private PolynomialRegression PRLearning(double[] independentVariables, double[] dependentVariables)
{
var polyTeacher = new PolynomialLeastSquares()
{
Degree = 6
};
PolynomialRegression objRegressionLocal = polyTeacher.Learn(independentVariables, dependentVariables);
double[] prediction = objRegressionLocal.Transform(independentVariables);
predictionPL[indexPredictionPL] = prediction;
//PredictedData[1] = prediction;
errorPR += new SquareLoss(independentVariables).Loss(prediction);
double[] coefOfFunction = new double[objRegressionLocal.Weights.Length + 1];
coefOfFunction[0] = objRegressionLocal.Intercept;
int index = objRegressionLocal.Weights.Length - 1;
for (int i = 1; i <= objRegressionLocal.Weights.Length; i++)
{
coefOfFunction[i] = objRegressionLocal.Weights[index];
index--;
}
double func(double x)
{
double y = 0;
for (int i = 0; i <= polyTeacher.Degree; i++)
{
y += coefOfFunction[i] * Math.Pow(x, i);
}
return(y);
}
double min = NormalizedInputData.GetColumn(indexPredictionPL).Min(),
max = NormalizedInputData.GetColumn(indexPredictionPL).Max();
XYpairPL[indexPredictionPL] = new double[2][];
XYpairPL[indexPredictionPL][0] = new double[100];
XYpairPL[indexPredictionPL][1] = new double[100];
index = 0;
for (double i = min; i <= max; i += 0.01)
{
XYpairPL[indexPredictionPL][0][index] = i;
XYpairPL[indexPredictionPL][1][index] = func(i);
index++;
}
indexPredictionPL++;
return(objRegressionLocal);
}
public void PolynomialRegressionLearning()
{
int n = LearningData.Length;
double[] dependentVariables = LearningData.GetColumn(3);
double[] independentVariables = new double[n];
//InputDataNormalization();
for (int i = 0; i < 3; i++)
{
independentVariables = NormalizedInputData.GetColumn(i);
objRegression[i] = PRLearning(independentVariables, dependentVariables);
}
PredictedData[1] = new double[predictionPL[0].Length];
for (int i = 0; i < predictionPL[0].Length; i++)
{
PredictedData[1][i] = (predictionPL[0][i] + predictionPL[1][i] + predictionPL[2][i]) / 3;
}
}
