ClassesClasses | | Operators

create_class_gmmcreate_class_gmmCreateClassGmmCreateClassGmm (Operator)

Name

create_class_gmmcreate_class_gmmCreateClassGmmCreateClassGmm — Create a Gaussian Mixture Model for classification

Signature

create_class_gmm( : : NumDim, NumClasses, NumCenters, CovarType, Preprocessing, NumComponents, RandSeed : GMMHandle)

Herror create_class_gmm(const Hlong NumDim, const Hlong NumClasses, const Hlong NumCenters, const char* CovarType, const char* Preprocessing, const Hlong NumComponents, const Hlong RandSeed, Hlong* GMMHandle)

Herror T_create_class_gmm(const Htuple NumDim, const Htuple NumClasses, const Htuple NumCenters, const Htuple CovarType, const Htuple Preprocessing, const Htuple NumComponents, const Htuple RandSeed, Htuple* GMMHandle)

void CreateClassGmm(const HTuple& NumDim, const HTuple& NumClasses, const HTuple& NumCenters, const HTuple& CovarType, const HTuple& Preprocessing, const HTuple& NumComponents, const HTuple& RandSeed, HTuple* GMMHandle)

void HClassGmm::HClassGmm(Hlong NumDim, Hlong NumClasses, const HTuple& NumCenters, const HString& CovarType, const HString& Preprocessing, Hlong NumComponents, Hlong RandSeed)

void HClassGmm::HClassGmm(Hlong NumDim, Hlong NumClasses, Hlong NumCenters, const HString& CovarType, const HString& Preprocessing, Hlong NumComponents, Hlong RandSeed)

void HClassGmm::HClassGmm(Hlong NumDim, Hlong NumClasses, Hlong NumCenters, const char* CovarType, const char* Preprocessing, Hlong NumComponents, Hlong RandSeed)

void HClassGmm::CreateClassGmm(Hlong NumDim, Hlong NumClasses, const HTuple& NumCenters, const HString& CovarType, const HString& Preprocessing, Hlong NumComponents, Hlong RandSeed)

void HClassGmm::CreateClassGmm(Hlong NumDim, Hlong NumClasses, Hlong NumCenters, const HString& CovarType, const HString& Preprocessing, Hlong NumComponents, Hlong RandSeed)

void HClassGmm::CreateClassGmm(Hlong NumDim, Hlong NumClasses, Hlong NumCenters, const char* CovarType, const char* Preprocessing, Hlong NumComponents, Hlong RandSeed)

static void HOperatorSet.CreateClassGmm(HTuple numDim, HTuple numClasses, HTuple numCenters, HTuple covarType, HTuple preprocessing, HTuple numComponents, HTuple randSeed, out HTuple GMMHandle)

public HClassGmm(int numDim, int numClasses, HTuple numCenters, string covarType, string preprocessing, int numComponents, int randSeed)

public HClassGmm(int numDim, int numClasses, int numCenters, string covarType, string preprocessing, int numComponents, int randSeed)

void HClassGmm.CreateClassGmm(int numDim, int numClasses, HTuple numCenters, string covarType, string preprocessing, int numComponents, int randSeed)

void HClassGmm.CreateClassGmm(int numDim, int numClasses, int numCenters, string covarType, string preprocessing, int numComponents, int randSeed)

Description

create_class_gmmcreate_class_gmmCreateClassGmmCreateClassGmmCreateClassGmm creates a Gaussian Mixture Model (GMM) for classification. NumDimNumDimNumDimNumDimnumDim specifies the number of dimensions of the feature space, NumClassesNumClassesNumClassesNumClassesnumClasses specifies the number of classes. A GMM consists of NumCentersNumCentersNumCentersNumCentersnumCenters Gaussian centers per class. NumCentersNumCentersNumCentersNumCentersnumCenters can not only be the exact number of centers to be used, but, depending on the number of parameters, can specify upper and lower bounds for the number of centers:

exactly one parameter:

The parameter determines the exact number of centers to be used for all classes.

exactly two parameters:

The first parameter determines the mimimum number of centers, the second determines the maximum number of centers for all classes.

exactly parameters:

Alternatingly every first parameter determines the minimum number of centers per class and every second parameters determines the maximum number of centers per class.

When upper and lower bounds are specified, the optimum number of centers will be determined with the help of the Mimimum Message Length Criterion (MML). In general, we recommend to start the training with (too) many centers as maximum and the expected number of centers as minimum.

Each center is described by the parameters center , covariance matrix , and mixing coefficient . These parameters are calculated from the training data by means of the Expectation Maximization (EM) algorithm. A GMM can approximate an arbitrary probability density, provided that enough centers are being used. The covariance matrices have the dimensions NumDimNumDimNumDimNumDimnumDim x NumDimNumDimNumDimNumDimnumDim (NumComponentsNumComponentsNumComponentsNumComponentsnumComponents x NumComponentsNumComponentsNumComponentsNumComponentsnumComponents if preprocessing is used) and are symmetric. Further constraints can be given by CovarTypeCovarTypeCovarTypeCovarTypecovarType:

For CovarTypeCovarTypeCovarTypeCovarTypecovarType = 'spherical'"spherical""spherical""spherical""spherical", is a scalar multiple of the identity matrix . The center density function p(x|j) is

For CovarTypeCovarTypeCovarTypeCovarTypecovarType = 'diag'"diag""diag""diag""diag", is a diagonal matrix . The center density function p(x|j) is

For CovarTypeCovarTypeCovarTypeCovarTypecovarType = 'full'"full""full""full""full", is a positive definite matrix. The center density function p(x|j) is

The complexity of the calculations increases from CovarTypeCovarTypeCovarTypeCovarTypecovarType = 'spherical'"spherical""spherical""spherical""spherical" over CovarTypeCovarTypeCovarTypeCovarTypecovarType = 'diag'"diag""diag""diag""diag" to CovarTypeCovarTypeCovarTypeCovarTypecovarType = 'full'"full""full""full""full". At the same time the flexibility of the centers increases. In general, 'spherical'"spherical""spherical""spherical""spherical" therefore needs higher values for NumCentersNumCentersNumCentersNumCentersnumCenters than 'full'"full""full""full""full".

The procedure to use GMM is as follows: First, a GMM is created by create_class_gmmcreate_class_gmmCreateClassGmmCreateClassGmmCreateClassGmm. Then, training vectors are added by add_sample_class_gmmadd_sample_class_gmmAddSampleClassGmmAddSampleClassGmmAddSampleClassGmm, afterwards they can be written to disk with write_samples_class_gmmwrite_samples_class_gmmWriteSamplesClassGmmWriteSamplesClassGmmWriteSamplesClassGmm. With train_class_gmmtrain_class_gmmTrainClassGmmTrainClassGmmTrainClassGmm the classifier center parameters (defined above) are determined. Furthermore, they can be saved with write_class_gmmwrite_class_gmmWriteClassGmmWriteClassGmmWriteClassGmm for later classifications.

From the mixing probabilities and the center density function p(x|j), the probability density function p(x) can be calculated by:

The probability density function p(x) can be evaluated with evaluate_class_gmmevaluate_class_gmmEvaluateClassGmmEvaluateClassGmmEvaluateClassGmm for a feature vector x. classify_class_gmmclassify_class_gmmClassifyClassGmmClassifyClassGmmClassifyClassGmm sorts the p(x) and therefore discovers the most probable class of the feature vector.

The parameters PreprocessingPreprocessingPreprocessingPreprocessingpreprocessing and NumComponentsNumComponentsNumComponentsNumComponentsnumComponents can be used to preprocess the training data and reduce its dimensions. These parameters are explained in the description of the operator create_class_mlpcreate_class_mlpCreateClassMlpCreateClassMlpCreateClassMlp.

create_class_gmmcreate_class_gmmCreateClassGmmCreateClassGmmCreateClassGmm initializes the coordinates of the centers with random numbers. To ensure that the results of training the classifier with train_class_gmmtrain_class_gmmTrainClassGmmTrainClassGmmTrainClassGmm are reproducible, the seed value of the random number generator is passed in RandSeedRandSeedRandSeedRandSeedrandSeed.

Execution Information

This operator returns a handle. Note that the state of an instance of this handle type may be changed by specific operators even though the handle is used as an input parameter by those operators.

Parameters

NumDimNumDimNumDimNumDimnumDim (input_control)  integer HTupleHTupleHtuple (integer) (int / long) (Hlong) (Hlong)

Number of dimensions of the feature space.

Default value: 3

Suggested values: 1, 2, 3, 4, 5, 8, 10, 15, 20, 30, 40, 50, 60, 70, 80, 90, 100

Restriction: NumDim >= 1

NumClassesNumClassesNumClassesNumClassesnumClasses (input_control)  integer HTupleHTupleHtuple (integer) (int / long) (Hlong) (Hlong)

Number of classes of the GMM.

Default value: 5

Suggested values: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10

Restriction: NumClasses >= 1

NumCentersNumCentersNumCentersNumCentersnumCenters (input_control)  integer(-array) HTupleHTupleHtuple (integer) (int / long) (Hlong) (Hlong)

Number of centers per class.

Default value: 1

Suggested values: 1, 2, 3, 4, 5, 8, 10, 15, 20, 30

Restriction: NumClasses >= 1

CovarTypeCovarTypeCovarTypeCovarTypecovarType (input_control)  string HTupleHTupleHtuple (string) (string) (HString) (char*)

Type of the covariance matrices.

Default value: 'spherical' "spherical" "spherical" "spherical" "spherical"

List of values: 'diag'"diag""diag""diag""diag", 'full'"full""full""full""full", 'spherical'"spherical""spherical""spherical""spherical"

PreprocessingPreprocessingPreprocessingPreprocessingpreprocessing (input_control)  string HTupleHTupleHtuple (string) (string) (HString) (char*)

Type of preprocessing used to transform the feature vectors.

Default value: 'normalization' "normalization" "normalization" "normalization" "normalization"

List of values: 'canonical_variates'"canonical_variates""canonical_variates""canonical_variates""canonical_variates", 'none'"none""none""none""none", 'normalization'"normalization""normalization""normalization""normalization", 'principal_components'"principal_components""principal_components""principal_components""principal_components"

NumComponentsNumComponentsNumComponentsNumComponentsnumComponents (input_control)  integer HTupleHTupleHtuple (integer) (int / long) (Hlong) (Hlong)

Preprocessing parameter: Number of transformed features (ignored for PreprocessingPreprocessingPreprocessingPreprocessingpreprocessing = 'none'"none""none""none""none" and PreprocessingPreprocessingPreprocessingPreprocessingpreprocessing = 'normalization'"normalization""normalization""normalization""normalization").

Default value: 10

Suggested values: 1, 2, 3, 4, 5, 8, 10, 15, 20, 30, 40, 50, 60, 70, 80, 90, 100

Restriction: NumComponents >= 1

RandSeedRandSeedRandSeedRandSeedrandSeed (input_control)  integer HTupleHTupleHtuple (integer) (int / long) (Hlong) (Hlong)

Seed value of the random number generator that is used to initialize the GMM with random values.

Default value: 42

GMMHandleGMMHandleGMMHandleGMMHandleGMMHandle (output_control)  class_gmm HClassGmm, HTupleHTupleHtuple (integer) (IntPtr) (Hlong) (Hlong)

GMM handle.

Example (HDevelop)

* Classification with Gaussian Mixture Models
create_class_gmm (NumDim , NumClasses, [1,5], 'full', 'none',\
                  NumComponents, 42, GMMHandle)
* Add the training data
for J := 0 to NumData-1 by 1
    * Features := [...]
    * ClassID := [...]
    add_sample_class_gmm (GMMHandle, Features, ClassID, Randomize)
endfor
* Train the GMM
train_class_gmm (GMMHandle, 100, 0.001, 'training', 0.0001, Centers, Iter)
* Classify unknown data in 'Features'
classify_class_gmm (GMMHandle, Features, 1, ID, Prob, Density, KSigmaProb)
clear_class_gmm (GMMHandle)

Result

If the parameters are valid, the operator create_class_gmmcreate_class_gmmCreateClassGmmCreateClassGmmCreateClassGmm returns the value 2 (H_MSG_TRUE). If necessary an exception is raised.

Possible Successors

add_sample_class_gmmadd_sample_class_gmmAddSampleClassGmmAddSampleClassGmmAddSampleClassGmm, add_samples_image_class_gmmadd_samples_image_class_gmmAddSamplesImageClassGmmAddSamplesImageClassGmmAddSamplesImageClassGmm

Alternatives

create_class_mlpcreate_class_mlpCreateClassMlpCreateClassMlpCreateClassMlp, create_class_svmcreate_class_svmCreateClassSvmCreateClassSvmCreateClassSvm

See also

clear_class_gmmclear_class_gmmClearClassGmmClearClassGmmClearClassGmm, train_class_gmmtrain_class_gmmTrainClassGmmTrainClassGmmTrainClassGmm, classify_class_gmmclassify_class_gmmClassifyClassGmmClassifyClassGmmClassifyClassGmm, evaluate_class_gmmevaluate_class_gmmEvaluateClassGmmEvaluateClassGmmEvaluateClassGmm, classify_image_class_gmmclassify_image_class_gmmClassifyImageClassGmmClassifyImageClassGmmClassifyImageClassGmm

References

Christopher M. Bishop: “Neural Networks for Pattern Recognition”; Oxford University Press, Oxford; 1995.
Mario A.T. Figueiredo: “Unsupervised Learning of Finite Mixture Models”; IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 24, No. 3; March 2002.

Module

Foundation


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