public void GaussianMixtureModelTest4()
{
// Suppose we have a weighted data set. Those are the input points:
double[][] points =
{
new double[] { 0 }, new double[] { 3 }, new double[] { 1 },
new double[] { 7 }, new double[] { 3 }, new double[] { 5 },
new double[] { 1 }, new double[] { 2 }, new double[] { -1 },
new double[] { 2 }, new double[] { 7 }, new double[] { 6 },
new double[] { 8 }, new double[] { 6 } // (14 points)
};
// And those are their respective unnormalized weights:
double[] weights = { 10, 1, 1, 2, 2, 1, 1, 1, 8, 1, 2, 5, 1, 1 }; // (14 weights)
// with weights
{
Accord.Math.Tools.SetupGenerator(0);
// If we need the GaussianMixtureModel functionality, we can
// use the estimated mixture to initialize a new model:
GaussianMixtureModel gmm = new GaussianMixtureModel(2);
gmm.Initializations = 1;
var options = new GaussianMixtureModelOptions();
options.Weights = weights;
options.ParallelOptions.MaxDegreeOfParallelism = 1;
gmm.Compute(points, options);
int a = 1;
int b = 0;
double tol = 1e-6;
if (!gmm.Gaussians[a].Mean[0].IsRelativelyEqual(6.41922, 1e-4))
{
var t = a;
a = b;
b = t;
}
Assert.AreEqual(6.4192285647145395, gmm.Gaussians[a].Mean[0], tol);
Assert.AreEqual(0.2888226129013588, gmm.Gaussians[b].Mean[0], tol);
Assert.AreEqual(0.32321638614859777, gmm.Gaussians[a].Proportion, tol);
Assert.AreEqual(0.67678361385140218, gmm.Gaussians[b].Proportion, tol);
Assert.AreEqual(1, gmm.Gaussians[0].Proportion + gmm.Gaussians[1].Proportion);
}
// with weights, new style
{
Accord.Math.Tools.SetupGenerator(0);
// If we need the GaussianMixtureModel functionality, we can
// use the estimated mixture to initialize a new model:
GaussianMixtureModel gmm = new GaussianMixtureModel(2);
gmm.Initializations = 1;
gmm.UseLogarithm = false;
gmm.ParallelOptions.MaxDegreeOfParallelism = 1;
gmm.Learn(points, weights);
int a = 1;
int b = 0;
double tol = 1e-6;
if (!gmm.Gaussians[a].Mean[0].IsRelativelyEqual(6.41922, 1e-4))
{
var t = a;
a = b;
b = t;
}
Assert.AreEqual(6.4192285647145395, gmm.Gaussians[a].Mean[0], tol);
Assert.AreEqual(0.2888226129013588, gmm.Gaussians[b].Mean[0], tol);
Assert.AreEqual(0.32321638614859777, gmm.Gaussians[a].Proportion, tol);
Assert.AreEqual(0.67678361385140218, gmm.Gaussians[b].Proportion, tol);
Assert.AreEqual(1, gmm.Gaussians[0].Proportion + gmm.Gaussians[1].Proportion);
}
// without weights
{
Accord.Math.Random.Generator.Seed = 0;
// If we need the GaussianMixtureModel functionality, we can
// use the estimated mixture to initialize a new model:
GaussianMixtureModel gmm = new GaussianMixtureModel(2);
gmm.Initializations = 1;
var options = new GaussianMixtureModelOptions();
options.ParallelOptions.MaxDegreeOfParallelism = 1;
gmm.Compute(points, options);
int a = 1;
int b = 0;
double tol = 1e-2;
if (!6.5149525060859848.IsRelativelyEqual(gmm.Gaussians[a].Mean[0], tol))
{
var t = a;
a = b;
b = t;
}
Assert.AreEqual(6.5149525060859848, gmm.Gaussians[a].Mean[0], tol);
Assert.AreEqual(1.4191977895308987, gmm.Gaussians[b].Mean[0], tol);
Assert.AreEqual(0.4195042394315267, gmm.Gaussians[a].Proportion, tol);
Assert.AreEqual(0.58049576056847307, gmm.Gaussians[b].Proportion, tol);
Assert.AreEqual(1, gmm.Gaussians[0].Proportion + gmm.Gaussians[1].Proportion, 1e-8);
}
}
public void GaussianMixtureModelTest4()
{
// Suppose we have a weighted data set. Those are the input points:
double[][] points =
{
new double[] { 0 }, new double[] { 3 }, new double[] { 1 },
new double[] { 7 }, new double[] { 3 }, new double[] { 5 },
new double[] { 1 }, new double[] { 2 }, new double[] { -1 },
new double[] { 2 }, new double[] { 7 }, new double[] { 6 },
new double[] { 8 }, new double[] { 6 } // (14 points)
};
// And those are their respective unnormalized weights:
double[] weights = { 10, 1, 1, 2, 2, 1, 1, 1, 8, 1, 2, 5, 1, 1 }; // (14 weights)
// with weights
{
Accord.Math.Tools.SetupGenerator(0);
// If we need the GaussianMixtureModel functionality, we can
// use the estimated mixture to initialize a new model:
GaussianMixtureModel gmm = new GaussianMixtureModel(2);
gmm.Initializations = 1;
var options = new GaussianMixtureModelOptions();
options.Weights = weights;
options.ParallelOptions.MaxDegreeOfParallelism = 1;
gmm.Compute(points, options);
int a = 1;
int b = 0;
double tol = 1e-6;
if (!gmm.Gaussians[a].Mean[0].IsRelativelyEqual(6.41922, 1e-4))
{
var t = a;
a = b;
b = t;
}
Assert.AreEqual(6.4192285647145395, gmm.Gaussians[a].Mean[0], tol);
Assert.AreEqual(0.2888226129013588, gmm.Gaussians[b].Mean[0], tol);
Assert.AreEqual(0.32321638614859777, gmm.Gaussians[a].Proportion, tol);
Assert.AreEqual(0.67678361385140218, gmm.Gaussians[b].Proportion, tol);
Assert.AreEqual(1, gmm.Gaussians[0].Proportion + gmm.Gaussians[1].Proportion);
}
// with weights, new style
{
Accord.Math.Tools.SetupGenerator(0);
// If we need the GaussianMixtureModel functionality, we can
// use the estimated mixture to initialize a new model:
GaussianMixtureModel gmm = new GaussianMixtureModel(2);
gmm.Initializations = 1;
gmm.UseLogarithm = false;
gmm.ParallelOptions.MaxDegreeOfParallelism = 1;
gmm.Learn(points, weights);
int a = 1;
int b = 0;
double tol = 1e-6;
if (!gmm.Gaussians[a].Mean[0].IsRelativelyEqual(6.41922, 1e-4))
{
var t = a;
a = b;
b = t;
}
Assert.AreEqual(6.4192285647145395, gmm.Gaussians[a].Mean[0], tol);
Assert.AreEqual(0.2888226129013588, gmm.Gaussians[b].Mean[0], tol);
Assert.AreEqual(0.32321638614859777, gmm.Gaussians[a].Proportion, tol);
Assert.AreEqual(0.67678361385140218, gmm.Gaussians[b].Proportion, tol);
Assert.AreEqual(1, gmm.Gaussians[0].Proportion + gmm.Gaussians[1].Proportion);
}
// without weights
{
Accord.Math.Random.Generator.Seed = 0;
// If we need the GaussianMixtureModel functionality, we can
// use the estimated mixture to initialize a new model:
GaussianMixtureModel gmm = new GaussianMixtureModel(2);
gmm.Initializations = 1;
var options = new GaussianMixtureModelOptions();
options.ParallelOptions.MaxDegreeOfParallelism = 1;
gmm.Compute(points, options);
int a = 1;
int b = 0;
double tol = 1e-2;
if (!6.5149525060859848.IsRelativelyEqual(gmm.Gaussians[a].Mean[0], tol))
{
var t = a;
a = b;
b = t;
}
Assert.AreEqual(6.5149525060859848, gmm.Gaussians[a].Mean[0], tol);
Assert.AreEqual(1.4191977895308987, gmm.Gaussians[b].Mean[0], tol);
Assert.AreEqual(0.4195042394315267, gmm.Gaussians[a].Proportion, tol);
Assert.AreEqual(0.58049576056847307, gmm.Gaussians[b].Proportion, tol);
Assert.AreEqual(1, gmm.Gaussians[0].Proportion + gmm.Gaussians[1].Proportion, 1e-8);
}
}
public void GenerateGMM()
{
double[][] mixture = new double[DeltaScores.Count][];
for (int i = 0; i < DeltaScores.Count; i++)
{
mixture[i] = new double[] { DeltaScores[i] };
}
gmm = new GaussianMixtureModel(2);
// If available, initialize with k-means
//if (kmeans != null) gmm.Initialize(kmeans);
// Compute the model
GaussianMixtureModelOptions gmmo = new GaussianMixtureModelOptions();
gmmo.Logarithm = true;
gmm.Compute(mixture, 0.000000000000000000000001);
//Find out who is the big median gaussian and small media gaussian
double[] meanTmp1 = gmm.Gaussians[0].Mean;
double[] meanTmp2 = gmm.Gaussians[1].Mean;
ProportionSmallGaussian = -1;
ProportionBigGaussian = -1;
if (meanTmp1[0] < meanTmp2[0])
{
GaussianSmallMean = gmm.Gaussians[0].ToDistribution();
ProportionSmallGaussian = gmm.Gaussians[0].Proportion;
GaussianBigMean = gmm.Gaussians[1].ToDistribution();
ProportionBigGaussian = gmm.Gaussians[1].Proportion;
}
else
{
GaussianSmallMean = gmm.Gaussians[1].ToDistribution();
ProportionSmallGaussian = gmm.Gaussians[1].Proportion;
GaussianBigMean = gmm.Gaussians[0].ToDistribution();
ProportionBigGaussian = gmm.Gaussians[0].Proportion;
}
//And now find GMM intersect and the cumulative intersect
double min = DeltaScores.Min();
double max = DeltaScores.Max();
List <double> smallMeanValues = new List <double>();
List <double> largeMeanValues = new List <double>();
//Determine intersect of the two Gaussians
double GMintersect = double.PositiveInfinity;
double GMdistance2 = double.PositiveInfinity;
double Cintersect = double.PositiveInfinity;
double Cdistance2 = double.PositiveInfinity;
for (double i = GaussianSmallMean.Median[0]; i <= GaussianBigMean.Median[0]; i += 0.02)
{
//Gaussian Intersect
//double GMredG = GaussianSmallMean.ProbabilityDensityFunction(i) * ProportionSmallGaussian;
//double GMblueG = GaussianBigMean.ProbabilityDensityFunction(i) * ProportionBigGaussian;
double GMredG = GaussianSmallMean.ProbabilityDensityFunction(i);
double GMblueG = GaussianBigMean.ProbabilityDensityFunction(i);
double GMdistanceThis = Math.Abs(GMredG - GMblueG);
if (GMdistanceThis < Math.Abs(GMdistance2))
{
GMdistance2 = GMdistanceThis;
GMintersect = i;
}
//Cumulative Intersect
//double CredG = GaussianSmallMean.ComplementaryDistributionFunction(i) * ProportionSmallGaussian;
//double CblueG = GaussianBigMean.DistributionFunction(i) * ProportionBigGaussian;
double CredG = GaussianSmallMean.ComplementaryDistributionFunction(i);
double CblueG = GaussianBigMean.DistributionFunction(i);
double CdistanceThis = Math.Abs(CredG - CblueG);
if (CdistanceThis < Math.Abs(Cdistance2))
{
Cdistance2 = CdistanceThis;
Cintersect = i;
}
}
Console.WriteLine("Gaussian Meeting point : " + GMintersect);
Console.WriteLine("Cumulative Meeting point : " + Cintersect);
GaussianIntersect = GMintersect;
CumulativeIntersect = Cintersect;
}
