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- Givens matrix operations routines.
- Householder transformation routines.
- Routines for determining Hessenberg factorisations.
- Routines for symmetric eigenvalue problems.
- Collection of matrix factorisation operation functions.
- Collection of matrix operation functions.
- Collection of permutations operation functions.
- Some simple functions for log operations.
- Some simple functions for string operations.

- Allocator
*Class do easily allocate/deallocate memory in Torch.* - Bagging
*This class represents a*`Trainer`that implements the well-known Bagging algorithm (Breiman, 1996). - BayesClassifier
*A multi class bayes classifier -- maximizes the likelihood of each class separately using a trainer for distribution.* - BayesClassifierMachine
*BayesClassifierMachine is the machine used by the*`BayesClassifier`trainer to perform a Bayes Classification using different distributions. - BeamSearchDecoder
*This class implements a Viterbi decoder with beam search capabilities.* - BoolCmdOption
*This class defines a bool command-line option.* - Boosting
*Boosting implementation.* - BoostingMeasurer
*Compute the classification weighted error (in %) for*`BoostingMachine`of the`inputs`with respect to the`targets`of`data`. - ClassFormat
*Used to define a class code.* - ClassFormatDataSet
*Given a DataSet, convert (on-the-fly) targets using a conversion table.* - ClassMeasurer
*Compute the classification error (in %) of the*`inputs`with respect to the`targets`of`data`. - ClassNLLCriterion
*This criterion can be used to train *in classification* a*`GradientMachine`object using the`StochasticGradient`trainer. - ClassNLLMeasurer
*This class measures the negative log likelihood.* - CmdLine
*This class provides a useful interface for the user, to easily read some arguments/options from the command-line.* - CmdOption
*This class defines an option for the command line.* - ConnectedMachine
*Easy connections between several*`GradientMachine`. - Criterion
`Criterion`class for`StochasticGradient`. - DataSet
*Provides an interface to manipulate all kind of data.* - DecoderBatchTest
*This class is used to decode a set of test files and display statistics and results for each file (ie.* - DecoderSingleTest
*This class is used to recognise a single input data file, post-process the recognition result and output the result.* - DecodingHMM
*This class contains all HMM information required for decoding.* - DecodingHypothesis
*This class contains all hypothesis data that needs to be updated and propagated as hypotheses are extended through word models and across word boundaries.* - DiagonalGMM
*This class can be used to model Diagonal Gaussian Mixture Models.* - DiskDataSet
*Provides an interface to manipulate all kind of data which are kept on disk, and not fully loaded in memory.* - DiskHTKDataSet
*Provides an interface to manipulate HTK data which are kept on disk, and not fully loaded in memory.* - DiskMatDataSet
*Matrix DataSet On Disk.* - DiskXFile
*A file on the disk.* - Distribution
*This class is designed to handle generative distribution models such as Gaussian Mixture Models and Hidden Markov Models.* - DotKernel
*DotProduct Kernel.* - EMTrainer
*This class is used to train any distribution using the EM algorithm.* - EditDistance
*This class can be used to compute the "edit distance" between two sequences.* - EditDistanceMeasurer
*This class can be used to measure and print an*`EditDistance`object. - ExampleFrameSelectorDataSet
*This dataset is empty at the begining.* - Exp
*Exponentiel layer for*`GradientMachine`. - FileListCmdOption
*This class take a file name in the command line, and reads a list of files contained in this file.* - FrameSeg
*This class keeps track of all information to compute errors at the frame level* - FrameSegMeasurer
*This class can be used to save the frame segmentation of a*`SimpleDecoderSpeechHMM`in a file. - FrameSelectorDataSet
*A dataset used to select some frames of another dataset.* - GaussianKernel
*Gaussian Kernel* - GradientMachine
*Gradient machine: machine which can be trained with a gradient descent.* - Grammar
*This class contains the grammar of accepted sentences for a speech recognition experiment such as the one using SimpleDecoderSpeechHMM A grammar is a transition table where each node is a word.* - HMM
*This class implements a Hidden Markov Model distribution.* - HTKDataSet
*This dataset can deal with the HTK format for features and targets.* - IOAscii
*Handles the standard Ascii sequence format in Torch.* - IOBin
*Handles the standard binary sequence format in Torch.* - IOBufferize
*This IO bufferizes the asked sequence of a given IO when calling*`getSequence()`. - IOHTK
*Handles the standard binary sequence format in HTK.* - IOHTKTarget
*Handles the standard Ascii HTK targets/labels format in Torch.* - IOMulti
*This IO takes several IOSequence, and will act as if you had concatened all these IOSequence when calling*`getMatrix()`. - IOSequence
*Class which provides an ensemble of sequences, which have the same frame size, but could have different number of frames.* - IOSequenceArray
*Load and save in an efficiently manner an array of sequences.* - IOSub
*IOSequence which does a selection of adjacent columns on another IOSequence, when calling*`getMatrix()`. - InputsSelect
*Machine which select a block of adjacent inputs, and put them in the outputs.* - IntCmdOption
*This class defines a integer command-line option.* - KFold
*Provides an interface to sample data, for use by methods such as cross-validation* - KMeans
*This class can be used to do a "kmeans" on a given set of data.* - KNN
*This machine implements the K-nearest-neighbors (KNN) algorithm.* - Kernel
*General Kernel class.* - LMCache
*This class implements a rudimentary caching scheme for language model lookup.* - LMCacheEntry
*This class implements the internal entries within the LMCache class.* - LMInteriorLevelWordEntry
*This class is used internally within the language model n-gram data structures.* - LMInteriorLevelWordList
- LMNGram
*This class is the main class for N-gram language modelling.* - LanguageModel
*This object implements an n-gram language model.* - LexiconInfo
*This class stores information about how phonemes are assembled into pronunciations.* - Linear
*Linear layer for*`GradientMachine`. - LinearLexicon
*This class is essentially an array of DecodingHMM instances, representing the HMM's for each pronunciation we can recognise.* - LogMixer
*LogMixer useful for experts mixtures.* - LogRBF
*LogRBF layer for*`GradientMachine`. - LogSigmoid
*Log-Sigmoid layer for*`GradientMachine`. - LogSoftMax
*Log-SoftMax layer for*`GradientMachine`. - MAPDiagonalGMM
*This class is a special case of a DiagonalGMM that implements the MAP algorithm instead of the EM algorithm.* - MAPHMM
*This class is a special case of a HMM that implements the MAP algorithm for HMM transitions probabilities.* - MLP
*A Multi-Layer Perceptron.* - MSECriterion
*Mean Squared Error criterion.* - MSEMeasurer
*Mean Squared Error measurer.* - Machine
`Object`which can compute some outputs, given some inputs. - Mat
*Matrix object.* - MatDataSet
*Matrix DataSet.* - MeanVarNorm
*In the constructor, it computes the mean and the standard deviation over all the frames in the given DataSet (by default, only for inputs).* - Measurer
*Used to measure what you want during training/testing.* - MemoryDataSet
*DataSet where data is fully loaded in memory.* - MemoryXFile
*A file in the memory.* - Mixer
*Mixer useful for experts mixtures.* - MultiClassFormat
*Define the multi class encoding format.* - MultiCriterion
*MultiCriterion can be used to handle multiple criterions.* - Multinomial
*This class can be used to model Multinomial Distributions.* - NLLCriterion
*This criterion can be used to train*`Distribution`object using the`GMTrainer`trainer. - NLLMeasurer
*This class measures the negative log likelihood.* - NPTrainer
*Trainer for Non Parametric Machines.* - NullXFile
*NullXFile.* - Object
*Almost all classes in Torch should be a sub-class of this class.* - OneHotClassFormat
*Define the one hot class encoding format.* - OutputMeasurer
*Compute the outputification error (in %) of the*`inputs`with respect to the`targets`of`data`. - Parameters
*Parameters definition.* - ParzenDistribution
*This class can be used to model a Parzen density estimator with a Gaussian kernel:* - ParzenMachine
*This machine implements the Parzen Window estimator.* - Perm
*Permutation object.* - PhoneInfo
*This class contains the names of the phonemes that make up the words in the lexicon.* - PhoneModels
- PipeXFile
*A file in a pipe.* - PolynomialKernel
*Polynomial Kernel .* - PreProcessing
*This class is able to do pre-processing on examples in a*`DataSet`. - QCCache
*"Cache" used by the Quadratic Constrained Trainer (*`QCTrainer`). - QCMachine
*"Quadratic Constrained Machine".* - QCTrainer
*Train a*`QCMachine`. - Random
*Random class which contains several static random methods.* - RealCmdOption
*This class defines a real command-line option.* - SVM
*Support Vector Machine.* - SVMCache
`QCCache`implementation for SVMs. - SVMCacheClassification
*Cache for SVM classification.* - SVMCacheRegression
*Cache for SVM regression.* - SVMClassification
*SVM in classification.* - SVMRegression
*SVM in regression.* - SaturationMeasurer
*Measure the saturation of a*`GradientMachine`. - Sequence
*Sequence definition.* - Sigmoid
*Sigmoid layer for*`GradientMachine`. - SigmoidKernel
*Sigmoid Kernel* - SimpleDecoderSpeechHMM
*This class implements a special case of Hidden Markov Models that can be used to do connected word speech recognition for small vocabulary, using embedded training.* - SoftMax
*SoftMax layer for*`GradientMachine`. - SoftPlus
*SoftPlus layer for*`GradientMachine`. - SpatialConvolution
*Class for doing convolution over images.* - SpatialSubSampling
*Class for doing sub-sampling over images.* - SpeechHMM
*This class implements a special case of Hidden Markov Models that can be used to do connected word speech recognition for small vocabulary, using embedded training.* - SpeechMLP
- SpeechMLPDistr
- Stack
*This is an implementation of a "stack".* - StochasticGradient
*Trainer for GradientMachine.* - StringCmdOption
*This class defines a string command-line option.* - SumMachine
*This machine simply adds up its input vectors.* - TableLookupDistribution
*This class outputs one of the observations as the logProbability.* - Tanh
*Tanh layer for*`GradientMachine`. - TemporalConvolution
*Class for doing a convolution over a sequence.* - TemporalMean
*Given an input sequence, it does the mean over all input frames.* - TemporalSubSampling
*Class for doing sub-sampling over a sequence.* - TimeMeasurer
*Measure the time (in seconds) between two*`measureIteration()`calls. - Timer
*Timer.* - Trainer
*Trainer.* - TwoClassFormat
*Define the two class encoding format.* - Vec
*Vector object.* - Vocabulary
*This object contains the list of words we want our recogniser to recognise plus a few "special" words (eg.* - WeightedMSECriterion
*Similar to*`MSECriterion`, but you can put a weight on each example. - WeightedSumMachine
*Weighted-sum machine.* - WordChainElemPool
*This class is a pool of pre-allocated WordChainElem structures.* - WordSeg
*This class keeps track of all information to compute word errors* - WordSegMeasurer
*This class can be used to save the word segmentation of a*`SimpleDecoderSpeechHMM`in a file. - XFile
*XFile.*

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