GClasses::GCalibrator Class Reference
#include <GLearner.h>
Inheritance diagram for GClasses::GCalibrator:
Public Member Functions | |
| GCalibrator (GSupervisedLearner *pLearner) | |
| This takes ownership of pLearner. More... | |
| GCalibrator (const GDomNode *pNode, GLearnerLoader &ll) | |
| Deserialization constructor. More... | |
| virtual | ~GCalibrator () |
| Deletes the supervised learner and the transform. More... | |
| virtual void | predict (const GVec &in, GVec &out) |
| See the comment for GSupervisedLearner::predict. More... | |
| virtual void | predictDistribution (const GVec &in, GPrediction *pOut) |
| See the comment for GSupervisedLearner::predictDistribution. More... | |
| virtual const GVec & | prefilterFeatures (const GVec &in) |
| Transform a feature vector to the form for presenting to the inner learner. More... | |
| virtual const GVec & | prefilterLabels (const GVec &in) |
| Transform a label vector to the form for presenting to the inner learner. More... | |
| virtual GDomNode * | serialize (GDom *pDoc) const |
| Marshal this object into a DOM, which can then be converted to a variety of serial formats. More... | |
| virtual void | trainIncremental (const GVec &in, const GVec &out) |
| See the comment for GIncrementalLearner::trainIncremental. More... | |
 Public Member Functions inherited from GClasses::GFilter | |
| virtual void | clear () |
| See the comment for GSupervisedLearner::clear. More... | |
| void | initShellOnly (const GRelation &featureRel, const GRelation &labelRel) |
| Initialize (or train) this filter without calling train on any of the interior components. (This might be used when filtering a learner that has already been trained with a transform that has also already been trained.) More... | |
| GSupervisedLearner * | innerLearner () |
| Returns a pointer to the inner learner. More... | |
| virtual bool | isFilter () |
| Returns true. More... | |
| GMatrix * | prefilterFeatures (const GMatrix &in) |
| Transform a feature matrix to the form for presenting to the inner learner. More... | |
| GMatrix * | prefilterLabels (const GMatrix &in) |
| Transform a label matrix to the form for presenting to the inner learner. More... | |
| virtual void | trainSparse (GSparseMatrix &features, GMatrix &labels) |
| Throws an exception. More... | |
| Public Member Functions inherited from GClasses::GIncrementalLearner | |
| GIncrementalLearner () | |
| General-purpose constructor. More... | |
| GIncrementalLearner (const GDomNode *pNode) | |
| 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... | |
| void | beginIncrementalLearning (const GMatrix &features, const GMatrix &labels) |
| A version of beginIncrementalLearning that supports data-dependent filters. More... | |
| Public Member Functions inherited from GClasses::GSupervisedLearner | |
| GSupervisedLearner () | |
| General-purpose constructor. More... | |
| GSupervisedLearner (const GDomNode *pNode) | |
| 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 GRelation & | relFeatures () |
| Returns a reference to the feature relation (meta-data about the input attributes). More... | |
| const GRelation & | relLabels () |
| Returns a reference to the label relation (meta-data about the output attributes). More... | |
| double | sumSquaredError (const GMatrix &features, const GMatrix &labels, double *pOutSAE=NULL) |
| 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, double *pOutSAE=NULL) |
| 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... | |
| virtual bool | canImplicitlyHandleNominalFeatures () |
| Returns true iff this algorithm can implicitly handle nominal features. If it cannot, then the GNominalToCat transform will be used to convert nominal features to continuous values before passing them to it. More... | |
| virtual bool | canImplicitlyHandleNominalLabels () |
| Returns true iff this algorithm can implicitly handle nominal labels (a.k.a. classification). If it cannot, then the GNominalToCat transform will be used during training to convert nominal labels to continuous values, and to convert categorical predictions back to nominal labels. More... | |
| double | crossValidate (const GMatrix &features, const GMatrix &labels, size_t nFolds, double *pOutSAE=NULL, 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. if pOutSAE is not NULL, the sum absolute error will be placed there. More... | |
| GTransducer & | operator= (const GTransducer &other) |
| Throws an exception to prevent models from being copied by value. More... | |
| GRand & | rand () |
| 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, double *pOutSAE=NULL, 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. if pOutSAE is not NULL, the sum absolute error will be placed there. 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... | |
| std::unique_ptr< GMatrix > | transduce (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... | |
Protected Member Functions | |
| virtual void | beginIncrementalLearningInner (const GRelation &featureRel, const GRelation &labelRel) |
| See the comment for GIncrementalLearner::beginIncrementalLearningInner. More... | |
| virtual void | trainInner (const GMatrix &features, const GMatrix &labels) |
| See the comment for GSupervisedLearner::trainInner. More... | |
| Protected Member Functions inherited from GClasses::GFilter | |
| GFilter (GSupervisedLearner *pLearner, bool ownLearner=true) | |
| GFilter (const GDomNode *pNode, GLearnerLoader &ll) | |
| Deserialization constructor. More... | |
| virtual | ~GFilter () |
| virtual bool | canTrainIncrementally () |
| Returns true. More... | |
| void | discardIntermediateFilters () |
| Discards any filters between this filter and the base learner. More... | |
| void | discardIntermediateFilters_helper (GSupervisedLearner *pOriginal) |
| Recursive method used by discardIntermediateFilters. More... | |
| GDomNode * | domNode (GDom *pDoc, const char *szClassName) const |
| Helper function for serialization. More... | |
| Protected Member Functions inherited from GClasses::GIncrementalLearner | |
| virtual void | beginIncrementalLearningInner (const GMatrix &features, const GMatrix &labels) |
| Protected Member Functions inherited from GClasses::GSupervisedLearner | |
| GDomNode * | baseDomNode (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 std::unique_ptr< GMatrix > | transduceInner (const GMatrix &features1, const GMatrix &labels1, const GMatrix &features2) |
| See GTransducer::transduce. More... | |
Protected Attributes | |
| GNeuralNetLearner ** | m_pCalibrations |
| Protected Attributes inherited from GClasses::GFilter | |
| bool | m_ownLearner |
| GIncrementalLearner * | m_pIncrementalLearner |
| GSupervisedLearner * | m_pLearner |
| GSupervisedLearner * | m_pOriginal |
| Protected Attributes inherited from GClasses::GSupervisedLearner | |
| GRelation * | m_pRelFeatures |
| GRelation * | m_pRelLabels |
| Protected Attributes inherited from GClasses::GTransducer | |
| GRand | m_rand |
Additional Inherited Members | |
| 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... | |
| Static Protected Member Functions inherited from GClasses::GSupervisedLearner | |
| static void | addInterpolatedFunction (double *pOut, size_t nOutVals, double *pIn, size_t nInVals) |
| Adds the function pIn to pOut after interpolating pIn to be the same size as pOut. (This is a helper-function used by precisionRecall.) More... | |
Constructor & Destructor Documentation
| GClasses::GCalibrator::GCalibrator | ( | GSupervisedLearner * | pLearner | ) |
This takes ownership of pLearner.
| GClasses::GCalibrator::GCalibrator | ( | const GDomNode * | pNode, |
| GLearnerLoader & | ll | ||
| ) |
Deserialization constructor.
|
virtual |
Deletes the supervised learner and the transform.
Member Function Documentation
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protectedvirtual |
See the comment for GIncrementalLearner::beginIncrementalLearningInner.
Implements GClasses::GIncrementalLearner.
See the comment for GSupervisedLearner::predict.
Implements GClasses::GSupervisedLearner.
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virtual |
See the comment for GSupervisedLearner::predictDistribution.
Implements GClasses::GSupervisedLearner.
Transform a feature vector to the form for presenting to the inner learner.
Implements GClasses::GFilter.
Transform a label vector to the form for presenting to the inner learner.
Implements GClasses::GFilter.
Marshal this object into a DOM, which can then be converted to a variety of serial formats.
Implements GClasses::GSupervisedLearner.
See the comment for GIncrementalLearner::trainIncremental.
Implements GClasses::GIncrementalLearner.
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protectedvirtual |
See the comment for GSupervisedLearner::trainInner.
Implements GClasses::GSupervisedLearner.
Member Data Documentation
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protected |
