GClasses
GClasses::GGaussianProcess Class Reference

A Gaussian Process model. This class was implemented according to the specification in Algorithm 2.1 on page 19 of chapter 2 of http://www.gaussianprocesses.org/gpml/chapters/ by Carl Edward Rasmussen and Christopher K. I. Williams. More...

#include <GGaussianProcess.h>

Inheritance diagram for GClasses::GGaussianProcess:
GClasses::GSupervisedLearner GClasses::GTransducer

Public Member Functions

 GGaussianProcess ()
 General-purpose constructor. More...
 
 GGaussianProcess (GDomNode *pNode, GLearnerLoader &ll)
 Deserialization constructor. More...
 
virtual ~GGaussianProcess ()
 Destructor. More...
 
virtual void clear ()
 See the comment for GSupervisedLearner::clear. More...
 
virtual GDomNodeserialize (GDom *pDoc) const
 Marshal this object into a DOM, which can then be converted to a variety of serial formats. More...
 
void setKernel (GKernel *pKernel)
 Sets the kernel to use. Takes ownership of pKernel. (The default is the identity kernel, which just does linear regression, so you almost certainly will want to change the kernel before using this model.) More...
 
void setMaxSamples (size_t m)
 Sets the maximum number of samples to train with. If the training data contains more than 'm' samples, it will sub-sample the training data in order to train efficiently. The default is 350. More...
 
void setNoiseVariance (double v)
 Sets the noise variance term. (The default is 0.0.) More...
 
void setWeightsPriorVariance (double v)
 Sets the weight prior variance term. (The default is 1024.0.) More...
 
- Public Member Functions inherited from GClasses::GSupervisedLearner
 GSupervisedLearner ()
 General-purpose constructor. More...
 
 GSupervisedLearner (GDomNode *pNode, GLearnerLoader &ll)
 Deserialization constructor. More...
 
virtual ~GSupervisedLearner ()
 Destructor. More...
 
void basicTest (double minAccuracy1, double minAccuracy2, double deviation=1e-6, bool printAccuracy=false, double warnRange=0.035)
 This is a helper method used by the unit tests of several model learners. More...
 
virtual bool canGeneralize ()
 Returns true because fully supervised learners have an internal model that allows them to generalize previously unseen rows. More...
 
void confusion (GMatrix &features, GMatrix &labels, std::vector< GMatrix * > &stats)
 Generates a confusion matrix containing the total counts of the number of times each value was expected and predicted. (Rows represent target values, and columns represent predicted values.) stats should be an empty vector. This method will resize stats to the number of dimensions in the label vector. The caller is responsible to delete all of the matrices that it puts in this vector. For continuous labels, the value will be NULL. More...
 
virtual bool isFilter ()
 Returns false. More...
 
void precisionRecall (double *pOutPrecision, size_t nPrecisionSize, GMatrix &features, GMatrix &labels, size_t label, size_t nReps)
 label specifies which output to measure. (It should be 0 if there is only one label dimension.) The measurement will be performed "nReps" times and results averaged together nPrecisionSize specifies the number of points at which the function is sampled pOutPrecision should be an array big enough to hold nPrecisionSize elements for every possible label value. (If the attribute is continuous, it should just be big enough to hold nPrecisionSize elements.) If bLocal is true, it computes the local precision instead of the global precision. More...
 
const GRelationrelFeatures ()
 Returns a reference to the feature relation (meta-data about the input attributes). More...
 
const GRelationrelLabels ()
 Returns a reference to the label relation (meta-data about the output attributes). More...
 
double sumSquaredError (const GMatrix &features, const GMatrix &labels)
 Computes the sum-squared-error for predicting the labels from the features. For categorical labels, Hamming distance is used. More...
 
void train (const GMatrix &features, const GMatrix &labels)
 Call this method to train the model. More...
 
virtual double trainAndTest (const GMatrix &trainFeatures, const GMatrix &trainLabels, const GMatrix &testFeatures, const GMatrix &testLabels)
 Trains and tests this learner. Returns sum-squared-error. More...
 
- Public Member Functions inherited from GClasses::GTransducer
 GTransducer ()
 General-purpose constructor. More...
 
 GTransducer (const GTransducer &that)
 Copy-constructor. Throws an exception to prevent models from being copied by value. More...
 
virtual ~GTransducer ()
 
virtual bool canImplicitlyHandleContinuousFeatures ()
 Returns true iff this algorithm can implicitly handle continuous features. If it cannot, then the GDiscretize transform will be used to convert continuous features to nominal values before passing them to it. More...
 
virtual bool canImplicitlyHandleContinuousLabels ()
 Returns true iff this algorithm can implicitly handle continuous labels (a.k.a. regression). If it cannot, then the GDiscretize transform will be used during training to convert nominal labels to continuous values, and to convert nominal predictions back to continuous labels. More...
 
virtual bool canTrainIncrementally ()
 Returns false because semi-supervised learners cannot be trained incrementally. More...
 
double crossValidate (const GMatrix &features, const GMatrix &labels, size_t nFolds, RepValidateCallback pCB=NULL, size_t nRep=0, void *pThis=NULL)
 Perform n-fold cross validation on pData. Returns sum-squared error. Uses trainAndTest for each fold. pCB is an optional callback method for reporting intermediate stats. It can be NULL if you don't want intermediate reporting. nRep is just the rep number that will be passed to the callback. pThis is just a pointer that will be passed to the callback for you to use however you want. It doesn't affect this method. More...
 
GTransduceroperator= (const GTransducer &other)
 Throws an exception to prevent models from being copied by value. More...
 
GRandrand ()
 Returns a reference to the random number generator associated with this object. For example, you could use it to change the random seed, to make this algorithm behave differently. This might be important, for example, in an ensemble of learners. More...
 
double repValidate (const GMatrix &features, const GMatrix &labels, size_t reps, size_t nFolds, RepValidateCallback pCB=NULL, void *pThis=NULL)
 Perform cross validation "nReps" times and return the average score. pCB is an optional callback method for reporting intermediate stats It can be NULL if you don't want intermediate reporting. pThis is just a pointer that will be passed to the callback for you to use however you want. It doesn't affect this method. More...
 
virtual bool supportedFeatureRange (double *pOutMin, double *pOutMax)
 Returns true if this algorithm supports any feature value, or if it does not implicitly handle continuous features. If a limited range of continuous values is supported, returns false and sets pOutMin and pOutMax to specify the range. More...
 
virtual bool supportedLabelRange (double *pOutMin, double *pOutMax)
 Returns true if this algorithm supports any label value, or if it does not implicitly handle continuous labels. If a limited range of continuous values is supported, returns false and sets pOutMin and pOutMax to specify the range. More...
 
GMatrixtransduce (const GMatrix &features1, const GMatrix &labels1, const GMatrix &features2)
 Predicts a set of labels to correspond with features2, such that these labels will be consistent with the patterns exhibited by features1 and labels1. More...
 
void transductiveConfusionMatrix (const GMatrix &trainFeatures, const GMatrix &trainLabels, const GMatrix &testFeatures, const GMatrix &testLabels, std::vector< GMatrix * > &stats)
 Makes a confusion matrix for a transduction algorithm. More...
 

Static Public Member Functions

static void test ()
 
- Static Public Member Functions inherited from GClasses::GSupervisedLearner
static void test ()
 Runs some unit tests related to supervised learning. Throws an exception if any problems are found. More...
 

Protected Member Functions

virtual bool canImplicitlyHandleMissingFeatures ()
 See the comment for GTransducer::canImplicitlyHandleMissingFeatures. More...
 
virtual bool canImplicitlyHandleNominalFeatures ()
 See the comment for GTransducer::canImplicitlyHandleNominalFeatures. More...
 
virtual bool canImplicitlyHandleNominalLabels ()
 See the comment for GTransducer::canImplicitlyHandleNominalLabels. More...
 
virtual void predict (const double *pIn, double *pOut)
 See the comment for GSupervisedLearner::predict. More...
 
virtual void predictDistribution (const double *pIn, GPrediction *pOut)
 See the comment for GSupervisedLearner::predictDistribution. More...
 
virtual void trainInner (const GMatrix &features, const GMatrix &labels)
 See the comment for GSupervisedLearner::trainInner. More...
 
void trainInnerInner (const GMatrix &features, const GMatrix &labels)
 Called by trainInner. More...
 
- Protected Member Functions inherited from GClasses::GSupervisedLearner
GDomNodebaseDomNode (GDom *pDoc, const char *szClassName) const
 Child classes should use this in their implementation of serialize. More...
 
size_t precisionRecallContinuous (GPrediction *pOutput, double *pFunc, GMatrix &trainFeatures, GMatrix &trainLabels, GMatrix &testFeatures, GMatrix &testLabels, size_t label)
 This is a helper method used by precisionRecall. More...
 
size_t precisionRecallNominal (GPrediction *pOutput, double *pFunc, GMatrix &trainFeatures, GMatrix &trainLabels, GMatrix &testFeatures, GMatrix &testLabels, size_t label, int value)
 This is a helper method used by precisionRecall. More...
 
void setupFilters (const GMatrix &features, const GMatrix &labels)
 This method determines which data filters (normalize, discretize, and/or nominal-to-cat) are needed and trains them. More...
 
virtual GMatrixtransduceInner (const GMatrix &features1, const GMatrix &labels1, const GMatrix &features2)
 See GTransducer::transduce. More...
 

Protected Attributes

size_t m_maxSamples
 
double m_noiseVar
 
GMatrixm_pAlpha
 
GMatrixm_pBuf
 
GKernelm_pKernel
 
GMatrixm_pLInv
 
GMatrixm_pStoredFeatures
 
double m_weightsPriorVar
 
- Protected Attributes inherited from GClasses::GSupervisedLearner
GRelationm_pRelFeatures
 
GRelationm_pRelLabels
 
- Protected Attributes inherited from GClasses::GTransducer
GRand m_rand
 

Detailed Description

A Gaussian Process model. This class was implemented according to the specification in Algorithm 2.1 on page 19 of chapter 2 of http://www.gaussianprocesses.org/gpml/chapters/ by Carl Edward Rasmussen and Christopher K. I. Williams.

Constructor & Destructor Documentation

GClasses::GGaussianProcess::GGaussianProcess ( )

General-purpose constructor.

GClasses::GGaussianProcess::GGaussianProcess ( GDomNode pNode,
GLearnerLoader ll 
)

Deserialization constructor.

virtual GClasses::GGaussianProcess::~GGaussianProcess ( )
virtual

Destructor.

Member Function Documentation

virtual bool GClasses::GGaussianProcess::canImplicitlyHandleMissingFeatures ( )
inlineprotectedvirtual
virtual bool GClasses::GGaussianProcess::canImplicitlyHandleNominalFeatures ( )
inlineprotectedvirtual
virtual bool GClasses::GGaussianProcess::canImplicitlyHandleNominalLabels ( )
inlineprotectedvirtual

See the comment for GTransducer::canImplicitlyHandleNominalLabels.

Reimplemented from GClasses::GTransducer.

virtual void GClasses::GGaussianProcess::clear ( )
virtual

See the comment for GSupervisedLearner::clear.

Implements GClasses::GSupervisedLearner.

virtual void GClasses::GGaussianProcess::predict ( const double *  pIn,
double *  pOut 
)
protectedvirtual
virtual void GClasses::GGaussianProcess::predictDistribution ( const double *  pIn,
GPrediction pOut 
)
protectedvirtual
virtual GDomNode* GClasses::GGaussianProcess::serialize ( GDom pDoc) const
virtual

Marshal this object into a DOM, which can then be converted to a variety of serial formats.

Implements GClasses::GSupervisedLearner.

void GClasses::GGaussianProcess::setKernel ( GKernel pKernel)

Sets the kernel to use. Takes ownership of pKernel. (The default is the identity kernel, which just does linear regression, so you almost certainly will want to change the kernel before using this model.)

void GClasses::GGaussianProcess::setMaxSamples ( size_t  m)
inline

Sets the maximum number of samples to train with. If the training data contains more than 'm' samples, it will sub-sample the training data in order to train efficiently. The default is 350.

void GClasses::GGaussianProcess::setNoiseVariance ( double  v)
inline

Sets the noise variance term. (The default is 0.0.)

void GClasses::GGaussianProcess::setWeightsPriorVariance ( double  v)
inline

Sets the weight prior variance term. (The default is 1024.0.)

static void GClasses::GGaussianProcess::test ( )
static
virtual void GClasses::GGaussianProcess::trainInner ( const GMatrix features,
const GMatrix labels 
)
protectedvirtual
void GClasses::GGaussianProcess::trainInnerInner ( const GMatrix features,
const GMatrix labels 
)
protected

Called by trainInner.

Member Data Documentation

size_t GClasses::GGaussianProcess::m_maxSamples
protected
double GClasses::GGaussianProcess::m_noiseVar
protected
GMatrix* GClasses::GGaussianProcess::m_pAlpha
protected
GMatrix* GClasses::GGaussianProcess::m_pBuf
protected
GKernel* GClasses::GGaussianProcess::m_pKernel
protected
GMatrix* GClasses::GGaussianProcess::m_pLInv
protected
GMatrix* GClasses::GGaussianProcess::m_pStoredFeatures
protected
double GClasses::GGaussianProcess::m_weightsPriorVar
protected