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.Classify(sample);
Assert.AreEqual(2, gmm.Gaussians.Count);
for (int i = 0; i < samples.Length; i++)
{
sample = samples[i];
c = gmm.Classify(sample);
Assert.AreEqual(c, i >= 5 ? 1 : 0);
}
}
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);
}
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);
}
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.Classify(sample);
Assert.AreEqual(2, gmm.Gaussians.Count);
for (int i = 0; i < samples.Length; i++)
{
sample = samples[i];
c = gmm.Classify(sample);
Assert.AreEqual(c, i >= 5 ? 1 : 0);
}
}
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);
}
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);
}
internal void Classify()
{
classes = gmm.Classify(currentProjection);
}