- abs() - Method in class de.jungblut.ner.UnrollableDoubleVector
-
- AbstractActivationFunction - Class in de.jungblut.math.activation
-
Implements the boiler plate code for applying functions on container classes
like vectors and matrices by applying the function on every element.
- AbstractActivationFunction() - Constructor for class de.jungblut.math.activation.AbstractActivationFunction
-
- AbstractClassifier - Class in de.jungblut.classification
-
Abstract base class for classifiers.
- AbstractClassifier() - Constructor for class de.jungblut.classification.AbstractClassifier
-
- AbstractKNearestNeighbours - Class in de.jungblut.classification.knn
-
K nearest neighbour classification algorithm that is seeded with a "database"
of known examples and predicts based on the k-nearest neighbours majority
vote for a class.
- AbstractKNearestNeighbours(int, int) - Constructor for class de.jungblut.classification.knn.AbstractKNearestNeighbours
-
Constructs a new knn classifier.
- AbstractMiniBatchCostFunction - Class in de.jungblut.math.minimize
-
Mini Batch cost function.
- AbstractMiniBatchCostFunction(DoubleVector[], DoubleVector[], int, int) - Constructor for class de.jungblut.math.minimize.AbstractMiniBatchCostFunction
-
An abstract minibatch costfunction.
- AbstractMiniBatchCostFunction(DoubleVector[], DoubleVector[], int, int, boolean) - Constructor for class de.jungblut.math.minimize.AbstractMiniBatchCostFunction
-
An abstract minibatch costfunction.
- AbstractMinimizer - Class in de.jungblut.math.minimize
-
Abstract minimizer class that adds functionality that can be shared between
many minimizers.
- AbstractMinimizer() - Constructor for class de.jungblut.math.minimize.AbstractMinimizer
-
- AbstractPredictor - Class in de.jungblut.classification
-
- AbstractPredictor() - Constructor for class de.jungblut.classification.AbstractPredictor
-
- AbstractTreeNode - Class in de.jungblut.classification.tree
-
- AbstractTreeNode() - Constructor for class de.jungblut.classification.tree.AbstractTreeNode
-
- ActivationFunction - Interface in de.jungblut.math.activation
-
Squashing function interface to provide multiple activation functions, e.G.
- ActivationFunctionSelector - Enum in de.jungblut.math.activation
-
Singleton helper to get the activation functions as singleton.
- activations - Variable in class de.jungblut.classification.nn.MultilayerPerceptronCostFunction.NetworkConfiguration
-
- add(Evaluator.EvaluationResult) - Method in class de.jungblut.classification.eval.Evaluator.EvaluationResult
-
- add(T) - Method in class de.jungblut.crawl.FetchResultPersister
-
Add a crawled result to the back queue.
- add(E) - Method in class de.jungblut.datastructure.DiskList
-
Writes the given element to the disk.
- add(T) - Method in class de.jungblut.datastructure.SingleLinkedList
-
- add(int, T) - Method in class de.jungblut.datastructure.SingleLinkedList
-
- add(DoubleVector) - Method in class de.jungblut.ner.UnrollableDoubleVector
-
- add(double) - Method in class de.jungblut.ner.UnrollableDoubleVector
-
- add(T) - Method in class de.jungblut.sketching.CountMinSketch
-
Adds this object to the min sketch.
- add(double) - Method in class de.jungblut.utils.Statistics
-
Adds a new data item into the current statistics object.
- addAll(Multiset<VALUE>) - Method in class de.jungblut.datastructure.HuffmanTree
-
Bulk inserts the given multiset into this huffman tree.
- addIterationCompletionCallback(IterationCompletionListener) - Method in class de.jungblut.math.minimize.AbstractMinimizer
-
Add a callback listener that triggers after a iteration.
- addStartAndEndTags(String[]) - Static method in class de.jungblut.nlp.TokenizerUtils
-
Adds and to the beginning of the array and the end.
- AgglomerativeClustering - Class in de.jungblut.clustering
-
"Bottom Up" clustering (agglomerative) using average single linkage
clustering.
- AgglomerativeClustering() - Constructor for class de.jungblut.clustering.AgglomerativeClustering
-
- AgglomerativeClustering.ClusterNode - Class in de.jungblut.clustering
-
Tree structure for containing information about linkages and distances.
- allNominal(int) - Static method in enum de.jungblut.classification.tree.FeatureType
-
Creates an array that denotes the type of a feature at a given feature
index.
- allNumerical(int) - Static method in enum de.jungblut.classification.tree.FeatureType
-
Creates an array that denotes the type of a feature at a given feature
index.
- annealingAfter(int) - Method in class de.jungblut.math.minimize.GradientDescent.GradientDescentBuilder
-
Sets a simple annealing (alpha / (1+current_iteration / phi)) where phi
is the given parameter here.
- apply(DoubleVector) - Method in class de.jungblut.math.activation.AbstractActivationFunction
-
- apply(DoubleMatrix) - Method in class de.jungblut.math.activation.AbstractActivationFunction
-
- apply(double) - Method in interface de.jungblut.math.activation.ActivationFunction
-
Applies the activation function on the given element.
- apply(DoubleVector) - Method in interface de.jungblut.math.activation.ActivationFunction
-
Applies the activation function on each element in the given vector.
- apply(DoubleMatrix) - Method in interface de.jungblut.math.activation.ActivationFunction
-
Applies the gradient of the activation function on each element in the
given matrix.
- apply(double) - Method in class de.jungblut.math.activation.ElliotActivationFunction
-
- apply(double) - Method in class de.jungblut.math.activation.LinearActivationFunction
-
- apply(DoubleVector) - Method in class de.jungblut.math.activation.LinearActivationFunction
-
- apply(DoubleMatrix) - Method in class de.jungblut.math.activation.LinearActivationFunction
-
- apply(double) - Method in class de.jungblut.math.activation.LogActivationFunction
-
- apply(double) - Method in class de.jungblut.math.activation.ReluActivationFunction
-
- apply(double) - Method in class de.jungblut.math.activation.SigmoidActivationFunction
-
- apply(double) - Method in class de.jungblut.math.activation.SoftMaxActivationFunction
-
- apply(DoubleVector) - Method in class de.jungblut.math.activation.SoftMaxActivationFunction
-
- apply(DoubleMatrix) - Method in class de.jungblut.math.activation.SoftMaxActivationFunction
-
- apply(double) - Method in class de.jungblut.math.activation.SoftplusReluActivationFunction
-
- apply(double) - Method in class de.jungblut.math.activation.StepActivationFunction
-
- apply(double) - Method in class de.jungblut.math.activation.TanhActivationFunction
-
- apply(DoubleVectorFunction) - Method in class de.jungblut.ner.UnrollableDoubleVector
-
- apply(DoubleVector, DoubleDoubleVectorFunction) - Method in class de.jungblut.ner.UnrollableDoubleVector
-
- approximateCount(T) - Method in class de.jungblut.sketching.CountMinSketch
-
- ArrayIterator<E> - Class in de.jungblut.datastructure
-
Generic ArrayIterator.
- ArrayIterator(E[]) - Constructor for class de.jungblut.datastructure.ArrayIterator
-
Get a new ArrayIterator for the given array.
- ArrayJoiner - Class in de.jungblut.datastructure
-
A Joiner utility that works for primitive arrays which Guava's Joiner
can't deal with.
- ArrayUtils - Class in de.jungblut.datastructure
-
Array utils for stuff that isn't included in
Arrays.
- ArticleContentExtrator - Class in de.jungblut.crawl.extraction
-
Extractor for news articles.
- ArticleContentExtrator() - Constructor for class de.jungblut.crawl.extraction.ArticleContentExtrator
-
- ArticleContentExtrator.ContentFetchResult - Class in de.jungblut.crawl.extraction
-
Article content fetch result.
- asClassifier() - Method in interface de.jungblut.classification.Predictor
-
Backward compatibility method to make online-ml project's predictors work
with almost everything in this library.
- asStream() - Method in class de.jungblut.reader.Dataset
-
- AsyncBufferedOutputStream - Class in de.jungblut.datastructure
-
BufferedOutputStream that asynchronously flushes to disk, so callers don't
have to wait until the flush happens.
- AsyncBufferedOutputStream(OutputStream) - Constructor for class de.jungblut.datastructure.AsyncBufferedOutputStream
-
Creates an asynchronous buffered output stream with 8K buffer and 5 maximal
buffers.
- AsyncBufferedOutputStream(OutputStream, int) - Constructor for class de.jungblut.datastructure.AsyncBufferedOutputStream
-
Creates an asynchronous buffered output stream with defined buffersize and
5 maximal buffers.
- AsyncBufferedOutputStream(OutputStream, int, int) - Constructor for class de.jungblut.datastructure.AsyncBufferedOutputStream
-
Creates an asynchronous buffered output stream.
- average(int) - Method in class de.jungblut.classification.eval.Evaluator.EvaluationResult
-
- averageTransitionProbability(int[]) - Method in class de.jungblut.nlp.MarkovChain
-
- backwardPropagate(DoubleMatrix, DoubleMatrix[], DoubleMatrix[], DoubleMatrix[], MultilayerPerceptronCostFunction.NetworkConfiguration) - Static method in class de.jungblut.classification.nn.MultilayerPerceptronCostFunction
-
- BasicFeatureExtractor - Class in de.jungblut.ner
-
Basic feature extraction for sequence learning, takes the current word into
account and the previous label - as well as the joint version of both.
- BasicFeatureExtractor() - Constructor for class de.jungblut.ner.BasicFeatureExtractor
-
- batchParallelism(int) - Method in class de.jungblut.classification.nn.MultilayerPerceptron.MultilayerPerceptronBuilder
-
- batchParallelism(int) - Method in class de.jungblut.classification.nn.RBM.RBMBuilder
-
- BigramTokenizer - Class in de.jungblut.nlp
-
Advanced tokenizer that lowercases, adds start and end tags, deduplicates
tokens and builds bigrams.
- BigramTokenizer() - Constructor for class de.jungblut.nlp.BigramTokenizer
-
- BitSetWritable - Class in de.jungblut.writable
-
- BitSetWritable() - Constructor for class de.jungblut.writable.BitSetWritable
-
- BitSetWritable(BitSet) - Constructor for class de.jungblut.writable.BitSetWritable
-
- BlockPartitioner - Class in de.jungblut.partition
-
This partitioner partitions connected ranges from 0 to numberOfRows into
sizeOfCluster buckets.
- BlockPartitioner() - Constructor for class de.jungblut.partition.BlockPartitioner
-
- boldDriver() - Method in class de.jungblut.math.minimize.GradientDescent.GradientDescentBuilder
-
BoldDriver will change the learning rate over time by observing the cost
of the costfunction.
- boldDriver(double, double) - Method in class de.jungblut.math.minimize.GradientDescent.GradientDescentBuilder
-
BoldDriver will change the learning rate over time by observing the cost
of the costfunction.
- Boundaries - Class in de.jungblut.partition
-
- Boundaries.Range - Class in de.jungblut.partition
-
- breakOnDifference(double) - Method in class de.jungblut.math.minimize.GradientDescent.GradientDescentBuilder
-
Breaks minimization process when the given delta in costs have been
archieved.
- breakOnDivergence() - Method in class de.jungblut.math.minimize.GradientDescent.GradientDescentBuilder
-
If called, this breaks when the gradient descent minimizer starts to
diverge (costs are growing).
- build() - Method in class de.jungblut.classification.nn.MultilayerPerceptron.MultilayerPerceptronBuilder
-
- build() - Method in class de.jungblut.classification.nn.RBM.RBMBuilder
-
- build(List<DOCUMENT_TYPE>) - Method in class de.jungblut.datastructure.InvertedIndex
-
Builds this inverted index.
- build() - Method in class de.jungblut.math.minimize.GradientDescent.GradientDescentBuilder
-
- buildDictionary(Stream<String[]>) - Static method in class de.jungblut.nlp.VectorizerUtils
-
Builds a sorted dictionary of tokens from a list of (tokenized) documents.
- buildDictionary(Stream<String[]>, float, int) - Static method in class de.jungblut.nlp.VectorizerUtils
-
Builds a sorted dictionary of tokens from a list of (tokenized) documents.
- buildInvertedIndexArray(List<String[]>, String[]) - Static method in class de.jungblut.nlp.VectorizerUtils
-
Builds an inverted index based on the given dictionary, adds just the
document index mappings to it.
- buildInvertedIndexDocumentCount(List<String[]>, String[]) - Static method in class de.jungblut.nlp.VectorizerUtils
-
Builds an inverted index document count based on the given dictionary, so
at each dimension of the returned array, there is a count of how many
documents contained that token.
- buildInvertedIndexMap(List<String[]>, String[]) - Static method in class de.jungblut.nlp.VectorizerUtils
-
Builds an inverted index as multi map.
- buildNGrams(String[], int) - Static method in class de.jungblut.nlp.TokenizerUtils
-
This tokenizer uses the given tokens and then concatenates the words based
on size.
- buildNGramsRange(String[], int, int) - Static method in class de.jungblut.nlp.TokenizerUtils
-
- buildTransitionVector(String[], String[]) - Static method in class de.jungblut.nlp.VectorizerUtils
-
Builds a transition array by traversing the documents and checking the
dictionary.
- ByteBufferInputStream - Class in de.jungblut.datastructure
-
- ByteBufferInputStream(ByteBuffer) - Constructor for class de.jungblut.datastructure.ByteBufferInputStream
-
- calculateGradient(DoubleVector, DoubleVector, DoubleVector) - Method in class de.jungblut.math.loss.CrossEntropyLoss
-
- calculateGradient(DoubleVector, DoubleVector, DoubleVector) - Method in class de.jungblut.math.loss.HingeLoss
-
- calculateGradient(DoubleVector, DoubleVector, DoubleVector) - Method in class de.jungblut.math.loss.LogLoss
-
- calculateGradient(DoubleVector, DoubleVector, DoubleVector) - Method in interface de.jungblut.math.loss.LossFunction
-
Calculate the gradient with the given parameters.
- calculateGradient(DoubleVector, DoubleVector, DoubleVector) - Method in class de.jungblut.math.loss.SquaredLoss
-
- calculateGradient(DoubleVector, DoubleVector, DoubleVector) - Method in class de.jungblut.math.loss.StepLoss
-
- calculateGradients(DoubleMatrix[], DoubleMatrix[], DoubleMatrix[], DoubleMatrix[], int, MultilayerPerceptronCostFunction.NetworkConfiguration) - Static method in class de.jungblut.classification.nn.MultilayerPerceptronCostFunction
-
- calculateLoss(DoubleMatrix, DoubleMatrix) - Method in class de.jungblut.math.loss.CrossEntropyLoss
-
- calculateLoss(DoubleVector, DoubleVector) - Method in class de.jungblut.math.loss.CrossEntropyLoss
-
- calculateLoss(DoubleMatrix, DoubleMatrix) - Method in class de.jungblut.math.loss.HingeLoss
-
- calculateLoss(DoubleVector, DoubleVector) - Method in class de.jungblut.math.loss.HingeLoss
-
- calculateLoss(DoubleMatrix, DoubleMatrix) - Method in class de.jungblut.math.loss.LogLoss
-
- calculateLoss(DoubleVector, DoubleVector) - Method in class de.jungblut.math.loss.LogLoss
-
- calculateLoss(DoubleMatrix, DoubleMatrix) - Method in interface de.jungblut.math.loss.LossFunction
-
Calculate the error with the given parameters.
- calculateLoss(DoubleVector, DoubleVector) - Method in interface de.jungblut.math.loss.LossFunction
-
Calculate the error with the given parameters.
- calculateLoss(DoubleMatrix, DoubleMatrix) - Method in class de.jungblut.math.loss.SquaredLoss
-
- calculateLoss(DoubleVector, DoubleVector) - Method in class de.jungblut.math.loss.SquaredLoss
-
- calculateLoss(DoubleMatrix, DoubleMatrix) - Method in class de.jungblut.math.loss.StepLoss
-
- calculateLoss(DoubleVector, DoubleVector) - Method in class de.jungblut.math.loss.StepLoss
-
- calculateRegularization(DoubleMatrix[], int, MultilayerPerceptronCostFunction.NetworkConfiguration) - Static method in class de.jungblut.classification.nn.MultilayerPerceptronCostFunction
-
- call() - Method in class de.jungblut.crawl.FetchThread
-
- CanopyClustering - Class in de.jungblut.clustering
-
Sequential canopy clusterer.
- Classifier - Interface in de.jungblut.classification
-
Classifier interface for predicting categorial variables.
- ClassifierFactory<A extends Classifier> - Interface in de.jungblut.classification
-
Factory interface for building new classifiers, majorly used in
crossvalidation to generate new classifiers when needed.
- classNames - Variable in class de.jungblut.reader.Dataset
-
- cleanup(Reducer<Text, TextIntPairWritable, Text, TextIntIntIntWritable>.Context) - Method in class de.jungblut.nlp.mr.WordCorpusFrequencyJob.DocumentSumReducer
-
- cleanup(Mapper<LongWritable, Text, Text, LongWritable>.Context) - Method in class de.jungblut.nlp.mr.WordCountJob.WordFrequencyMapper
-
- close() - Method in class de.jungblut.crawl.ResultWriterAdapter
-
- close() - Method in class de.jungblut.crawl.SequenceFileResultWriter
-
- close() - Method in class de.jungblut.datastructure.AsyncBufferedOutputStream
-
- close() - Method in class de.jungblut.datastructure.DiskList
-
Closes read and write, also deletes the file.
- close() - Method in class de.jungblut.datastructure.SortedFile
-
- cluster(List<DoubleVector>, DistanceMeasurer, boolean) - Static method in class de.jungblut.clustering.AgglomerativeClustering
-
Starts the clustering process.
- Cluster - Class in de.jungblut.clustering
-
- Cluster(DoubleVector, List<DoubleVector>) - Constructor for class de.jungblut.clustering.Cluster
-
- cluster(List<DoubleVector>, DistanceMeasurer, int, double) - Method in class de.jungblut.clustering.DBSCAN
-
Clusters the points.
- cluster(List<DoubleVector>, DistanceMeasurer, int, double) - Static method in class de.jungblut.clustering.DBSCANClustering
-
Clusters the given points.
- cluster(int, DistanceMeasurer, double, boolean) - Method in class de.jungblut.clustering.KMeansClustering
-
Starts the clustering process.
- cluster(List<DoubleVector>, double, double, int, boolean) - Static method in class de.jungblut.clustering.MeanShiftClustering
-
Clusters a bunch of given points using the Mean Shift algorithm.
- cluster(List<DoubleVector>, boolean) - Method in class de.jungblut.clustering.OnePassExclusiveClustering
-
Cluster the given items.
- collect(M) - Method in class de.jungblut.datastructure.SortedFile
-
Collects a message.
- CollectionInputProvider<T> - Class in de.jungblut.datastructure
-
Provider for generic collections to read.
- CollectionInputProvider(Collection<T>) - Constructor for class de.jungblut.datastructure.CollectionInputProvider
-
- compare(int, int) - Method in class de.jungblut.datastructure.SortedFile
-
- compareTo(MathUtils.PredictionOutcomePair) - Method in class de.jungblut.math.MathUtils.PredictionOutcomePair
-
- compareTo(IntIntPairWritable) - Method in class de.jungblut.nlp.mr.IntIntPairWritable
-
- compareTo(TextDoublePairWritable) - Method in class de.jungblut.nlp.mr.TextDoublePairWritable
-
- compareTo(TextIntIntIntWritable) - Method in class de.jungblut.nlp.mr.TextIntIntIntWritable
-
- compareTo(TextIntPairWritable) - Method in class de.jungblut.nlp.mr.TextIntPairWritable
-
- compareTo(TextTextPairWritable) - Method in class de.jungblut.nlp.mr.TextTextPairWritable
-
- compareTo(Boundaries.Range) - Method in class de.jungblut.partition.Boundaries.Range
-
- compareTo(VectorWritable) - Method in class de.jungblut.writable.VectorWritable
-
- compareVector(VectorWritable, VectorWritable) - Static method in class de.jungblut.writable.VectorWritable
-
- compareVector(DoubleVector, DoubleVector) - Static method in class de.jungblut.writable.VectorWritable
-
- compile() - Method in class de.jungblut.classification.tree.RandomForest
-
- compileAndLoad(String, AbstractTreeNode) - Static method in class de.jungblut.classification.tree.TreeCompiler
-
Compiles the given node and directly loads it.
- compileNode(String, AbstractTreeNode) - Static method in class de.jungblut.classification.tree.TreeCompiler
-
Compiles the given tree node and name into a class.
- compileTree() - Method in class de.jungblut.classification.tree.DecisionTree
-
Compiles this current tree representation into byte code and loads it into
this class.
- completeStateSequence(Optional<Random>, int[], int...) - Method in class de.jungblut.nlp.MarkovChain
-
Completes the given state sequence by picking the best next state on the
transition probabilities (so a transition with a high probability is picked
more often).
- computeAUC(List<MathUtils.PredictionOutcomePair>) - Static method in class de.jungblut.math.MathUtils
-
This is actually taken from Kaggle's C# implementation: ://www.kaggle.com/c/SemiSupervisedFeatureLearning
/forums/t/919/auc-implementation/6136#post6136.
- computeFeatures(List<String>, int, int) - Method in class de.jungblut.ner.BasicFeatureExtractor
-
- computeFeatures(List<K>, int, int) - Method in interface de.jungblut.ner.SequenceFeatureExtractor
-
Compute a feature for the given sequence (the complete list words).
- computeNextStep(DoubleVector, DoubleMatrix, DoubleMatrix, MultilayerPerceptronCostFunction.NetworkConfiguration) - Static method in class de.jungblut.classification.nn.MultilayerPerceptronCostFunction
-
Do a full forward pass and backpropagate the error.
- computeUnfoldParameters(int[]) - Static method in class de.jungblut.classification.nn.MultilayerPerceptronCostFunction
-
Calculates the unfold parameters to unroll a learned theta vector in their
matrix.
- concat(int[]...) - Static method in class de.jungblut.datastructure.ArrayUtils
-
Concats the given arrays.
- concat(long[]...) - Static method in class de.jungblut.datastructure.ArrayUtils
-
Concats the given arrays.
- concat(double[]...) - Static method in class de.jungblut.datastructure.ArrayUtils
-
Concats the given arrays.
- concat(T[]...) - Static method in class de.jungblut.datastructure.ArrayUtils
-
Concats the given arrays.
- concat(String[], String) - Static method in class de.jungblut.nlp.TokenizerUtils
-
Concats the given tokens with the given delimiter.
- ConditionalLikelihoodCostFunction - Class in de.jungblut.ner
-
Conditional likelihood cost function, used in a maximum entropy markov model
to optimize the weights.
- ConditionalLikelihoodCostFunction(DoubleMatrix, DoubleMatrix) - Constructor for class de.jungblut.ner.ConditionalLikelihoodCostFunction
-
- ConsoleResultWriter<T extends FetchResult> - Class in de.jungblut.crawl
-
Simple class that outputs to console.
- ConsoleResultWriter() - Constructor for class de.jungblut.crawl.ConsoleResultWriter
-
- consumeNext(Iterator<K>, Iterator<V>) - Static method in class de.jungblut.datastructure.Iterables
-
Consumes the next entries from a parallel interator.
- consumeStream(InputStream) - Static method in class de.jungblut.crawl.extraction.OutlinkExtractor
-
Consumes a given
InputStream and returns a string consisting of the
html code of the site.
- ContentFetchResult(String, HashSet<String>) - Constructor for class de.jungblut.crawl.extraction.ArticleContentExtrator.ContentFetchResult
-
- ContentFetchResult(String, HashSet<String>, String, String) - Constructor for class de.jungblut.crawl.extraction.ArticleContentExtrator.ContentFetchResult
-
- copy(int[]) - Static method in class de.jungblut.datastructure.ArrayUtils
-
Copies the given array into a new one.
- copy(double[]) - Static method in class de.jungblut.datastructure.ArrayUtils
-
Copies the given array into a new one.
- copy(long[]) - Static method in class de.jungblut.datastructure.ArrayUtils
-
Copies the given array into a new one.
- copy(T[]) - Static method in class de.jungblut.datastructure.ArrayUtils
-
Copies the given array into a new one.
- CosineDistance - Class in de.jungblut.distance
-
- CosineDistance() - Constructor for class de.jungblut.distance.CosineDistance
-
- CostFunction - Interface in de.jungblut.math.minimize
-
Cost function interface to be implemented when using with a optimizer like
conjugate gradient for example.
- CostGradientTuple - Class in de.jungblut.math.minimize
-
More readable variant of the before used Tuple<> in
CostFunction.
- CostGradientTuple(double, DoubleVector) - Constructor for class de.jungblut.math.minimize.CostGradientTuple
-
- countingSort(int[], int, int) - Static method in class de.jungblut.datastructure.ArrayUtils
-
Counting sort that sorts the integer array in O(n+k) where n is the number
of elements and k is the length of the integer intervals given (high -
low).
- CountMinSketch<T> - Class in de.jungblut.sketching
-
- CountMinSketch(int, int, Funnel<T>) - Constructor for class de.jungblut.sketching.CountMinSketch
-
Creates a new CountMinSketch.
- Crawler<T extends FetchResult> - Interface in de.jungblut.crawl
-
Basic Crawler Interface, all implements should implicit give a constructor
with the same arguments like setup and redirect the call to it.
- create(DoubleVector[], DoubleVector[], float, boolean) - Static method in class de.jungblut.classification.eval.EvaluationSplit
-
Creates a new evaluation split.
- create(int, Voter.CombiningType, ClassifierFactory<K>) - Static method in class de.jungblut.classification.meta.Voter
-
Creates a new voting classificator.
- create(int[], ActivationFunction[], LossFunction, Minimizer, int) - Static method in class de.jungblut.classification.nn.MultilayerPerceptron.MultilayerPerceptronBuilder
-
Creates a new TrainingConfiguration with the mandatory configurations of
the activation functions, the to be used minimizer and the maximum
iterations.
- create(ActivationFunction, int...) - Static method in class de.jungblut.classification.nn.RBM.RBMBuilder
-
Creates a new
RBM.RBMBuilder from an activation function and
layersizes.
- create() - Static method in class de.jungblut.classification.tree.DecisionTree
-
- create(FeatureType[]) - Static method in class de.jungblut.classification.tree.DecisionTree
-
Creates a new decision tree with the given feature types.
- create(int) - Static method in class de.jungblut.classification.tree.RandomForest
-
Creates a new random forest, trains on one thread with the number of trees
supplied.
- create(int, FeatureType[]) - Static method in class de.jungblut.classification.tree.RandomForest
-
Creates a new random forest, trains on one thread with the number of trees
supplied.
- create(int...) - Static method in class de.jungblut.datastructure.ArrayUtils
-
Creates the given array from a varargs parameter.
- create(long...) - Static method in class de.jungblut.datastructure.ArrayUtils
-
Creates the given array from a varargs parameter.
- create(double...) - Static method in class de.jungblut.datastructure.ArrayUtils
-
Creates the given array from a varargs parameter.
- create(byte...) - Static method in class de.jungblut.datastructure.ArrayUtils
-
Creates the given array from a varargs parameter.
- create(T...) - Static method in class de.jungblut.datastructure.ArrayUtils
-
Creates the given array from a varargs parameter.
- create(InvertedIndex.DocumentMapper<DOCUMENT_TYPE, KEY_TYPE>, InvertedIndex.DocumentDistanceMeasurer<DOCUMENT_TYPE, KEY_TYPE>) - Static method in class de.jungblut.datastructure.InvertedIndex
-
Create an inverted index out of two mapping interfaces: a mapper that maps
documents to its key parts and a distance measurer that measures distance
between two documents.
- create(double) - Static method in class de.jungblut.math.minimize.GradientDescent.GradientDescentBuilder
-
Creates a new builder.
- create(int) - Static method in class de.jungblut.nlp.MarkovChain
-
Creates a new markov chain with the supplied number of states.
- create(int, DoubleMatrix) - Static method in class de.jungblut.nlp.MarkovChain
-
Creates a new markov chain with the supplied number of states and its
predefined transition matrix.
- create(int) - Static method in class de.jungblut.nlp.MinHash
-
Creates a
MinHash instance with the given number of hash functions
with a linear hashing function.
- create(int, long) - Static method in class de.jungblut.nlp.MinHash
-
Creates a
MinHash instance with the given number of hash functions
and a seed to be used in parallel systems.
- create(int, MinHash.HashType) - Static method in class de.jungblut.nlp.MinHash
-
Creates a
MinHash instance with the given number of hash functions.
- create(int, MinHash.HashType, long) - Static method in class de.jungblut.nlp.MinHash
-
Creates a
MinHash instance with the given number of hash functions
and a seed to be used in parallel systems.
- createCanopies(List<DoubleVector>, DistanceMeasurer, double, double, boolean) - Static method in class de.jungblut.clustering.CanopyClustering
-
Creates a list of canopies.
- createClusterKeys(int[], int) - Method in class de.jungblut.nlp.MinHash
-
Generates cluster keys from the minhashes.
- createCompiledTree() - Static method in class de.jungblut.classification.tree.DecisionTree
-
- createCompiledTree(FeatureType[]) - Static method in class de.jungblut.classification.tree.DecisionTree
-
Creates a new compiled decision tree with the given feature types.
- createJob(String, String, Configuration, long, long) - Static method in class de.jungblut.nlp.mr.TfIdfCalculatorJob
-
Creates a tf-idf job.
- createJob(String, String, String, Configuration) - Static method in class de.jungblut.nlp.mr.WordCorpusFrequencyJob
-
Creates a token frequency job.
- createJob(String, String, Configuration) - Static method in class de.jungblut.nlp.mr.WordCountJob
-
Creates a token frequency job.
- createPolynomials(DenseDoubleMatrix, int) - Static method in class de.jungblut.math.MathUtils
-
Creates a new matrix consisting out of polynomials of the input matrix.
Considering you want to do a 2 polynomial out of 3 columns you get:
(SEED: x^1 | y^1 | z^1 )| x^2 | y^2 | z^2 for the columns of the returned
matrix.
- createStratified(DoubleVector[], DoubleVector[], float, boolean) - Static method in class de.jungblut.classification.eval.EvaluationSplit
-
Creates a new stratified evaluation split.
- createVectorIndex(DistanceMeasurer) - Static method in class de.jungblut.datastructure.InvertedIndex
-
Creates an inverted index for vectors (usually sparse vectors are used)
that maps dimensions to the corresponding vectors if they are non-zero.
- CrossEntropyLoss - Class in de.jungblut.math.loss
-
- CrossEntropyLoss() - Constructor for class de.jungblut.math.loss.CrossEntropyLoss
-
- crossValidateClassifier(ClassifierFactory<A>, DoubleVector[], DoubleVector[], int, int, Double, boolean) - Static method in class de.jungblut.classification.eval.Evaluator
-
Does a k-fold crossvalidation on the given classifiers with features and
outcomes.
- crossValidateClassifier(ClassifierFactory<A>, DoubleVector[], DoubleVector[], int, int, Double, int, boolean) - Static method in class de.jungblut.classification.eval.Evaluator
-
Does a k-fold crossvalidation on the given classifiers with features and
outcomes.
- CsvDatasetReader - Class in de.jungblut.reader
-
Binary dataset reader from CSVs.
- CUBLAS2_AVAILABLE - Static variable in class de.jungblut.math.cuda.JCUDAMatrixUtils
-
- Dataset - Class in de.jungblut.reader
-
Simplistic dataset to carry information about them.
- Dataset(DoubleVector[], DoubleVector[]) - Constructor for class de.jungblut.reader.Dataset
-
- DBSCAN - Class in de.jungblut.clustering
-
Sequential version of DBSCAN to evaluate if this algorithm is suitable for
arbitrary parallelization paradigms that can crunch graphs.
- DBSCAN() - Constructor for class de.jungblut.clustering.DBSCAN
-
- DBSCANClustering - Class in de.jungblut.clustering
-
Plain sequential DBSCAN clustering.
- DBSCANClustering() - Constructor for class de.jungblut.clustering.DBSCANClustering
-
- de.jungblut.classification - package de.jungblut.classification
-
- de.jungblut.classification.bayes - package de.jungblut.classification.bayes
-
- de.jungblut.classification.eval - package de.jungblut.classification.eval
-
- de.jungblut.classification.knn - package de.jungblut.classification.knn
-
- de.jungblut.classification.meta - package de.jungblut.classification.meta
-
- de.jungblut.classification.nn - package de.jungblut.classification.nn
-
- de.jungblut.classification.regression - package de.jungblut.classification.regression
-
- de.jungblut.classification.tree - package de.jungblut.classification.tree
-
- de.jungblut.clustering - package de.jungblut.clustering
-
- de.jungblut.crawl - package de.jungblut.crawl
-
- de.jungblut.crawl.extraction - package de.jungblut.crawl.extraction
-
- de.jungblut.datastructure - package de.jungblut.datastructure
-
- de.jungblut.distance - package de.jungblut.distance
-
- de.jungblut.math - package de.jungblut.math
-
- de.jungblut.math.activation - package de.jungblut.math.activation
-
- de.jungblut.math.cuda - package de.jungblut.math.cuda
-
- de.jungblut.math.loss - package de.jungblut.math.loss
-
- de.jungblut.math.minimize - package de.jungblut.math.minimize
-
- de.jungblut.ner - package de.jungblut.ner
-
- de.jungblut.nlp - package de.jungblut.nlp
-
- de.jungblut.nlp.model - package de.jungblut.nlp.model
-
- de.jungblut.nlp.mr - package de.jungblut.nlp.mr
-
- de.jungblut.online.ml - package de.jungblut.online.ml
-
- de.jungblut.partition - package de.jungblut.partition
-
- de.jungblut.reader - package de.jungblut.reader
-
- de.jungblut.sketching - package de.jungblut.sketching
-
- de.jungblut.utils - package de.jungblut.utils
-
- de.jungblut.writable - package de.jungblut.writable
-
- DecisionTree - Class in de.jungblut.classification.tree
-
A decision tree that can be used for classification with numerical or
categorical features.
- decode(SparseBitVector) - Method in class de.jungblut.datastructure.HuffmanTree
-
Decodes a given vector.
- decode(DoubleMatrix, DoubleMatrix, DoubleMatrix, int) - Static method in class de.jungblut.math.ViterbiUtils
-
Do a decoding pass on the given HMM weights, the features to decode and how
many classes to predict.
- decode(DoubleVector[], DoubleVector[]) - Method in class de.jungblut.nlp.HMM
-
Decodes the given observation sequence (features) with the current HMM.
- deduplicate(int[]) - Static method in class de.jungblut.datastructure.ArrayUtils
-
Deduplicates an array in linear time, it does not change the order of the
elements.
- deduplicate(T[]) - Static method in class de.jungblut.datastructure.ArrayUtils
-
Deduplicates an array in linear time, it does not change the order of the
elements.
- deduplicateTokens(String[]) - Static method in class de.jungblut.nlp.TokenizerUtils
-
Deduplicates the given tokens, but maintains the order.
- deepCopy() - Method in class de.jungblut.ner.UnrollableDoubleVector
-
- DENSE_DOUBLE_MATRIX - Static variable in class de.jungblut.writable.MatrixWritable
-
- DenseMatrixFolder - Class in de.jungblut.math.minimize
-
- DenseMatrixFolder() - Constructor for class de.jungblut.math.minimize.DenseMatrixFolder
-
- deserialize(DataInput) - Static method in class de.jungblut.classification.bayes.MultinomialNaiveBayes
-
Deserializes a new MultinomialNaiveBayesClassifier from the given input
stream.
- deserialize(DataInput) - Static method in class de.jungblut.classification.nn.MultilayerPerceptron
-
Deserializes a new neural network from the given input stream.
- deserialize(DataInputStream) - Static method in class de.jungblut.classification.nn.RBM
-
Deserializes the RBM back from the binary stream input.
- deserialize(DataInput) - Static method in class de.jungblut.classification.tree.DecisionTree
-
Reads a new tree from the given stream.
- deserialize(DataInput) - Static method in class de.jungblut.classification.tree.RandomForest
-
Reads a new forest from the given stream.
- DICT_OUT_PATH_KEY - Static variable in class de.jungblut.nlp.mr.WordCorpusFrequencyJob
-
- DiskList<E extends org.apache.hadoop.io.Writable> - Class in de.jungblut.datastructure
-
A file backed disk for adding elements and reading from them in a sequential
fashion.
- DiskList(String) - Constructor for class de.jungblut.datastructure.DiskList
-
Opens a new disk list at the given path.
- DiskList(String, int) - Constructor for class de.jungblut.datastructure.DiskList
-
Opens a new disk list at the given path with the given buffersize.
- DiskList(String, E) - Constructor for class de.jungblut.datastructure.DiskList
-
Opens a new disk list at the given path.
- DiskList(String, int, E) - Constructor for class de.jungblut.datastructure.DiskList
-
Opens a new disk list at the given path with the given buffersize.
- DiskList.IORuntimeException - Exception in de.jungblut.datastructure
-
- DistanceMeasurer - Interface in de.jungblut.distance
-
- DistanceResult<TYPE> - Class in de.jungblut.datastructure
-
Immutable generic distance result that contains a document type object and
its distance (to some artificial queried document).
- DistanceResult(double, TYPE) - Constructor for class de.jungblut.datastructure.DistanceResult
-
Create a new DistanceResultImpl with a distance and a document.
- divide(double) - Method in class de.jungblut.ner.UnrollableDoubleVector
-
- divide(DoubleVector) - Method in class de.jungblut.ner.UnrollableDoubleVector
-
- divideFrom(double) - Method in class de.jungblut.ner.UnrollableDoubleVector
-
- divideFrom(DoubleVector) - Method in class de.jungblut.ner.UnrollableDoubleVector
-
- DocumentSimilarity - Class in de.jungblut.nlp
-
Simply distance measure wrapper for debug string similarity measuring.
- DocumentSumReducer() - Constructor for class de.jungblut.nlp.mr.WordCorpusFrequencyJob.DocumentSumReducer
-
- DocumentVectorizerReducer() - Constructor for class de.jungblut.nlp.mr.TfIdfCalculatorJob.DocumentVectorizerReducer
-
- doGradChecks() - Method in class de.jungblut.math.minimize.OWLQN
-
Set to true this will check the gradients every iteration and print out if
it aligns with the numerical gradient.
- dot(DoubleVector) - Method in class de.jungblut.ner.UnrollableDoubleVector
-
- dropout(Random, DoubleMatrix, double) - Static method in class de.jungblut.classification.nn.MultilayerPerceptronCostFunction
-
Computes dropout for the activations matrix.
- dropoutVisibleLayer(DoubleMatrix, DoubleMatrix[], MultilayerPerceptronCostFunction.NetworkConfiguration) - Static method in class de.jungblut.classification.nn.MultilayerPerceptronCostFunction
-
- ElliotActivationFunction - Class in de.jungblut.math.activation
-
Implementation of the elliot activation function.
- ElliotActivationFunction() - Constructor for class de.jungblut.math.activation.ElliotActivationFunction
-
- END_TAG - Static variable in class de.jungblut.nlp.TokenizerUtils
-
- EPS - Static variable in class de.jungblut.math.MathUtils
-
- equals(Object) - Method in class de.jungblut.crawl.FetchResult
-
- equals(Object) - Method in class de.jungblut.nlp.model.Pair
-
- equals(Object) - Method in class de.jungblut.nlp.model.ReferencedContext
-
- equals(Object) - Method in class de.jungblut.nlp.mr.IntIntPairWritable
-
- equals(Object) - Method in class de.jungblut.nlp.mr.TextDoublePairWritable
-
- equals(Object) - Method in class de.jungblut.nlp.mr.TextIntIntIntWritable
-
- equals(Object) - Method in class de.jungblut.nlp.mr.TextIntPairWritable
-
- equals(Object) - Method in class de.jungblut.partition.Boundaries.Range
-
- equals(Object) - Method in class de.jungblut.writable.VectorWritable
-
- error - Variable in class de.jungblut.classification.nn.MultilayerPerceptronCostFunction.NetworkConfiguration
-
- estimateLikelihood(DoubleVector[]) - Method in class de.jungblut.nlp.HMM
-
Likelihood estimation on the current HMM.
- EuclidianDistance - Class in de.jungblut.distance
-
- EuclidianDistance() - Constructor for class de.jungblut.distance.EuclidianDistance
-
- evaluateBatch(DoubleVector, DoubleMatrix, DoubleMatrix) - Method in class de.jungblut.classification.nn.MultilayerPerceptronCostFunction
-
- evaluateBatch(DoubleVector, DoubleMatrix, DoubleMatrix) - Method in class de.jungblut.classification.nn.RBMCostFunction
-
- evaluateBatch(DoubleVector, DoubleMatrix, DoubleMatrix) - Method in class de.jungblut.math.minimize.AbstractMiniBatchCostFunction
-
Evaluate the batch.
- evaluateClassifier(Classifier, DoubleVector[], DoubleVector[], float, boolean) - Static method in class de.jungblut.classification.eval.Evaluator
-
Trains and evaluates the given classifier with a test split.
- evaluateClassifier(Classifier, DoubleVector[], DoubleVector[], float, boolean, Double) - Static method in class de.jungblut.classification.eval.Evaluator
-
Trains and evaluates the given classifier with a test split.
- evaluateCost(DoubleVector) - Method in class de.jungblut.classification.regression.LogisticRegressionCostFunction
-
- evaluateCost(DoubleVector) - Method in class de.jungblut.math.minimize.AbstractMiniBatchCostFunction
-
- evaluateCost(DoubleVector) - Method in interface de.jungblut.math.minimize.CostFunction
-
Evaluation for the cost function to retrieve cost and gradient.
- evaluateCost(DoubleVector) - Method in class de.jungblut.math.minimize.NegatedCostFunction
-
- evaluateCost(DoubleVector) - Method in class de.jungblut.ner.ConditionalLikelihoodCostFunction
-
- evaluateSplit(Classifier, EvaluationSplit) - Static method in class de.jungblut.classification.eval.Evaluator
-
Evaluates a given train/test split with the given classifier.
- evaluateSplit(Classifier, EvaluationSplit, Double) - Static method in class de.jungblut.classification.eval.Evaluator
-
Evaluates a given train/test split with the given classifier.
- evaluateSplit(Classifier, DoubleVector[], DoubleVector[], DoubleVector[], DoubleVector[], Double) - Static method in class de.jungblut.classification.eval.Evaluator
-
Evaluates a given train/test split with the given classifier.
- EvaluationListener<A extends Classifier> - Class in de.jungblut.classification.eval
-
The evaluation listener is majorly used to track the overfitting of a
classifier while training.
- EvaluationListener(WeightMapper<A>, EvaluationSplit) - Constructor for class de.jungblut.classification.eval.EvaluationListener
-
Initializes this listener.
- EvaluationListener(WeightMapper<A>, EvaluationSplit, int) - Constructor for class de.jungblut.classification.eval.EvaluationListener
-
Initializes this listener.
- EvaluationResult() - Constructor for class de.jungblut.classification.eval.Evaluator.EvaluationResult
-
- EvaluationSplit - Class in de.jungblut.classification.eval
-
Split data class that contains the division of train/test vectors.
- EvaluationSplit(DoubleVector[], DoubleVector[], DoubleVector[], DoubleVector[]) - Constructor for class de.jungblut.classification.eval.EvaluationSplit
-
Sets a split internally.
- Evaluator - Class in de.jungblut.classification.eval
-
Binary-/Multi-class classification evaluator utility that takes care of
test/train splitting and its evaluation with various metrics.
- Evaluator.EvaluationResult - Class in de.jungblut.classification.eval
-
- EXCEPTIONS_ENABLED - Static variable in class de.jungblut.math.cuda.JCUDAMatrixUtils
-
- exp() - Method in class de.jungblut.ner.UnrollableDoubleVector
-
- EXT - Static variable in class de.jungblut.math.minimize.Fmincg
-
- extract(String) - Method in class de.jungblut.crawl.extraction.ArticleContentExtrator
-
- extract(String) - Method in interface de.jungblut.crawl.extraction.Extractor
-
Extracts from a given URL all the content needed and return it.
- extract(String) - Method in class de.jungblut.crawl.extraction.HtmlExtrator
-
- extract(String) - Method in class de.jungblut.crawl.extraction.OutlinkExtractor
-
- extractBaseUrl(String) - Static method in class de.jungblut.crawl.extraction.OutlinkExtractor
-
Extracts a base url from the given url (to make relative outlinks to
absolute ones).
- Extractor<T extends FetchResult> - Interface in de.jungblut.crawl.extraction
-
Simple extraction logic interface for a site and a result.
- extractOutlinks(String, String) - Static method in class de.jungblut.crawl.extraction.OutlinkExtractor
-
Extracts outlinks of the given HTML doc in string.
- extractPredictedClass(DoubleVector) - Method in class de.jungblut.classification.AbstractPredictor
-
- extractPredictedClass(DoubleVector, double) - Method in class de.jungblut.classification.AbstractPredictor
-
- extractPredictedClass(DoubleVector) - Method in interface de.jungblut.classification.Predictor
-
Given an already done prediction, choose the class.
- extractPredictedClass(DoubleVector, double) - Method in interface de.jungblut.classification.Predictor
-
Given an already done prediction, choose the class with a threshold.
- extractPredictedClass(DoubleVector) - Method in class de.jungblut.classification.UntrainableClassifier
-
- extractPredictedClass(DoubleVector, double) - Method in class de.jungblut.classification.UntrainableClassifier
-
- extractTitle(String) - Static method in class de.jungblut.crawl.extraction.ArticleContentExtrator
-
Extracts the title from the given HTML.
- featureNames - Variable in class de.jungblut.reader.Dataset
-
- FeatureOutcomePair - Class in de.jungblut.online.ml
-
- FeatureOutcomePair(DoubleVector, DoubleVector) - Constructor for class de.jungblut.online.ml.FeatureOutcomePair
-
- features - Variable in class de.jungblut.reader.Dataset
-
- FeatureType - Enum in de.jungblut.classification.tree
-
Denotes a feature type, either numerical or nominal.
- FetchResult - Class in de.jungblut.crawl
-
Fetch Result class, contains the origin url and its outlinks for further
crawling.
- FetchResult(String, HashSet<String>) - Constructor for class de.jungblut.crawl.FetchResult
-
- FetchResultPersister<T extends FetchResult> - Class in de.jungblut.crawl
-
Asynchronous persister thread, taking a resultwriter and handles the logic
behind asynchronous writing to disk or an arbitrary sink implemented by the
ResultWriter.
- FetchResultPersister(ResultWriter<T>) - Constructor for class de.jungblut.crawl.FetchResultPersister
-
- FetchResultPersister(ResultWriter<T>, Configuration) - Constructor for class de.jungblut.crawl.FetchResultPersister
-
- FetchThread<T extends FetchResult> - Class in de.jungblut.crawl
-
Callable fetcher that extracts, for a given list of URLs and with a
given
Extractor, the content from the list of urls.
- FetchThread(List<String>, Extractor<T>) - Constructor for class de.jungblut.crawl.FetchThread
-
- filter(HashSet<String>, Pattern) - Static method in class de.jungblut.crawl.extraction.OutlinkExtractor
-
Filters outlinks from a parsed page that NOT matches the given matcher.
- finalizeComputation() - Method in class de.jungblut.utils.Statistics
-
Finalize the computation to calculate the mean/median and deviation.
- find(T[], T) - Static method in class de.jungblut.datastructure.ArrayUtils
-
Finds the occurence of the given key in the given array.
- find(int[], int) - Static method in class de.jungblut.datastructure.ArrayUtils
-
Finds the occurence of the given key in the given array.
- find(long[], long) - Static method in class de.jungblut.datastructure.ArrayUtils
-
Finds the occurence of the given key in the given array.
- findNoise(List<DoubleVector>, List<List<DoubleVector>>) - Static method in class de.jungblut.clustering.DBSCANClustering
-
Find the noise in the given clustering, by taking a set difference.
- flush() - Method in class de.jungblut.datastructure.AsyncBufferedOutputStream
-
Flushes this buffered output stream.
- Fmincg - Class in de.jungblut.math.minimize
-
Minimize a continuous differentialble multivariate function.
- Fmincg() - Constructor for class de.jungblut.math.minimize.Fmincg
-
- foldMatrices(DoubleMatrix...) - Static method in class de.jungblut.math.minimize.DenseMatrixFolder
-
Folds the given matrices column-wise into a single vector.
- foldMatrix(DoubleMatrix) - Static method in class de.jungblut.math.minimize.DenseMatrixFolder
-
Folds a single matrix into a single vector by rows.
- forwardPropagate(DoubleMatrix[], DoubleMatrix[], DoubleMatrix[], MultilayerPerceptronCostFunction.NetworkConfiguration) - Static method in class de.jungblut.classification.nn.MultilayerPerceptronCostFunction
-
- freePointer(Pointer) - Static method in class de.jungblut.math.cuda.JCUDAMatrixUtils
-
Frees the given pointer.
- from(Iterator<T>) - Static method in class de.jungblut.datastructure.Iterables
-
- from(int, double) - Static method in class de.jungblut.math.MathUtils.PredictionOutcomePair
-
- fromCIEXYZ(float[]) - Method in class de.jungblut.reader.LUVColorSpace
-
- fromRGB(float[]) - Method in class de.jungblut.reader.LUVColorSpace
-
- fromTrainedModels(List<K>) - Static method in class de.jungblut.classification.meta.Voter
-
Creates a voter from the given trained models for prediction purposes.
- fromUpTo(int, int, int) - Static method in class de.jungblut.datastructure.ArrayUtils
-
Creates an integer array from the given start up to a end number with a
stepsize.
- fromUpTo(long, long, long) - Static method in class de.jungblut.datastructure.ArrayUtils
-
Creates a long array from the given start up to a end number with a
stepsize.
- fromUpTo(double, double, double) - Static method in class de.jungblut.datastructure.ArrayUtils
-
Creates a double array from the given start up to a end number with a
stepsize.
- funnel(Integer, PrimitiveSink) - Method in class de.jungblut.utils.IntegerFunnel
-
- funnel(Long, PrimitiveSink) - Method in class de.jungblut.utils.LongFunnel
-
- funnel(DoubleVector, PrimitiveSink) - Method in class de.jungblut.utils.VectorFunnel
-
- generateClassName() - Static method in class de.jungblut.classification.tree.TreeCompiler
-
- get(int) - Method in class de.jungblut.datastructure.DiskList
-
- get() - Method in class de.jungblut.datastructure.DistanceResult
-
- get(K) - Method in class de.jungblut.datastructure.LRUCache
-
- get(int) - Method in class de.jungblut.datastructure.SingleLinkedList
-
- get(K) - Method in class de.jungblut.datastructure.StackMap
-
HashMap access to get the value for a key.
- get() - Static method in class de.jungblut.distance.EuclidianDistance
-
- get() - Method in enum de.jungblut.math.activation.ActivationFunctionSelector
-
- get(int) - Method in class de.jungblut.ner.UnrollableDoubleVector
-
- getAccuracy() - Method in class de.jungblut.classification.eval.Evaluator.EvaluationResult
-
- getActivations() - Method in class de.jungblut.classification.nn.MultilayerPerceptron
-
- getAssignments() - Method in class de.jungblut.clustering.Cluster
-
- getAUC() - Method in class de.jungblut.classification.eval.Evaluator.EvaluationResult
-
- getBestResult() - Method in class de.jungblut.classification.eval.TestSetIterationCallback
-
- getBestWeights() - Method in class de.jungblut.classification.eval.TestSetIterationCallback
-
- getBitSet() - Method in class de.jungblut.writable.BitSetWritable
-
- getBoundaries() - Method in class de.jungblut.partition.Boundaries
-
- getCardinality() - Method in class de.jungblut.datastructure.HuffmanTree
-
- getCenter() - Method in class de.jungblut.clustering.Cluster
-
- getCenters() - Method in class de.jungblut.clustering.KMeansClustering
-
- getClassifier() - Method in class de.jungblut.classification.meta.Voter
-
- getClassNames() - Method in class de.jungblut.reader.Dataset
-
- getClusteringCost() - Method in class de.jungblut.clustering.KMeansClustering
-
- getCoefficientOfVariation() - Method in class de.jungblut.utils.Statistics
-
- getConfusionMatrix() - Method in class de.jungblut.classification.eval.Evaluator.EvaluationResult
-
- getConnection(String) - Static method in class de.jungblut.crawl.extraction.OutlinkExtractor
-
- getContext() - Method in class de.jungblut.nlp.model.ReferencedContext
-
- getCorrect() - Method in class de.jungblut.classification.eval.Evaluator.EvaluationResult
-
- getCost() - Method in class de.jungblut.math.minimize.CostGradientTuple
-
- getCount() - Method in class de.jungblut.utils.Statistics
-
- getCurrentState() - Method in class de.jungblut.datastructure.DiskList
-
- getDictionary() - Method in class de.jungblut.ner.SparseFeatureExtractorHelper
-
- getDimension() - Method in class de.jungblut.ner.UnrollableDoubleVector
-
- getDispersionIndex() - Method in class de.jungblut.utils.Statistics
-
- getDistance() - Method in class de.jungblut.datastructure.DistanceResult
-
- getEmissionProbabilitiyMatrix() - Method in class de.jungblut.nlp.HMM
-
- getEnd() - Method in class de.jungblut.partition.Boundaries.Range
-
- getEvaluationSplit(Dataset) - Static method in class de.jungblut.reader.IrisReader
-
- getF1Score() - Method in class de.jungblut.classification.eval.Evaluator.EvaluationResult
-
- getFalseNegative() - Method in class de.jungblut.classification.eval.Evaluator.EvaluationResult
-
- getFalsePositive() - Method in class de.jungblut.classification.eval.Evaluator.EvaluationResult
-
- getFalsePositiveRate() - Method in class de.jungblut.classification.eval.Evaluator.EvaluationResult
-
- getFeature() - Method in class de.jungblut.online.ml.FeatureOutcomePair
-
- getFeatureNames() - Method in class de.jungblut.reader.Dataset
-
- getFeatures() - Method in class de.jungblut.reader.Dataset
-
- getFirst() - Method in class de.jungblut.nlp.model.Pair
-
- getFirst() - Method in class de.jungblut.nlp.mr.IntIntPairWritable
-
- getFirst() - Method in class de.jungblut.nlp.mr.TextDoublePairWritable
-
- getFirst() - Method in class de.jungblut.nlp.mr.TextIntIntIntWritable
-
- getFirst() - Method in class de.jungblut.nlp.mr.TextIntPairWritable
-
- getFirst() - Method in class de.jungblut.nlp.mr.TextTextPairWritable
-
- getFoldedThetaVector() - Method in class de.jungblut.classification.nn.MultilayerPerceptron
-
- getFourth() - Method in class de.jungblut.nlp.mr.TextIntIntIntWritable
-
- getGradient() - Method in class de.jungblut.math.minimize.CostGradientTuple
-
- getHiddenPriorProbability() - Method in class de.jungblut.nlp.HMM
-
- getHtml() - Method in class de.jungblut.crawl.extraction.HtmlExtrator.HtmlFetchResult
-
- getHuffmanCodes() - Method in class de.jungblut.datastructure.HuffmanTree
-
Bulk returns all generated Huffman codes as a bit vector representation.
- getIndex() - Method in class de.jungblut.datastructure.ArrayIterator
-
- getInformationGain() - Method in class de.jungblut.classification.tree.Split
-
- getK() - Method in class de.jungblut.math.cuda.MatrixDimension
-
- getKey() - Method in class de.jungblut.datastructure.StackMap.StackMapEntry
-
- getLayers() - Method in class de.jungblut.classification.nn.MultilayerPerceptron
-
- getLdA() - Method in class de.jungblut.math.cuda.MatrixDimension
-
- getLdB() - Method in class de.jungblut.math.cuda.MatrixDimension
-
- getLdC() - Method in class de.jungblut.math.cuda.MatrixDimension
-
- getLeft() - Method in class de.jungblut.clustering.AgglomerativeClustering.ClusterNode
-
- getLength() - Method in class de.jungblut.ner.UnrollableDoubleVector
-
- getLogLoss() - Method in class de.jungblut.classification.eval.Evaluator.EvaluationResult
-
- getM() - Method in class de.jungblut.math.cuda.MatrixDimension
-
- getMainVector() - Method in class de.jungblut.ner.UnrollableDoubleVector
-
- getMatrix(Pointer, int, int) - Static method in class de.jungblut.math.cuda.JCUDAMatrixUtils
-
Read a matrix from device memory.
- getMatrix() - Method in class de.jungblut.writable.MatrixWritable
-
- getMax() - Method in class de.jungblut.utils.Statistics
-
- getMean() - Method in class de.jungblut.clustering.AgglomerativeClustering.ClusterNode
-
- getMean() - Method in class de.jungblut.utils.Statistics
-
- getMedian() - Method in class de.jungblut.utils.Statistics
-
- getMin() - Method in class de.jungblut.utils.Statistics
-
- getMostFrequentItems(Multiset<E>) - Static method in class de.jungblut.nlp.VectorizerUtils
-
Given a multiset of generic elements we are going to return a list of all
the elements, sorted descending by their frequency.
- getMostFrequentItems(Multiset<E>, Predicate<Multiset.Entry<E>>) - Static method in class de.jungblut.nlp.VectorizerUtils
-
Given a multiset of generic elements we are going to return a list of all
the elements, sorted descending by their frequency.
- getN() - Method in class de.jungblut.math.cuda.MatrixDimension
-
- getName() - Method in class de.jungblut.ner.UnrollableDoubleVector
-
- getNearestNeighbours(DoubleVector, int) - Method in class de.jungblut.classification.knn.AbstractKNearestNeighbours
-
Find the k nearest neighbours for the given feature.
- getNearestNeighbours(DoubleVector, int) - Method in class de.jungblut.classification.knn.KNearestNeighbours
-
- getNearestNeighbours(DoubleVector, int) - Method in class de.jungblut.classification.knn.SparseKNearestNeighbours
-
- getNeuralNetworkWeights(int) - Method in class de.jungblut.classification.nn.RBM
-
Creates a weight matrix that can be used for unsupervised weight
initialization in the
MultilayerPerceptron.
- getNoise() - Method in class de.jungblut.clustering.DBSCAN
-
- getNumberOfDocuments(Job) - Static method in class de.jungblut.nlp.mr.WordCorpusFrequencyJob
-
Gets the counter of the input lines read, in this case it should be the
number of documents.
- getNumberOfTokens(Job) - Static method in class de.jungblut.nlp.mr.WordCorpusFrequencyJob
-
Gets the counter of the reduce output values.
- getNumericalSplitValue() - Method in class de.jungblut.classification.tree.Split
-
- getNumHiddenStates() - Method in class de.jungblut.nlp.HMM
-
- getNumLabels() - Method in class de.jungblut.classification.eval.Evaluator.EvaluationResult
-
- getNumStates() - Method in class de.jungblut.nlp.MarkovChain
-
- getNumVisibleStates() - Method in class de.jungblut.nlp.HMM
-
- getOutcome() - Method in class de.jungblut.online.ml.FeatureOutcomePair
-
- getOutcomeClass() - Method in class de.jungblut.math.MathUtils.PredictionOutcomePair
-
- getOutcomes() - Method in class de.jungblut.reader.Dataset
-
- getOutlinks() - Method in class de.jungblut.crawl.FetchResult
-
- getOutputPath() - Method in class de.jungblut.crawl.SequenceFileResultWriter
-
- getParent() - Method in class de.jungblut.clustering.AgglomerativeClustering.ClusterNode
-
- getPool() - Static method in class de.jungblut.datastructure.StringPool
-
- getPrecision() - Method in class de.jungblut.classification.eval.Evaluator.EvaluationResult
-
- getPrediction() - Method in class de.jungblut.math.MathUtils.PredictionOutcomePair
-
- getProbabilityForSequence(int[]) - Method in class de.jungblut.nlp.MarkovChain
-
Calculates the probability that the given sequence occurs.
- getRecall() - Method in class de.jungblut.classification.eval.Evaluator.EvaluationResult
-
- getReference() - Method in class de.jungblut.nlp.model.ReferencedContext
-
- getRight() - Method in class de.jungblut.clustering.AgglomerativeClustering.ClusterNode
-
- getSecond() - Method in class de.jungblut.nlp.model.Pair
-
- getSecond() - Method in class de.jungblut.nlp.mr.IntIntPairWritable
-
- getSecond() - Method in class de.jungblut.nlp.mr.TextDoublePairWritable
-
- getSecond() - Method in class de.jungblut.nlp.mr.TextIntIntIntWritable
-
- getSecond() - Method in class de.jungblut.nlp.mr.TextIntPairWritable
-
- getSecond() - Method in class de.jungblut.nlp.mr.TextTextPairWritable
-
- getSideVectors() - Method in class de.jungblut.ner.UnrollableDoubleVector
-
- getSignalToNoise() - Method in class de.jungblut.utils.Statistics
-
- getSlidingWindowPatches(BufferedImage, int, int, int, int) - Static method in class de.jungblut.reader.ImageReader
-
Calculates subimage windows with the sliding window algorithm.
- getSplitAttributeIndex() - Method in class de.jungblut.classification.tree.Split
-
- getSplitDistance() - Method in class de.jungblut.clustering.AgglomerativeClustering.ClusterNode
-
- getStandardDeviation() - Method in class de.jungblut.utils.Statistics
-
- getStart() - Method in class de.jungblut.partition.Boundaries.Range
-
- getSum() - Method in class de.jungblut.utils.Statistics
-
- getSynchronizedPool() - Static method in class de.jungblut.datastructure.StringPool
-
- getTestFeatures() - Method in class de.jungblut.classification.eval.EvaluationSplit
-
- getTestOutcome() - Method in class de.jungblut.classification.eval.EvaluationSplit
-
- getTestSize() - Method in class de.jungblut.classification.eval.Evaluator.EvaluationResult
-
- getText() - Method in class de.jungblut.crawl.extraction.ArticleContentExtrator.ContentFetchResult
-
- getTheta() - Method in class de.jungblut.classification.regression.LogisticRegression
-
- getTheta() - Method in class de.jungblut.ner.MaxEntMarkovModel
-
- getThird() - Method in class de.jungblut.nlp.mr.TextIntIntIntWritable
-
- getTitle() - Method in class de.jungblut.crawl.extraction.ArticleContentExtrator.ContentFetchResult
-
- getTokenizer(Configuration) - Static method in class de.jungblut.nlp.mr.WordCorpusFrequencyJob
-
Gets a tokenizer, based on the configured class in "tokenizer.class".
- getTrainFeatures() - Method in class de.jungblut.classification.eval.EvaluationSplit
-
- getTrainFeatures() - Method in class de.jungblut.classification.meta.TrainingSplit
-
- getTrainOutcome() - Method in class de.jungblut.classification.eval.EvaluationSplit
-
- getTrainOutcome() - Method in class de.jungblut.classification.meta.TrainingSplit
-
- getTransitionProbabilities(int[]) - Method in class de.jungblut.nlp.MarkovChain
-
- getTransitionProbabilities() - Method in class de.jungblut.nlp.MarkovChain
-
- getTransitionProbabilityMatrix() - Method in class de.jungblut.nlp.HMM
-
- getTrueNegative() - Method in class de.jungblut.classification.eval.Evaluator.EvaluationResult
-
- getTruePositive() - Method in class de.jungblut.classification.eval.Evaluator.EvaluationResult
-
- getType() - Method in class de.jungblut.classification.tree.AbstractTreeNode
-
- getType() - Method in class de.jungblut.classification.tree.LeafNode
-
- getType() - Method in class de.jungblut.classification.tree.NominalNode
-
- getType() - Method in class de.jungblut.classification.tree.NumericalNode
-
- getUrl() - Method in class de.jungblut.crawl.FetchResult
-
- getValue() - Method in class de.jungblut.datastructure.StackMap.StackMapEntry
-
- getVariance() - Method in class de.jungblut.utils.Statistics
-
- getVector() - Method in class de.jungblut.writable.VectorWritable
-
- getWeights() - Method in class de.jungblut.classification.nn.MultilayerPerceptron
-
- getWeights() - Method in class de.jungblut.classification.nn.RBM
-
- getWeights() - Method in class de.jungblut.classification.nn.WeightMatrix
-
- gradient(DoubleVector) - Method in class de.jungblut.math.activation.AbstractActivationFunction
-
- gradient(DoubleMatrix) - Method in class de.jungblut.math.activation.AbstractActivationFunction
-
- gradient(double) - Method in interface de.jungblut.math.activation.ActivationFunction
-
Applies the gradient of the activation function on the given element.
- gradient(DoubleVector) - Method in interface de.jungblut.math.activation.ActivationFunction
-
Applies the gradient of the activation function on each element in the
given vector.
- gradient(DoubleMatrix) - Method in interface de.jungblut.math.activation.ActivationFunction
-
Applies the gradient of the activation function on each element in the
given matrix.
- gradient(double) - Method in class de.jungblut.math.activation.ElliotActivationFunction
-
- gradient(double) - Method in class de.jungblut.math.activation.LinearActivationFunction
-
- gradient(DoubleVector) - Method in class de.jungblut.math.activation.LinearActivationFunction
-
- gradient(DoubleMatrix) - Method in class de.jungblut.math.activation.LinearActivationFunction
-
- gradient(double) - Method in class de.jungblut.math.activation.LogActivationFunction
-
- gradient(double) - Method in class de.jungblut.math.activation.ReluActivationFunction
-
- gradient(double) - Method in class de.jungblut.math.activation.SigmoidActivationFunction
-
- gradient(double) - Method in class de.jungblut.math.activation.SoftMaxActivationFunction
-
- gradient(DoubleVector) - Method in class de.jungblut.math.activation.SoftMaxActivationFunction
-
- gradient(DoubleMatrix) - Method in class de.jungblut.math.activation.SoftMaxActivationFunction
-
- gradient(double) - Method in class de.jungblut.math.activation.SoftplusReluActivationFunction
-
- gradient(double) - Method in class de.jungblut.math.activation.StepActivationFunction
-
- gradient(double) - Method in class de.jungblut.math.activation.TanhActivationFunction
-
- GradientDescent - Class in de.jungblut.math.minimize
-
Gradient descent implementation with some neat features like momentum,
divergence detection, delta breaks and bold driver and scheduled annealing
adaptive learning rates.
- GradientDescent(double, double) - Constructor for class de.jungblut.math.minimize.GradientDescent
-
- GradientDescent.GradientDescentBuilder - Class in de.jungblut.math.minimize
-
- guardedLogarithm(double) - Static method in class de.jungblut.math.MathUtils
-
- ImageReader - Class in de.jungblut.reader
-
BufferedImage reader that exports the raw bytes as a feature vectors with
different encodings.
- inputLayerDropout(double) - Method in class de.jungblut.classification.nn.MultilayerPerceptron.MultilayerPerceptronBuilder
-
Sets the input layer dropout probability.
- InputProvider<T> - Class in de.jungblut.datastructure
-
A provider that provides generic input from a generic source as an iterator
that can be read over and over again.
- InputProvider() - Constructor for class de.jungblut.datastructure.InputProvider
-
- IntegerFunnel - Class in de.jungblut.utils
-
- IntegerFunnel() - Constructor for class de.jungblut.utils.IntegerFunnel
-
- internStrings(String[]) - Static method in class de.jungblut.nlp.TokenizerUtils
-
Interns the given strings inplace.
- internStrings(String[], StringPool) - Static method in class de.jungblut.nlp.TokenizerUtils
-
Interns the given strings inplace with the given pool.
- intersection(int[], int[]) - Static method in class de.jungblut.datastructure.ArrayUtils
-
Computes the intersection of two sorted arrays.
- intersectionUnsorted(int[], int[]) - Static method in class de.jungblut.datastructure.ArrayUtils
-
Computes the intersection of two unsorted arrays.
- IntIntPairWritable - Class in de.jungblut.nlp.mr
-
- IntIntPairWritable() - Constructor for class de.jungblut.nlp.mr.IntIntPairWritable
-
- IntIntPairWritable(IntWritable, IntWritable) - Constructor for class de.jungblut.nlp.mr.IntIntPairWritable
-
- InvertedIndex<DOCUMENT_TYPE,KEY_TYPE> - Class in de.jungblut.datastructure
-
Inverted Index, mainly developed for sparse vectors to speedup dimension
lookups for fast distance measurement and search space reduction.
- InvertedIndex.DocumentDistanceMeasurer<DOCUMENT_TYPE,KEY_TYPE> - Interface in de.jungblut.datastructure
-
Measurer that measures distance of two documents.
- InvertedIndex.DocumentMapper<DOCUMENT_TYPE,KEY_TYPE> - Interface in de.jungblut.datastructure
-
Mapper that maps a document to its keys.
- IORuntimeException() - Constructor for exception de.jungblut.datastructure.DiskList.IORuntimeException
-
- IORuntimeException(Throwable) - Constructor for exception de.jungblut.datastructure.DiskList.IORuntimeException
-
- IrisReader - Class in de.jungblut.reader
-
Dataset vectorizer for the iris dataset.
- isBinary() - Method in class de.jungblut.classification.eval.Evaluator.EvaluationResult
-
- isNamed() - Method in class de.jungblut.ner.UnrollableDoubleVector
-
- isNominal() - Method in enum de.jungblut.classification.tree.FeatureType
-
- isNumerical() - Method in enum de.jungblut.classification.tree.FeatureType
-
- isSingle() - Method in class de.jungblut.ner.UnrollableDoubleVector
-
- isSparse() - Method in class de.jungblut.ner.UnrollableDoubleVector
-
- isTransposeA() - Method in class de.jungblut.math.cuda.MatrixDimension
-
- isTransposeB() - Method in class de.jungblut.math.cuda.MatrixDimension
-
- isValid(String) - Static method in class de.jungblut.crawl.extraction.OutlinkExtractor
-
Checks if the site does not end with unparsable suffixes likes PDF and if
its a valid url by extracting a base url at at index 0.
- isValidIndex(int[], int) - Static method in class de.jungblut.datastructure.ArrayUtils
-
- isValidIndex(double[], int) - Static method in class de.jungblut.datastructure.ArrayUtils
-
- isValidIndex(float[], int) - Static method in class de.jungblut.datastructure.ArrayUtils
-
- isValidIndex(long[], int) - Static method in class de.jungblut.datastructure.ArrayUtils
-
- isValidIndex(boolean[], int) - Static method in class de.jungblut.datastructure.ArrayUtils
-
- isValidIndex(byte[], int) - Static method in class de.jungblut.datastructure.ArrayUtils
-
- isValidIndex(T[], int) - Static method in class de.jungblut.datastructure.ArrayUtils
-
- Iterables - Class in de.jungblut.datastructure
-
Some fancy utilities for iterables, e.G.
- iterate() - Method in class de.jungblut.datastructure.CollectionInputProvider
-
- iterate() - Method in class de.jungblut.datastructure.InputProvider
-
- iterate() - Method in class de.jungblut.datastructure.TextLineInputProvider
-
- iterate() - Method in class de.jungblut.ner.UnrollableDoubleVector
-
- iterateNonZero() - Method in class de.jungblut.ner.UnrollableDoubleVector
-
- IterationCompletionListener - Interface in de.jungblut.math.minimize
-
Callback that should be triggered when a iteration was finished.
- IterativeSimilarityAggregation - Class in de.jungblut.ner
-
Iterative similarity aggregation for named entity recognition and set
expansion based on the paper
"SEISA: Set Expansion by Iterative Similarity Aggregation".
- IterativeSimilarityAggregation(String[], Tuple<String[], DoubleMatrix>) - Constructor for class de.jungblut.ner.IterativeSimilarityAggregation
-
Constructs the similarity aggregation by seed tokens to expand and a given
bipartite graph.
- IterativeSimilarityAggregation(String[], Tuple<String[], DoubleMatrix>, double, DistanceMeasurer) - Constructor for class de.jungblut.ner.IterativeSimilarityAggregation
-
Constructs the similarity aggregation by seed tokens to expand and a given
bipartite graph.
- iterator() - Method in class de.jungblut.datastructure.DiskList
-
- iterator() - Method in class de.jungblut.datastructure.SingleLinkedList
-
- main(String[]) - Static method in class de.jungblut.crawl.extraction.ArticleContentExtrator
-
- main(String[]) - Static method in class de.jungblut.crawl.MultithreadedCrawler
-
- main(String[]) - Static method in class de.jungblut.math.cuda.JCUDAMatrixUtils
-
- main(String[]) - Static method in class de.jungblut.nlp.mr.TfIdfCalculatorJob
-
Calculates TF-IDF vectors from text input in the following format:
- main(String[]) - Static method in class de.jungblut.nlp.mr.WordCorpusFrequencyJob
-
- main(String[]) - Static method in class de.jungblut.nlp.mr.WordCountJob
-
- ManhattanDistance - Class in de.jungblut.distance
-
- ManhattanDistance() - Constructor for class de.jungblut.distance.ManhattanDistance
-
- map(LongWritable, Text, Mapper<LongWritable, Text, Text, TextIntPairWritable>.Context) - Method in class de.jungblut.nlp.mr.WordCorpusFrequencyJob.TokenMapper
-
- map(LongWritable, Text, Mapper<LongWritable, Text, Text, LongWritable>.Context) - Method in class de.jungblut.nlp.mr.WordCountJob.WordFrequencyMapper
-
- mapDocument(DOCUMENT_TYPE) - Method in interface de.jungblut.datastructure.InvertedIndex.DocumentMapper
-
Maps the document into its smaller parts.
- mapDocument(DoubleVector) - Method in class de.jungblut.nlp.SparseVectorDocumentMapper
-
- mapper - Variable in class de.jungblut.classification.eval.EvaluationListener
-
- mapWeights(DoubleVector) - Method in interface de.jungblut.classification.eval.WeightMapper
-
Maps the given weights to a classifier implementation.
- mapWeights(DoubleVector) - Method in class de.jungblut.classification.nn.MLPWeightMapper
-
- MarkovChain - Class in de.jungblut.nlp
-
Markov chain, that can "learn" the state transition probabilities by a given
input and returns the probability for a given sequence of states.
- MathUtils - Class in de.jungblut.math
-
Math utils that features normalizations and other fancy stuff.
- MathUtils.PredictionOutcomePair - Class in de.jungblut.math
-
- MatrixDimension - Class in de.jungblut.math.cuda
-
Helper class and data holder for matrices and their operations.
- MatrixDimension(DoubleMatrix, DoubleMatrix) - Constructor for class de.jungblut.math.cuda.MatrixDimension
-
Creates matrix dimensions from two matrices.
- MatrixDimension(DoubleMatrix, DoubleMatrix, boolean, boolean) - Constructor for class de.jungblut.math.cuda.MatrixDimension
-
Creates matrix dimensions from two matrices.
- MatrixWritable - Class in de.jungblut.writable
-
Writable class for dense and sparse matrices.
- MatrixWritable() - Constructor for class de.jungblut.writable.MatrixWritable
-
- MatrixWritable(DoubleMatrix) - Constructor for class de.jungblut.writable.MatrixWritable
-
- max(int[]) - Static method in class de.jungblut.datastructure.ArrayUtils
-
- max(long[]) - Static method in class de.jungblut.datastructure.ArrayUtils
-
- max(double[]) - Static method in class de.jungblut.datastructure.ArrayUtils
-
- max() - Method in class de.jungblut.ner.UnrollableDoubleVector
-
- MaxEntMarkovModel - Class in de.jungblut.ner
-
Maximum entropy markov model for named entity recognition (classifying labels
in sequence learning).
- MaxEntMarkovModel(Minimizer, int, boolean) - Constructor for class de.jungblut.ner.MaxEntMarkovModel
-
- MaxEntMarkovModel(DenseDoubleMatrix, int) - Constructor for class de.jungblut.ner.MaxEntMarkovModel
-
- maxIndex(int[]) - Static method in class de.jungblut.datastructure.ArrayUtils
-
- maxIndex(long[]) - Static method in class de.jungblut.datastructure.ArrayUtils
-
- maxIndex(double[]) - Static method in class de.jungblut.datastructure.ArrayUtils
-
- maxIndex() - Method in class de.jungblut.ner.UnrollableDoubleVector
-
- meanNormalizeColumns(DoubleMatrix) - Static method in class de.jungblut.math.MathUtils
-
- meanNormalizeColumns(Dataset) - Static method in class de.jungblut.math.MathUtils
-
Normalizes the given dataset (inplace), by subtracting the mean and
dividing by the stddev.
- meanNormalizeColumns(Dataset, Predicate<FeatureOutcomePair>) - Static method in class de.jungblut.math.MathUtils
-
Normalizes the given dataset (inplace), by subtracting the mean and
dividing by the stddev.
- meanNormalizeRows(DoubleMatrix) - Static method in class de.jungblut.math.MathUtils
-
- MeanShiftClustering - Class in de.jungblut.clustering
-
Sequential Mean Shift Clustering using a gaussian kernel and euclidian
distance measurement.
- MeanShiftClustering() - Constructor for class de.jungblut.clustering.MeanShiftClustering
-
- measure(DOCUMENT_TYPE, Set<KEY_TYPE>, DOCUMENT_TYPE, Set<KEY_TYPE>) - Method in interface de.jungblut.datastructure.InvertedIndex.DocumentDistanceMeasurer
-
Measures the distance (value between 0.0 and 1.0) between a reference
document and a candidate document.
- measure(DoubleVector, Set<T>, DoubleVector, Set<T>) - Method in class de.jungblut.distance.VectorDocumentDistanceMeasurer
-
- measureDistance(double[], double[]) - Method in class de.jungblut.distance.CosineDistance
-
- measureDistance(DoubleVector, DoubleVector) - Method in class de.jungblut.distance.CosineDistance
-
- measureDistance(double[], double[]) - Method in interface de.jungblut.distance.DistanceMeasurer
-
- measureDistance(DoubleVector, DoubleVector) - Method in interface de.jungblut.distance.DistanceMeasurer
-
- measureDistance(double[], double[]) - Method in class de.jungblut.distance.EuclidianDistance
-
- measureDistance(DoubleVector, DoubleVector) - Method in class de.jungblut.distance.EuclidianDistance
-
- measureDistance(double[], double[]) - Method in class de.jungblut.distance.HaversineDistance
-
- measureDistance(DoubleVector, DoubleVector) - Method in class de.jungblut.distance.HaversineDistance
-
- measureDistance(double[], double[]) - Method in class de.jungblut.distance.JaccardDistance
-
- measureDistance(DoubleVector, DoubleVector) - Method in class de.jungblut.distance.JaccardDistance
-
- measureDistance(double[], double[]) - Method in class de.jungblut.distance.ManhattanDistance
-
- measureDistance(DoubleVector, DoubleVector) - Method in class de.jungblut.distance.ManhattanDistance
-
- measureDistance(double[], double[]) - Method in class de.jungblut.distance.ZeroDistance
-
- measureDistance(DoubleVector, DoubleVector) - Method in class de.jungblut.distance.ZeroDistance
-
- measureDocumentSimilarity(String[], String[]) - Method in class de.jungblut.nlp.DocumentSimilarity
-
- measureSimilarity(double[], double[]) - Method in class de.jungblut.distance.SimilarityMeasurer
-
- measureSimilarity(DoubleVector, DoubleVector) - Method in class de.jungblut.distance.SimilarityMeasurer
-
- measureSimilarity(int[], int[]) - Method in class de.jungblut.nlp.MinHash
-
Measures the similarity between two min hash arrays by comparing the hashes
at the same index.
- medianOfMedians(int[]) - Static method in class de.jungblut.datastructure.ArrayUtils
-
Finds the median of medians in the given array.
- memcpyMatrix(DenseDoubleMatrix) - Static method in class de.jungblut.math.cuda.JCUDAMatrixUtils
-
Copies the given matrix to the device memory in column major format.
- merge(int[], int[]) - Static method in class de.jungblut.datastructure.ArrayUtils
-
Merges two sorted arrays to a single new array.
- merge(int[], int, int, int) - Static method in class de.jungblut.datastructure.ArrayUtils
-
Merges two sorted subparts of the given number array.
- merge(List<K>, List<K>) - Static method in class de.jungblut.datastructure.ListUtils
-
Merges two sorted segments into a single sorted list.
- merge(Class<M>, String, String...) - Static method in class de.jungblut.datastructure.Merger
-
- merge(Class<M>, String, List<String>) - Static method in class de.jungblut.datastructure.Merger
-
- merge(Class<M>, File, File...) - Static method in class de.jungblut.datastructure.Merger
-
- merge(Class<M>, File, List<File>) - Static method in class de.jungblut.datastructure.Merger
-
- merge(Class<M>, boolean, File, List<File>) - Static method in class de.jungblut.datastructure.Merger
-
- mergeIntermediate(Class<M>, String, String...) - Static method in class de.jungblut.datastructure.Merger
-
- mergeIntermediate(Class<M>, File, File...) - Static method in class de.jungblut.datastructure.Merger
-
- mergeIntermediate(Class<M>, File, List<File>) - Static method in class de.jungblut.datastructure.Merger
-
- mergeIntermediate(Class<M>, String, List<String>) - Static method in class de.jungblut.datastructure.Merger
-
- Merger<M extends org.apache.hadoop.io.WritableComparable> - Class in de.jungblut.datastructure
-
Sorted segment merger on disk.
- mergeSort(List<K>) - Static method in class de.jungblut.datastructure.ListUtils
-
MergeSorts the given list.
- min(int[]) - Static method in class de.jungblut.datastructure.ArrayUtils
-
- min(long[]) - Static method in class de.jungblut.datastructure.ArrayUtils
-
- min(double[]) - Static method in class de.jungblut.datastructure.ArrayUtils
-
- min() - Method in class de.jungblut.ner.UnrollableDoubleVector
-
- MIN_WORD_COUNT_KEY - Static variable in class de.jungblut.nlp.mr.WordCorpusFrequencyJob
-
- MIN_WORD_COUNT_KEY - Static variable in class de.jungblut.nlp.mr.WordCountJob
-
- MinHash - Class in de.jungblut.nlp
-
Linear MinHash algorithm to find near duplicates faster or to speedup nearest
neighbour searches.
- MinHash.HashType - Enum in de.jungblut.nlp
-
- minHashVector(DoubleVector) - Method in class de.jungblut.nlp.MinHash
-
Minhashes the given vector by iterating over all non zero items and hashing
each byte in its value (as an integer).
- miniBatchSize(int) - Method in class de.jungblut.classification.nn.MultilayerPerceptron.MultilayerPerceptronBuilder
-
- miniBatchSize(int) - Method in class de.jungblut.classification.nn.RBM.RBMBuilder
-
- minimize(CostFunction, DoubleVector, int, boolean) - Method in class de.jungblut.math.minimize.Fmincg
-
- minimize(CostFunction, DoubleVector, int, boolean) - Method in class de.jungblut.math.minimize.GradientDescent
-
- minimize(CostFunction, DoubleVector, int, boolean) - Method in interface de.jungblut.math.minimize.Minimizer
-
Minimizes the given costfunction with the starting parameter theta.
- minimize(CostFunction, DoubleVector, int, boolean) - Method in class de.jungblut.math.minimize.OWLQN
-
- minimize(CostFunction, DoubleVector, int, boolean) - Method in class de.jungblut.math.minimize.ParticleSwarmOptimization
-
- minimizeFunction(CostFunction, DoubleVector, int, boolean) - Static method in class de.jungblut.math.minimize.Fmincg
-
Minimizes the given CostFunction with Nonlinear conjugate gradient method.
- minimizeFunction(CostFunction, DoubleVector, double, double, int, boolean) - Static method in class de.jungblut.math.minimize.GradientDescent
-
Minimize a given cost function f with the initial parameters pInput (also
called theta) with a learning rate alpha and a fixed number of iterations.
- minimizeFunction(CostFunction, DoubleVector, int, boolean) - Static method in class de.jungblut.math.minimize.OWLQN
-
Minimizes the given cost function with L-BFGS.
- minimizeFunction(CostFunction, DoubleVector, int, double, double, double, int, int, boolean) - Static method in class de.jungblut.math.minimize.ParticleSwarmOptimization
-
Minimize a function using particle swarm optimization.
- Minimizer - Interface in de.jungblut.math.minimize
-
Minimizer interface for various function minimizers.
- minIndex(int[]) - Static method in class de.jungblut.datastructure.ArrayUtils
-
- minIndex(long[]) - Static method in class de.jungblut.datastructure.ArrayUtils
-
- minIndex(double[]) - Static method in class de.jungblut.datastructure.ArrayUtils
-
- minIndex() - Method in class de.jungblut.ner.UnrollableDoubleVector
-
- minMaxScale(DoubleMatrix, double, double, double, double) - Static method in class de.jungblut.math.MathUtils
-
Scales a matrix into the interval given by min and max.
- minMaxScale(DoubleVector, double, double, double, double) - Static method in class de.jungblut.math.MathUtils
-
Scales a vector into the interval given by min and max.
- minMaxScale(double, double, double, double, double) - Static method in class de.jungblut.math.MathUtils
-
Scales a single input into the interval given by min and max.
- missingNumber(int[]) - Static method in class de.jungblut.datastructure.ArrayUtils
-
If array contains unique integers in a range between 0 and n-1, this
function finds the only one missing in linear time and constant memory.
- MLPWeightMapper - Class in de.jungblut.classification.nn
-
- MLPWeightMapper(ClassifierFactory<MultilayerPerceptron>) - Constructor for class de.jungblut.classification.nn.MLPWeightMapper
-
- MNISTReader - Class in de.jungblut.reader
-
MNIST CSV reader from kaggle: www.kaggle.com/c/digit-recognizer/
- momentum(double) - Method in class de.jungblut.math.minimize.GradientDescent.GradientDescentBuilder
-
Add momentum to this gradient descent minimizer.
- MultilayerPerceptron - Class in de.jungblut.classification.nn
-
Multilayer perceptron implementation that works on GPU via JCuda and CPU.
- MultilayerPerceptron.MultilayerPerceptronBuilder - Class in de.jungblut.classification.nn
-
Configuration for training a neural net through the
Classifier
- MultilayerPerceptronCostFunction - Class in de.jungblut.classification.nn
-
Neural network costfunction for a multilayer perceptron.
- MultilayerPerceptronCostFunction(MultilayerPerceptron, DoubleVector[], DoubleVector[]) - Constructor for class de.jungblut.classification.nn.MultilayerPerceptronCostFunction
-
- MultilayerPerceptronCostFunction.NetworkConfiguration - Class in de.jungblut.classification.nn
-
- MultinomialNaiveBayes - Class in de.jungblut.classification.bayes
-
Multinomial naive bayes classifier.
- MultinomialNaiveBayes() - Constructor for class de.jungblut.classification.bayes.MultinomialNaiveBayes
-
Default constructor to construct this classifier.
- MultinomialNaiveBayes(boolean) - Constructor for class de.jungblut.classification.bayes.MultinomialNaiveBayes
-
Pass true if this classifier should output some progress information to
STDOUT.
- multiply(DenseDoubleMatrix, DenseDoubleMatrix) - Static method in class de.jungblut.math.cuda.JCUDAMatrixUtils
-
Multiplies matrix A with matrix B and returns a new matrix.
- multiply(Pointer, Pointer, MatrixDimension) - Static method in class de.jungblut.math.cuda.JCUDAMatrixUtils
-
Multiplies matrix A with matrix B (these are pointers, thus the dimension
must be passed and returns a new matrix.
- multiply(DenseDoubleMatrix, DenseDoubleMatrix, boolean, boolean) - Static method in class de.jungblut.math.cuda.JCUDAMatrixUtils
-
Multiplies matrix a with matrix b and returns a new matrix.
- multiply(double) - Method in class de.jungblut.ner.UnrollableDoubleVector
-
- multiply(DoubleVector) - Method in class de.jungblut.ner.UnrollableDoubleVector
-
- multiQuickSort(int[]...) - Static method in class de.jungblut.datastructure.ArrayUtils
-
Multi-sorts the given arrays with the quicksort algorithm.
- multiQuickSort(int, int[]...) - Static method in class de.jungblut.datastructure.ArrayUtils
-
Multi-sorts the given arrays with the quicksort algorithm.
- multiQuickSort(T[]...) - Static method in class de.jungblut.datastructure.ArrayUtils
-
Multi-sorts the given arrays with the quicksort algorithm.
- multiQuickSort(int, T[]...) - Static method in class de.jungblut.datastructure.ArrayUtils
-
Multi-sorts the given arrays with the quicksort algorithm.
- multiShuffle(T[], T[]...) - Static method in class de.jungblut.datastructure.ArrayUtils
-
Shuffles the given array.
- multiShuffle(T[], Random, T[]...) - Static method in class de.jungblut.datastructure.ArrayUtils
-
Shuffles the given array with the given random function.
- MultithreadedCrawler<T extends FetchResult> - Class in de.jungblut.crawl
-
Fast multithreaded crawler, will start a fixed threadpool of 32 threads each
will be fed by 10 urls at once.
- MultithreadedCrawler(int, int, int, Extractor<T>, ResultWriter<T>) - Constructor for class de.jungblut.crawl.MultithreadedCrawler
-
Constructs a new Multithreaded Crawler.
- MultithreadedCrawler(int, Extractor<T>, ResultWriter<T>) - Constructor for class de.jungblut.crawl.MultithreadedCrawler
-
Constructs a new Multithreaded Crawler with 32 threads working on 10 url
batches at each time.
- MushroomReader - Class in de.jungblut.reader
-
Dataset vectorizer for the mushroom dataset.
- Pair<S,T> - Class in de.jungblut.nlp.model
-
Pair implementation, unlike Tuple this implements hashcode and equals
on both parts of this pair.
- Pair(S, T) - Constructor for class de.jungblut.nlp.model.Pair
-
- ParticleSwarmOptimization - Class in de.jungblut.math.minimize
-
Particle Swarm Optimization algorithm to minimize costfunctions.
- ParticleSwarmOptimization(int, double, double, double, int) - Constructor for class de.jungblut.math.minimize.ParticleSwarmOptimization
-
- partition(T[]) - Static method in class de.jungblut.datastructure.ArrayUtils
-
Partitions the given array in-place and uses the last element as pivot,
everything less than the pivot will be placed left and everything greater
will be placed right of the pivot.
- partition(T[], int, int) - Static method in class de.jungblut.datastructure.ArrayUtils
-
Partitions the given array in-place and uses the end element as pivot,
everything less than the pivot will be placed left and everything greater
will be placed right of the pivot.
- partition(int[]) - Static method in class de.jungblut.datastructure.ArrayUtils
-
Partitions the given array in-place and uses the last element as pivot,
everything less than the pivot will be placed left and everything greater
will be placed right of the pivot.
- partition(int[], int, int) - Static method in class de.jungblut.datastructure.ArrayUtils
-
Partitions the given array in-place and uses the end element as pivot,
everything less than the pivot will be placed left and everything greater
will be placed right of the pivot.
- partition(long[]) - Static method in class de.jungblut.datastructure.ArrayUtils
-
Partitions the given array in-place and uses the last element as pivot,
everything less than the pivot will be placed left and everything greater
will be placed right of the pivot.
- partition(long[], int, int) - Static method in class de.jungblut.datastructure.ArrayUtils
-
Partitions the given array in-place and uses the end element as pivot,
everything less than the pivot will be placed left and everything greater
will be placed right of the pivot.
- partition(double[]) - Static method in class de.jungblut.datastructure.ArrayUtils
-
Partitions the given array in-place and uses the last element as pivot,
everything less than the pivot will be placed left and everything greater
will be placed right of the pivot.
- partition(double[], int, int) - Static method in class de.jungblut.datastructure.ArrayUtils
-
Partitions the given array in-place and uses the end element as pivot,
everything less than the pivot will be placed left and everything greater
will be placed right of the pivot.
- partition(int, int) - Method in class de.jungblut.partition.BlockPartitioner
-
- partition(int, int) - Method in interface de.jungblut.partition.Partitioner
-
- Partitioner - Interface in de.jungblut.partition
-
Used to partition a list/matrix-like structure to a number of cores /
buckets.
- peek() - Method in class de.jungblut.datastructure.StackMap
-
Retrieves the first item in the stack, but does not remove it.
- Permutations<T extends Comparable<? super T>> - Class in de.jungblut.datastructure
-
- Permutations(T[]) - Constructor for class de.jungblut.datastructure.Permutations
-
- poll(E) - Method in class de.jungblut.datastructure.DiskList
-
Polls the next element from the input stream.
- pool(String) - Method in class de.jungblut.datastructure.StringPool
-
Pools the given string and returns a reference to an existing string (if
exists).
- pop() - Method in class de.jungblut.datastructure.StackMap
-
Retrieves the first item in the stack and removes it.
- pow(double) - Method in class de.jungblut.ner.UnrollableDoubleVector
-
- predict(DoubleVector) - Method in class de.jungblut.classification.bayes.MultinomialNaiveBayes
-
- predict(DoubleVector) - Method in class de.jungblut.classification.knn.AbstractKNearestNeighbours
-
- predict(DoubleVector) - Method in class de.jungblut.classification.meta.Voter
-
- predict(DoubleVector) - Method in class de.jungblut.classification.nn.MultilayerPerceptron
-
Predicts the outcome of the given input by doing a forward pass.
- predict(DoubleVector, double) - Method in class de.jungblut.classification.nn.MultilayerPerceptron
-
Predicts the outcome of the given input by doing a forward pass.
- predict(DoubleVector) - Method in class de.jungblut.classification.nn.RBM
-
Returns the hidden activations of the last RBM.
- predict(DoubleVector) - Method in interface de.jungblut.classification.Predictor
-
Classifies the given features.
- predict(DoubleVector) - Method in class de.jungblut.classification.regression.LogisticRegression
-
- predict(DoubleVector) - Method in class de.jungblut.classification.tree.AbstractTreeNode
-
- predict(DoubleVector) - Method in class de.jungblut.classification.tree.DecisionTree
-
- predict(DoubleVector) - Method in class de.jungblut.classification.tree.LeafNode
-
- predict(DoubleVector) - Method in class de.jungblut.classification.tree.NominalNode
-
- predict(DoubleVector) - Method in class de.jungblut.classification.tree.NumericalNode
-
- predict(DoubleVector) - Method in class de.jungblut.classification.tree.RandomForest
-
- predict(DoubleVector) - Method in class de.jungblut.classification.UntrainableClassifier
-
- predict(DoubleVector) - Method in class de.jungblut.ner.MaxEntMarkovModel
-
- predict(DoubleVector, DoubleVector[]) - Method in class de.jungblut.ner.MaxEntMarkovModel
-
- predict(DoubleMatrix, DoubleMatrix) - Method in class de.jungblut.ner.MaxEntMarkovModel
-
- predict(DoubleVector) - Method in class de.jungblut.nlp.HMM
-
- predict(DoubleVector, DoubleVector) - Method in class de.jungblut.nlp.HMM
-
- predictedClass(DoubleVector, double) - Method in class de.jungblut.classification.AbstractPredictor
-
- predictedClass(DoubleVector) - Method in class de.jungblut.classification.AbstractPredictor
-
- predictedClass(DoubleVector, double) - Method in interface de.jungblut.classification.Predictor
-
Classifies the given features.
- predictedClass(DoubleVector) - Method in interface de.jungblut.classification.Predictor
-
Classifies the given features.
- predictedClass(DoubleVector, double) - Method in class de.jungblut.classification.UntrainableClassifier
-
- predictedClass(DoubleVector) - Method in class de.jungblut.classification.UntrainableClassifier
-
- Predictor - Interface in de.jungblut.classification
-
- predictProbability(DoubleVector) - Method in class de.jungblut.classification.AbstractPredictor
-
- predictProbability(DoubleVector) - Method in class de.jungblut.classification.knn.AbstractKNearestNeighbours
-
- predictProbability(DoubleVector) - Method in interface de.jungblut.classification.Predictor
-
Classifies the given features.
- predictProbability(DoubleVector) - Method in class de.jungblut.classification.tree.RandomForest
-
- predictProbability(DoubleVector) - Method in class de.jungblut.classification.UntrainableClassifier
-
- print() - Method in class de.jungblut.classification.eval.Evaluator.EvaluationResult
-
- print(Logger) - Method in class de.jungblut.classification.eval.Evaluator.EvaluationResult
-
- printConfusionMatrix() - Method in class de.jungblut.classification.eval.Evaluator.EvaluationResult
-
- printConfusionMatrix(String[]) - Method in class de.jungblut.classification.eval.Evaluator.EvaluationResult
-
- process(String...) - Method in interface de.jungblut.crawl.Crawler
-
Starts the crawler, starting by the seedURL.
- process(String...) - Method in class de.jungblut.crawl.MultithreadedCrawler
-
- process(String...) - Method in class de.jungblut.crawl.SequentialCrawler
-
- put(K, V) - Method in class de.jungblut.datastructure.LRUCache
-
- put(K, V) - Method in class de.jungblut.datastructure.StackMap
-
Put method which puts the k/v mapping into the map and pushes the key on
the stack.
- radixSort(int[]) - Static method in class de.jungblut.datastructure.ArrayUtils
-
Radix sorts an integer array in O(m*n), where m is the length of the key
(here 32 bit) and n the number of elements.
- RandomForest - Class in de.jungblut.classification.tree
-
A decision tree forest, using bagging.
- Range() - Constructor for class de.jungblut.partition.Boundaries.Range
-
- Range(int, int) - Constructor for class de.jungblut.partition.Boundaries.Range
-
- RBM - Class in de.jungblut.classification.nn
-
Class for training and stacking Restricted Boltzmann Machines (RBMs).
- RBM.RBMBuilder - Class in de.jungblut.classification.nn
-
- RBMCostFunction - Class in de.jungblut.classification.nn
-
Restricted Boltzmann machine implementation using Contrastive Divergence 1
(CD1).
- RBMCostFunction(DoubleVector[], int, int, int, ActivationFunction, TrainingType, double, long, boolean) - Constructor for class de.jungblut.classification.nn.RBMCostFunction
-
- read(DataInput) - Static method in class de.jungblut.classification.tree.AbstractTreeNode
-
- read() - Method in class de.jungblut.datastructure.ByteBufferInputStream
-
- read(byte[], int, int) - Method in class de.jungblut.datastructure.ByteBufferInputStream
-
- readCsv(String, char, Character, int, int) - Static method in class de.jungblut.reader.CsvDatasetReader
-
Reads a csv into feature and outcome arrays.
- readDenseMatrix(DataInput) - Static method in class de.jungblut.writable.MatrixWritable
-
Reads a dense matrix from the given input stream.
- readFields(DataInput) - Method in class de.jungblut.classification.tree.LeafNode
-
- readFields(DataInput) - Method in class de.jungblut.classification.tree.NominalNode
-
- readFields(DataInput) - Method in class de.jungblut.classification.tree.NumericalNode
-
- readFields(DataInput) - Method in class de.jungblut.nlp.HMM
-
- readFields(DataInput) - Method in class de.jungblut.nlp.mr.IntIntPairWritable
-
- readFields(DataInput) - Method in class de.jungblut.nlp.mr.TextDoublePairWritable
-
- readFields(DataInput) - Method in class de.jungblut.nlp.mr.TextIntIntIntWritable
-
- readFields(DataInput) - Method in class de.jungblut.nlp.mr.TextIntPairWritable
-
- readFields(DataInput) - Method in class de.jungblut.nlp.mr.TextTextPairWritable
-
- readFields(DataInput) - Method in class de.jungblut.partition.Boundaries.Range
-
- readFields(DataInput) - Method in class de.jungblut.utils.Statistics
-
- readFields(DataInput) - Method in class de.jungblut.writable.BitSetWritable
-
- readFields(DataInput) - Method in class de.jungblut.writable.MatrixWritable
-
- readFields(DataInput) - Method in class de.jungblut.writable.VectorWritable
-
- readImageAsGreyScale(BufferedImage) - Static method in class de.jungblut.reader.ImageReader
-
Returns the given image as a vector, where each dimension is mapped to a
given pixel in the image.
- readImageAsLUV(BufferedImage) - Static method in class de.jungblut.reader.ImageReader
-
Returns the given image as a list of points in space, where each point is
encoded by its LUV value.
- readImageAsRGB(BufferedImage) - Static method in class de.jungblut.reader.ImageReader
-
Returns the given image as a list of points in space, where each point is
encoded by its RGB values.
- readIrisDataset(String) - Static method in class de.jungblut.reader.IrisReader
-
- readMNISTTrainImages(String) - Static method in class de.jungblut.reader.MNISTReader
-
- readMushroomDataset(String) - Static method in class de.jungblut.reader.MushroomReader
-
- readSparseMatrix(DataInput) - Static method in class de.jungblut.writable.MatrixWritable
-
Reads a sparse matrix from the given input stream.
- readTwentyNewsgroups(File) - Static method in class de.jungblut.reader.TwentyNewsgroupReader
-
Needs the "20news-bydate" directory that has test and train subdirectories
given.
- readVector(DataInput) - Static method in class de.jungblut.writable.VectorWritable
-
- reconstructInput(DoubleVector) - Method in class de.jungblut.classification.nn.RBM
-
Creates a reconstruction of the input using the given hidden activations.
- reduce(Text, Iterable<TextIntIntIntWritable>, Reducer<Text, TextIntIntIntWritable, Text, VectorWritable>.Context) - Method in class de.jungblut.nlp.mr.TfIdfCalculatorJob.DocumentVectorizerReducer
-
Input is the document ID with several (token, document frequency, term
frequency, token index) pairs.
- reduce(Text, Iterable<TextIntPairWritable>, Reducer<Text, TextIntPairWritable, Text, TextIntIntIntWritable>.Context) - Method in class de.jungblut.nlp.mr.WordCorpusFrequencyJob.DocumentSumReducer
-
- reduce(Text, Iterable<LongWritable>, Reducer<Text, LongWritable, Text, LongWritable>.Context) - Method in class de.jungblut.nlp.mr.WordCountJob.WordFrequencyReducer
-
- ReferencedContext<REF_TYPE,CONTEXT_TYPE> - Class in de.jungblut.nlp.model
-
Reference and its context.
- ReferencedContext(REF_TYPE, CONTEXT_TYPE...) - Constructor for class de.jungblut.nlp.model.ReferencedContext
-
- ReferencedContext(REF_TYPE, Collection<CONTEXT_TYPE>) - Constructor for class de.jungblut.nlp.model.ReferencedContext
-
- ReluActivationFunction - Class in de.jungblut.math.activation
-
Rectified linear units implementation.
- ReluActivationFunction() - Constructor for class de.jungblut.math.activation.ReluActivationFunction
-
- remove() - Method in class de.jungblut.datastructure.ArrayIterator
-
Removes the current item from the list.
- remove(int) - Method in class de.jungblut.datastructure.SingleLinkedList
-
- removeEmpty(String[]) - Static method in class de.jungblut.nlp.TokenizerUtils
-
Removes empty tokens from given array.
- removeMatchingRegex(String, String, String[], boolean) - Static method in class de.jungblut.nlp.TokenizerUtils
-
Applies given regex on tokens and may optionally delete when a token gets
empty.
- removeRow(int) - Method in class de.jungblut.partition.Boundaries
-
- ResultWriter<T extends FetchResult> - Interface in de.jungblut.crawl
-
Result writing interface.
- ResultWriterAdapter<T extends FetchResult> - Class in de.jungblut.crawl
-
- ResultWriterAdapter() - Constructor for class de.jungblut.crawl.ResultWriterAdapter
-
- rnd - Variable in class de.jungblut.classification.nn.MultilayerPerceptronCostFunction.NetworkConfiguration
-
- run() - Method in class de.jungblut.crawl.FetchResultPersister
-
- runInterval - Variable in class de.jungblut.classification.eval.EvaluationListener
-
- SEED - Static variable in class de.jungblut.classification.nn.MultilayerPerceptron
-
- seek(int) - Method in class de.jungblut.datastructure.SingleLinkedList
-
- selectionType(Voter.SelectionType) - Method in class de.jungblut.classification.meta.Voter
-
- SEPARATORS - Static variable in class de.jungblut.nlp.TokenizerUtils
-
- SequenceFeatureExtractor<K> - Interface in de.jungblut.ner
-
Interface for feature extraction in sequence learning.
- SequenceFileResultWriter<T extends FetchResult> - Class in de.jungblut.crawl
-
Writes the result into a sequencefile "files/crawl/result.seq".
- SequenceFileResultWriter() - Constructor for class de.jungblut.crawl.SequenceFileResultWriter
-
- SequentialCrawler<T extends FetchResult> - Class in de.jungblut.crawl
-
Sequential crawler, mainly for debugging or development.
- SequentialCrawler(int, Extractor<T>, ResultWriter<T>) - Constructor for class de.jungblut.crawl.SequentialCrawler
-
- serialize(MultinomialNaiveBayes, DataOutput) - Static method in class de.jungblut.classification.bayes.MultinomialNaiveBayes
-
- serialize(MultilayerPerceptron, DataOutput) - Static method in class de.jungblut.classification.nn.MultilayerPerceptron
-
Serializes this network at its current state to a binary file.
- serialize(RBM, DataOutput) - Static method in class de.jungblut.classification.nn.RBM
-
Serializes this RBM model into the given output stream.
- serialize(DecisionTree, DataOutput) - Static method in class de.jungblut.classification.tree.DecisionTree
-
Writes the given tree to the output stream.
- serialize(RandomForest, DataOutput) - Static method in class de.jungblut.classification.tree.RandomForest
-
Writes the given forest to the output stream.
- set(int, T) - Method in class de.jungblut.datastructure.SingleLinkedList
-
- set(int, double) - Method in class de.jungblut.ner.UnrollableDoubleVector
-
- setClassNames(String[]) - Method in class de.jungblut.reader.Dataset
-
- setCombiningType(Voter.CombiningType) - Method in class de.jungblut.classification.meta.Voter
-
- setCompiled(boolean) - Method in class de.jungblut.classification.tree.DecisionTree
-
If set to true, this tree will be compiled after training time
automatically.
- setFeatureNames(String[]) - Method in class de.jungblut.reader.Dataset
-
- setFeatureTypes(FeatureType[]) - Method in class de.jungblut.classification.tree.DecisionTree
-
Sets the type of feature per index.
- setFeatureTypes(FeatureType[]) - Method in class de.jungblut.classification.tree.RandomForest
-
- setL1Weight(double) - Method in class de.jungblut.math.minimize.OWLQN
-
This implementation also supports l1 weight adjustment (without the
costfunction knowing about it).
- setM(int) - Method in class de.jungblut.math.minimize.OWLQN
-
The amount of directions and gradients to keep, this is the "limited" part
of L-BFGS.
- setMaxHeight(int) - Method in class de.jungblut.classification.tree.DecisionTree
-
Sets the maximum height of this tree.
- setMaxHeight(int) - Method in class de.jungblut.classification.tree.RandomForest
-
Sets the maximum height of this random forest.
- setNumRandomFeaturesToChoose(int) - Method in class de.jungblut.classification.tree.DecisionTree
-
Sets the number of random features to choose from all features.Zero,
negative numbers or numbers greater than the really available features
indicate all features to be used.
- setNumRandomFeaturesToChoose(int) - Method in class de.jungblut.classification.tree.RandomForest
-
- setRunInterval(int) - Method in class de.jungblut.classification.eval.EvaluationListener
-
Sets the run intervall of this listener.
- setSeed(long) - Method in class de.jungblut.classification.nn.RBM
-
Sets the internally used rng seed.
- setSeed(long) - Method in class de.jungblut.classification.tree.DecisionTree
-
Sets the seed for a random number generator if used.
- setTolerance(double) - Method in class de.jungblut.math.minimize.OWLQN
-
The breaking tolerance over a window of five iterations.
- setup(int, Extractor<T>, ResultWriter<T>) - Method in interface de.jungblut.crawl.Crawler
-
Setups this crawler.
- setup(int, Extractor<T>, ResultWriter<T>) - Method in class de.jungblut.crawl.MultithreadedCrawler
-
- setup(int, Extractor<T>, ResultWriter<T>) - Method in class de.jungblut.crawl.SequentialCrawler
-
- setup(Reducer<Text, TextIntIntIntWritable, Text, VectorWritable>.Context) - Method in class de.jungblut.nlp.mr.TfIdfCalculatorJob.DocumentVectorizerReducer
-
- setup(Reducer<Text, TextIntPairWritable, Text, TextIntIntIntWritable>.Context) - Method in class de.jungblut.nlp.mr.WordCorpusFrequencyJob.DocumentSumReducer
-
- setup(Mapper<LongWritable, Text, Text, TextIntPairWritable>.Context) - Method in class de.jungblut.nlp.mr.WordCorpusFrequencyJob.TokenMapper
-
- setup(Mapper<LongWritable, Text, Text, LongWritable>.Context) - Method in class de.jungblut.nlp.mr.WordCountJob.WordFrequencyMapper
-
- setValue(VALUE) - Method in class de.jungblut.datastructure.StackMap.StackMapEntry
-
- setWeights(DoubleMatrix) - Method in class de.jungblut.classification.nn.WeightMatrix
-
- shuffle(T[]) - Static method in class de.jungblut.datastructure.ArrayUtils
-
Shuffles the given array.
- shuffle(T[], Random) - Static method in class de.jungblut.datastructure.ArrayUtils
-
Shuffles the given array with the given random function.
- SigmoidActivationFunction - Class in de.jungblut.math.activation
-
Implementation of the sigmoid function.
- SigmoidActivationFunction() - Constructor for class de.jungblut.math.activation.SigmoidActivationFunction
-
- SimilarityMeasurer - Class in de.jungblut.distance
-
Similarity measurer wrapper.
- SimilarityMeasurer(DistanceMeasurer) - Constructor for class de.jungblut.distance.SimilarityMeasurer
-
- single(int, ActivationFunction) - Static method in class de.jungblut.classification.nn.RBM
-
- single(int) - Static method in class de.jungblut.classification.nn.RBM
-
- singleGPU(int, ActivationFunction) - Static method in class de.jungblut.classification.nn.RBM
-
- SingleLinkedList<T> - Class in de.jungblut.datastructure
-
Single Linked list with less overhead in memory than the double linked list
of Java utils.
- SingleLinkedList() - Constructor for class de.jungblut.datastructure.SingleLinkedList
-
- SingleLinkedList(Collection<? extends T>) - Constructor for class de.jungblut.datastructure.SingleLinkedList
-
- size() - Method in class de.jungblut.datastructure.DiskList
-
- size() - Method in class de.jungblut.datastructure.SingleLinkedList
-
- slice(int) - Method in class de.jungblut.ner.UnrollableDoubleVector
-
- slice(int, int) - Method in class de.jungblut.ner.UnrollableDoubleVector
-
- sliceByLength(int, int) - Method in class de.jungblut.ner.UnrollableDoubleVector
-
- SoftMaxActivationFunction - Class in de.jungblut.math.activation
-
Softmax activation that only works on vectors, because it needs to sum and
divide the probabilities.
- SoftMaxActivationFunction() - Constructor for class de.jungblut.math.activation.SoftMaxActivationFunction
-
- SoftplusReluActivationFunction - Class in de.jungblut.math.activation
-
- SoftplusReluActivationFunction() - Constructor for class de.jungblut.math.activation.SoftplusReluActivationFunction
-
- SortedFile<M extends org.apache.hadoop.io.WritableComparable> - Class in de.jungblut.datastructure
-
A file that serializes WritableComparables to a buffer, once it hits a
threshold this buffer will be sorted in memory.
- SortedFile(String, String, int, Class<M>) - Constructor for class de.jungblut.datastructure.SortedFile
-
Creates a single sorted file.
- SortedFile(String, String, int, Class<M>, boolean) - Constructor for class de.jungblut.datastructure.SortedFile
-
Creates a single sorted file.
- sortInternal() - Method in class de.jungblut.classification.tree.NominalNode
-
- SPAM_DOCUMENT_PERCENTAGE_KEY - Static variable in class de.jungblut.nlp.mr.TfIdfCalculatorJob
-
- SPARSE_DOUBLE_ROW_MATRIX - Static variable in class de.jungblut.writable.MatrixWritable
-
- SparseFeatureExtractorHelper<K> - Class in de.jungblut.ner
-
Convenient helper for creating vectors out of text features for sequence
learning.
- SparseFeatureExtractorHelper(List<K>, List<Integer>, SequenceFeatureExtractor<K>) - Constructor for class de.jungblut.ner.SparseFeatureExtractorHelper
-
Constructs this feature factory.
- SparseFeatureExtractorHelper(List<K>, List<Integer>, SequenceFeatureExtractor<K>, String[]) - Constructor for class de.jungblut.ner.SparseFeatureExtractorHelper
-
Constructs this feature factory via a given dictionary.
- sparseHashVectorize(Stream<String[]>, HashFunction, Supplier<DoubleVector>) - Static method in class de.jungblut.nlp.VectorizerUtils
-
Uses the hashing trick to provide a sparse numeric representation of the
given input.
- sparseHashVectorize(String[], HashFunction, Supplier<DoubleVector>) - Static method in class de.jungblut.nlp.VectorizerUtils
-
Uses the hashing trick to provide a sparse numeric representation of the
given input.
- SparseKNearestNeighbours - Class in de.jungblut.classification.knn
-
K nearest neighbour classification algorithm that is seeded with a "database"
of known examples and predicts based on the k-nearest neighbours majority
vote for a class.
- SparseKNearestNeighbours(int, int, DistanceMeasurer) - Constructor for class de.jungblut.classification.knn.SparseKNearestNeighbours
-
Constructs a new knn classifier.
- SparseVectorDocumentMapper - Class in de.jungblut.nlp
-
Mapper that maps sparse vectors into a set of their indices so they can be
used in the
InvertedIndex for fast lookup.
- SparseVectorDocumentMapper() - Constructor for class de.jungblut.nlp.SparseVectorDocumentMapper
-
- split - Variable in class de.jungblut.classification.eval.EvaluationListener
-
- Split - Class in de.jungblut.classification.tree
-
From Mahout, split class with better naming.
- Split(int, double, double) - Constructor for class de.jungblut.classification.tree.Split
-
- Split(int, double) - Constructor for class de.jungblut.classification.tree.Split
-
- sqrt() - Method in class de.jungblut.ner.UnrollableDoubleVector
-
- SquaredLoss - Class in de.jungblut.math.loss
-
- SquaredLoss() - Constructor for class de.jungblut.math.loss.SquaredLoss
-
- stacked(ActivationFunction, int...) - Static method in class de.jungblut.classification.nn.RBM
-
Creates a new stacked RBM with sigmoid activation and with the given number
of hidden nodes in each stacked layer.
- stacked(int...) - Static method in class de.jungblut.classification.nn.RBM
-
Creates a new stacked RBM with sigmoid activation and with the given number
of hidden nodes in each stacked layer.
- stackedGPU(ActivationFunction, int...) - Static method in class de.jungblut.classification.nn.RBM
-
Creates a new stacked RBM with sigmoid activation and with the given number
of hidden nodes in each stacked layer.
- StackMap<K,V> - Class in de.jungblut.datastructure
-
A stack that also provides random access lookup of values.
- StackMap() - Constructor for class de.jungblut.datastructure.StackMap
-
- StackMap.StackMapEntry<KEY,VALUE> - Class in de.jungblut.datastructure
-
Immutable class for a Key/Value tuple.
- StackMapEntry(KEY, VALUE) - Constructor for class de.jungblut.datastructure.StackMap.StackMapEntry
-
- StandardTokenizer - Class in de.jungblut.nlp
-
Just a basic tokenizer by certain attributes with normalization.
- StandardTokenizer() - Constructor for class de.jungblut.nlp.StandardTokenizer
-
- START_TAG - Static variable in class de.jungblut.nlp.TokenizerUtils
-
- startStaticThresholding(double, int, boolean) - Method in class de.jungblut.ner.IterativeSimilarityAggregation
-
Starts the static thresholding algorithm and returns the expandedset of
newly found related tokens.
- Statistics - Class in de.jungblut.utils
-
Small statistics utility to describe data based on its
min/max/mean/median/deviation.
- Statistics() - Constructor for class de.jungblut.utils.Statistics
-
- StepActivationFunction - Class in de.jungblut.math.activation
-
Classic perceptron-like step function.
- StepActivationFunction(double) - Constructor for class de.jungblut.math.activation.StepActivationFunction
-
- StepLoss - Class in de.jungblut.math.loss
-
- StepLoss() - Constructor for class de.jungblut.math.loss.StepLoss
-
- stochastic() - Method in class de.jungblut.classification.nn.MultilayerPerceptron.MultilayerPerceptronBuilder
-
Sets the training mode to stochastic.
- stochastic(boolean) - Method in class de.jungblut.classification.nn.MultilayerPerceptron.MultilayerPerceptronBuilder
-
If verbose is true, stochastic training will be used.
- stochastic() - Method in class de.jungblut.classification.nn.RBM.RBMBuilder
-
Sets the training mode to stochastic.
- stochastic(boolean) - Method in class de.jungblut.classification.nn.RBM.RBMBuilder
-
If verbose is true, stochastic training will be used.
- stop() - Method in class de.jungblut.crawl.FetchResultPersister
-
Stop this persister and make the queue read-only.
- StringPool - Class in de.jungblut.datastructure
-
Simple map based StringPool that is considered faster than using
String.intern(), but uses a bit more memory.
- subArray(T[], int) - Static method in class de.jungblut.datastructure.ArrayUtils
-
Splits the given array from 0 to the given splitindex (included).
- subArray(T[], int, int) - Static method in class de.jungblut.datastructure.ArrayUtils
-
Splits the given array from the given startIndex to the given splitIndex
(included).
- subList(List<K>, int, int) - Static method in class de.jungblut.datastructure.ListUtils
-
Sublists the given list.
- subtract(DoubleVector) - Method in class de.jungblut.ner.UnrollableDoubleVector
-
- subtract(double) - Method in class de.jungblut.ner.UnrollableDoubleVector
-
- subtractFrom(double) - Method in class de.jungblut.ner.UnrollableDoubleVector
-
- sum() - Method in class de.jungblut.ner.UnrollableDoubleVector
-
- swap(int[], int, int) - Static method in class de.jungblut.datastructure.ArrayUtils
-
Swaps the given index x with y in the array.
- swap(long[], int, int) - Static method in class de.jungblut.datastructure.ArrayUtils
-
Swaps the given index x with y in the array.
- swap(double[], int, int) - Static method in class de.jungblut.datastructure.ArrayUtils
-
Swaps the given index x with y in the array.
- swap(boolean[], int, int) - Static method in class de.jungblut.datastructure.ArrayUtils
-
Swaps the given index x with y in the array.
- swap(T[], int, int) - Static method in class de.jungblut.datastructure.ArrayUtils
-
Swaps the given index x with y in the array.
- swap(int, int) - Method in class de.jungblut.datastructure.SortedFile
-
- TanhActivationFunction - Class in de.jungblut.math.activation
-
Implementation of the Tanh activation based on FastMath.
- TanhActivationFunction() - Constructor for class de.jungblut.math.activation.TanhActivationFunction
-
- tenFoldCrossValidation(ClassifierFactory<A>, DoubleVector[], DoubleVector[], int, Double, boolean) - Static method in class de.jungblut.classification.eval.Evaluator
-
Does a 10 fold crossvalidation.
- tenFoldCrossValidation(ClassifierFactory<A>, DoubleVector[], DoubleVector[], int, Double, int, boolean) - Static method in class de.jungblut.classification.eval.Evaluator
-
Does a 10 fold crossvalidation.
- testClassifier(Predictor, DoubleVector[], DoubleVector[]) - Static method in class de.jungblut.classification.eval.Evaluator
-
Tests the given classifier without actually training it.
- testClassifier(Predictor, DoubleVector[], DoubleVector[], Double) - Static method in class de.jungblut.classification.eval.Evaluator
-
Tests the given classifier without actually training it.
- TestSetIterationCallback<T extends Classifier> - Class in de.jungblut.classification.eval
-
This callback is used to evaluate the performance on a held-out test set.
- TestSetIterationCallback(EvaluationSplit, WeightMapper<T>, Comparator<Evaluator.EvaluationResult>, int) - Constructor for class de.jungblut.classification.eval.TestSetIterationCallback
-
Creates a new test set iteration callback instance.
- TestSetIterationCallback(EvaluationSplit, WeightMapper<T>, Comparator<Evaluator.EvaluationResult>) - Constructor for class de.jungblut.classification.eval.TestSetIterationCallback
-
Creates a new test set iteration callback instance.
- TextDoublePairWritable - Class in de.jungblut.nlp.mr
-
- TextDoublePairWritable() - Constructor for class de.jungblut.nlp.mr.TextDoublePairWritable
-
- TextDoublePairWritable(Text, DoubleWritable) - Constructor for class de.jungblut.nlp.mr.TextDoublePairWritable
-
- TextIntIntIntWritable - Class in de.jungblut.nlp.mr
-
- TextIntIntIntWritable() - Constructor for class de.jungblut.nlp.mr.TextIntIntIntWritable
-
- TextIntIntIntWritable(Text, IntWritable, IntWritable, IntWritable) - Constructor for class de.jungblut.nlp.mr.TextIntIntIntWritable
-
- TextIntPairWritable - Class in de.jungblut.nlp.mr
-
- TextIntPairWritable() - Constructor for class de.jungblut.nlp.mr.TextIntPairWritable
-
- TextIntPairWritable(Text, IntWritable) - Constructor for class de.jungblut.nlp.mr.TextIntPairWritable
-
- TextLineInputProvider - Class in de.jungblut.datastructure
-
Line reader for plain text that contains data in lines.
- TextLineInputProvider(String) - Constructor for class de.jungblut.datastructure.TextLineInputProvider
-
- TextLineInputProvider(URI) - Constructor for class de.jungblut.datastructure.TextLineInputProvider
-
- TextLineInputProvider(Path) - Constructor for class de.jungblut.datastructure.TextLineInputProvider
-
- TextTextPairWritable - Class in de.jungblut.nlp.mr
-
- TextTextPairWritable() - Constructor for class de.jungblut.nlp.mr.TextTextPairWritable
-
- TextTextPairWritable(Text, Text) - Constructor for class de.jungblut.nlp.mr.TextTextPairWritable
-
- TfIdfCalculatorJob - Class in de.jungblut.nlp.mr
-
- TfIdfCalculatorJob() - Constructor for class de.jungblut.nlp.mr.TfIdfCalculatorJob
-
- TfIdfCalculatorJob.DocumentVectorizerReducer - Class in de.jungblut.nlp.mr
-
Calculate the sparse vector with TF-IDF.
- tfIdfVectorize(List<String[]>, String[], int[]) - Static method in class de.jungblut.nlp.VectorizerUtils
-
Vectorizes the given documents by the TF-IDF weighting.
- tfIdfVectorize(int, String[], String[], int[]) - Static method in class de.jungblut.nlp.VectorizerUtils
-
Vectorizes the given single document by the TF-IDF weighting.
- toArray() - Method in class de.jungblut.ner.UnrollableDoubleVector
-
- toCIEXYZ(float[]) - Method in class de.jungblut.reader.LUVColorSpace
-
- tokenize(String) - Method in class de.jungblut.nlp.BigramTokenizer
-
- tokenize(String) - Method in class de.jungblut.nlp.StandardTokenizer
-
- tokenize(String) - Method in interface de.jungblut.nlp.Tokenizer
-
Tokenizes the given String to a array of Strings.
- Tokenizer - Interface in de.jungblut.nlp
-
Standard tokenizer interface.
- TOKENIZER_CLASS_KEY - Static variable in class de.jungblut.nlp.mr.WordCorpusFrequencyJob
-
- TokenizerUtils - Class in de.jungblut.nlp
-
Nifty text utility for majorly tokenizing tasks.
- TokenMapper() - Constructor for class de.jungblut.nlp.mr.WordCorpusFrequencyJob.TokenMapper
-
- toObjectList(int[]) - Static method in class de.jungblut.datastructure.ArrayUtils
-
Converts the given int array to a list of object wrappers.
- toObjectList(long[]) - Static method in class de.jungblut.datastructure.ArrayUtils
-
Converts the given long array to a list of object wrappers.
- toObjectList(double[]) - Static method in class de.jungblut.datastructure.ArrayUtils
-
Converts the given double array to a list of object wrappers.
- toPrimitiveArray(List<Integer>) - Static method in class de.jungblut.datastructure.ArrayUtils
-
Converts the given list of object type to its primitive counterpart.
- toPrimitiveArray(Integer[]) - Static method in class de.jungblut.datastructure.ArrayUtils
-
Converts the given array of object type to its primitive counterpart.
- toPrimitiveArray(Long[]) - Static method in class de.jungblut.datastructure.ArrayUtils
-
Converts the given array of object type to its primitive counterpart.
- toPrimitiveArray(Double[]) - Static method in class de.jungblut.datastructure.ArrayUtils
-
Converts the given array of object type to its primitive counterpart.
- toRGB(float[]) - Method in class de.jungblut.reader.LUVColorSpace
-
- toString() - Method in class de.jungblut.classification.nn.WeightMatrix
-
- toString() - Method in class de.jungblut.classification.tree.Split
-
- toString() - Method in class de.jungblut.clustering.AgglomerativeClustering.ClusterNode
-
- toString() - Method in class de.jungblut.crawl.extraction.ArticleContentExtrator.ContentFetchResult
-
- toString() - Method in class de.jungblut.crawl.extraction.HtmlExtrator.HtmlFetchResult
-
- toString() - Method in class de.jungblut.crawl.FetchResult
-
- toString() - Method in class de.jungblut.datastructure.DistanceResult
-
- toString() - Method in class de.jungblut.math.activation.AbstractActivationFunction
-
- toString() - Method in class de.jungblut.math.cuda.MatrixDimension
-
- toString() - Method in class de.jungblut.nlp.model.ReferencedContext
-
- toString() - Method in class de.jungblut.nlp.mr.IntIntPairWritable
-
- toString() - Method in class de.jungblut.nlp.mr.TextDoublePairWritable
-
- toString() - Method in class de.jungblut.nlp.mr.TextIntIntIntWritable
-
- toString() - Method in class de.jungblut.nlp.mr.TextIntPairWritable
-
- toString() - Method in class de.jungblut.online.ml.FeatureOutcomePair
-
- toString() - Method in class de.jungblut.partition.Boundaries.Range
-
- toString() - Method in class de.jungblut.partition.Boundaries
-
- toString() - Method in class de.jungblut.utils.Statistics
-
- toString() - Method in class de.jungblut.writable.VectorWritable
-
- train(DoubleVector[], DoubleVector[]) - Method in class de.jungblut.classification.AbstractClassifier
-
- train(Iterable<DoubleVector>, Iterable<DoubleVector>) - Method in class de.jungblut.classification.AbstractClassifier
-
- train(Iterable<DoubleVector>, Iterable<DoubleVector>) - Method in class de.jungblut.classification.bayes.MultinomialNaiveBayes
-
- train(DoubleVector[], DoubleVector[]) - Method in interface de.jungblut.classification.Classifier
-
Trains this classifier with the given features and the outcome.
- train(Iterable<DoubleVector>, Iterable<DoubleVector>) - Method in interface de.jungblut.classification.Classifier
-
Trains this classifier with the given features and the outcome.
- train(Iterable<DoubleVector>, Iterable<DoubleVector>) - Method in class de.jungblut.classification.knn.KNearestNeighbours
-
- train(Iterable<DoubleVector>, Iterable<DoubleVector>) - Method in class de.jungblut.classification.knn.SparseKNearestNeighbours
-
- train(DoubleVector[], DoubleVector[]) - Method in class de.jungblut.classification.meta.Voter
-
- train(DoubleVector[], DoubleVector[]) - Method in class de.jungblut.classification.nn.MultilayerPerceptron
-
- train(DoubleVector[], DoubleVector[], Minimizer, int, double, boolean) - Method in class de.jungblut.classification.nn.MultilayerPerceptron
-
Full backpropagation training method.
- train(DoubleVector[], double, int) - Method in class de.jungblut.classification.nn.RBM
-
Trains the RBM on the given training set.
- train(DoubleVector[], Minimizer, int) - Method in class de.jungblut.classification.nn.RBM
-
Trains the RBM on the given training set.
- train(DoubleVector[], DoubleVector[]) - Method in class de.jungblut.classification.regression.LogisticRegression
-
- train(DoubleVector[], DoubleVector[]) - Method in class de.jungblut.classification.tree.DecisionTree
-
- train(DoubleVector[], DoubleVector[]) - Method in class de.jungblut.classification.tree.RandomForest
-
- train(Iterable<DoubleVector>, Iterable<DoubleVector>) - Method in class de.jungblut.classification.UntrainableClassifier
-
- train(DoubleVector[], DoubleVector[]) - Method in class de.jungblut.classification.UntrainableClassifier
-
- train(DoubleVector[], DoubleVector[]) - Method in class de.jungblut.ner.MaxEntMarkovModel
-
- train(DoubleVector[], DoubleVector[]) - Method in class de.jungblut.nlp.HMM
-
- train(Stream<int[]>) - Method in class de.jungblut.nlp.MarkovChain
-
Trains the transition probabilities of the markov chain.
- TrainingSplit - Class in de.jungblut.classification.meta
-
- TrainingSplit(DoubleVector[], DoubleVector[]) - Constructor for class de.jungblut.classification.meta.TrainingSplit
-
Sets the split internally.
- trainingType(TrainingType) - Method in class de.jungblut.classification.nn.MultilayerPerceptron.MultilayerPerceptronBuilder
-
Sets the training type, it defaults to CPU- so only use if you want to
use the GPU.
- trainingType - Variable in class de.jungblut.classification.nn.MultilayerPerceptronCostFunction.NetworkConfiguration
-
- trainingType(TrainingType) - Method in class de.jungblut.classification.nn.RBM.RBMBuilder
-
Sets the training type, it defaults to CPU- so only use if you want to
use the GPU.
- TrainingType - Enum in de.jungblut.classification.nn
-
Train normally on the CPU or on the GPU via CUDA?
- trainSupervised(DoubleVector[], DoubleVector[]) - Method in class de.jungblut.nlp.HMM
-
Trains the current models parameters by executing a forwad pass over the
given observations (hidden and visible states).
- trainUnsupervised(DoubleVector[], double, int, boolean) - Method in class de.jungblut.nlp.HMM
-
Trains the current models parameters by executing a baum-welch expectation
maximization algorithm.
- transformToByteCode(MethodVisitor, Label) - Method in class de.jungblut.classification.tree.AbstractTreeNode
-
Transforms this node to byte code, given a visitor that already starts
containing the methods and a label that must be jumped to in case of a
return.
- transformToByteCode(MethodVisitor, Label) - Method in class de.jungblut.classification.tree.LeafNode
-
- transformToByteCode(MethodVisitor, Label) - Method in class de.jungblut.classification.tree.NominalNode
-
- transformToByteCode(MethodVisitor, Label) - Method in class de.jungblut.classification.tree.NumericalNode
-
- TreeCompiler - Class in de.jungblut.classification.tree
-
Compilation unit for the object tree structure of the
DecisionTree.
- TreeCompiler() - Constructor for class de.jungblut.classification.tree.TreeCompiler
-
- TwentyNewsgroupReader - Class in de.jungblut.reader
-
Reads the "20news-bydate" dataset into a vector space model as well as
predictions based on the category.
- valueOf(String) - Static method in enum de.jungblut.classification.meta.Voter.CombiningType
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum de.jungblut.classification.meta.Voter.SelectionType
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum de.jungblut.classification.nn.TrainingType
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum de.jungblut.classification.tree.FeatureType
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum de.jungblut.math.activation.ActivationFunctionSelector
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum de.jungblut.nlp.MinHash.HashType
-
Returns the enum constant of this type with the specified name.
- valueOf(String) - Static method in enum de.jungblut.nlp.mr.WordCorpusFrequencyJob.WordCorpusCounter
-
Returns the enum constant of this type with the specified name.
- values() - Static method in enum de.jungblut.classification.meta.Voter.CombiningType
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum de.jungblut.classification.meta.Voter.SelectionType
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum de.jungblut.classification.nn.TrainingType
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum de.jungblut.classification.tree.FeatureType
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum de.jungblut.math.activation.ActivationFunctionSelector
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum de.jungblut.nlp.MinHash.HashType
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- values() - Static method in enum de.jungblut.nlp.mr.WordCorpusFrequencyJob.WordCorpusCounter
-
Returns an array containing the constants of this enum type, in
the order they are declared.
- VectorDocumentDistanceMeasurer<T> - Class in de.jungblut.distance
-
Document distance measurer on vectors (basically a proxy to the real
DistanceMeasurer).
- VectorFunnel - Class in de.jungblut.utils
-
A funnel that funnels a DoubleVector into bytes by taking the non-zero items
from a vector for sparse instances.
- VectorFunnel() - Constructor for class de.jungblut.utils.VectorFunnel
-
- vectorize() - Method in class de.jungblut.ner.SparseFeatureExtractorHelper
-
Vectorizes the given data from the constructor.
- vectorize(K) - Method in class de.jungblut.ner.SparseFeatureExtractorHelper
-
Vectorizes the given word.
- vectorize(K, Integer) - Method in class de.jungblut.ner.SparseFeatureExtractorHelper
-
Vectorizes the given word with the previous outcome.
- vectorize(List<K>, List<Integer>) - Method in class de.jungblut.ner.SparseFeatureExtractorHelper
-
Vectorizes the given data.
- vectorizeAdditionals(List<K>, List<Integer>) - Method in class de.jungblut.ner.SparseFeatureExtractorHelper
-
Vectorizes the given data.
- vectorizeEachLabel(List<K>) - Method in class de.jungblut.ner.SparseFeatureExtractorHelper
-
Vectorizes the given data for each label.
- VectorizerUtils - Class in de.jungblut.nlp
-
Vectorizing utility for basic tf-idf and wordcount vectorizing of
tokens/strings.
- VectorizerUtils() - Constructor for class de.jungblut.nlp.VectorizerUtils
-
- VectorWritable - Class in de.jungblut.writable
-
New and updated VectorWritable class that has all the other fancy
combinations of vectors that are possible in my math library.
This class is not compatible to the one in the clustering package that has a
totally different byte alignment in binary files.
- VectorWritable() - Constructor for class de.jungblut.writable.VectorWritable
-
- VectorWritable(VectorWritable) - Constructor for class de.jungblut.writable.VectorWritable
-
- VectorWritable(DoubleVector) - Constructor for class de.jungblut.writable.VectorWritable
-
- verbose() - Method in class de.jungblut.classification.meta.Voter
-
- verbose(boolean) - Method in class de.jungblut.classification.meta.Voter
-
- verbose() - Method in class de.jungblut.classification.nn.MultilayerPerceptron.MultilayerPerceptronBuilder
-
Sets verbose to true.
- verbose(boolean) - Method in class de.jungblut.classification.nn.MultilayerPerceptron.MultilayerPerceptronBuilder
-
If verbose is true, progress indicators will be printed to STDOUT.
- verbose() - Method in class de.jungblut.classification.nn.RBM.RBMBuilder
-
Sets verbose to true.
- verbose(boolean) - Method in class de.jungblut.classification.nn.RBM.RBMBuilder
-
If verbose is true, progress indicators will be printed to STDOUT.
- verbose() - Method in class de.jungblut.classification.tree.RandomForest
-
- verbose(boolean) - Method in class de.jungblut.classification.tree.RandomForest
-
- visibleDropoutProbability - Variable in class de.jungblut.classification.nn.MultilayerPerceptronCostFunction.NetworkConfiguration
-
- ViterbiUtils - Class in de.jungblut.math
-
Viterbi Utilities for forward backward passes and his famous decoding
algorithm for hidden markov models.
- Voter<A extends Classifier> - Class in de.jungblut.classification.meta
-
Implementation of vote ensembling.
- Voter.CombiningType - Enum in de.jungblut.classification.meta
-
- Voter.SelectionType - Enum in de.jungblut.classification.meta
-
- WeightMapper<A extends Classifier> - Interface in de.jungblut.classification.eval
-
- WeightMatrix - Class in de.jungblut.classification.nn
-
Weight matrix wrapper to encapsulate the random initialization.
- WeightMatrix(int, int) - Constructor for class de.jungblut.classification.nn.WeightMatrix
-
Creates a [unitsRightLayer x (unitsLeftLayer + 1)] matrix of weights and
seed the values using the famous uniform distribution formula of LeCun.
- WeightMatrix(DoubleMatrix) - Constructor for class de.jungblut.classification.nn.WeightMatrix
-
- whiteSpaceTokenize(String) - Static method in class de.jungblut.nlp.TokenizerUtils
-
Tokenizes on normal whitespaces "\\s+" in java regex.
- whiteSpaceTokenizeNGrams(String, int) - Static method in class de.jungblut.nlp.TokenizerUtils
-
This tokenizer first splits on whitespaces and then concatenates the words
based on size.
- with(DistanceMeasurer) - Static method in class de.jungblut.distance.VectorDocumentDistanceMeasurer
-
- with(DistanceMeasurer) - Static method in class de.jungblut.nlp.DocumentSimilarity
-
- withWeights(WeightMatrix[]) - Method in class de.jungblut.classification.nn.MultilayerPerceptron.MultilayerPerceptronBuilder
-
Sets the initial weights, maybe from an already trained network, or from
a fancy random initialization technique.
- WORD_COUNT_OUTPUT_KEY - Static variable in class de.jungblut.nlp.mr.TfIdfCalculatorJob
-
- WordCorpusFrequencyJob - Class in de.jungblut.nlp.mr
-
MapReduce job that calculates the word frequency over all documents by
inverting document->words and writing the sum of the assigned documents per
word and its document.
- WordCorpusFrequencyJob() - Constructor for class de.jungblut.nlp.mr.WordCorpusFrequencyJob
-
- WordCorpusFrequencyJob.DocumentSumReducer - Class in de.jungblut.nlp.mr
-
Sums up all the documents per token index by docID.
- WordCorpusFrequencyJob.TokenMapper - Class in de.jungblut.nlp.mr
-
Write a token with its document id.
- WordCorpusFrequencyJob.WordCorpusCounter - Enum in de.jungblut.nlp.mr
-
- WordCountJob - Class in de.jungblut.nlp.mr
-
MapReduce job that calculates the token frequency by an improved word count.
- WordCountJob() - Constructor for class de.jungblut.nlp.mr.WordCountJob
-
- WordCountJob.WordFrequencyMapper - Class in de.jungblut.nlp.mr
-
Group the tokens in memory for each chunk, write it in the cleanup step.
- WordCountJob.WordFrequencyReducer - Class in de.jungblut.nlp.mr
-
Group the tokens by reducing the mappers output and summing the sums for
each token.
- WordFrequencyMapper() - Constructor for class de.jungblut.nlp.mr.WordCountJob.WordFrequencyMapper
-
- WordFrequencyReducer() - Constructor for class de.jungblut.nlp.mr.WordCountJob.WordFrequencyReducer
-
- wordFrequencyVectorize(String[]...) - Static method in class de.jungblut.nlp.VectorizerUtils
-
Vectorizes a given list of documents.
- wordFrequencyVectorize(Stream<String[]>) - Static method in class de.jungblut.nlp.VectorizerUtils
-
Vectorizes a given list of documents.
- wordFrequencyVectorize(Stream<String[]>, String[]) - Static method in class de.jungblut.nlp.VectorizerUtils
-
Vectorizes a given list of documents and a dictionary.
- wordTokenize(String) - Static method in class de.jungblut.nlp.TokenizerUtils
-
Tokenizes on several indicators of a word, regex is [
\r\n\t.,;:'\"()?!\\-/|]
- wordTokenize(String, boolean) - Static method in class de.jungblut.nlp.TokenizerUtils
-
- wordTokenize(String, String) - Static method in class de.jungblut.nlp.TokenizerUtils
-
Tokenizes on several indicators of a word, regex to detect these must be
given.
- wrap(DoubleVector) - Static method in class de.jungblut.writable.VectorWritable
-
- write(DataOutput) - Method in class de.jungblut.classification.tree.AbstractTreeNode
-
- write(T) - Method in class de.jungblut.crawl.ConsoleResultWriter
-
- write(T) - Method in interface de.jungblut.crawl.ResultWriter
-
Writes a single item to the output.
- write(T) - Method in class de.jungblut.crawl.ResultWriterAdapter
-
- write(FetchResult) - Method in class de.jungblut.crawl.SequenceFileResultWriter
-
- write(int) - Method in class de.jungblut.datastructure.AsyncBufferedOutputStream
-
Writes the specified byte to this buffered output stream.
- write(byte[]) - Method in class de.jungblut.datastructure.AsyncBufferedOutputStream
-
- write(byte[], int, int) - Method in class de.jungblut.datastructure.AsyncBufferedOutputStream
-
Writes len bytes from the specified byte array starting at
offset off to this buffered output stream.
- write(DataOutput) - Method in class de.jungblut.nlp.HMM
-
- write(DataOutput) - Method in class de.jungblut.nlp.mr.IntIntPairWritable
-
- write(DataOutput) - Method in class de.jungblut.nlp.mr.TextDoublePairWritable
-
- write(DataOutput) - Method in class de.jungblut.nlp.mr.TextIntIntIntWritable
-
- write(DataOutput) - Method in class de.jungblut.nlp.mr.TextIntPairWritable
-
- write(DataOutput) - Method in class de.jungblut.nlp.mr.TextTextPairWritable
-
- write(DataOutput) - Method in class de.jungblut.partition.Boundaries.Range
-
- write(DataOutput) - Method in class de.jungblut.utils.Statistics
-
- write(DataOutput) - Method in class de.jungblut.writable.BitSetWritable
-
- write(DataOutput) - Method in class de.jungblut.writable.MatrixWritable
-
- write(DataOutput) - Method in class de.jungblut.writable.VectorWritable
-
- writeDenseMatrix(DenseDoubleMatrix, DataOutput) - Static method in class de.jungblut.writable.MatrixWritable
-
Writes a dense matrix to the given output stream.
- writeInternal(DataOutput) - Method in class de.jungblut.classification.tree.AbstractTreeNode
-
serialize internal state.
- writeInternal(DataOutput) - Method in class de.jungblut.classification.tree.LeafNode
-
- writeInternal(DataOutput) - Method in class de.jungblut.classification.tree.NominalNode
-
- writeInternal(DataOutput) - Method in class de.jungblut.classification.tree.NumericalNode
-
- writer - Variable in class de.jungblut.crawl.SequenceFileResultWriter
-
- writeSparseMatrix(SparseDoubleRowMatrix, DataOutput) - Static method in class de.jungblut.writable.MatrixWritable
-
Writes a sparse matrix to the given output stream.
- writeVector(DoubleVector, DataOutput) - Static method in class de.jungblut.writable.VectorWritable
-