0, 0];
}
return(outputValues);
}
/*
* Calculate weight values from training points
*/
public List <Matrix> calculateWeights()
{
Matrix A = new Matrix(m_trainingPoints.Count);
int i = 0;
int j = 0;
//Constructs A matrix using RBF function (gaussian)
for (i = 0; i < m_trainingPoints.Count; i++)
{
for (j = 0; j < m_trainingPoints.Count; j++)
{
double[] distPoints = new double[m_numInputDimensions];
for (int inputIndex = 0; inputIndex < m_numInputDimensions; inputIndex++)
{
distPoints[inputIndex] = m_trainingPoints[i].inputDim[inputIndex] - m_trainingPoints[j].inputDim[inputIndex];
}
A[i, j] = RBFFunctions.gaussKernel(summedDistance(distPoints), m_sigma);
}
}
Matrix Ainv = A.Inverse();
//Get weights from inverse A matrix and training outputs
for (i = 0; i < m_numOutputDimensions; i++)
{
double[] groupDim = getDimensionFromGroup(m_trainingPoints, DimensionType.OUTPUT, i);
//Converts double array to single column matrix
Matrix col = new Matrix(m_trainingPoints.Count, 1);
for (j = 0; j < groupDim.Length; j++)
{
col[j, 0] = groupDim[j];
}
Matrix mult = Ainv.Multiply(col);
m_weights.Add(mult);
}
return(m_weights);
}
