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Factorizers, provides ID to index mappingOnlineLogisticRegression learners.AdaptiveLogisticRegression.DEFAULT_THREAD_COUNT and AdaptiveLogisticRegression.DEFAULT_POOL_SIZE
containsKey method on the map returned by AbstractJob.parseArguments(String[]);
AbstractJob.parseArguments(String[]) is called.
AbstractJob.parseArguments(String[]) is called.
AbstractJob.parseArguments(String[]) is called.
AbstractJob.parseArguments(String[]) is called.
ItemSimilarity.allSimilarItemIDs(long) as candidate itemsRecommenderEvaluator which computes the average absolute
difference between predicted and actual ratings for users.BooleanUserPreferenceArray but stores preferences for one item (all item IDs the same) rather
than one user.GenericUserPreferenceArray but stores, conceptually, BooleanPreference objects which
have no associated preference value.DataModel implementation to be used in an evaluation, given training data.
Recommender implementation to be evaluated, using the given DataModel.
Collection if the method parameter is null.
Retriever.
Retriever and with given maximum size.
ModelTrainer with two TopicModel instances:
one from the previous iteration, the other empty.CachingCVB0PerplexityMapper, to aid in debugging.ItemSimilarity implementation.ItemSimilarity.
ItemSimilarity.
Recommender which caches the results from another Recommender in memory.UserNeighborhood implementation.UserSimilarity implementation.UserSimilarity.
UserSimilarity.
SequenceFile format.
GenericRecommenderIRStatsEvaluator.evaluate(RecommenderBuilder, DataModelBuilder, DataModel, IDRescorer, int, double, double) to
have it attempt to compute a reasonable threshold.
n-1 scores, where
n is equal to numCategories(), given an input
vector instance.
n-1, for each row of a matrix, where n is equal
to numCategories().
n scores, where
n is numCategories(), given an input vector
instance.
n scores, where
n is numCategories(), given an input vector
instance.
n probabilities, one for each category.
AbstractVectorClassifier.classify(Vector) would return a vector with only one element.
closeables (to prevent repeating close attempts), re-throw the
last one at the end.
Parametered.createParameters(String,org.apache.hadoop.conf.Configuration)
on parameter parmetered, and then recur down its composite tree to invoke
Parametered.createParameters(String,org.apache.hadoop.conf.Configuration)
and Parametered.configure(org.apache.hadoop.conf.Configuration) on
each composite part.
Reducer classReducer for PFPGrowth which updates the status as well as writes the
patterns generated by the algorithmPairs whose second element is a count.to-1.SequenceFileDirValueIterator
Version.
Version.
SequenceFile format.
CachingCVB0Mapper for more details on scalability and room for improvement.Preferences
for items.DataModel to be
used while evaluating a Recommender.MatrixWritable.StringTuples.The
SequenceFile input should have a Text key
containing the unique document identifier and a
Text value containing the whole document.SequenceFile, select k vectors and write them to the
output file as a Kluster representing the initial centroid to use.WritableComparable encapsulating two items.Writable encapsulating an item ID and a preference value.Recommender's recommendations.
FastByIDMap with default capacity.
FastByIDMap whose capacity can accommodate the given number of entries without rehash.
FastIDSet with default capacity.
Map implementation, based on algorithms described in Knuth's "Art of Computer
Programming", Vol.FastMap with default capacity.
DataModel backed by a delimited file.IDMigrator backed by a file.ItemSimilarity backed by a comma-delimited file.FileLineIterable over a given file, assuming a UTF-8 encoding.
FileLineIterable over a given file, assuming a UTF-8 encoding.
FileLineIterable over a given file, using the given encoding.
FileLineIterator over a given file, assuming a UTF-8 encoding.
FileLineIterator over a given file, assuming a UTF-8 encoding.
FileLineIterator over a given file, using the given encoding.
FileItemSimilarityFPGrowth algorithmObject for each level of the recursive
FPGrowth algorithm to reduce allocation overhead.FullRunningAverage to add a running standard deviation computation.DictionaryVectorizer job
DataModel which uses given user data as its data source.GenericDataModel from the given users (and their preferences).
GenericDataModel from the given users (and their preferences).
GenericBooleanPrefDataModel.toDataMap(DataModel) with GenericBooleanPrefDataModel.GenericBooleanPrefDataModel(FastByIDMap)
GenericItemBasedRecommender which is appropriate for use when no notion of preference
value exists in the data.GenericUserBasedRecommender which is appropriate for use when no notion of preference
value exists in the data.DataModel which uses a given List of users as its data source.GenericDataModel from the given users (and their preferences).
GenericDataModel from the given users (and their preferences).
GenericDataModel.toDataMap(DataModel) with GenericDataModel.GenericDataModel(FastByIDMap)
Recommender which uses a given
DataModel and
ItemSimilarity to produce recommendations.GenericUserPreferenceArray but stores preferences for one item (all item IDs the same) rather
than one user.GenericItemSimilarity.ItemItemSimilarity which takes a static list of precomputed item similarities and bases its
responses on that alone.GenericItemSimilarity from a precomputed list of GenericItemSimilarity.ItemItemSimilaritys.
GenericItemSimilarity.GenericItemSimilarity(Iterable), but will only keep the specified number of similarities
from the given Iterable of similarities.
GenericItemSimilarity.ItemItemSimilarity implementation and a
DataModel, rather than a list of GenericItemSimilarity.ItemItemSimilaritys.
GenericItemSimilarity.GenericItemSimilarity(ItemSimilarity, DataModel) )}, but will only keep the specified
number of similarities from the given DataModel.
Preference encapsulating an item and preference value.RecommendedItem.n preferences, then evaluate the IR
statistics based on a DataModel that does not have these values.Recommender
which uses a given DataModel and UserNeighborhood to produce recommendations.GenericItemPreferenceArray but stores preferences for one user (all user IDs the same) rather
than one item.AbstractJob.parseArguments(String[]).
DataModel.getMaxPreference()
DataModel.getMinPreference()
AbstractJob.parseArguments(String[]).
Rescorer which operates on long primitive IDs, rather than arbitrary Objects.CVB0Driver, but sequentially, in memory.AbstractJob.parseArguments(String[])
FastIDSet.
WritableComparable which encapsulates an ordered pair of signed integers.Closeable too,
where file is wiped on close and thus the disk resource is released
('closed').Recommender's recommendations.true to exclude the given thing.
true to exclude the given thing.
AggregateAndRecommendReducer
automatically exclude themItemSimilarity.itemSimilarity(long, long).
ItemAverageRecommender, except that estimated preferences are adjusted for the users' average
preference value.DistributedCache.
LongPrimitiveArrayIterator over an entire array.
long primitives in the style of an Iterator -- as
opposed to iterating over Long.DataSource by name from JNDI.
Refreshable to the given collection of Refreshables if it is not
already there and immediately refreshes it.
RandomAccessSparseVectors into the complete Document
RandomAccessSparseVector
TopicModel and use it to iteratively learn the p(topic|term, doc)
distribution for documents (this can be done in parallel across many documents, as the
"read-only" model is, well, read-only.SharingMappers.len lowest bytes
of val.
RecommendedItemPersistenceStrategy which does nothing.Rescorer which always returns the original score.AbstractJob.parseArguments(String[])
addOption methods.
PathFilter.SequenceFileDirIterable and the like to select whether the input path specifies a
directory to list, or a glob pattern.FactorizationsPlusAnonymousUserDataModel
which allow multiple concurrent anonymous requests.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.Preference encapsulates an item and a preference value, which indicates the strength of the
preference for it.Preference.GenericOptionsParser.
FastByIDMap data structure which maps user IDs
to preferences.
SequenceFile format.
ResultSet, Statement and Connection (if not null) and logs (but does not
rethrow) any resulting SQLException.
SequenceFile, randomly select k vectors and
write them to the output file as a Kluster representing the
initial centroid to use.Recommender.recommend(long, int, org.apache.mahout.cf.taste.recommender.IDRescorer), with a
Rescorer that does nothing.
Writable which encapsulates a list of RecommendedItems.Recommender to be
evaluated based on the given DataModel.Recommender's recommendations.Recommender's performance, including precision, recall and
f-measure.Refreshable.refresh(java.util.Collection) and is the entire body of
that method.
Refreshable.RecommenderIRStatsEvaluator.FileDataModel.setPreference(long, long, float).
DataModel.removePreference(long, long) (Object, Object)}.
Rescorer simply assigns a new "score" to a thing like an ID of an item or user which a
Recommender is considering returning as a top recommendation.RecommenderEvaluator which computes the "root mean squared"
difference between predicted and actual ratings for users.ClusterClassifier to classify input vectors into their
respective clusters.
RunningAverage by adding standard deviation too.SamplingCandidateItemsStrategy.NO_LIMIT_FACTOR) for all factors, except
candidatesPerUserFactor which defaults to SamplingCandidateItemsStrategy.DEFAULT_FACTOR.
Iterable whose Iterable.iterator() returns only some subset of the elements that
it would, as determined by a iterator rate parameter.Iterator and returns only some subset of the elements that it would, as determined by a
iterator rate parameter.LongPrimitiveIterator and returns only some subset of the elements that it would,
as determined by a sampling rate parameter.Iterable counterpart to SequenceFileDirIterator.SequenceFileIterator, but iterates not just over one sequence file, but many.FileSystem.listStatus(Path) or
FileSystem.globStatus(Path) to obtain list of files to iterate over
(depending on pathType parameter).
Iterable counterpart to SequenceFileDirValueIterator.SequenceFileValueIterator, but iterates not just over one
sequence file, but many.FileSystem.listStatus(Path) or
FileSystem.globStatus(Path) to obtain list of files to iterate over
(depending on pathType parameter).
Iterable counterpart to SequenceFileIterator.SequenceFileIterable.SequenceFileIterable(Path, boolean, Configuration) but key and value instances are not reused
by default.
Iterator over a SequenceFile's keys and values, as a Pair
containing key and value.Writable key and Writable value, and writes them into a SequenceFileIterable counterpart to SequenceFileValueIterator.SequenceFileValueIterable.SequenceFileValueIterable(Path, boolean, Configuration) but instances are not reused
by default.
Iterator over a SequenceFile's values only.Preference
FileDataModel
maintains; it does not modify any data on disk.
DataModel.setPreference(long, long, float).
PreferenceInferrer to the UserSimilarity implementation.
MultithreadedSharingMapper.BatchItemSimilarities implementationVectorOrPrefWritable
actually a column from that matrix has to be used but as the similarity matrix is symmetric,
we can use a row instead of having to transpose itFeatureVectorEncoder the
input and writes it to the output as a sequence file.Iterator.
Iterator.next()
repeatedly.PearsonCorrelationSimilarity, but compares relative ranking of preference values instead of
preference values themselves.EuclideanDistanceMeasure but it does not take the square root.MemoryUtil.startMemoryLogger(long) or
MemoryUtil.startMemoryLogger().
Recommender that uses matrix factorization (a projection of users
and items onto a feature space)AbstractJob.parseArguments(String[])
Weight based on term frequency onlyLists for arrays in Map values .
StringTuple The input documents has to be
in the SequenceFile format
Matrix of counts of occurrences of (topic, term) pairs.FrequentPatternMaxHeapFPTree This reduces plenty of space and speeds up
Map/Reduce of PFPGrowth algorithm by reducing data size passed from the Mapper to the reducer where
FPGrowth mining is doneVectorDistanceMapper, except it outputs
<input, Vector>, where the vector is a dense vector contain one entry for every seed vector
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