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Accord.MachineLearning Namespace

 
Classes
  ClassDescription
Public classCode exampleBagOfWords
Bag of words.
Public classCode exampleBalancedKMeans
Balanced K-Means algorithm. Note: The balanced clusters will be available in the Labels property of this instance!
Public classBaseKNearestNeighborsTModel, TInput, TDistance
Base class for K-Nearest Neighbor (k-NN) algorithms.
Public classBinaryClassifierBaseTInput
Base class for binary classifiers.
Public classBinaryLearningBaseTModel, TInput
Common base class for supervised learning algorithms for binary classifiers.
Public classBinaryLikelihoodClassifierBaseTInput
Public classBinaryScoreClassifierBaseTInput
Public classCode exampleBinarySplit
Binary split clustering algorithm.
Public classBoltzmannExploration
Boltzmann distribution exploration policy.
Public classCode exampleBootstrap
Bootstrap method for generalization performance measurements.
Public classBootstrapResult
Bootstrap validation analysis results.
Public classBootstrapValues
Information class to store the training and validation errors of a model.
Public classClassifierBaseTInput, TClasses
Base class for multi-class and multi-label classifiers.
Public classClusterCollectionTData, TCluster
Cluster collection.
Public classClusterCollectionTData, TCentroids, TCluster
Cluster collection.
Public classClusterCollectionTData, TCentroids, TClusterClusterTCollection
Data cluster.
Public classCode exampleCrossValidation
k-Fold cross-validation.
Public classCode exampleCrossValidationTModel
k-Fold cross-validation.
Public classCrossValidationResultTModel
Class for representing results acquired through a k-fold cross-validation analysis.
Public classCrossValidationStatistics
Summary statistics for a cross-validation trial.
Public classCrossValidationValues
Information class to store the training and validation errors of a model.
Public classCrossValidationValuesTModel
Training and validation errors of a model.
Public classEarlyStoppingTModel
Early stopping training procedure.
Public classEpsilonGreedyExploration
Epsilon greedy exploration policy.
Public classGaussianClusterCollection
Gaussian Mixture Model Cluster Collection.
Public classGaussianClusterCollectionGaussianCluster
Gaussian Mixture Model cluster.
Public classCode exampleGaussianMixtureModel
Gaussian mixture model clustering.
Public classGaussianMixtureModelOptions Obsolete.
Options for Gaussian Mixture Model fitting.
Public classCode exampleGridSearchTModel
Grid search procedure for automatic parameter tuning.
Public classGridSearchParameterCollection
Grid search parameter collection.
Public classGridSearchRange
Range of parameters to be tested in a grid search.
Public classGridSearchRangeCollection
GridSearchRange collection.
Public classGridSearchResultTModel
Contains results from the grid-search procedure.
Public classInnerParametersTBinary, TInput
Public classCode exampleKMeans
Lloyd's k-Means clustering algorithm.
Public classKMeansClusterCollection
k-Means cluster collection.
Public classKMeansClusterCollectionKMeansCluster
k-Means' cluster.
Public classKModes
k-Modes algorithm.
Public classKModesT
k-Modes algorithm.
Public classKModesClusterCollectionT
k-Modes cluster collection.
Public classKModesClusterCollectionTKModesCluster
k-Modes' cluster.
Public classKNearestNeighbors
K-Nearest Neighbor (k-NN) algorithm.
Public classCode exampleKNearestNeighborsTInput
K-Nearest Neighbor (k-NN) algorithm.
Public classLikelihoodTaggerBaseTInput
Base implementation for generative observation sequence taggers. A sequence tagger can predict the class label of each individual observation in a input sequence vector.
Public classCode exampleMeanShift
Mean shift clustering algorithm.
Public classMeanShiftClusterCollection
Mean shift cluster collection.
Public classMeanShiftClusterCollectionMeanShiftCluster
Mean shift cluster.
Public classCode exampleMinimumMeanDistanceClassifier
Minimum (Mean) Distance Classifier.
Public classMulticlassClassifierBase
Base class for multi-class classifiers.
Public classMulticlassClassifierBaseTInput
Base class for multi-class classifiers.
Public classMulticlassLearningBaseTModel
Base class for multi-class learning algorithm.
Public classMulticlassLearningBaseTModel, TInput
Base class for multi-class learning algorithm.
Public classMulticlassLikelihoodClassifierBaseTInput
Base class for generative multi-class classifiers.
Public classMulticlassScoreClassifierBaseTInput
Public classMultilabelClassifierBaseTInput
Base class for multi-label classifiers.
Public classMultilabelLikelihoodClassifierBaseTInput
Public classMultilabelScoreClassifierBaseTInput
Public classMultipleTransformBaseTInput, TOutput
Base class for data transformation algorithms.
Public classOneVsOneTBinary
One-Vs-One construction for solving multi-class classification using a set of binary classifiers.
Public classOneVsOneTBinary, TInput
One-Vs-One construction for solving multi-class classification using a set of binary classifiers.
Public classOneVsOneLearningTBinary, TModel
Public classOneVsOneLearningTInput, TBinary, TModel
Public classOneVsRestTModel
Base class for multi-class classifiers based on the "one-vs-rest" construction based on binary classifiers.
Public classOneVsRestTModel, TInput
Base class for multi-class classifiers based on the "one-vs-rest" construction based on binary classifiers.
Public classOneVsRestLearningTBinary, TModel
Public classOneVsRestLearningTInput, TBinary, TModel
Public classParallelLearningBase
Base class for parallel learning algorithms.
Public classParallelMulticlassLearningBaseTModel, TInput
Base class for multi-class learning algorithms that employ parallel algorithms.
Public classQLearning
QLearning learning algorithm.
Public classCode exampleRANSACTModel
Multipurpose RANSAC algorithm.
Public classRouletteWheelExploration
Roulette wheel exploration policy.
Public classSarsa
Sarsa learning algorithm.
Public classScoreTaggerBaseTInput
Common base class for observation sequence taggers.
Public classSplitSetResultTModel
Class for representing results acquired through a split-set validation analysis.
Public classSplitSetStatistics
Summary statistics for a Split-set validation trial.
Public classSplitSetStatisticsTModel
Summary statistics for a Split-set validation trial.
Public classSplitSetValidation
Split-Set Validation.
Public classSplitSetValidationTModel
Split-Set Validation.
Public classSubproblemEventArgs
Subproblem progress event argument.
Public classTabuSearchExploration
Tabu search exploration policy.
Public classTaggerBaseTInput
Base class for multi-class and multi-label classifiers.
Public classCode exampleTFIDF
Term Frequency - Inverse Term Frequency.
Public classTools
Set of machine learning tools.
Public classTransformBase
Base class for data transformation algorithms.
Public classTransformBaseTInput
Base class for data transformation algorithms.
Public classTransformBaseTInput, TOutput
Base class for data transformation algorithms.
Structures
  StructureDescription
Public structureClassPair
Pair of class labels.
Public structureDecision
Decision between two class labels. Indicates the class index of the first class, the class index of the adversary, and the class index of the winner.
Public structureGridSearchParameter
Contains the name and value of a parameter that should be used during fitting.
Interfaces
  InterfaceDescription
Public interfaceIBagOfWordsT
Common interface for Bag of Words objects.
Public interfaceIBinaryClassifier
Common interface for classification models. Classification models learn how to produce a class-label (or a set of class labels) y from an input vector x.
Public interfaceIBinaryClassifierTInput
Common interface for classification models. Classification models learn how to produce a class-label (or a set of class labels) y from an input vector x.
Public interfaceIBinaryLikelihoodClassifierTInput
Common interface for generative binary classifiers. A binary classifier can predict whether or not an instance belongs to a class, while at the same time being able to provide the probability of this sample belonging to the positive class.
Public interfaceIBinaryScoreClassifierTInput
Common interface for score-based binary classifiers. A binary classifier can predict whether or not an instance belongs to a class based on a decision score (a real number) that measures the association of the input with the negative and positive class.
Public interfaceIClassifierTInput, TClasses
Common interface for classification models. Classification models learn how to produce a class-label (or a set of class labels) y from an input vector x.
Public interfaceIClusterCollectionTData
Common interface for cluster collections.
Public interfaceIClusterCollectionTData, TCluster
Common interface for cluster collections.
Public interfaceIClusteringAlgorithmTData
Common interface for clustering algorithms.
Public interfaceIClusteringAlgorithmTData, TWeights
Common interface for clustering algorithms.
Public interfaceIDescriptiveLearningTModel, TInput
Common interface for unsupervised learning algorithms.
Public interfaceIExplorationPolicy
Exploration policy interface.
Public interfaceIGenerativeTInput
Common interface for generative models.
Public interfaceILikelihoodTaggerTInput
Common interface for generative observation sequence taggers. A sequence tagger can predict the class label of each individual observation in a input sequence vector.
Public interfaceIMulticlassClassifier
Common interface for multi-class models. Classification models learn how to produce a class-label y from an input vector x.
Public interfaceIMulticlassClassifierTInput
Common interface for multi-class models. Classification models learn how to produce a class-label y from an input vector x.
Public interfaceIMulticlassClassifierTInput, TClasses
Common interface for multi-class models. Classification models learn how to produce a class-label y from an input vector x.
Public interfaceIMulticlassLikelihoodClassifierTInput
Common interface for generative multi-class classifiers. A multi-class classifier can predicts a class label based on an input instance vector.
Public interfaceIMulticlassLikelihoodClassifierTInput, TClasses
Common interface for generative multi-class classifiers. A multi-class classifier can predicts a class label based on an input instance vector.
Public interfaceIMulticlassOutLikelihoodClassifierTInput, TClasses
Common interface for generative multi-class classifiers. A multi-class classifier can predicts a class label based on an input instance vector.
Public interfaceIMulticlassOutScoreClassifierTInput, TClasses
Common interface for score-based multi-class classifiers. A multi-class classifier can predict to which class an instance belongs based on a decision score (a real number) that measures the association of the input with each class.
Public interfaceIMulticlassRefLikelihoodClassifierTInput, TClasses
Common interface for generative multi-class classifiers. A multi-class classifier can predicts a class label based on an input instance vector.
Public interfaceIMulticlassRefScoreClassifierTInput, TClasses
Common interface for score-based multi-class classifiers. A multi-class classifier can predict to which class an instance belongs based on a decision score (a real number) that measures the association of the input with each class.
Public interfaceIMulticlassScoreClassifierTInput
Common interface for score-based multi-class classifiers. A multi-class classifier can predict to which class an instance belongs based on a decision score (a real number) that measures the association of the input with each class.
Public interfaceIMulticlassScoreClassifierTInput, TClasses
Common interface for score-based multi-class classifiers. A multi-class classifier can predict to which class an instance belongs based on a decision score (a real number) that measures the association of the input with each class.
Public interfaceIMultilabelClassifier
Common interface for multi-label classifiers. A multi-label classifier can predict the occurrence of multiple class labels at once.
Public interfaceIMultilabelClassifierTInput
Common interface for multi-label classifiers. A multi-label classifier can predict the occurrence of multiple class labels at once.
Public interfaceIMultilabelClassifierTInput, TClasses
Common interface for multi-label classifiers. A multi-label classifier can predict the occurrence of multiple class labels at once.
Public interfaceIMultilabelLikelihoodClassifierTInput
Common interface for generative multi-label classifiers. A multi-label classifier can predict the occurrence of multiple class labels at once, as well as their probabilities.
Public interfaceIMultilabelLikelihoodClassifierTInput, TClasses
Common interface for generative multi-label classifiers. A multi-label classifier can predict the occurrence of multiple class labels at once.
Public interfaceIMultilabelOutLikelihoodClassifierTInput, TClasses
Common interface for generative multi-label classifiers. A multi-label classifier can predict the occurrence of multiple class labels at once, as well as their probabilities.
Public interfaceIMultilabelOutScoreClassifierTInput, TClasses
Common interface for score-based multi-label classifiers. A multi-label classifier can predict the occurrence of multiple class labels at once based on a decision score (a real number) computed for each class.
Public interfaceIMultilabelRefLikelihoodClassifierTInput, TClasses
Common interface for generative multi-label classifiers. A multi-label classifier can predict the occurrence of multiple class labels at once, as well as their probabilities.
Public interfaceIMultilabelRefScoreClassifierTInput, TClasses
Common interface for score-based multi-label classifiers. A multi-label classifier can predict the occurrence of multiple class labels at once based on a decision score (a real number) computed for each class.
Public interfaceIMultilabelScoreClassifierTInput
Common interface for score-based multi-label classifiers. A multi-label classifier can predict the occurrence of multiple class labels at once based on a decision score (a real number) computed for each class.
Public interfaceIMultilabelScoreClassifierTInput, TClasses
Common interface for score-based multi-label classifiers. A multi-label classifier can predict the occurrence of multiple class labels at once based on a decision score (a real number) computed for each class.
Public interfaceIMultipleRegressionTInput
Common interface for multiple regression models. Multiple regression models learn how to produce a set of real values (a real-valued vector) from an input vector x.
Public interfaceIMultipleRegressionTInput, TOutput
Common interface for multiple regression models. Multiple regression models learn how to produce a set of real values (a real-valued vector) from an input vector x.
Public interfaceIMultipleTransformTInput, TOutput
Common interface for data transformation algorithms. Examples of transformations include classifiers, regressions and other machine learning techniques.
Public interfaceIParallel
Common interface for parallel algorithms.
Public interfaceIRegressionTInput
Common interface for regression models. Regression models learn how to produce a real value (or a set of real values) y from an input vector x.
Public interfaceIRegressionTInput, TOutput
Common interface for regression models. Regression models learn how to produce a real value (or a set of real values) y from an input vector x.
Public interfaceIScoreTaggerTInput
Common interface for observation sequence taggers.
Public interfaceISupervisedBinaryLearningTModel
Common interface for supervised learning algorithms for binary classifiers.
Public interfaceISupervisedBinaryLearningTModel, TInput
Common interface for supervised learning algorithms for binary classifiers.
Public interfaceISupervisedLearningTModel, TInput, TOutput
Common interface for supervised learning algorithms.
Public interfaceISupervisedMulticlassLearningTModel
Common interface for supervised learning algorithms for multi-class classifiers.
Public interfaceISupervisedMulticlassLearningTModel, TInput
Common interface for supervised learning algorithms for multi-class classifiers.
Public interfaceISupervisedMultilabelLearningTModel
Common interface for supervised learning algorithms for multi-label classifiers.
Public interfaceISupervisedMultilabelLearningTModel, TInput
Common interface for supervised learning algorithms for multi-label classifiers.
Public interfaceISupportsCancellation
Common interface for algorithms that can be canceled in the middle of execution.
Public interfaceITaggerTInput
Common interface for generative observation sequence taggers. A sequence tagger can predict the class label of each individual observation in a input sequence vector.
Public interfaceITransform
Common interface for data transformation algorithms. Examples of transformations include classifiers, regressions and other machine learning techniques.
Public interfaceITransformTInput
Common interface for data transformation algorithms. Examples of transformations include classifiers, regressions and other machine learning techniques.
Public interfaceITransformTInput, TOutput
Common interface for data transformation algorithms. Examples of transformations include classifiers, regressions and other machine learning techniques.
Public interfaceIUnsupervisedLearningTModel, TInput, TOutput
Common interface for unsupervised learning algorithms.
Delegates
Enumerations
  EnumerationDescription
Public enumerationInverseDocumentFrequency
Weighting schemes for Inverse Document Frequency (IDF).
Public enumerationModelStorageMode
Modes for storing models.
Public enumerationSeeding
Initialization schemes for clustering algorithms.
Public enumerationTermFrequency
Weighting schemes for term-frequency (TF).