|
| 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...
|
| |
| virtual const double * | prefilterFeatures (const double *pIn)=0 |
| | Transform a feature vector to the form for presenting to the inner learner. More...
|
| |
| GMatrix * | prefilterFeatures (const GMatrix &in) |
| | Transform a feature matrix to the form for presenting to the inner learner. More...
|
| |
| virtual const double * | prefilterLabels (const double *pIn)=0 |
| | Transform a label vector 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...
|
| |
| | 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 void | trainIncremental (const double *pIn, const double *pOut)=0 |
| | Pass a single input row and the corresponding label to incrementally train this model. More...
|
| |
| | 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...
|
| |
| virtual void | predict (const double *pIn, double *pOut)=0 |
| | Evaluate pIn to compute a prediction for pOut. The model must be trained (by calling train) before the first time that this method is called. pIn and pOut should point to arrays of doubles of the same size as the number of columns in the training matrices that were passed to the train method. More...
|
| |
| virtual void | predictDistribution (const double *pIn, GPrediction *pOut)=0 |
| | Evaluate pIn and compute a prediction for pOut. pOut is expected to point to an array of GPrediction objects which have already been allocated. There should be labelDims() elements in this array. The distributions will be more accurate if the model is calibrated before the first time that this method is called. 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...
|
| |
| virtual GDomNode * | serialize (GDom *pDoc) const =0 |
| | Marshal this object into a DOM that can be converted to a variety of formats. (Implementations of this method should use baseDomNode.) 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...
|
| |
| | 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, 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...
|
| |
| 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, 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...
|
| |
| 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...
|
| |
|
| | GFilter (GSupervisedLearner *pLearner, bool ownLearner=true) |
| |
| | GFilter (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...
|
| |
| GDomNode * | domNode (GDom *pDoc, const char *szClassName) const |
| | Helper function for serialization. More...
|
| |
| virtual void | beginIncrementalLearningInner (const GRelation &featureRel, const GRelation &labelRel)=0 |
| | Prepare the model for incremental learning. More...
|
| |
| 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 void | trainInner (const GMatrix &features, const GMatrix &labels)=0 |
| | This is the implementation of the model's training algorithm. (This method is called by train). More...
|
| |
| virtual GMatrix * | transduceInner (const GMatrix &features1, const GMatrix &labels1, const GMatrix &features2) |
| | See GTransducer::transduce. More...
|
| |
Public Member Functions inherited from GClasses::GIncrementalLearner