## Uses of Interface
org.apache.mahout.cf.taste.model.DataModel

Packages that use DataModel
org.apache.mahout.cf.taste.eval
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.model
org.apache.mahout.cf.taste.recommender

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

Methods in org.apache.mahout.cf.taste.eval that return DataModel
`DataModel` `DataModelBuilder.buildDataModel(FastByIDMap<PreferenceArray> trainingData)` Builds a `DataModel` implementation to be used in an evaluation, given training data.

Methods in org.apache.mahout.cf.taste.eval with parameters of type DataModel
`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 DataModel in org.apache.mahout.cf.taste.impl.eval

Methods in org.apache.mahout.cf.taste.impl.eval with parameters of type DataModel
`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)`
`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)`

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

Classes in org.apache.mahout.cf.taste.impl.model that implement DataModel
`class` `AbstractDataModel` Contains some features common to all implementations.
`class` `GenericBooleanPrefDataModel` A simple `DataModel` which uses given user data as its data source.
`class` `GenericDataModel` A simple `DataModel` which uses a given `List` of users as its data source.
`class` `PlusAnonymousConcurrentUserDataModel` This is a special thread-safe version of `PlusAnonymousUserDataModel` which allow multiple concurrent anonymous requests.
`class` `PlusAnonymousUserDataModel` This `DataModel` decorator class is useful in a situation where you wish to recommend to a user that doesn't really exist yet in your actual `DataModel`.

Methods in org.apache.mahout.cf.taste.impl.model that return DataModel
`protected DataModel` `PlusAnonymousUserDataModel.getDelegate()`

Methods in org.apache.mahout.cf.taste.impl.model with parameters of type DataModel
`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.

Constructors in org.apache.mahout.cf.taste.impl.model with parameters of type DataModel
`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)`
`PlusAnonymousConcurrentUserDataModel(DataModel delegate, int maxConcurrentUsers)`
`PlusAnonymousUserDataModel(DataModel delegate)`

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

Classes in org.apache.mahout.cf.taste.impl.model.file that implement DataModel
`class` `FileDataModel` A `DataModel` backed by a delimited file.

Methods in org.apache.mahout.cf.taste.impl.model.file that return DataModel
`protected DataModel` `FileDataModel.buildModel()`

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

Constructors in org.apache.mahout.cf.taste.impl.neighborhood with parameters of type DataModel
`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)`
`ThresholdUserNeighborhood(double threshold, UserSimilarity userSimilarity, DataModel dataModel)`
`ThresholdUserNeighborhood(double threshold, UserSimilarity userSimilarity, DataModel dataModel, double samplingRate)`

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

Methods in org.apache.mahout.cf.taste.impl.recommender that return DataModel
`DataModel` `CachingRecommender.getDataModel()`
`DataModel` `AbstractRecommender.getDataModel()`

Methods in org.apache.mahout.cf.taste.impl.recommender with parameters of type DataModel
`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)`
`FastIDSet` `AbstractCandidateItemsStrategy.getCandidateItems(long[] itemIDs, DataModel dataModel)`
`FastIDSet` `AbstractCandidateItemsStrategy.getCandidateItems(long userID, PreferenceArray preferencesFromUser, DataModel dataModel)`

Constructors in org.apache.mahout.cf.taste.impl.recommender with parameters of type DataModel
`AbstractRecommender(DataModel dataModel)`
`AbstractRecommender(DataModel dataModel, CandidateItemsStrategy candidateItemsStrategy)`
`EstimatedPreferenceCapper(DataModel model)`
`GenericBooleanPrefItemBasedRecommender(DataModel dataModel, ItemSimilarity similarity)`
`GenericBooleanPrefItemBasedRecommender(DataModel dataModel, ItemSimilarity similarity, CandidateItemsStrategy candidateItemsStrategy, MostSimilarItemsCandidateItemsStrategy mostSimilarItemsCandidateItemsStrategy)`
`GenericBooleanPrefUserBasedRecommender(DataModel dataModel, UserNeighborhood neighborhood, UserSimilarity similarity)`
`GenericItemBasedRecommender(DataModel dataModel, ItemSimilarity similarity)`
`GenericItemBasedRecommender(DataModel dataModel, ItemSimilarity similarity, CandidateItemsStrategy candidateItemsStrategy, MostSimilarItemsCandidateItemsStrategy mostSimilarItemsCandidateItemsStrategy)`
`GenericUserBasedRecommender(DataModel dataModel, UserNeighborhood neighborhood, UserSimilarity similarity)`
`ItemAverageRecommender(DataModel dataModel)`
`ItemUserAverageRecommender(DataModel dataModel)`
`RandomRecommender(DataModel dataModel)`

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

Fields in org.apache.mahout.cf.taste.impl.recommender.svd declared as DataModel
`protected DataModel` `RatingSGDFactorizer.dataModel`

Constructors in org.apache.mahout.cf.taste.impl.recommender.svd with parameters of type DataModel
`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 DataModel in org.apache.mahout.cf.taste.impl.similarity

Methods in org.apache.mahout.cf.taste.impl.similarity that return DataModel
`protected DataModel` `AbstractItemSimilarity.getDataModel()`

Constructors in org.apache.mahout.cf.taste.impl.similarity with parameters of type DataModel
`AbstractItemSimilarity(DataModel dataModel)`
`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`.
`CityBlockSimilarity(DataModel dataModel)`
`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)`
`LogLikelihoodSimilarity(DataModel dataModel)`
`PearsonCorrelationSimilarity(DataModel dataModel)`
`PearsonCorrelationSimilarity(DataModel dataModel, Weighting weighting)`
`SpearmanCorrelationSimilarity(DataModel dataModel)`
`TanimotoCoefficientSimilarity(DataModel dataModel)`
`UncenteredCosineSimilarity(DataModel dataModel)`
`UncenteredCosineSimilarity(DataModel dataModel, Weighting weighting)`

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

Subinterfaces of DataModel in org.apache.mahout.cf.taste.model
`interface` `JDBCDataModel`

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

Methods in org.apache.mahout.cf.taste.recommender that return DataModel
`DataModel` `Recommender.getDataModel()`

Methods in org.apache.mahout.cf.taste.recommender with parameters of type DataModel
`FastIDSet` `MostSimilarItemsCandidateItemsStrategy.getCandidateItems(long[] itemIDs, DataModel dataModel)`
`FastIDSet` `CandidateItemsStrategy.getCandidateItems(long userID, PreferenceArray preferencesFromUser, DataModel dataModel)`

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