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| KNN (UINT K=10, bool useScaling=false, bool useNullRejection=false, double nullRejectionCoeff=10.0, bool searchForBestKValue=false, UINT minKSearchValue=1, UINT maxKSearchValue=10) |
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| KNN (const KNN &rhs) |
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virtual | ~KNN (void) |
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KNN & | operator= (const KNN &rhs) |
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virtual bool | deepCopyFrom (const Classifier *classifier) |
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virtual bool | train_ (ClassificationData &trainingData) |
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virtual bool | predict_ (VectorDouble &inputVector) |
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virtual bool | clear () |
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virtual bool | saveModelToFile (fstream &file) const |
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virtual bool | loadModelFromFile (fstream &file) |
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virtual bool | recomputeNullRejectionThresholds () |
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UINT | getK () |
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UINT | getDistanceMethod () |
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bool | setK (UINT K) |
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bool | setMinKSearchValue (UINT minKSearchValue) |
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bool | setMaxKSearchValue (UINT maxKSearchValue) |
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bool | enableBestKValueSearch (bool searchForBestKValue) |
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bool | setNullRejectionCoeff (double nullRejectionCoeff) |
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bool | setDistanceMethod (UINT distanceMethod) |
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| Classifier (void) |
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virtual | ~Classifier (void) |
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bool | copyBaseVariables (const Classifier *classifier) |
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virtual bool | reset () |
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string | getClassifierType () const |
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bool | getSupportsNullRejection () const |
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bool | getNullRejectionEnabled () const |
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double | getNullRejectionCoeff () const |
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double | getMaximumLikelihood () const |
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double | getBestDistance () const |
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double | getPhase () const |
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virtual UINT | getNumClasses () const |
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UINT | getClassLabelIndexValue (UINT classLabel) const |
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UINT | getPredictedClassLabel () const |
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VectorDouble | getClassLikelihoods () const |
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VectorDouble | getClassDistances () const |
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VectorDouble | getNullRejectionThresholds () const |
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vector< UINT > | getClassLabels () const |
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vector< MinMax > | getRanges () const |
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bool | enableNullRejection (bool useNullRejection) |
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virtual bool | setNullRejectionThresholds (VectorDouble newRejectionThresholds) |
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bool | getTimeseriesCompatible () const |
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Classifier * | createNewInstance () const |
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Classifier * | deepCopy () const |
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const Classifier * | getClassifierPointer () const |
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const Classifier & | getBaseClassifier () const |
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| MLBase (void) |
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virtual | ~MLBase (void) |
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bool | copyMLBaseVariables (const MLBase *mlBase) |
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virtual bool | train (ClassificationData trainingData) |
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virtual bool | train (RegressionData trainingData) |
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virtual bool | train_ (RegressionData &trainingData) |
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virtual bool | train (TimeSeriesClassificationData trainingData) |
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virtual bool | train_ (TimeSeriesClassificationData &trainingData) |
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virtual bool | train (TimeSeriesClassificationDataStream trainingData) |
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virtual bool | train_ (TimeSeriesClassificationDataStream &trainingData) |
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virtual bool | train (UnlabelledData trainingData) |
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virtual bool | train_ (UnlabelledData &trainingData) |
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virtual bool | train (MatrixDouble data) |
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virtual bool | train_ (MatrixDouble &data) |
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virtual bool | predict (VectorDouble inputVector) |
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virtual bool | predict (MatrixDouble inputMatrix) |
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virtual bool | predict_ (MatrixDouble &inputMatrix) |
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virtual bool | map (VectorDouble inputVector) |
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virtual bool | map_ (VectorDouble &inputVector) |
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virtual bool | print () const |
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virtual bool | save (const string filename) const |
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virtual bool | load (const string filename) |
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virtual bool | saveModelToFile (string filename) const |
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virtual bool | loadModelFromFile (string filename) |
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virtual bool | getModel (ostream &stream) const |
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double | scale (const double &x, const double &minSource, const double &maxSource, const double &minTarget, const double &maxTarget, const bool constrain=false) |
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virtual string | getModelAsString () const |
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UINT | getBaseType () const |
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UINT | getNumInputFeatures () const |
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UINT | getNumInputDimensions () const |
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UINT | getNumOutputDimensions () const |
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UINT | getMinNumEpochs () const |
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UINT | getMaxNumEpochs () const |
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UINT | getValidationSetSize () const |
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UINT | getNumTrainingIterationsToConverge () const |
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double | getMinChange () const |
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double | getLearningRate () const |
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double | getRootMeanSquaredTrainingError () const |
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double | getTotalSquaredTrainingError () const |
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bool | getUseValidationSet () const |
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bool | getRandomiseTrainingOrder () const |
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bool | getTrained () const |
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bool | getModelTrained () const |
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bool | getScalingEnabled () const |
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bool | getIsBaseTypeClassifier () const |
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bool | getIsBaseTypeRegressifier () const |
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bool | getIsBaseTypeClusterer () const |
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bool | enableScaling (bool useScaling) |
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bool | setMaxNumEpochs (const UINT maxNumEpochs) |
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bool | setMinNumEpochs (const UINT minNumEpochs) |
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bool | setMinChange (const double minChange) |
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bool | setLearningRate (double learningRate) |
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bool | setUseValidationSet (const bool useValidationSet) |
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bool | setValidationSetSize (const UINT validationSetSize) |
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bool | setRandomiseTrainingOrder (const bool randomiseTrainingOrder) |
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bool | registerTrainingResultsObserver (Observer< TrainingResult > &observer) |
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bool | registerTestResultsObserver (Observer< TestInstanceResult > &observer) |
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bool | removeTrainingResultsObserver (const Observer< TrainingResult > &observer) |
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bool | removeTestResultsObserver (const Observer< TestInstanceResult > &observer) |
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bool | removeAllTrainingObservers () |
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bool | removeAllTestObservers () |
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bool | notifyTrainingResultsObservers (const TrainingResult &data) |
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bool | notifyTestResultsObservers (const TestInstanceResult &data) |
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MLBase * | getMLBasePointer () |
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const MLBase * | getMLBasePointer () const |
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vector< TrainingResult > | getTrainingResults () const |
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| GRTBase (void) |
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virtual | ~GRTBase (void) |
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bool | copyGRTBaseVariables (const GRTBase *GRTBase) |
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string | getClassType () const |
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string | getLastWarningMessage () const |
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string | getLastErrorMessage () const |
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string | getLastInfoMessage () const |
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GRTBase * | getGRTBasePointer () |
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const GRTBase * | getGRTBasePointer () const |
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virtual void | notify (const TrainingResult &data) |
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virtual void | notify (const TestInstanceResult &data) |
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UINT | K |
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UINT | distanceMethod |
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The number of neighbours to search for
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bool | searchForBestKValue |
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The distance method used to compute the distance between each data point
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UINT | minKSearchValue |
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Sets if the best K value should be searched for or if the model should be trained with K
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UINT | maxKSearchValue |
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The minimum K value to start the search from
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ClassificationData | trainingData |
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The maximum K value to end the search at
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VectorDouble | trainingMu |
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Holds the trainingData to perform the predictions
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VectorDouble | trainingSigma |
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Holds the average max-class distance of the training data for each of classes
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string | classifierType |
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bool | supportsNullRejection |
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bool | useNullRejection |
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UINT | numClasses |
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UINT | predictedClassLabel |
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UINT | classifierMode |
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double | nullRejectionCoeff |
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double | maxLikelihood |
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double | bestDistance |
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double | phase |
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VectorDouble | classLikelihoods |
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VectorDouble | classDistances |
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VectorDouble | nullRejectionThresholds |
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vector< UINT > | classLabels |
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vector< MinMax > | ranges |
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bool | trained |
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bool | useScaling |
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UINT | baseType |
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UINT | numInputDimensions |
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UINT | numOutputDimensions |
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UINT | numTrainingIterationsToConverge |
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UINT | minNumEpochs |
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UINT | maxNumEpochs |
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UINT | validationSetSize |
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double | learningRate |
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double | minChange |
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double | rootMeanSquaredTrainingError |
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double | totalSquaredTrainingError |
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bool | useValidationSet |
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bool | randomiseTrainingOrder |
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Random | random |
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vector< TrainingResult > | trainingResults |
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TrainingResultsObserverManager | trainingResultsObserverManager |
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TestResultsObserverManager | testResultsObserverManager |
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string | classType |
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DebugLog | debugLog |
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ErrorLog | errorLog |
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InfoLog | infoLog |
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TrainingLog | trainingLog |
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TestingLog | testingLog |
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WarningLog | warningLog |
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Definition at line 51 of file KNN.h.