public static double CalculateBiasAndVariance(ParserResults trainingData, List <List <bool> > predictorsPredictions, out double bias)
{
double variance = 0;
bias = 0;
// Calculate biar and variance for all trees
for (int trainingDataIndex = 0; trainingDataIndex < trainingData.Values.Count; trainingDataIndex++)
{
var trainingDataValue = trainingData.Values[trainingDataIndex];
double realValue = Transformer.BoolToDouble(trainingDataValue.Output);
List <double> allPredictions = new List <double>(predictorsPredictions.Count);
foreach (var predictorPredictions in predictorsPredictions)
{
bool prediction = predictorPredictions[trainingDataIndex];
allPredictions.Add(Transformer.BoolToDouble(prediction));
}
double mode = ModeFinder.FindMode(allPredictions);
double averagePrediction = allPredictions.Average();
// Now that we have the mode, realValue, and average of all predictions, we can calculate variance and bias
double varianceForThisDataPoint = 0;
double diffOfRealValueAndAveragePrediction = (realValue - averagePrediction);
double biasForThisDataPoint = diffOfRealValueAndAveragePrediction * diffOfRealValueAndAveragePrediction;
foreach (var prediction in allPredictions)
{
var diffForModeAndPrediction = prediction - mode;
varianceForThisDataPoint += diffForModeAndPrediction * diffForModeAndPrediction;
}
varianceForThisDataPoint = varianceForThisDataPoint / allPredictions.Count;
// Accumulate
variance += varianceForThisDataPoint;
bias += biasForThisDataPoint;
}
variance = variance / trainingData.Values.Count;
bias = bias / trainingData.Values.Count;
return(variance);
}
public static double CalculateBiasAndVariance(ParserResults trainingData, List <List <DecisionTreeLevel> > listOfTreesToRunTestOn, out double bias)
{
double variance = 0;
bias = 0;
// Calculate biar and variance for all trees
foreach (var trainingDataValue in trainingData.Values)
{
double realValue = Transformer.BoolToDouble(trainingDataValue.Output);
List <double> allPredictions = new List <double>(listOfTreesToRunTestOn.Count);
foreach (var bagger in listOfTreesToRunTestOn)
{
bool prediction = DecisionTreeScorer.CalculatePrediction(bagger, trainingDataValue);
allPredictions.Add(Transformer.BoolToDouble(prediction));
}
double mode = ModeFinder.FindMode(allPredictions);
double averagePrediction = allPredictions.Average();
// Now that we have the mode, realValue, and average of all predictions, we can calculate variance and bias
double varianceForThisDataPoint = 0;
double diffOfRealValueAndAveragePrediction = (realValue - averagePrediction);
double biasForThisDataPoint = diffOfRealValueAndAveragePrediction * diffOfRealValueAndAveragePrediction;
foreach (var prediction in allPredictions)
{
var diffForModeAndPrediction = prediction - mode;
varianceForThisDataPoint += diffForModeAndPrediction * diffForModeAndPrediction;
}
varianceForThisDataPoint = varianceForThisDataPoint / allPredictions.Count;
// Accumulate
variance += varianceForThisDataPoint;
bias += biasForThisDataPoint;
}
variance = variance / trainingData.Values.Count;
bias = bias / trainingData.Values.Count;
return(variance);
}
