public void GaussianMixtureModelTest7()
{
double[][] values =
{
new double[] { 0 },
new double[] { 1 },
new double[] { 2 },
new double[] { 3 },
new double[] { 4 },
new double[] { 5 },
new double[] { 6 },
new double[] { 7 },
new double[] { 8 },
new double[] { 9 },
new double[] { 10 },
new double[] { 11 },
new double[] { 12 }
};
double[] weights =
{
1, 3, 5, 4, 3, 5, 10, 17, 12, 6, 3, 1, 0
};
GaussianMixtureModel gmm = new GaussianMixtureModel(2);
gmm.Compute(values, new GaussianMixtureModelOptions()
{
Weights = weights
});
int[] classifications = gmm.Gaussians.Nearest(values);
}
public void GaussianMixtureModelTest5()
{
Accord.Math.Tools.SetupGenerator(0);
MemoryStream stream = new MemoryStream(Resources.CircleWithWeights);
ExcelReader reader = new ExcelReader(stream, xlsx: false);
DataTable table = reader.GetWorksheet("Sheet1");
double[,] matrix = table.ToMatrix();
double[][] points = matrix.Submatrix(null, 0, 1).ToArray();
double[] weights = matrix.GetColumn(2);
GaussianMixtureModel gmm = new GaussianMixtureModel(2);
gmm.Compute(points, new GaussianMixtureModelOptions()
{
Weights = weights
});
Assert.AreEqual(-0.010550720353814949, gmm.Gaussians[0].Mean[0]);
Assert.AreEqual(0.40799698773355553, gmm.Gaussians[0].Mean[1]);
Assert.AreEqual(0.011896812071918696, gmm.Gaussians[1].Mean[0]);
Assert.AreEqual(-0.40400708592859663, gmm.Gaussians[1].Mean[1]);
}
public void GaussianMixtureModelConstructorTest()
{
Accord.Math.Tools.SetupGenerator(0);
// 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 Model with 2 components
GaussianMixtureModel gmm = new GaussianMixtureModel(2);
// Compute the model (estimate)
gmm.Compute(samples, 0.0001);
// Classify a single sample
int c = gmm.Gaussians.Nearest(sample);
Assert.AreEqual(2, gmm.Gaussians.Count);
int first = 0;
for (int i = 0; i < samples.Length; i++)
{
sample = samples[i];
c = gmm.Gaussians.Nearest(sample);
if (i == 0)
{
first = c;
}
if (i < 5)
{
Assert.AreEqual(c, first);
}
else
{
Assert.AreNotEqual(c, first);
}
}
}
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 unormalized 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.Compute(points, new GaussianMixtureModelOptions()
{
Weights = weights
});
Assert.AreEqual(6.420790676635443, gmm.Gaussians[0].Mean[0]);
Assert.AreEqual(0.290536871335858, gmm.Gaussians[1].Mean[0]);
Assert.AreEqual(0.32294476897888613, gmm.Gaussians[0].Proportion);
Assert.AreEqual(0.67705523102111387, gmm.Gaussians[1].Proportion);
}
// without 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.Compute(points);
Assert.AreEqual(6.5149525060859865, gmm.Gaussians[0].Mean[0]);
Assert.AreEqual(1.4191977895308987, gmm.Gaussians[1].Mean[0]);
Assert.AreEqual(0.42235760973845654, gmm.Gaussians[0].Proportion);
Assert.AreEqual(0.57764239026154351, gmm.Gaussians[1].Proportion);
}
}
public void GaussianMixtureModelConstructorTest()
{
Accord.Math.Tools.SetupGenerator(0);
// 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 Model with 2 components
GaussianMixtureModel gmm = new GaussianMixtureModel(2);
// Compute the model (estimate)
gmm.Compute(samples, 0.0001);
// Classify a single sample
int c = gmm.Gaussians.Nearest(sample);
Assert.AreEqual(2, gmm.Gaussians.Count);
int first = 0;
for (int i = 0; i < samples.Length; i++)
{
sample = samples[i];
c = gmm.Gaussians.Nearest(sample);
if (i == 0)
first = c;
if (i < 5)
{
Assert.AreEqual(c, first);
}
else
{
Assert.AreNotEqual(c, first);
}
}
}
public void GaussianMixtureModelTest2()
{
Accord.Math.Tools.SetupGenerator(0);
int height = 16;
int width = 16;
var gmm = new GaussianMixtureModel(3);
// gmm.Regularization = 0;
Assert.AreEqual(3, gmm.Gaussians.Count);
Assert.IsNull(gmm.Gaussians[0].Covariance);
Assert.IsNull(gmm.Gaussians[0].Mean);
double[][][] A = new double[3][][];
A[0] = new double[height][];
A[1] = new double[height][];
A[2] = new double[height][];
for (int j = 0; j < height; j++)
{
A[0][j] = new double[width];
A[1][j] = new double[width];
A[2][j] = new double[width];
for (int k = 0; k < width; k++)
{
A[0][j][k] = 102;
A[1][j][k] = 57;
A[2][j][k] = 200;
}
}
double[][] B = Matrix.Stack(A);
bool thrown = false;
try
{
var result = gmm.Compute(B);
}
catch (NonPositiveDefiniteMatrixException)
{
thrown = true;
}
Assert.IsTrue(thrown);
}
public void GaussianMixtureModelConstructorTest()
{
Accord.Math.Tools.SetupGenerator(0);
// 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 Model with 2 components
GaussianMixtureModel gmm = new GaussianMixtureModel(2);
// Compute the model (estimate)
double ll = gmm.Compute(samples, 0.0001);
Assert.AreEqual(-35.930732550698494, ll, 1e-10);
Assert.AreEqual(2, gmm.Gaussians.Count);
Assert.IsTrue(gmm.Gaussians.Means[0].IsEqual(new[] { 5.8, 2.0 }, 1e-3));
Assert.IsTrue(gmm.Gaussians.Means[1].IsEqual(new[] { 0.6, 2.4 }, 1e-3));
int[] c = samples.Apply(gmm.Clusters.Decide);
for (int i = 0; i < samples.Length; i++)
{
double[] responses;
int e;
responses = gmm.Gaussians.Probabilities(samples[i], out e);
int a = responses.ArgMax();
Assert.AreEqual(a, e);
Assert.AreEqual(c[i], (i < 5) ? 1 : 0);
}
}
public void GaussianMixtureModelTest3()
{
Accord.Math.Tools.SetupGenerator(0);
var gmm = new GaussianMixtureModel(3);
Assert.AreEqual(3, gmm.Gaussians.Count);
Assert.IsNull(gmm.Gaussians[0].Covariance);
Assert.IsNull(gmm.Gaussians[0].Mean);
double[][] B = Matrix.Random(56, 12).ToArray();
double result = gmm.Compute(B);
}
internal void Train()
{
logLike = new double[maxK]; // Log-likelihood estimate for a given model and training set
rissanen = new double[maxK]; // Rissanen estimate for a given model and training set
mdl = new double[maxK]; // Minimum description length for a given model and training set
kVar = new bool[maxK];
for (int i = 0; i < maxK; ++i)
{
// Step 1: Make a GMM with K subclasses
gmm = new GaussianMixtureModel(kVals[i]);
// Step 2: fit the gmm to the projection data
logLike[i] = gmm.Compute(currentProjection, 1e-3, 1.0);
// Step 3: perform a classification to detect spurious classification
kVar[i] = (kVals[i] == gmm.Classify(currentProjection).Distinct().ToArray().Length);
// Step 4: Calculate the MDL for this K
double L = (double)(kVals[i] * 3 - 1);
rissanen[i] = 0.5 * L * Math.Log(currentProjection.Length);
mdl[i] = -logLike[i] + rissanen[i];
}
// Which value of K supported the MDL:
int ind = Array.IndexOf(mdl, mdl.Min());
// Find the value of K that supports the lowest mdl and is verified
K = kVals[ind];
while (!kVar[ind])
{
K = kVals[ind];
++ind;
}
// Recreate the gmm with the trained value of K
gmm = new GaussianMixtureModel(K);
double LL = gmm.Compute(currentProjection, 1e-3, 1.0);
// Is this a functional classifier?
if (gmm != null)
{
trained = true;
}
// Set the STD ellipsoid
gmm.SetPValue(pValue);
}
public void GaussianMixtureModelConstructorTest()
{
Accord.Math.Tools.SetupGenerator(0);
// 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 Model with 2 components
GaussianMixtureModel gmm = new GaussianMixtureModel(2);
// Compute the model (estimate)
double ll = gmm.Compute(samples, 0.0001);
Assert.AreEqual(-35.930732550698494, ll, 1e-10);
Assert.AreEqual(2, gmm.Gaussians.Count);
Assert.IsTrue(gmm.Gaussians.Means[0].IsEqual(new[] { 5.8, 2.0 }, 1e-3));
Assert.IsTrue(gmm.Gaussians.Means[1].IsEqual(new[] { 0.6, 2.4 }, 1e-3));
int[] c = samples.Apply(gmm.Clusters.Nearest);
for (int i = 0; i < samples.Length; i++)
{
double[] responses;
int e = gmm.Gaussians.Nearest(samples[i], out responses);
int a = responses.ArgMax();
Assert.AreEqual(a, e);
Assert.AreEqual(c[i], (i < 5) ? 1 : 0);
}
}
public void GaussianMixtureModelTest5()
{
Accord.Math.Tools.SetupGenerator(0);
string path = Path.Combine(TestContext.CurrentContext.TestDirectory, "Resources", "CircleWithWeights.xls");
ExcelReader reader = new ExcelReader(path);
DataTable table = reader.GetWorksheet("Sheet1");
double[,] matrix = table.ToMatrix();
double[][] points = matrix.Submatrix(null, 0, 1).ToJagged();
double[] weights = matrix.GetColumn(2);
GaussianMixtureModel gmm = new GaussianMixtureModel(2);
gmm.Initializations = 1;
gmm.ParallelOptions.MaxDegreeOfParallelism = 1;
gmm.Compute(points, new GaussianMixtureModelOptions()
{
Weights = weights
});
int a = 0;
int b = 1;
double tol = 1e-3;
if ((-0.407859903454185).IsRelativelyEqual(gmm.Gaussians[1].Mean[0], tol))
{
a = 1;
b = 0;
}
Assert.AreEqual(-0.407859903454185, gmm.Gaussians[a].Mean[0], tol);
Assert.AreEqual(-0.053911705279706859, gmm.Gaussians[a].Mean[1], tol);
Assert.AreEqual(0.39380877640250328, gmm.Gaussians[b].Mean[0], tol);
Assert.AreEqual(0.047186154880776772, gmm.Gaussians[b].Mean[1], tol);
Assert.AreEqual(1, gmm.Gaussians[0].Proportion + gmm.Gaussians[1].Proportion, 1e-15);
Assert.IsFalse(gmm.Gaussians[0].Mean.HasNaN());
Assert.IsFalse(gmm.Gaussians[1].Mean.HasNaN());
}
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);
}
private void btnCompute_Click(object sender, EventArgs e)
{
// Create a new Gaussian Mixture Model
GaussianMixtureModel gmm = new GaussianMixtureModel(k);
// If available, initialize with k-means
if (kmeans != null)
{
gmm.Initialize(kmeans);
}
// Compute the model
gmm.Compute(mixture);
// Classify all instances in mixture data
int[] classifications = gmm.Classify(mixture);
// Draw the classifications
updateGraph(classifications);
}
private void DoFitGMM()
{
// Create a new Gaussian Mixture Model
var gmm = new GaussianMixtureModel(this.k);
// If available, initialize with k-means
if (this.kmeans != null)
{
gmm.Initialize(this.kmeans);
}
// Compute the model
gmm.Compute(this.mixture);
// Classify all instances in mixture data
var classifications = gmm.Gaussians.Nearest(this.mixture);
// Draw the classifications
this.UpdateGraph(classifications);
}
public void buildModel()
{
if (inputMatrix == null)
{
getMatrix();
}
switch (cType)
{
case clusterType.KMEANS:
KMeans kmeans = new KMeans(k);
kmeans.Compute(inputMatrix, precision);
clusterCollection = kmeans.Clusters;
model = kmeans;
break;
case clusterType.BINARY:
BinarySplit bSplit = new BinarySplit(k);
bSplit.Compute(inputMatrix, precision);
clusterCollection = bSplit.Clusters;
model = bSplit;
//Console.WriteLine("BinarySplit");
break;
case clusterType.GAUSSIANMIXTURE:
GaussianMixtureModel gModel = new GaussianMixtureModel(k);
gModel.Compute(inputMatrix, precision);
clusterCollection = gModel.Gaussians;
model = gModel;
break;
default:
break;
}
lbl = new List <string>();
for (int i = 0; i < k; i++)
{
lbl.Add(i.ToString());
}
}
public void GaussianMixtureModelTest3()
{
Accord.Math.Tools.SetupGenerator(0);
var gmm = new GaussianMixtureModel(3);
Assert.AreEqual(3, gmm.Gaussians.Count);
Assert.IsNull(gmm.Gaussians[0].Covariance);
Assert.IsNull(gmm.Gaussians[0].Mean);
double[][] B = Matrix.Random(56, 12).ToJagged();
Accord.Math.Tools.SetupGenerator(0);
gmm.Options.Robust = true;
var result = gmm.Compute(B, new GaussianMixtureModelOptions()
{
NormalOptions = new NormalOptions
{
Robust = true
}
});
}
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 GaussianMixtureModelTest7()
{
double[][] values =
{
new double[] {0},
new double[] {1},
new double[] {2},
new double[] {3},
new double[] {4},
new double[] {5},
new double[] {6},
new double[] {7},
new double[] {8},
new double[] {9},
new double[] {10},
new double[] {11},
new double[] {12}
};
double[] weights =
{
1, 3, 5, 4, 3, 5, 10, 17, 12, 6, 3, 1, 0
};
GaussianMixtureModel gmm = new GaussianMixtureModel(2);
gmm.Compute(values, new GaussianMixtureModelOptions()
{
Weights = weights
});
int[] classifications = gmm.Gaussians.Nearest(values);
}
public void GaussianMixtureModelTest3()
{
Accord.Math.Tools.SetupGenerator(0);
var gmm = new GaussianMixtureModel(3);
Assert.AreEqual(3, gmm.Gaussians.Count);
Assert.IsNull(gmm.Gaussians[0].Covariance);
Assert.IsNull(gmm.Gaussians[0].Mean);
double[][] B = Matrix.Random(56, 12).ToJagged();
Accord.Math.Tools.SetupGenerator(0);
gmm.Options.Robust = true;
var result = gmm.Compute(B, new GaussianMixtureModelOptions()
{
NormalOptions = new NormalOptions
{
Robust = true
}
});
}
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);
}
public CalPAC(UISettings ui, string grouping, int n, List <double> AreaPool)
{
try {
//Stopwatch sw = new Stopwatch();
//sw.Start();
Grouping = grouping; N = n;
AreaPool.Sort(); // for weight sampling
//double px2um2=ui.PixelScale*ui.PixelScale/ui.ResizeRatio/ui.ResizeRatio;
var CountDis = new List <double[]>();
double AreaSum = 0.0d;
for (int i = 0; i < AreaPool.Count; i++)
{
double[] data = new double[1];
data[0] = Math.Log10(AreaPool[i] + 1); //data[1] = (double)(mFull[i]/100.0d);
CountDis.Add(data);
AreaSum += AreaPool[i];
}
///1-Dist fitted with 1-Population
GaussianMixtureModel gmm1 = new GaussianMixtureModel(1);
gmm1Fit = gmm1.Compute(CountDis.ToArray());
p1Mean = gmm1.Gaussians[0].Mean[0];
p1Covariance = gmm1.Gaussians[0].Covariance[0, 0];
p1Proportion = gmm1.Gaussians[0].Proportion;
///1-Dist fitted with 2-Populations / take the larger proportion
//GaussianMixtureModel gmm1=new GaussianMixtureModel(2);
//gmm1Fit=gmm1.Compute(countDis.ToArray());
//int m=(gmm1.Gaussians[0].Proportion>gmm1.Gaussians[1].Proportion) ? 0 : 1;
//p1Mean=gmm1.Gaussians[m].Mean[0];
//p1Covariance=gmm1.Gaussians[m].Covariance[0, 0];
//p1Proportion=gmm1.Gaussians[m].Proportion;
///List<double[]> areaDis = WeightExpansion(AreaPool, divider=10.0d);
//var AreaDis = new List<double[]>();
//double divider = 10.0d;
//for (int i = 0; i<AreaPool.Count; i++) {
// double[] data = new double[1];
// data[0]=Math.Log10(AreaPool[i]+1);
// for (int r = 0; r<Math.Round(AreaPool[i]/divider); r++) { AreaDis.Add(data); }
//}
///List<double[]> areaDis = WeightSampling(AreaPool, AreaSum, Math.Max((int)Math.Round(AreaSum/5000.0d), 1)); // sample every divided by 5000 evenly or every one if not too large
var AreaDis = new List <double[]>();
int askip = (int)Math.Round(Math.Max(AreaSum / 5000.0d, 1));
for (int sn = 0; sn < (int)Math.Floor(AreaSum); sn += askip) // sample number
{
double s = 0.0d;
for (int bi = 0; bi < AreaPool.Count; bi++)
{
s += AreaPool[bi];
if (s > sn)
{
double[] data = new double[1];
data[0] = Math.Log10(AreaPool[bi] + 1);
AreaDis.Add(data); break;
}
}
}
///2-Dist fitted with 2-Populations /
GaussianMixtureModel gmm2 = new GaussianMixtureModel(2);
gmm2Fit = gmm2.Compute(AreaDis.ToArray());
int a = (gmm2.Gaussians[0].Mean[0] < gmm2.Gaussians[1].Mean[0]) ? 0 : 1;
p2Mean = gmm2.Gaussians[a].Mean[0];
p2Covariance = gmm2.Gaussians[a].Covariance[0, 0];
p2Proportion = gmm2.Gaussians[a].Proportion;
p3Mean = gmm2.Gaussians[1 - a].Mean[0];
p3Covariance = gmm2.Gaussians[1 - a].Covariance[0, 0];
p3Proportion = gmm2.Gaussians[1 - a].Proportion;
DistCount = Bucketize(CountDis);
DistArea = Bucketize(AreaDis);
//sw.Stop();
//Debug.Write(sw.Elapsed);
} catch { throw new Exception("Error Occured During Airspace Categorization"); }
}
public void GaussianMixtureModelTest5()
{
Accord.Math.Tools.SetupGenerator(0);
MemoryStream stream = new MemoryStream(Resources.CircleWithWeights);
ExcelReader reader = new ExcelReader(stream, xlsx: false);
DataTable table = reader.GetWorksheet("Sheet1");
double[,] matrix = table.ToMatrix();
double[][] points = matrix.Submatrix(null, 0, 1).ToArray();
double[] weights = matrix.GetColumn(2);
GaussianMixtureModel gmm = new GaussianMixtureModel(2);
gmm.Compute(points, new GaussianMixtureModelOptions()
{
Weights = weights
});
int a = 0;
int b = 1;
if ((-0.010550720353814949).IsRelativelyEqual(gmm.Gaussians[1].Mean[0], 1e-4))
{
a = 1;
b = 0;
}
Assert.AreEqual(-0.010550720353814949, gmm.Gaussians[a].Mean[0], 1e-4);
Assert.AreEqual(0.40799698773355553, gmm.Gaussians[a].Mean[1], 1e-3);
Assert.AreEqual(0.011896812071918696, gmm.Gaussians[b].Mean[0], 1e-4);
Assert.AreEqual(-0.40400708592859663, gmm.Gaussians[b].Mean[1], 1e-4);
Assert.AreEqual(1, gmm.Gaussians[0].Proportion + gmm.Gaussians[1].Proportion, 1e-15);
Assert.IsFalse(gmm.Gaussians[0].Mean.HasNaN());
Assert.IsFalse(gmm.Gaussians[1].Mean.HasNaN());
}
/// <summary>
/// Estimates Gaussian distributions from the data.
/// </summary>
///
private void btnCompute_Click(object sender, EventArgs e)
{
// Create a new Gaussian Mixture Model
GaussianMixtureModel gmm = new GaussianMixtureModel(k);
// If available, initialize with k-means
if (kmeans != null) gmm.Initialize(kmeans);
// Compute the model
gmm.Compute(mixture);
// Classify all instances in mixture data
int[] classifications = gmm.Gaussians.Nearest(mixture);
// Draw the classifications
updateGraph(classifications);
}
internal void Train()
{
logLike = new double[maxK]; // Log-likelihood estimate for a given model and training set
rissanen = new double[maxK]; // Rissanen estimate for a given model and training set
mdl = new double[maxK]; // Minimum description length for a given model and training set
kVar = new bool[maxK];
for (int i = 0; i < maxK; ++i)
{
// Step 1: Make a GMM with K subclasses
gmm = new GaussianMixtureModel(kVals[i]);
// Step 2: fit the gmm to the projection data
logLike[i] = gmm.Compute(currentProjection, 1e-3, 1.0);
// Step 3: perform a classification to detect spurious classification
kVar[i] = (kVals[i] == gmm.Classify(currentProjection).Distinct().ToArray().Length);
// Step 4: Calculate the MDL for this K
double L = (double)(kVals[i] * 3 - 1);
rissanen[i] = 0.5 * L * Math.Log(currentProjection.Length);
mdl[i] = -logLike[i] + rissanen[i];
}
// Which value of K supported the MDL:
int ind = Array.IndexOf(mdl, mdl.Min());
// Find the value of K that supports the lowest mdl and is verified
K = kVals[ind];
while (!kVar[ind])
{
K = kVals[ind];
++ind;
}
// Recreate the gmm with the trained value of K
gmm = new GaussianMixtureModel(K);
double LL = gmm.Compute(currentProjection, 1e-3, 1.0);
// Is this a functional classifier?
if (gmm != null)
trained = true;
// Set the STD ellipsoid
gmm.SetPValue(pValue);
}
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;
}
// Method to rate mode goodness and then sort (use first mode as base)
private void ComputeModeGoodnessRatio()
{
// Use no more than Max_N (which is 5 I believe)
int n = lstCentroids.Count; // find as many Gaussians as modes.
// Don't do too many Gaussians because it will take a long time and perform poorly
if (n > GCount)
{
n = GCount;
}
// Multiple dist map and diff map if necessary
//DateTime startTime = DateTime.Now;
ComputeRealMap();
//DateTime stopTime = DateTime.Now;
//TimeSpan duration = stopTime - startTime;
//double RunTime = duration.TotalSeconds;
//System.Windows.Forms.MessageBox.Show("ComputeRealMap Run time " + RunTime + " seconds!");
// Allocate memory for output matrices
Array arrModes = new double[n];
Array arrProportions = new double[n];
Array arrMUs = new double[n, 2];
Array arrSigmaXSigmaY = new double[n];
Array junkModes = new double[n];
Array junkMUs = new double[n, 2];
Array junkSigmaXSigmaY = new double[n];
#region Using MATLAB for GMM
////startTime = DateTime.Now;
//double[,] arrSamplesR;
//double[,] arrSamplesI;
//// Generate samples from map to get ready to perform mixed Gaussian fitting
//PrepareSamples(out arrSamplesR, out arrSamplesI);
////stopTime = DateTime.Now;
////duration = stopTime - startTime;
////RunTime = duration.TotalSeconds;
////System.Windows.Forms.MessageBox.Show("PrepareSamples Run time " + RunTime + " seconds!");
//// Perform mixed Gaussian fitting and get parameters
////startTime = DateTime.Now;
//// Using MATLAB to do GMM
//GaussianFitting(n, arrSamplesR, arrSamplesI, ref arrModes, ref arrMUs, ref arrSigmaXSigmaY, ref junkModes, ref junkMUs, ref junkSigmaXSigmaY);
////stopTime = DateTime.Now;
////duration = stopTime - startTime;
////RunTime = duration.TotalSeconds;
////System.Windows.Forms.MessageBox.Show("GaussianFitting Run time " + RunTime + " seconds!");
//// Debug
//// curRequest.SetLog("\nMATLAB GMM results\n");
#endregion
#region Using C# for GMM
// Prepare samples into the format needed
double[][] arrSamples;
// Generate samples from map to get ready to perform mixed Gaussian fitting
PrepareSamplesAccord(out arrSamples);
// Using Accord.net library to do GMM
GaussianMixtureModel gmm = new GaussianMixtureModel(n);
List<MapMode> lstGaussians = new List<MapMode>(); // Results stored in a list of MapModes for sorting
// If Accord.net library fails, try it again up to 3 times
for (int ii = 0; ii < ProjectConstants.MaxAccordRun; ii++)
{
try
{
gmm.Compute(arrSamples, 10);
//// Debug code
//// Print out means and covariances and proportions so we can plot
//Console.WriteLine("Accord.net run number " + ii);
//for (int i = 0; i < n; i++)
//{
// Console.WriteLine("Gaussian number " + i);
// // Means
// Console.Write("Mean: (" + gmm.Gaussians[i].Mean[0] + " " + gmm.Gaussians[i].Mean[1] + ") ");
// // Area
// Console.Write("Covariance Matrix [" + gmm.Gaussians[i].Covariance[0, 0] + " "
// + gmm.Gaussians[i].Covariance[0, 1] + "; "
// + gmm.Gaussians[i].Covariance[1, 0] + " "
// + gmm.Gaussians[i].Covariance[1, 1] + "] ");
// Console.Write("Proportion: " + gmm.Gaussians[i].Proportion + "\n");
//}
// Getting arrays ready
for (int i = 0; i < n; i++)
{
// Means
arrMUs.SetValue(gmm.Gaussians[i].Mean[0], i, 0);
arrMUs.SetValue(gmm.Gaussians[i].Mean[1], i, 1);
// Area
DenseMatrix m = new DenseMatrix(gmm.Gaussians[i].Covariance);
System.Numerics.Complex[] d = m.Evd().EigenValues().ToArray();
double SigmaXSigmaY = Math.Sqrt(d[0].Real) * Math.Sqrt(d[1].Real);
arrSigmaXSigmaY.SetValue(SigmaXSigmaY, i);
// Modes
arrModes.SetValue(gmm.Gaussians[i].GetDistribution().ProbabilityDensityFunction(gmm.Gaussians[i].Mean), i);
// Scales
arrProportions.SetValue(gmm.Gaussians[i].Proportion, i);
}
// Debug
// curRequest.SetLog("\nAccord GMM results\n");
////Debug code
//for (int i = 0; i < arrMUs.Length / 2; i++)
//{
// curRequest.SetLog(arrMUs.GetValue(i, 0) + "," + arrMUs.GetValue(i, 1) + " ");
//}
//curRequest.SetLog("\n");
//for (int i = 0; i < arrSigmaXSigmaY.Length; i++)
//{
// curRequest.SetLog(arrSigmaXSigmaY.GetValue(i) + " ");
//}
//curRequest.SetLog("\n");
//for (int i=0; i< arrModes.Length; i++)
//{
// curRequest.SetLog(arrModes.GetValue(i) + " ");
//}
//curRequest.SetLog("\n");
//startTime = DateTime.Now;
// Match centroids to Gaussians
MatchCentroidsToGaussians(arrMUs, lstGaussians);
//stopTime = DateTime.Now;
//duration = stopTime - startTime;
//RunTime = duration.TotalSeconds;
//System.Windows.Forms.MessageBox.Show("MatchCentroidsToGaussians Run time " + RunTime + " seconds!");
ii = ProjectConstants.MaxAccordRun;
}
catch
{
Console.WriteLine("Something went wrong with Accord.net library.");
// System.Windows.Forms.MessageBox.Show("Something went wrong with Accord.net library.");
}
}
#endregion
//startTime = DateTime.Now;
// Evaluate Goodness Rating
EvaluateGoodnessRatings(arrModes, arrSigmaXSigmaY, lstGaussians, arrProportions);
//stopTime = DateTime.Now;
//duration = stopTime - startTime;
//RunTime = duration.TotalSeconds;
//System.Windows.Forms.MessageBox.Show("EvaluateGoodnessRatings Run time " + RunTime + " seconds!");
//// Debug code
//// Print out all Gaussians found
//Console.WriteLine("Goodness Ratio:");
//for (int ii = 0; ii < lstGaussians.Count; ii++)
//{
// Console.Write("Gaussian number " + ii + ": " + lstGaussians[ii].GoodnessRating + ", ");
//}
//Console.Write("\n");
// Find top N modes
lstGaussians.Sort();
lstGaussians.Reverse();
////Debug
//for (int i = 0; i < n; i++)
//{
// curRequest.SetLog("Mode (x,y): " + lstGaussians[i].Mode.X + "," + lstGaussians[i].Mode.Y + " MGR:" + lstGaussians[i].GoodnessRating + "\n");
//}
lstGaussians.RemoveRange(N, lstGaussians.Count - N);
// Rebuild lstCentroids
lstCentroids.Clear();
foreach (MapMode mm in lstGaussians)
{
lstCentroids.Add(mm.Mode);
}
}
public void buildModel()
{
if (inputMatrix == null) getMatrix();
switch (cType)
{
case clusterType.KMEANS:
KMeans kmeans = new KMeans(k);
kmeans.Compute(inputMatrix, precision);
clusterCollection = kmeans.Clusters;
model = kmeans;
break;
case clusterType.BINARY:
BinarySplit bSplit = new BinarySplit(k);
bSplit.Compute(inputMatrix, precision);
clusterCollection = bSplit.Clusters;
model = bSplit;
//Console.WriteLine("BinarySplit");
break;
case clusterType.GAUSSIANMIXTURE:
GaussianMixtureModel gModel = new GaussianMixtureModel(k);
gModel.Compute(inputMatrix, precision);
clusterCollection = gModel.Gaussians;
model = gModel;
break;
default:
break;
}
lbl = new List<string>();
for (int i = 0; i < k; i++)
{
lbl.Add(i.ToString());
}
}
public void GaussianMixtureModelTest5()
{
Accord.Math.Tools.SetupGenerator(0);
MemoryStream stream = new MemoryStream(Resources.CircleWithWeights);
ExcelReader reader = new ExcelReader(stream, xlsx: false);
DataTable table = reader.GetWorksheet("Sheet1");
double[,] matrix = table.ToMatrix();
double[][] points = matrix.Submatrix(null, 0, 1).ToJagged();
double[] weights = matrix.GetColumn(2);
GaussianMixtureModel gmm = new GaussianMixtureModel(2);
gmm.Initializations = 1;
gmm.Compute(points, new GaussianMixtureModelOptions()
{
Weights = weights
});
int a = 0;
int b = 1;
double tol = 1e-3;
if ((-0.407859903454185).IsRelativelyEqual(gmm.Gaussians[1].Mean[0], tol))
{
a = 1;
b = 0;
}
Assert.AreEqual(-0.407859903454185, gmm.Gaussians[a].Mean[0], tol);
Assert.AreEqual(-0.053911705279706859, gmm.Gaussians[a].Mean[1], tol);
Assert.AreEqual(0.39380877640250328, gmm.Gaussians[b].Mean[0], tol);
Assert.AreEqual(0.047186154880776772, gmm.Gaussians[b].Mean[1], tol);
Assert.AreEqual(1, gmm.Gaussians[0].Proportion + gmm.Gaussians[1].Proportion, 1e-15);
Assert.IsFalse(gmm.Gaussians[0].Mean.HasNaN());
Assert.IsFalse(gmm.Gaussians[1].Mean.HasNaN());
}
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.Compute(points, new GaussianMixtureModelOptions()
{
Weights = weights
});
int a = 0;
int b = 1;
if (gmm.Gaussians[1].Mean[0].IsRelativelyEqual(6.420790676635443, 1e-10))
{
a = 1;
b = 0;
}
Assert.AreEqual(6.420790676635443, gmm.Gaussians[a].Mean[0], 1e-10);
Assert.AreEqual(0.290536871335858, gmm.Gaussians[b].Mean[0], 1e-10);
Assert.AreEqual(0.32294476897888613, gmm.Gaussians[a].Proportion, 1e-6);
Assert.AreEqual(0.67705523102111387, gmm.Gaussians[b].Proportion, 1e-6);
Assert.AreEqual(1, gmm.Gaussians[0].Proportion + gmm.Gaussians[1].Proportion);
}
// without 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.Compute(points);
int a = 0;
int b = 1;
if (6.5149525060859865.IsRelativelyEqual(gmm.Gaussians[1].Mean[0], 1e-10))
{
a = 1;
b = 0;
}
Assert.AreEqual(6.5149525060859865, gmm.Gaussians[a].Mean[0], 1e-10);
Assert.AreEqual(1.4191977895308987, gmm.Gaussians[b].Mean[0], 1e-6);
Assert.AreEqual(0.42235760973845654, gmm.Gaussians[a].Proportion, 1e-6);
Assert.AreEqual(0.57764239026154351, gmm.Gaussians[b].Proportion, 1e-6);
Assert.AreEqual(1, gmm.Gaussians[0].Proportion + gmm.Gaussians[1].Proportion);
}
}
public void LargeSampleTest()
{
Accord.Math.Tools.SetupGenerator(0);
Func<double> r = () => Tools.Random.NextDouble();
Func<double> b = () => Tools.Random.NextDouble() > 0.3 ? 1 : -1;
// Test Samples
int thousand = 1000;
int million = thousand * thousand;
double[][] samples = new double[5 * million][];
Func<int, double[]> expand = (int label) =>
{
var d = new double[10];
d[label] = 1;
return d;
};
for (int j = 0; j < samples.Length; j++)
{
if (j % 10 > 8)
samples[j] = new double[] { r() }.Concatenate(expand(Tools.Random.Next() % 10));
else samples[j] = new double[] { r() * j }.Concatenate(expand(j % 10));
}
// Create a new Gaussian Mixture Model with 2 components
GaussianMixtureModel gmm = new GaussianMixtureModel(5);
// Compute the model
double result = gmm.Compute(samples, new GaussianMixtureModelOptions()
{
NormalOptions = new NormalOptions()
{
Regularization = 1e-5,
}
});
for (int i = 0; i < samples.Length; i++)
{
var sample = samples[i];
int c = gmm.Gaussians.Nearest(sample);
Assert.AreEqual(c, (i % 10) >= 5 ? 1 : 0);
}
}
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 GaussianMixtureModelTest2()
{
Accord.Math.Random.Generator.Seed = 0;
int height = 16;
int width = 16;
var gmm = new GaussianMixtureModel(3);
// gmm.Regularization = 0;
Assert.AreEqual(3, gmm.Gaussians.Count);
Assert.IsNull(gmm.Gaussians[0].Covariance);
Assert.IsNull(gmm.Gaussians[0].Mean);
double[][][] A = new double[3][][];
A[0] = new double[height][];
A[1] = new double[height][];
A[2] = new double[height][];
for (int j = 0; j < height; j++)
{
A[0][j] = new double[width];
A[1][j] = new double[width];
A[2][j] = new double[width];
for (int k = 0; k < width; k++)
{
A[0][j][k] = 102;
A[1][j][k] = 57;
A[2][j][k] = 200;
}
}
double[][] B = Matrix.Stack(A);
bool thrown = false;
try
{
var result = gmm.Compute(B);
}
catch (NonPositiveDefiniteMatrixException)
{
thrown = true;
}
Assert.IsTrue(thrown);
}
public void GaussianMixtureModelTest3()
{
Accord.Math.Tools.SetupGenerator(0);
var gmm = new GaussianMixtureModel(3);
Assert.AreEqual(3, gmm.Gaussians.Count);
Assert.IsNull(gmm.Gaussians[0].Covariance);
Assert.IsNull(gmm.Gaussians[0].Mean);
double[][] B = Matrix.Random(56, 12).ToArray();
var result = gmm.Compute(B);
}