GClasses
GClasses::GLinearRegressor Class Reference

A linear regression model. Let f be a feature vector of real values, and let l be a label vector of real values, then this model estimates l=Bf+e, where B is a matrix of real values, and e is a vector of real values. (In the Wikipedia article on linear regression, B is called "beta", and e is called "epsilon". The approach used by this model to compute beta and epsilon, however, is much more efficient than the approach currently described in that article.) More...

#include <GLinear.h>

Inheritance diagram for GClasses::GLinearRegressor:
GClasses::GSupervisedLearner GClasses::GTransducer

Public Member Functions

 GLinearRegressor ()
 
 GLinearRegressor (GDomNode *pNode, GLearnerLoader &ll)
 Load from a text-format. More...
 
virtual ~GLinearRegressor ()
 
void autoTune (GMatrix &features, GMatrix &labels)
 This model has no parameters to tune, so this method is a noop. More...
 
GMatrixbeta ()
 Returns the matrix that represents the linear transformation. More...
 
virtual void clear ()
 See the comment for GSupervisedLearner::clear. More...
 
double * epsilon ()
 Returns the vector that is added to the results after the linear transformation is applied. 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...
 
void refine (const GMatrix &features, const GMatrix &labels, double learningRate, size_t epochs, double learningRateDecayFactor)
 Performs on-line gradient descent to refine the model. More...
 
virtual GDomNodeserialize (GDom *pDoc) const
 Saves the model to a text file. (This doesn't save the short-term memory used for incremental learning, so if you're doing "incremental" learning, it will wake up with amnesia when you load it again.) 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 ()
 Performs unit tests for this class. Throws an exception if there is a failure. More...
 
- 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 trainInner (const GMatrix &features, const GMatrix &labels)
 See the comment for GSupervisedLearner::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

GMatrixm_pBeta
 
double * m_pEpsilon
 
- Protected Attributes inherited from GClasses::GSupervisedLearner
GRelationm_pRelFeatures
 
GRelationm_pRelLabels
 
- Protected Attributes inherited from GClasses::GTransducer
GRand m_rand
 

Detailed Description

A linear regression model. Let f be a feature vector of real values, and let l be a label vector of real values, then this model estimates l=Bf+e, where B is a matrix of real values, and e is a vector of real values. (In the Wikipedia article on linear regression, B is called "beta", and e is called "epsilon". The approach used by this model to compute beta and epsilon, however, is much more efficient than the approach currently described in that article.)

Constructor & Destructor Documentation

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

Load from a text-format.

virtual GClasses::GLinearRegressor::~GLinearRegressor ( )
virtual

Member Function Documentation

void GClasses::GLinearRegressor::autoTune ( GMatrix features,
GMatrix labels 
)

This model has no parameters to tune, so this method is a noop.

GMatrix* GClasses::GLinearRegressor::beta ( )
inline

Returns the matrix that represents the linear transformation.

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

See the comment for GTransducer::canImplicitlyHandleNominalLabels.

Reimplemented from GClasses::GTransducer.

virtual void GClasses::GLinearRegressor::clear ( )
virtual

See the comment for GSupervisedLearner::clear.

Implements GClasses::GSupervisedLearner.

double* GClasses::GLinearRegressor::epsilon ( )
inline

Returns the vector that is added to the results after the linear transformation is applied.

virtual void GClasses::GLinearRegressor::predict ( const double *  pIn,
double *  pOut 
)
virtual
virtual void GClasses::GLinearRegressor::predictDistribution ( const double *  pIn,
GPrediction pOut 
)
virtual
void GClasses::GLinearRegressor::refine ( const GMatrix features,
const GMatrix labels,
double  learningRate,
size_t  epochs,
double  learningRateDecayFactor 
)

Performs on-line gradient descent to refine the model.

virtual GDomNode* GClasses::GLinearRegressor::serialize ( GDom pDoc) const
virtual

Saves the model to a text file. (This doesn't save the short-term memory used for incremental learning, so if you're doing "incremental" learning, it will wake up with amnesia when you load it again.)

Implements GClasses::GSupervisedLearner.

static void GClasses::GLinearRegressor::test ( )
static

Performs unit tests for this class. Throws an exception if there is a failure.

virtual void GClasses::GLinearRegressor::trainInner ( const GMatrix features,
const GMatrix labels 
)
protectedvirtual

Member Data Documentation

GMatrix* GClasses::GLinearRegressor::m_pBeta
protected
double* GClasses::GLinearRegressor::m_pEpsilon
protected