public void ProcessInput(string line)
{
string[] items;
int i = TrainingData.Count;
TrainingData.Add(i, new List <int>(64));
items = line.Split(',');
for (int j = 0; j < 64; j++)
{
TrainingData[i].Add(int.Parse(items[j]));
}
ExpectedResults.Add(int.Parse(items[64]));
//Console.WriteLine(line);
}
public void Learn_Clicked(object sender, EventArgs args)
{
Task.Factory.StartNew(() =>
{
var bow = CreateBow();
foreach (var image in Images)
{
TrainingData.Add(GetData(image, bow));
}
var kernel = new Polynomial();
var teacher = new MulticlassSupportVectorLearning <IKernel>()
{
Kernel = kernel,
Learner = (param) => new SequentialMinimalOptimization <IKernel>()
{
Kernel = kernel
}
};
var svm = teacher.Learn(TrainingData.ToArray(), Tags.ToArray());
var error = new ZeroOneLoss(Tags.ToArray()).Loss(svm.Decide(TrainingData.ToArray()));
Error.Dispatcher.Invoke(() => Error.Text = error.ToString());
//var kernel = new Polynomial(16, 5);
//var complexity = CalculateComplexity(kernel);
//var ml = new MulticlassSupportVectorLearning<IKernel>()
//{
// Learner = (param) => new SequentialMinimalOptimization<IKernel>()
// {
// Complexity = complexity,
// Kernel = kernel
// }
//};
//machine = ml.Learn(TrainingData.ToArray(), Tags.ToArray());
//var result = machine.Decide(TrainingData.ToArray());
//var error = new ZeroOneLoss(Tags.ToArray())
//{
// Mean = true
//}.Loss(result);
//Error.Dispatcher.Invoke(() => Error.Text = error.ToString());
});
}
/// <summary>
/// Generates both training set consisting of feasible and infeasible examples
/// </summary>
public void GenerateTrainingData()
{
Feasibles = GenerateFeasibleExamples(GlobalVariables.FeasibleExamplesCount);
// Fill training data
TrainingData = Feasibles.AddColumnWithValues(1.0);
// Fill output List
var tempArr = new double[TrainingData.Count];
Output.AddRange(tempArr.Select(x => 1));
_dal.TrainingFeasibleExamples = Feasibles.ToArray();
GaussianMixtureModel gmm = new GaussianMixtureModel(GlobalVariables.Components);
gmm = new GaussianMixtureModel(GlobalVariables.Components)
{
Initializations = 100,
MaxIterations = 10000,
ParallelOptions = new System.Threading.Tasks.ParallelOptions()
{
MaxDegreeOfParallelism = 1
},
Tolerance = 10E-11,
Options = new Accord.Statistics.Distributions.Fitting.NormalOptions()
{
Regularization = double.Epsilon
}
};
// Estimate the Gaussian Mixture
gmm.Learn(_dal.TrainingFeasibleExamples);
var iterations = gmm.Iterations;
var distribution = gmm.ToMixtureDistribution();
// Get minimal probability of probability density function from distribution (percentile 0)
var minimalProbability = _dal.TrainingFeasibleExamples
.Select(item => distribution.ProbabilityDensityFunction(item))
.Min();
// Rescale data range for infeasible example creation
NewBoundries = BoundryRescaler.Rescale(Feasibles);
// Generate infeasible examples
var infeasibles = new List <double[]>();
while (infeasibles.Count < GlobalVariables.InfeasibleExamplesCount)
{
// Generate points within new boundry
var x = GenerateLimitedInputs(GlobalVariables.Dimensions, NewBoundries);
// Calculate probability density function value for given input
var probability = distribution.ProbabilityDensityFunction(x);
// Check if the value is smaller than smallest probability of all feasible examples
if (probability > minimalProbability)
{
continue;
}
infeasibles.Add(x);
TrainingData.Add(x.ExtendArrayWithValue(0.0));
Output.Add(0);
}
_dal.TrainingInfeasibleExamples = infeasibles.ToArray();
_dal.TrainingData = TrainingData.ToArray();
}
