public override IScore Run(double[][] featureValues, IContext ctx)
{
m_Dimensions = ctx.DataDescriptor.Features.Count();
int numOfInputVectors = featureValues.Length;
if (m_Weights == null)
{
m_Weights = new double[m_Dimensions];
initializeWeights();
}
//m_Errors = new double[numOfInputVectors];
double lastError = 0;
double totalError = 0.0;
var score = new PerceptronAlgorithmScore();
for (int i = 0; i < m_Iterations; i++)
{
double maxError = 0;
totalError = 0;
for (int inputVectIndx = 0; inputVectIndx < numOfInputVectors; inputVectIndx++)
{
// Calculate the output value with current weights.
double calculatedOutput = calculateResult(featureValues[inputVectIndx], m_Dimensions);
// Get expected output.
double expectedOutput = featureValues[inputVectIndx][ctx.DataDescriptor.LabelIndex];
// Error is difference between calculated output and expected output.
double error = expectedOutput - calculatedOutput;
// Total error for all input vectors inside of a single iteration.
totalError += Math.Abs(error);
if (this.m_TraceTotalError)
{
if (maxError < totalError)
{
maxError = totalError;
}
else if (inputVectIndx == numOfInputVectors - 1)
{
lastError = totalError;
}
}
if (error != 0)
{
// Y = W * X
// error = expectedOutput - calculatedOutput
// W = Y/X
//
// Updating of weights
for (int dimensionIndx = 0; dimensionIndx < m_Dimensions; dimensionIndx++)
{
double delta = m_LearningRate * featureValues[inputVectIndx][dimensionIndx] * error;
m_Weights[dimensionIndx] += delta;
}
}
//
// Updating of threshold
this.m_Threshold += this.m_LearningRate * error;
//if (totalError == 0 && i >= (m_Iterations * 0.1)
// && inputVectIndx >= (numOfInputVectors * 0.1)) // We let it calculate at least 10% of max iterations
// break;
}
if (this.m_TraceTotalError)
{
Debug.WriteLine($"Interation: {i}\tmaxError:{maxError}\tlastErr:{lastError}");
}
if (totalError == 0 && i >= (m_Iterations * 0.1)) // We let it calculate at least 10% of max iterations
{
break;
}
//if (totalError == 0)
// break;
}
score.Weights = this.m_Weights;
//score.Errors = this.m_Errors;//numberofsample
score.TotolEpochError = totalError;//all 0
ctx.Score = score;
return(ctx.Score);
}
//private bool m_PersistConvergenceData = false;
public PerceptronAlgorithm(PerceptronAlgorithmScore score)
{
}
