public void GaussianMixtureModelExample()
{
// Test Samples
double[][] samples =
{
new double[] { 0, 1 },
new double[] { 1, 2 },
new double[] { 1, 1 },
new double[] { 0, 7 },
new double[] { 1, 1 },
new double[] { 6, 2 },
new double[] { 6, 5 },
new double[] { 5, 1 },
new double[] { 7, 1 },
new double[] { 5, 1 }
};
double[] sample = samples[0];
// Create a new Gaussian mixture with 2 components
var gmm = new GaussianMixtureModel(components: 2);
// Compute the model (estimate)
var clusters = gmm.Learn(samples);
double error = gmm.LogLikelihood;
// Classify a single sample
int c0 = clusters.Decide(samples[0]);
int c1 = clusters.Decide(samples[1]);
int c7 = clusters.Decide(samples[7]);
int c8 = clusters.Decide(samples[8]);
Assert.AreEqual(c0, c1);
Assert.AreEqual(c7, c8);
Assert.AreNotEqual(c0, c8);
// Extract the multivariate Normal distribution from it
MultivariateMixture <MultivariateNormalDistribution> mixture =
gmm.ToMixtureDistribution();
Assert.AreEqual(2, mixture.Dimension);
Assert.AreEqual(2, mixture.Components.Length);
Assert.AreEqual(2, mixture.Coefficients.Length);
}
public void GaussianMixtureModelExample()
{
// Test Samples
double[][] samples =
{
new double[] { 0, 1 },
new double[] { 1, 2 },
new double[] { 1, 1 },
new double[] { 0, 7 },
new double[] { 1, 1 },
new double[] { 6, 2 },
new double[] { 6, 5 },
new double[] { 5, 1 },
new double[] { 7, 1 },
new double[] { 5, 1 }
};
double[] sample = samples[0];
// Create a new Gaussian mixture with 2 components
var gmm = new GaussianMixtureModel(components: 2);
// Compute the model (estimate)
double error = gmm.Compute(samples);
// Classify a single sample
int c0 = gmm.Gaussians.Nearest(samples[0]);
int c1 = gmm.Gaussians.Nearest(samples[1]);
int c7 = gmm.Gaussians.Nearest(samples[7]);
int c8 = gmm.Gaussians.Nearest(samples[8]);
Assert.AreEqual(c0, c1);
Assert.AreEqual(c7, c8);
Assert.AreNotEqual(c0, c8);
// Extract the multivariate Normal distribution from it
MultivariateMixture<MultivariateNormalDistribution> mixture =
gmm.ToMixtureDistribution();
Assert.AreEqual(2, mixture.Dimension);
Assert.AreEqual(2, mixture.Components.Length);
Assert.AreEqual(2, mixture.Coefficients.Length);
}
/// <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();
}