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KrisLibrary
1.0.0
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A model of a probability distribution consisting of k gaussians. More...
#include <GaussianMixtureModel.h>
Public Member Functions | |
| GaussianMixtureModel (int k, int d) | |
| GaussianMixtureModel (const GaussianMixtureModel &) | |
| int | NumDims () const |
| void | Resize (int k, int d) |
| void | SetLinearTransform (const GaussianMixtureModel &gmm, const Matrix &A, const Vector &b) |
| bool | TrainEM (const std::vector< Vector > &examples, Real &tol, int maxIters, int verbose=0) |
| Solves the max likelihood model from training data. More... | |
| bool | TrainDiagonalEM (const std::vector< Vector > &examples, Real &tol, int maxIters, int verbose=0) |
| Real | LogLikelihood (const std::vector< Vector > &data) |
| Computes the log likelihood of the data. | |
| int | PickGaussian () const |
| Real | Probability (const Vector &x) const |
| void | Generate (Vector &x) const |
| void | GetMean (Vector &x) const |
| void | GetMode (Vector &x) const |
| void | GetCovariance (Matrix &cov) const |
| void | GetVariance (Vector &var) const |
| void | Resample (int k) |
| void | Cluster (const GaussianMixtureModel &gmm, int k) |
Public Attributes | |
| std::vector< Gaussian< Real > > | gaussians |
| std::vector< Real > | phi |
| phi[i] gives probability of choosing gaussian[i] | |
A model of a probability distribution consisting of k gaussians.
| bool Statistics::GaussianMixtureModel::TrainEM | ( | const std::vector< Vector > & | examples, |
| Real & | tol, | ||
| int | maxIters, | ||
| int | verbose = 0 |
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Solves the max likelihood model from training data.
Runs expectation-maximization, starting from current values of gaussians, given the training data, a tolerance, # of max iterations. Returns true if tolerance was reached, returns log-likelihood of data in tol.
References ArrayUtils::copy(), Statistics::ExpNormalize(), Math::GaussianTransform(), Math::GetElements(), Math::IsFinite(), Math::IsInf(), Math::Gaussian< T >::L, Math::Gaussian< T >::logProbability(), Math::Gaussian< T >::mu, Statistics::Normalize(), phi, Statistics::GaussianMixtureModelRaw::phi, Math::RandomAllocate(), Math::Gaussian< T >::setCovariance(), Math::Sign(), and Math::WeightedSample().
1.8.11