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 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);
}