public void Add(double x, double y)
{
// no need to check validity of values explicitly here as it is checked in all three evaluators
covCalculator.Add(x, y);
sxCalculator.Add(x);
syCalculator.Add(y);
}
/// <summary>
/// Calculates linear scaling parameters in one pass.
/// The formulas to calculate the scaling parameters were taken from Scaled Symblic Regression by Maarten Keijzer.
/// http://www.springerlink.com/content/x035121165125175/
/// </summary>
public void Add(double original, double target)
{
// validity of values is checked in mean calculator and covariance calculator
targetMeanCalculator.Add(target);
originalMeanAndVarianceCalculator.Add(original);
originalTargetCovarianceCalculator.Add(original, target);
}
public void Add(double original, double estimated)
{
// no need to check for validity of values explicitly as it is checked in the meanAndVariance calculator anyway
double error = estimated - original;
meanSquaredErrorCalculator.Add(error * error);
originalVarianceCalculator.Add(original);
}
public static void Calculate(IEnumerable <double> x, out double mean, out double variance, out OnlineCalculatorError meanErrorState, out OnlineCalculatorError varianceErrorState)
{
OnlineMeanAndVarianceCalculator meanAndVarianceCalculator = new OnlineMeanAndVarianceCalculator();
foreach (double xi in x)
{
meanAndVarianceCalculator.Add(xi);
}
mean = meanAndVarianceCalculator.Mean;
variance = meanAndVarianceCalculator.Variance;
meanErrorState = meanAndVarianceCalculator.MeanErrorState;
varianceErrorState = meanAndVarianceCalculator.VarianceErrorState;
}
public void Add(double startValue, IEnumerable <double> actualContinuation, IEnumerable <double> referenceContinuation, IEnumerable <double> predictedContinuation)
{
if (double.IsNaN(startValue) || (errorState & OnlineCalculatorError.InvalidValueAdded) > 0)
{
errorState = errorState | OnlineCalculatorError.InvalidValueAdded;
}
else
{
var actualEnumerator = actualContinuation.GetEnumerator();
var predictedEnumerator = predictedContinuation.GetEnumerator();
var referenceEnumerator = referenceContinuation.GetEnumerator();
while (actualEnumerator.MoveNext() & predictedEnumerator.MoveNext() & referenceEnumerator.MoveNext()
& ErrorState != OnlineCalculatorError.InvalidValueAdded)
{
double actual = actualEnumerator.Current;
double predicted = predictedEnumerator.Current;
double reference = referenceEnumerator.Current;
if (double.IsNaN(actual) || double.IsNaN(predicted) || double.IsNaN(reference))
{
errorState = errorState | OnlineCalculatorError.InvalidValueAdded;
}
else
{
// error of predicted change
double errorPredictedChange = (predicted - startValue) - (actual - startValue);
squaredErrorMeanCalculator.Add(errorPredictedChange * errorPredictedChange);
double errorReference = (reference - startValue) - (actual - startValue);
unbiasedEstimatorMeanCalculator.Add(errorReference * errorReference);
}
}
// check if both enumerators are at the end to make sure both enumerations have the same length
if (actualEnumerator.MoveNext() || predictedEnumerator.MoveNext() || referenceEnumerator.MoveNext())
{
errorState = errorState | OnlineCalculatorError.InvalidValueAdded;
}
else
{
errorState = errorState & (~OnlineCalculatorError.InsufficientElementsAdded); // n >= 1
}
}
}
public static void Calculate(IEnumerable<double> x, out double mean, out double variance, out OnlineCalculatorError meanErrorState, out OnlineCalculatorError varianceErrorState) {
OnlineMeanAndVarianceCalculator meanAndVarianceCalculator = new OnlineMeanAndVarianceCalculator();
foreach (double xi in x) {
meanAndVarianceCalculator.Add(xi);
}
mean = meanAndVarianceCalculator.Mean;
variance = meanAndVarianceCalculator.Variance;
meanErrorState = meanAndVarianceCalculator.MeanErrorState;
varianceErrorState = meanAndVarianceCalculator.VarianceErrorState;
}
public void Add(double original, double estimated)
{
meanAndVarianceCalculator.Add(estimated - original);
}