GClasses
GClasses::GNaiveInstance Class Reference

This is an instance-based learner. Instead of finding the k-nearest neighbors of a feature vector, it finds the k-nearst neighbors in each dimension. That is, it finds n*k neighbors, considering each dimension independently. It then combines the label from all of these neighbors to make a prediction. Finding neighbors in this way makes it more robust to high-dimensional datasets. It tends to perform worse than k-nn in low-dimensional space, and better than k-nn in high-dimensional space. (It may be thought of as a cross between a k-nn instance learner and a Naive Bayes learner. It only supports continuous features and labels (so it is common to wrap it in a Categorize filter which will convert nominal features to a categorical distribution of continuous values). More...

#include <GNaiveInstance.h>

Inheritance diagram for GClasses::GNaiveInstance:
GClasses::GIncrementalLearner GClasses::GSupervisedLearner GClasses::GTransducer

Public Member Functions

 GNaiveInstance ()
 nNeighbors is the number of neighbors (in each dimension) that will contribute to the output value. More...
 
 GNaiveInstance (GDomNode *pNode, GLearnerLoader &ll)
 Deserializing constructor. More...
 
virtual ~GNaiveInstance ()
 
void autoTune (GMatrix &features, GMatrix &labels)
 Uses cross-validation to find a set of parameters that works well with the provided data. More...
 
virtual void clear ()
 See the comment for GSupervisedLearner::clear. More...
 
size_t neighbors ()
 Returns the number of neighbors. 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 GDomNodeserialize (GDom *pDoc) const
 Marshal this object into a DOM, which can then be converted to a variety of serial formats. More...
 
void setNeighbors (size_t k)
 Specify the number of neighbors to use. More...
 
virtual void trainIncremental (const double *pIn, const double *pOut)
 Incrementally train with a single instance. More...
 
virtual void trainSparse (GSparseMatrix &features, GMatrix &labels)
 See the comment for GIncrementalLearner::trainSparse. More...
 
- Public Member Functions inherited from GClasses::GIncrementalLearner
 GIncrementalLearner ()
 General-purpose constructor. More...
 
 GIncrementalLearner (GDomNode *pNode, GLearnerLoader &ll)
 Deserialization constructor. More...
 
virtual ~GIncrementalLearner ()
 Destructor. More...
 
void beginIncrementalLearning (const GRelation &featureRel, const GRelation &labelRel)
 You must call this method before you call trainIncremental. More...
 
virtual bool canTrainIncrementally ()
 Returns true. More...
 
virtual bool isFilter ()
 Only the GFilter class should return true to this method. 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...
 
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 canImplicitlyHandleMissingFeatures ()
 Returns true iff this algorithm supports missing feature values. If it cannot, then an imputation filter will be used to predict missing values before any feature-vectors are passed to the algorithm. 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 void beginIncrementalLearningInner (const GRelation &featureRel, const GRelation &labelRel)
 See the comment for GIncrementalLearner::beginIncrementalLearningInner. More...
 
virtual bool canImplicitlyHandleNominalFeatures ()
 See the comment for GTransducer::canImplicitlyHandleNominalFeatures. More...
 
virtual bool canImplicitlyHandleNominalLabels ()
 See the comment for GTransducer::canImplicitlyHandleNominalLabels. More...
 
void evalInput (size_t nInputDim, double dInput)
 
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

size_t m_nNeighbors
 
GNaiveInstanceAttr ** m_pAttrs
 
GHeapm_pHeap
 
double * m_pSumBuffer
 
double * m_pSumOfSquares
 
double * m_pValueSums
 
double * m_pWeightSums
 
- Protected Attributes inherited from GClasses::GSupervisedLearner
GRelationm_pRelFeatures
 
GRelationm_pRelLabels
 
- Protected Attributes inherited from GClasses::GTransducer
GRand m_rand
 

Detailed Description

This is an instance-based learner. Instead of finding the k-nearest neighbors of a feature vector, it finds the k-nearst neighbors in each dimension. That is, it finds n*k neighbors, considering each dimension independently. It then combines the label from all of these neighbors to make a prediction. Finding neighbors in this way makes it more robust to high-dimensional datasets. It tends to perform worse than k-nn in low-dimensional space, and better than k-nn in high-dimensional space. (It may be thought of as a cross between a k-nn instance learner and a Naive Bayes learner. It only supports continuous features and labels (so it is common to wrap it in a Categorize filter which will convert nominal features to a categorical distribution of continuous values).

Constructor & Destructor Documentation

GClasses::GNaiveInstance::GNaiveInstance ( )

nNeighbors is the number of neighbors (in each dimension) that will contribute to the output value.

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

Deserializing constructor.

virtual GClasses::GNaiveInstance::~GNaiveInstance ( )
virtual

Member Function Documentation

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

Uses cross-validation to find a set of parameters that works well with the provided data.

virtual void GClasses::GNaiveInstance::beginIncrementalLearningInner ( const GRelation featureRel,
const GRelation labelRel 
)
protectedvirtual
virtual bool GClasses::GNaiveInstance::canImplicitlyHandleNominalFeatures ( )
inlineprotectedvirtual
virtual bool GClasses::GNaiveInstance::canImplicitlyHandleNominalLabels ( )
inlineprotectedvirtual

See the comment for GTransducer::canImplicitlyHandleNominalLabels.

Reimplemented from GClasses::GTransducer.

virtual void GClasses::GNaiveInstance::clear ( )
virtual

See the comment for GSupervisedLearner::clear.

Implements GClasses::GSupervisedLearner.

void GClasses::GNaiveInstance::evalInput ( size_t  nInputDim,
double  dInput 
)
protected
size_t GClasses::GNaiveInstance::neighbors ( )
inline

Returns the number of neighbors.

virtual void GClasses::GNaiveInstance::predict ( const double *  pIn,
double *  pOut 
)
virtual
virtual void GClasses::GNaiveInstance::predictDistribution ( const double *  pIn,
GPrediction pOut 
)
virtual
virtual GDomNode* GClasses::GNaiveInstance::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::GNaiveInstance::setNeighbors ( size_t  k)
inline

Specify the number of neighbors to use.

static void GClasses::GNaiveInstance::test ( )
static

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

virtual void GClasses::GNaiveInstance::trainIncremental ( const double *  pIn,
const double *  pOut 
)
virtual

Incrementally train with a single instance.

Implements GClasses::GIncrementalLearner.

virtual void GClasses::GNaiveInstance::trainInner ( const GMatrix features,
const GMatrix labels 
)
protectedvirtual
virtual void GClasses::GNaiveInstance::trainSparse ( GSparseMatrix features,
GMatrix labels 
)
virtual

Member Data Documentation

size_t GClasses::GNaiveInstance::m_nNeighbors
protected
GNaiveInstanceAttr** GClasses::GNaiveInstance::m_pAttrs
protected
GHeap* GClasses::GNaiveInstance::m_pHeap
protected
double* GClasses::GNaiveInstance::m_pSumBuffer
protected
double* GClasses::GNaiveInstance::m_pSumOfSquares
protected
double* GClasses::GNaiveInstance::m_pValueSums
protected
double* GClasses::GNaiveInstance::m_pWeightSums
protected