private static double RunBPMGeneral(BPMDataModel model, bool addBias, Vector[] testSet, int[] testResults)
{
int correctCount = 0;
VectorGaussian[] posteriorWeights = _bpm.Train(model.GetInputs(), model.GetClasses());
string actualWeights = posteriorWeights[1].ToString();
int breakLocation = actualWeights.IndexOf("\r", StringComparison.Ordinal);
actualWeights = actualWeights.Substring(0, breakLocation);
if (!_onlyWriteAggregateResults && _writeGaussians)
{
_writer.WriteLine("Weights= " + actualWeights);
}
Discrete[] predictions = _bpm.Test(testSet);
int i = 0;
foreach (Discrete prediction in predictions)
{
if (FindMaxValPosition(prediction.GetProbs().ToArray()) == testResults[i])
{
correctCount++;
}
i++;
}
double accuracy = ((double)correctCount / predictions.Length) * 100;
//double logEvidence = bpm.GetLogEvidence();
return(accuracy);
}
public static BPMDataModel CreateNoisyModel(BPMDataModel model, double epsilon)
{
Random generator = new Random();
BPMDataModel noisyModel = new BPMDataModel(model.GetInputs().Length, model.GetNoOfFeatures());
List <double> ranges = model.GetRanges();
double[] values = new double[model.GetInputs()[0].Count];
List <Vector> x = new List <Vector>();
foreach (Vector vector in model.GetInputs())
{
for (int i = 0; i < ranges.Count; i++)
{
double randomVal = (0.5 - generator.NextDouble());
double newVal = CreateLaplacian(epsilon, 0, vector[i], ranges[i], randomVal);
values[i] = newVal;
}
x.Add(Vector.FromArray(values));
}
noisyModel.SetAllVectorFeatures(x.ToArray());
noisyModel.SetClasses(model.GetClasses());
//noisyModel.CalculateFeatureLimits();
//noisyModel.ScaleFeatures();
return(noisyModel);
}
public static BPMDataModel CreateNoisyModel(BPMDataModel model, double epsilon)
{
Random generator = new Random();
BPMDataModel noisyModel = new BPMDataModel(model.GetInputs().Length, model.GetNoOfFeatures());
List<double> ranges = model.GetRanges();
double[] values = new double[model.GetInputs()[0].Count];
List<Vector> x = new List<Vector>();
foreach (Vector vector in model.GetInputs())
{
for (int i = 0; i < ranges.Count; i++)
{
double randomVal = (0.5 - generator.NextDouble());
double newVal = CreateLaplacian(epsilon, 0, vector[i], ranges[i], randomVal);
values[i] = newVal;
}
x.Add(Vector.FromArray(values));
}
noisyModel.SetAllVectorFeatures(x.ToArray());
noisyModel.SetClasses(model.GetClasses());
//noisyModel.CalculateFeatureLimits();
//noisyModel.ScaleFeatures();
return noisyModel;
}
private static double RunBPMGeneral(BPMDataModel model, bool addBias, Vector[] testSet, int[] testResults)
{
int correctCount = 0;
VectorGaussian[] posteriorWeights = _bpm.Train(model.GetInputs(), model.GetClasses());
string actualWeights = posteriorWeights[1].ToString();
int breakLocation = actualWeights.IndexOf("\r", StringComparison.Ordinal);
actualWeights = actualWeights.Substring(0, breakLocation);
if (!_onlyWriteAggregateResults && _writeGaussians) _writer.WriteLine("Weights= " + actualWeights);
Discrete[] predictions = _bpm.Test(testSet);
int i = 0;
foreach (Discrete prediction in predictions)
{
if (FindMaxValPosition(prediction.GetProbs().ToArray()) == testResults[i]) correctCount++;
i++;
}
double accuracy = ((double)correctCount / predictions.Length) * 100;
//double logEvidence = bpm.GetLogEvidence();
return accuracy;
}