## Uses of Class
org.apache.mahout.cf.taste.common.TasteException

Packages that use TasteException
org.apache.mahout.cf.taste.common
org.apache.mahout.cf.taste.eval
org.apache.mahout.cf.taste.impl.common
org.apache.mahout.cf.taste.impl.common.jdbc
org.apache.mahout.cf.taste.impl.eval
org.apache.mahout.cf.taste.impl.model
org.apache.mahout.cf.taste.impl.model.file
org.apache.mahout.cf.taste.impl.neighborhood
org.apache.mahout.cf.taste.impl.recommender
org.apache.mahout.cf.taste.impl.recommender.svd
org.apache.mahout.cf.taste.impl.similarity
org.apache.mahout.cf.taste.impl.similarity.file
org.apache.mahout.cf.taste.model
org.apache.mahout.cf.taste.neighborhood
org.apache.mahout.cf.taste.recommender
org.apache.mahout.cf.taste.similarity

Uses of TasteException in org.apache.mahout.cf.taste.common

Subclasses of TasteException in org.apache.mahout.cf.taste.common
`class` `NoSuchItemException`
`class` `NoSuchUserException`

Uses of TasteException in org.apache.mahout.cf.taste.eval

Methods in org.apache.mahout.cf.taste.eval that throw TasteException
`Recommender` `RecommenderBuilder.buildRecommender(DataModel dataModel)` Builds a `Recommender` implementation to be evaluated, using the given `DataModel`.
`double` `RecommenderEvaluator.evaluate(RecommenderBuilder recommenderBuilder, DataModelBuilder dataModelBuilder, DataModel dataModel, double trainingPercentage, double evaluationPercentage)` Evaluates the quality of a `Recommender`'s recommendations.
`IRStatistics` `RecommenderIRStatsEvaluator.evaluate(RecommenderBuilder recommenderBuilder, DataModelBuilder dataModelBuilder, DataModel dataModel, IDRescorer rescorer, int at, double relevanceThreshold, double evaluationPercentage)`
`FastIDSet` `RelevantItemsDataSplitter.getRelevantItemsIDs(long userID, int at, double relevanceThreshold, DataModel dataModel)` During testing, relevant items are removed from a particular users' preferences, and a model is build using this user's other preferences and all other users.
`void` `RelevantItemsDataSplitter.processOtherUser(long userID, FastIDSet relevantItemIDs, FastByIDMap<PreferenceArray> trainingUsers, long otherUserID, DataModel dataModel)` Adds a single user and all their preferences to the training model.

Uses of TasteException in org.apache.mahout.cf.taste.impl.common

Methods in org.apache.mahout.cf.taste.impl.common that throw TasteException
`V` `Retriever.get(K key)`
`V` `Cache.get(K key)` Returns cached value for a key.

Uses of TasteException in org.apache.mahout.cf.taste.impl.common.jdbc

Methods in org.apache.mahout.cf.taste.impl.common.jdbc that throw TasteException
`static DataSource` `AbstractJDBCComponent.lookupDataSource(String dataSourceName)` Looks up a `DataSource` by name from JNDI.

Uses of TasteException in org.apache.mahout.cf.taste.impl.eval

Methods in org.apache.mahout.cf.taste.impl.eval that throw TasteException
`Void` `AbstractDifferenceRecommenderEvaluator.PreferenceEstimateCallable.call()`
`static void` `OrderBasedRecommenderEvaluator.evaluate(DataModel model1, DataModel model2, int samples, RunningAverage tracker, String tag)`
`double` `AbstractDifferenceRecommenderEvaluator.evaluate(RecommenderBuilder recommenderBuilder, DataModelBuilder dataModelBuilder, DataModel dataModel, double trainingPercentage, double evaluationPercentage)`
`IRStatistics` `GenericRecommenderIRStatsEvaluator.evaluate(RecommenderBuilder recommenderBuilder, DataModelBuilder dataModelBuilder, DataModel dataModel, IDRescorer rescorer, int at, double relevanceThreshold, double evaluationPercentage)`
`static void` `OrderBasedRecommenderEvaluator.evaluate(Recommender recommender, DataModel model, int samples, RunningAverage tracker, String tag)`
`static void` `OrderBasedRecommenderEvaluator.evaluate(Recommender recommender1, Recommender recommender2, int samples, RunningAverage tracker, String tag)`
`protected static void` `AbstractDifferenceRecommenderEvaluator.execute(Collection<Callable<Void>> callables, AtomicInteger noEstimateCounter, RunningAverageAndStdDev timing)`
`FastIDSet` `GenericRelevantItemsDataSplitter.getRelevantItemsIDs(long userID, int at, double relevanceThreshold, DataModel dataModel)`
`void` `GenericRelevantItemsDataSplitter.processOtherUser(long userID, FastIDSet relevantItemIDs, FastByIDMap<PreferenceArray> trainingUsers, long otherUserID, DataModel dataModel)`
`static LoadStatistics` `LoadEvaluator.runLoad(Recommender recommender)`
`static LoadStatistics` `LoadEvaluator.runLoad(Recommender recommender, int howMany)`

Uses of TasteException in org.apache.mahout.cf.taste.impl.model

Methods in org.apache.mahout.cf.taste.impl.model that throw TasteException
`LongPrimitiveIterator` `PlusAnonymousUserDataModel.getItemIDs()`
`FastIDSet` `PlusAnonymousUserDataModel.getItemIDsFromUser(long userID)`
`FastIDSet` `PlusAnonymousConcurrentUserDataModel.getItemIDsFromUser(long userID)`
`FastIDSet` `GenericDataModel.getItemIDsFromUser(long userID)`
`FastIDSet` `GenericBooleanPrefDataModel.getItemIDsFromUser(long userID)`
`int` `PlusAnonymousUserDataModel.getNumItems()`
`int` `PlusAnonymousUserDataModel.getNumUsers()`
`int` `PlusAnonymousConcurrentUserDataModel.getNumUsers()`
`int` `PlusAnonymousUserDataModel.getNumUsersWithPreferenceFor(long itemID)`
`int` `PlusAnonymousConcurrentUserDataModel.getNumUsersWithPreferenceFor(long itemID)`
`int` `PlusAnonymousUserDataModel.getNumUsersWithPreferenceFor(long itemID1, long itemID2)`
`int` `PlusAnonymousConcurrentUserDataModel.getNumUsersWithPreferenceFor(long itemID1, long itemID2)`
`PreferenceArray` `PlusAnonymousUserDataModel.getPreferencesForItem(long itemID)`
`PreferenceArray` `PlusAnonymousConcurrentUserDataModel.getPreferencesForItem(long itemID)`
`PreferenceArray` `PlusAnonymousUserDataModel.getPreferencesFromUser(long userID)`
`PreferenceArray` `PlusAnonymousConcurrentUserDataModel.getPreferencesFromUser(long userID)`
`Long` `PlusAnonymousUserDataModel.getPreferenceTime(long userID, long itemID)`
`Long` `PlusAnonymousConcurrentUserDataModel.getPreferenceTime(long userID, long itemID)`
`Long` `GenericDataModel.getPreferenceTime(long userID, long itemID)`
`Long` `GenericBooleanPrefDataModel.getPreferenceTime(long userID, long itemID)`
`Float` `PlusAnonymousUserDataModel.getPreferenceValue(long userID, long itemID)`
`Float` `PlusAnonymousConcurrentUserDataModel.getPreferenceValue(long userID, long itemID)`
`Float` `GenericDataModel.getPreferenceValue(long userID, long itemID)`
`LongPrimitiveIterator` `PlusAnonymousUserDataModel.getUserIDs()`
`LongPrimitiveIterator` `PlusAnonymousConcurrentUserDataModel.getUserIDs()`
`void` `AbstractJDBCIDMigrator.initialize(Iterable<String> stringIDs)`
`void` `PlusAnonymousUserDataModel.removePreference(long userID, long itemID)`
`void` `PlusAnonymousConcurrentUserDataModel.removePreference(long userID, long itemID)`
`void` `PlusAnonymousUserDataModel.setPreference(long userID, long itemID, float value)`
`void` `PlusAnonymousConcurrentUserDataModel.setPreference(long userID, long itemID, float value)`
`void` `AbstractJDBCIDMigrator.storeMapping(long longID, String stringID)`
`static FastByIDMap<PreferenceArray>` `GenericDataModel.toDataMap(DataModel dataModel)` Exports the simple user IDs and preferences in the data model.
`static FastByIDMap<FastIDSet>` `GenericBooleanPrefDataModel.toDataMap(DataModel dataModel)` Exports the simple user IDs and associated item IDs in the data model.
`String` `AbstractJDBCIDMigrator.toStringID(long longID)`

Constructors in org.apache.mahout.cf.taste.impl.model that throw TasteException
`GenericBooleanPrefDataModel(DataModel dataModel)` Deprecated. without direct replacement. Consider `GenericBooleanPrefDataModel.toDataMap(DataModel)` with `GenericBooleanPrefDataModel.GenericBooleanPrefDataModel(FastByIDMap)`
`GenericDataModel(DataModel dataModel)` Deprecated. without direct replacement. Consider `GenericDataModel.toDataMap(DataModel)` with `GenericDataModel.GenericDataModel(FastByIDMap)`

Uses of TasteException in org.apache.mahout.cf.taste.impl.model.file

Methods in org.apache.mahout.cf.taste.impl.model.file that throw TasteException
`LongPrimitiveIterator` `FileDataModel.getItemIDs()`
`FastIDSet` `FileDataModel.getItemIDsFromUser(long userID)`
`int` `FileDataModel.getNumItems()`
`int` `FileDataModel.getNumUsers()`
`int` `FileDataModel.getNumUsersWithPreferenceFor(long itemID)`
`int` `FileDataModel.getNumUsersWithPreferenceFor(long itemID1, long itemID2)`
`PreferenceArray` `FileDataModel.getPreferencesForItem(long itemID)`
`PreferenceArray` `FileDataModel.getPreferencesFromUser(long userID)`
`Long` `FileDataModel.getPreferenceTime(long userID, long itemID)`
`Float` `FileDataModel.getPreferenceValue(long userID, long itemID)`
`LongPrimitiveIterator` `FileDataModel.getUserIDs()`
`void` `FileDataModel.removePreference(long userID, long itemID)` See the warning at `FileDataModel.setPreference(long, long, float)`.
`void` `FileDataModel.setPreference(long userID, long itemID, float value)` Note that this method only updates the in-memory preference data that this `FileDataModel` maintains; it does not modify any data on disk.

Uses of TasteException in org.apache.mahout.cf.taste.impl.neighborhood

Methods in org.apache.mahout.cf.taste.impl.neighborhood that throw TasteException
`long[]` `ThresholdUserNeighborhood.getUserNeighborhood(long userID)`
`long[]` `NearestNUserNeighborhood.getUserNeighborhood(long userID)`
`long[]` `CachingUserNeighborhood.getUserNeighborhood(long userID)`

Constructors in org.apache.mahout.cf.taste.impl.neighborhood that throw TasteException
`CachingUserNeighborhood(UserNeighborhood neighborhood, DataModel dataModel)`
`NearestNUserNeighborhood(int n, double minSimilarity, UserSimilarity userSimilarity, DataModel dataModel)`
`NearestNUserNeighborhood(int n, double minSimilarity, UserSimilarity userSimilarity, DataModel dataModel, double samplingRate)`
`NearestNUserNeighborhood(int n, UserSimilarity userSimilarity, DataModel dataModel)`

Uses of TasteException in org.apache.mahout.cf.taste.impl.recommender

Methods in org.apache.mahout.cf.taste.impl.recommender that throw TasteException
`protected float` `GenericUserBasedRecommender.doEstimatePreference(long theUserID, long[] theNeighborhood, long itemID)`
`protected float` `GenericBooleanPrefUserBasedRecommender.doEstimatePreference(long theUserID, long[] theNeighborhood, long itemID)` This computation is in a technical sense, wrong, since in the domain of "boolean preference users" where all preference values are 1, this method should only ever return 1.0 or NaN.
`protected float` `GenericItemBasedRecommender.doEstimatePreference(long userID, PreferenceArray preferencesFromUser, long itemID)`
`protected float` `GenericBooleanPrefItemBasedRecommender.doEstimatePreference(long userID, PreferenceArray preferencesFromUser, long itemID)` This computation is in a technical sense, wrong, since in the domain of "boolean preference users" where all preference values are 1, this method should only ever return 1.0 or NaN.
`protected FastIDSet` `SamplingCandidateItemsStrategy.doGetCandidateItems(long[] preferredItemIDs, DataModel dataModel)`
`protected FastIDSet` `PreferredItemsNeighborhoodCandidateItemsStrategy.doGetCandidateItems(long[] preferredItemIDs, DataModel dataModel)` returns all items that have not been rated by the user and that were preferred by another user that has preferred at least one item that the current user has preferred too
`protected FastIDSet` `AllUnknownItemsCandidateItemsStrategy.doGetCandidateItems(long[] preferredItemIDs, DataModel dataModel)` return all items the user has not yet seen
`protected FastIDSet` `AllSimilarItemsCandidateItemsStrategy.doGetCandidateItems(long[] preferredItemIDs, DataModel dataModel)`
`protected abstract FastIDSet` `AbstractCandidateItemsStrategy.doGetCandidateItems(long[] preferredItemIDs, DataModel dataModel)`
`double` `GenericItemBasedRecommender.MostSimilarEstimator.estimate(Long itemID)`
`double` `TopItems.Estimator.estimate(T thing)`
`float` `ItemUserAverageRecommender.estimatePreference(long userID, long itemID)`
`float` `ItemAverageRecommender.estimatePreference(long userID, long itemID)`
`float` `GenericUserBasedRecommender.estimatePreference(long userID, long itemID)`
`float` `GenericItemBasedRecommender.estimatePreference(long userID, long itemID)`
`float` `CachingRecommender.estimatePreference(long userID, long itemID)`
`protected FastIDSet` `GenericUserBasedRecommender.getAllOtherItems(long[] theNeighborhood, long theUserID)`
`protected FastIDSet` `GenericBooleanPrefUserBasedRecommender.getAllOtherItems(long[] theNeighborhood, long theUserID)`
`protected FastIDSet` `AbstractRecommender.getAllOtherItems(long userID, PreferenceArray preferencesFromUser)`
`FastIDSet` `AbstractCandidateItemsStrategy.getCandidateItems(long[] itemIDs, DataModel dataModel)`
`FastIDSet` `AbstractCandidateItemsStrategy.getCandidateItems(long userID, PreferenceArray preferencesFromUser, DataModel dataModel)`
`static List<RecommendedItem>` `TopItems.getTopItems(int howMany, LongPrimitiveIterator possibleItemIDs, IDRescorer rescorer, TopItems.Estimator<Long> estimator)`
`static long[]` `TopItems.getTopUsers(int howMany, LongPrimitiveIterator allUserIDs, IDRescorer rescorer, TopItems.Estimator<Long> estimator)`
`List<RecommendedItem>` `GenericItemBasedRecommender.mostSimilarItems(long[] itemIDs, int howMany)`
`List<RecommendedItem>` `GenericItemBasedRecommender.mostSimilarItems(long[] itemIDs, int howMany, boolean excludeItemIfNotSimilarToAll)`
`List<RecommendedItem>` `GenericItemBasedRecommender.mostSimilarItems(long[] itemIDs, int howMany, Rescorer<LongPair> rescorer)`
`List<RecommendedItem>` `GenericItemBasedRecommender.mostSimilarItems(long[] itemIDs, int howMany, Rescorer<LongPair> rescorer, boolean excludeItemIfNotSimilarToAll)`
`List<RecommendedItem>` `GenericItemBasedRecommender.mostSimilarItems(long itemID, int howMany)`
`List<RecommendedItem>` `GenericItemBasedRecommender.mostSimilarItems(long itemID, int howMany, Rescorer<LongPair> rescorer)`
`long[]` `GenericUserBasedRecommender.mostSimilarUserIDs(long userID, int howMany)`
`long[]` `GenericUserBasedRecommender.mostSimilarUserIDs(long userID, int howMany, Rescorer<LongPair> rescorer)`
`List<RecommendedItem>` `CachingRecommender.recommend(long userID, int howMany)`
`List<RecommendedItem>` `AbstractRecommender.recommend(long userID, int howMany)` Default implementation which just calls `Recommender.recommend(long, int, org.apache.mahout.cf.taste.recommender.IDRescorer)`, with a `Rescorer` that does nothing.
`List<RecommendedItem>` `RandomRecommender.recommend(long userID, int howMany, IDRescorer rescorer)`
`List<RecommendedItem>` `ItemUserAverageRecommender.recommend(long userID, int howMany, IDRescorer rescorer)`
`List<RecommendedItem>` `ItemAverageRecommender.recommend(long userID, int howMany, IDRescorer rescorer)`
`List<RecommendedItem>` `GenericUserBasedRecommender.recommend(long userID, int howMany, IDRescorer rescorer)`
`List<RecommendedItem>` `GenericItemBasedRecommender.recommend(long userID, int howMany, IDRescorer rescorer)`
`List<RecommendedItem>` `CachingRecommender.recommend(long userID, int howMany, IDRescorer rescorer)`
`List<RecommendedItem>` `GenericItemBasedRecommender.recommendedBecause(long userID, long itemID, int howMany)`
`void` `ItemUserAverageRecommender.removePreference(long userID, long itemID)`
`void` `ItemAverageRecommender.removePreference(long userID, long itemID)`
`void` `CachingRecommender.removePreference(long userID, long itemID)`
`void` `AbstractRecommender.removePreference(long userID, long itemID)` Default implementation which just calls `DataModel.removePreference(long, long)` (Object, Object)}.
`void` `ItemUserAverageRecommender.setPreference(long userID, long itemID, float value)`
`void` `ItemAverageRecommender.setPreference(long userID, long itemID, float value)`
`void` `CachingRecommender.setPreference(long userID, long itemID, float value)`
`void` `AbstractRecommender.setPreference(long userID, long itemID, float value)` Default implementation which just calls `DataModel.setPreference(long, long, float)`.

Constructors in org.apache.mahout.cf.taste.impl.recommender that throw TasteException
`CachingRecommender(Recommender recommender)`
`ItemAverageRecommender(DataModel dataModel)`
`ItemUserAverageRecommender(DataModel dataModel)`
`RandomRecommender(DataModel dataModel)`

Uses of TasteException in org.apache.mahout.cf.taste.impl.recommender.svd

Methods in org.apache.mahout.cf.taste.impl.recommender.svd that throw TasteException
`float` `SVDRecommender.estimatePreference(long userID, long itemID)` a preference is estimated by computing the dot-product of the user and item feature vectors
`Factorization` `SVDPlusPlusFactorizer.factorize()`
`Factorization` `RatingSGDFactorizer.factorize()`
`Factorization` `ParallelSGDFactorizer.factorize()`
`Factorization` `Factorizer.factorize()`
`Factorization` `ALSWRFactorizer.factorize()`
`protected void` `ParallelSGDFactorizer.initialize()`
`protected void` `SVDPlusPlusFactorizer.prepareTraining()`
`protected void` `RatingSGDFactorizer.prepareTraining()`
`List<RecommendedItem>` `SVDRecommender.recommend(long userID, int howMany, IDRescorer rescorer)`

Constructors in org.apache.mahout.cf.taste.impl.recommender.svd that throw TasteException
`AbstractFactorizer(DataModel dataModel)`
`ALSWRFactorizer(DataModel dataModel, int numFeatures, double lambda, int numIterations)`
`ALSWRFactorizer(DataModel dataModel, int numFeatures, double lambda, int numIterations, boolean usesImplicitFeedback, double alpha)`
`ALSWRFactorizer(DataModel dataModel, int numFeatures, double lambda, int numIterations, boolean usesImplicitFeedback, double alpha, int numTrainingThreads)`
`ParallelSGDFactorizer.PreferenceShuffler(DataModel dataModel)`
`ParallelSGDFactorizer(DataModel dataModel, int numFeatures, double lambda, int numEpochs)`
`ParallelSGDFactorizer(DataModel dataModel, int numFeatures, double lambda, int numIterations, double mu0, double decayFactor, int stepOffset, double forgettingExponent)`
`ParallelSGDFactorizer(DataModel dataModel, int numFeatures, double lambda, int numIterations, double mu0, double decayFactor, int stepOffset, double forgettingExponent, double biasMuRatio, double biasLambdaRatio)`
`ParallelSGDFactorizer(DataModel dataModel, int numFeatures, double lambda, int numIterations, double mu0, double decayFactor, int stepOffset, double forgettingExponent, double biasMuRatio, double biasLambdaRatio, int numThreads)`
`ParallelSGDFactorizer(DataModel dataModel, int numFeatures, double lambda, int numIterations, double mu0, double decayFactor, int stepOffset, double forgettingExponent, int numThreads)`
`RatingSGDFactorizer(DataModel dataModel, int numFeatures, double learningRate, double preventOverfitting, double randomNoise, int numIterations, double learningRateDecay)`
`RatingSGDFactorizer(DataModel dataModel, int numFeatures, int numIterations)`
`SVDPlusPlusFactorizer(DataModel dataModel, int numFeatures, double learningRate, double preventOverfitting, double randomNoise, int numIterations, double learningRateDecay)`
`SVDPlusPlusFactorizer(DataModel dataModel, int numFeatures, int numIterations)`
`SVDRecommender(DataModel dataModel, Factorizer factorizer)`
`SVDRecommender(DataModel dataModel, Factorizer factorizer, CandidateItemsStrategy candidateItemsStrategy)`
`SVDRecommender(DataModel dataModel, Factorizer factorizer, CandidateItemsStrategy candidateItemsStrategy, PersistenceStrategy persistenceStrategy)` Create an SVDRecommender using a persistent store to cache factorizations.
`SVDRecommender(DataModel dataModel, Factorizer factorizer, PersistenceStrategy persistenceStrategy)` Create an SVDRecommender using a persistent store to cache factorizations.

Uses of TasteException in org.apache.mahout.cf.taste.impl.similarity

Methods in org.apache.mahout.cf.taste.impl.similarity that throw TasteException
`long[]` `CachingItemSimilarity.allSimilarItemIDs(long itemID)`
`long[]` `AbstractItemSimilarity.allSimilarItemIDs(long itemID)`
`float` `AveragingPreferenceInferrer.inferPreference(long userID, long itemID)`
`double[]` `TanimotoCoefficientSimilarity.itemSimilarities(long itemID1, long[] itemID2s)`
`double[]` `LogLikelihoodSimilarity.itemSimilarities(long itemID1, long[] itemID2s)`
`double[]` `CityBlockSimilarity.itemSimilarities(long itemID1, long[] itemID2s)`
`double[]` `CachingItemSimilarity.itemSimilarities(long itemID1, long[] itemID2s)`
`double` `TanimotoCoefficientSimilarity.itemSimilarity(long itemID1, long itemID2)`
`double` `LogLikelihoodSimilarity.itemSimilarity(long itemID1, long itemID2)`
`double` `CityBlockSimilarity.itemSimilarity(long itemID1, long itemID2)`
`double` `CachingItemSimilarity.itemSimilarity(long itemID1, long itemID2)`
`double` `TanimotoCoefficientSimilarity.userSimilarity(long userID1, long userID2)`
`double` `SpearmanCorrelationSimilarity.userSimilarity(long userID1, long userID2)`
`double` `LogLikelihoodSimilarity.userSimilarity(long userID1, long userID2)`
`double` `CityBlockSimilarity.userSimilarity(long userID1, long userID2)`
`double` `CachingUserSimilarity.userSimilarity(long userID1, long userID2)`

Constructors in org.apache.mahout.cf.taste.impl.similarity that throw TasteException
`AveragingPreferenceInferrer(DataModel dataModel)`
`CachingItemSimilarity(ItemSimilarity similarity, DataModel dataModel)` Creates this on top of the given `ItemSimilarity`.
`CachingUserSimilarity(UserSimilarity similarity, DataModel dataModel)` Creates this on top of the given `UserSimilarity`.
`EuclideanDistanceSimilarity(DataModel dataModel)`
`EuclideanDistanceSimilarity(DataModel dataModel, Weighting weighting)`
`GenericItemSimilarity(ItemSimilarity otherSimilarity, DataModel dataModel)` Builds a list of item-item similarities given an `GenericItemSimilarity.ItemItemSimilarity` implementation and a `DataModel`, rather than a list of `GenericItemSimilarity.ItemItemSimilarity`s.
`GenericItemSimilarity(ItemSimilarity otherSimilarity, DataModel dataModel, int maxToKeep)` Like `GenericItemSimilarity.GenericItemSimilarity(ItemSimilarity, DataModel)` )}, but will only keep the specified number of similarities from the given `DataModel`.
`GenericUserSimilarity(UserSimilarity otherSimilarity, DataModel dataModel)`
`GenericUserSimilarity(UserSimilarity otherSimilarity, DataModel dataModel, int maxToKeep)`
`PearsonCorrelationSimilarity(DataModel dataModel)`
`PearsonCorrelationSimilarity(DataModel dataModel, Weighting weighting)`
`UncenteredCosineSimilarity(DataModel dataModel)`
`UncenteredCosineSimilarity(DataModel dataModel, Weighting weighting)`

Uses of TasteException in org.apache.mahout.cf.taste.impl.similarity.file

Methods in org.apache.mahout.cf.taste.impl.similarity.file that throw TasteException
`long[]` `FileItemSimilarity.allSimilarItemIDs(long itemID)`
`double[]` `FileItemSimilarity.itemSimilarities(long itemID1, long[] itemID2s)`
`double` `FileItemSimilarity.itemSimilarity(long itemID1, long itemID2)`

Uses of TasteException in org.apache.mahout.cf.taste.model

Methods in org.apache.mahout.cf.taste.model that throw TasteException
`FastByIDMap<FastIDSet>` `JDBCDataModel.exportWithIDsOnly()`
`FastByIDMap<PreferenceArray>` `JDBCDataModel.exportWithPrefs()` Hmm, should this exist elsewhere? seems like most relevant for a DB implementation, which is not in memory, which might want to export to memory.
`LongPrimitiveIterator` `DataModel.getItemIDs()`
`FastIDSet` `DataModel.getItemIDsFromUser(long userID)`
`int` `DataModel.getNumItems()`
`int` `DataModel.getNumUsers()`
`int` `DataModel.getNumUsersWithPreferenceFor(long itemID)`
`int` `DataModel.getNumUsersWithPreferenceFor(long itemID1, long itemID2)`
`PreferenceArray` `DataModel.getPreferencesForItem(long itemID)`
`PreferenceArray` `DataModel.getPreferencesFromUser(long userID)`
`Long` `DataModel.getPreferenceTime(long userID, long itemID)` Retrieves the time at which a preference value from a user and item was set, if known.
`Float` `DataModel.getPreferenceValue(long userID, long itemID)` Retrieves the preference value for a single user and item.
`LongPrimitiveIterator` `DataModel.getUserIDs()`
`void` `UpdatableIDMigrator.initialize(Iterable<String> stringIDs)` Make the mapping aware of the given string IDs.
`void` `DataModel.removePreference(long userID, long itemID)` Removes a particular preference for a user.
`void` `DataModel.setPreference(long userID, long itemID, float value)` Sets a particular preference (item plus rating) for a user.
`void` `UpdatableIDMigrator.storeMapping(long longID, String stringID)` Stores the reverse long-to-String mapping in some kind of backing store.
`String` `IDMigrator.toStringID(long longID)`

Uses of TasteException in org.apache.mahout.cf.taste.neighborhood

Methods in org.apache.mahout.cf.taste.neighborhood that throw TasteException
`long[]` `UserNeighborhood.getUserNeighborhood(long userID)`

Uses of TasteException in org.apache.mahout.cf.taste.recommender

Methods in org.apache.mahout.cf.taste.recommender that throw TasteException
`float` `Recommender.estimatePreference(long userID, long itemID)`
`FastIDSet` `MostSimilarItemsCandidateItemsStrategy.getCandidateItems(long[] itemIDs, DataModel dataModel)`
`FastIDSet` `CandidateItemsStrategy.getCandidateItems(long userID, PreferenceArray preferencesFromUser, DataModel dataModel)`
`List<RecommendedItem>` `ItemBasedRecommender.mostSimilarItems(long[] itemIDs, int howMany)`
`List<RecommendedItem>` `ItemBasedRecommender.mostSimilarItems(long[] itemIDs, int howMany, boolean excludeItemIfNotSimilarToAll)`
`List<RecommendedItem>` `ItemBasedRecommender.mostSimilarItems(long[] itemIDs, int howMany, Rescorer<LongPair> rescorer)`
`List<RecommendedItem>` `ItemBasedRecommender.mostSimilarItems(long[] itemIDs, int howMany, Rescorer<LongPair> rescorer, boolean excludeItemIfNotSimilarToAll)`
`List<RecommendedItem>` `ItemBasedRecommender.mostSimilarItems(long itemID, int howMany)`
`List<RecommendedItem>` `ItemBasedRecommender.mostSimilarItems(long itemID, int howMany, Rescorer<LongPair> rescorer)`
`long[]` `UserBasedRecommender.mostSimilarUserIDs(long userID, int howMany)`
`long[]` `UserBasedRecommender.mostSimilarUserIDs(long userID, int howMany, Rescorer<LongPair> rescorer)`
`List<RecommendedItem>` `Recommender.recommend(long userID, int howMany)`
`List<RecommendedItem>` `Recommender.recommend(long userID, int howMany, IDRescorer rescorer)`
`List<RecommendedItem>` `ItemBasedRecommender.recommendedBecause(long userID, long itemID, int howMany)` Lists the items that were most influential in recommending a given item to a given user.
`void` `Recommender.removePreference(long userID, long itemID)`
`void` `Recommender.setPreference(long userID, long itemID, float value)`

Uses of TasteException in org.apache.mahout.cf.taste.similarity

Methods in org.apache.mahout.cf.taste.similarity that throw TasteException
`long[]` `ItemSimilarity.allSimilarItemIDs(long itemID)`
`float` `PreferenceInferrer.inferPreference(long userID, long itemID)` Infers the given user's preference value for an item.
`double[]` `ItemSimilarity.itemSimilarities(long itemID1, long[] itemID2s)` A bulk-get version of `ItemSimilarity.itemSimilarity(long, long)`.
`double` `ItemSimilarity.itemSimilarity(long itemID1, long itemID2)` Returns the degree of similarity, of two items, based on the preferences that users have expressed for the items.
`double` `UserSimilarity.userSimilarity(long userID1, long userID2)` Returns the degree of similarity, of two users, based on the their preferences.

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