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.ItemItemSimilaritys.
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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