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| | GNeuralDecomposition () |
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| | GNeuralDecomposition (const GDomNode *pNode) |
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| virtual | ~GNeuralDecomposition () |
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| bool | autoFilter () const |
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| virtual void | clear () |
| | Discards all training for the purpose of freeing memory. If you call this method, you must train before making any predictions. No settings or options are discarded, so you should be able to train again without specifying any other parameters and still get a comparable model. More...
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| void | clearFrozen () |
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| size_t | epochs () const |
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| virtual GMatrix * | extrapolate (double start=1.0, double length=1.0, double step=0.0002, bool outputFeatures=false) |
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| virtual GMatrix * | extrapolate (const GMatrix &features) |
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| double | featureBias () const |
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| double | featureScale () const |
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| bool | filterLogarithm () const |
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| void | freeze () |
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| double | learningRate () const |
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| GNeuralNet & | nn () const |
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| double | outputBias () const |
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| double | outputScale () const |
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| virtual void | predict (const GVec &in, GVec &out) |
| | 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...
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| virtual void | predictDistribution (const GVec &in, GPrediction *pOut) |
| | 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...
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| double | regularization () const |
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| void | restoreFrozen () |
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| virtual GDomNode * | serialize (GDom *pDoc) const |
| | Marshal this object into a DOM that can be converted to a variety of formats. (Implementations of this method should use baseDomNode.) More...
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| void | setAutoFilter (bool auto_Filter) |
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| void | setEpochs (size_t newepochs) |
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| void | setFeatureBias (double newfeatureBias) |
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| void | setFeatureScale (double newfeatureScale) |
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| void | setFilterLogarithm (bool filter_Logarithm) |
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| void | setLearningRate (double newlearningRate) |
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| void | setLinearUnits (size_t linearUnits) |
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| void | setLockPairs (bool lockPairs) |
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| void | setOutputBias (double newoutputBias) |
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| void | setOutputScale (double newoutputScale) |
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| void | setRegularization (double newregularization) |
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| void | setSigmoidUnits (size_t sigmoidUnits) |
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| void | setSinusoidUnits (size_t sinusoidUnits) |
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| void | setSoftplusUnits (size_t softplusUnits) |
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| virtual void | trainIncremental (const GVec &in, const GVec &pOut) |
| | Pass a single input row and the corresponding label to incrementally train this model. More...
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| virtual void | trainOnSeries (const GMatrix &series) |
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| virtual void | trainSparse (GSparseMatrix &features, GMatrix &labels) |
| | Train using a sparse feature matrix. (A Typical implementation of this method will first call beginIncrementalLearning, then it will iterate over all of the feature rows, and for each row it will convert the sparse row to a dense row, call trainIncremental using the dense row, then discard the dense row and proceed to the next row.) More...
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| | GIncrementalLearner () |
| | General-purpose constructor. More...
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| | GIncrementalLearner (const GDomNode *pNode) |
| | Deserialization constructor. More...
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| virtual | ~GIncrementalLearner () |
| | Destructor. More...
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| void | beginIncrementalLearning (const GRelation &featureRel, const GRelation &labelRel) |
| | You must call this method before you call trainIncremental. More...
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| void | beginIncrementalLearning (const GMatrix &features, const GMatrix &labels) |
| | A version of beginIncrementalLearning that supports data-dependent filters. More...
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| virtual bool | canTrainIncrementally () |
| | Returns true. More...
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| virtual bool | isFilter () |
| | Only the GFilter class should return true to this method. More...
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| | GSupervisedLearner () |
| | General-purpose constructor. More...
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| | GSupervisedLearner (const GDomNode *pNode) |
| | Deserialization constructor. More...
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| virtual | ~GSupervisedLearner () |
| | Destructor. More...
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| 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...
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| virtual bool | canGeneralize () |
| | Returns true because fully supervised learners have an internal model that allows them to generalize previously unseen rows. More...
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| 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...
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| 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...
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| const GRelation & | relFeatures () |
| | Returns a reference to the feature relation (meta-data about the input attributes). More...
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| const GRelation & | relLabels () |
| | Returns a reference to the label relation (meta-data about the output attributes). More...
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| 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...
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| void | train (const GMatrix &features, const GMatrix &labels) |
| | Call this method to train the model. More...
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| 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...
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| | GTransducer () |
| | General-purpose constructor. More...
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| | GTransducer (const GTransducer &that) |
| | Copy-constructor. Throws an exception to prevent models from being copied by value. More...
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| virtual | ~GTransducer () |
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| 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...
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| 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...
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| 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...
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| 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...
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| 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...
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| 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...
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| GTransducer & | operator= (const GTransducer &other) |
| | Throws an exception to prevent models from being copied by value. More...
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| 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...
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| 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...
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| 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...
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| 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...
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| 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...
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| 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...
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Public Member Functions inherited from GClasses::GIncrementalLearner