Copyright	(c) Amy de Buitléir 2012-2018
License	BSD-style
Maintainer	amy@nualeargais.ie
Stability	experimental
Portability	portable
Safe Haskell	Safe
Language	Haskell2010

Data.Datamining.Clustering.Classifier

Description

Tools for identifying patterns in data.

Synopsis

class Classifier (c :: * -> * -> * -> *) v k p where
- toList :: c v k p -> [(k, p)]
- numModels :: c v k p -> Int
- models :: c v k p -> [p]
- differences :: c v k p -> p -> [(k, v)]
- classify :: Ord v => c v k p -> p -> k
- train :: c v k p -> p -> c v k p
- trainBatch :: c v k p -> [p] -> c v k p
- classifyAndTrain :: c v k p -> p -> (k, c v k p)
- diffAndTrain :: c v k p -> p -> ([(k, v)], c v k p)
- reportAndTrain :: c v k p -> p -> (k, [(k, v)], c v k p)

Documentation

class Classifier (c :: * -> * -> * -> *) v k p where Source #

A machine which learns to classify input patterns. Minimal complete definition: trainBatch, reportAndTrain.

Minimal complete definition

toList, numModels, models, differences, trainBatch, reportAndTrain

Methods

toList :: c v k p -> [(k, p)] Source #

Returns a list of index/model pairs.

numModels :: c v k p -> Int Source #

Returns the number of models this classifier can learn.

models :: c v k p -> [p] Source #

Returns the current models of the classifier.

differences :: c v k p -> p -> [(k, v)] Source #

differences c target returns the indices of all nodes in c, paired with the difference between target and the node's model.

classify :: Ord v => c v k p -> p -> k Source #

classify c target returns the index of the node in c whose model best matches the target.

train :: c v k p -> p -> c v k p Source #

train c target returns a modified copy of the classifier c that has partially learned the target.

trainBatch :: c v k p -> [p] -> c v k p Source #

trainBatch c targets returns a modified copy of the classifier c that has partially learned the targets.

classifyAndTrain :: c v k p -> p -> (k, c v k p) Source #

classifyAndTrain c target returns a tuple containing the index of the node in c whose model best matches the input target, and a modified copy of the classifier c that has partially learned the target. Invoking classifyAndTrain c p may be faster than invoking (p classify c, train c p), but they should give identical results.

diffAndTrain :: c v k p -> p -> ([(k, v)], c v k p) Source #

diffAndTrain c target returns a tuple containing: 1. The indices of all nodes in c, paired with the difference between target and the node's model 2. A modified copy of the classifier c that has partially learned the target. Invoking diffAndTrain c p may be faster than invoking (p diff c, train c p), but they should give identical results.

reportAndTrain :: c v k p -> p -> (k, [(k, v)], c v k p) Source #

reportAndTrain c f target returns a tuple containing: 1. The index of the node in c whose model best matches the input target 2. The indices of all nodes in c, paired with the difference between target and the node's model 3. A modified copy of the classifier c that has partially learned the target Invoking diffAndTrain c p may be faster than invoking (p diff c, train c p), but they should give identical results.

Instances

(GridMap gm p, k ~ Index (BaseGrid gm p), FiniteGrid (gm p), GridMap gm x, k ~ Index (gm p), k ~ Index (gm x), k ~ Index (BaseGrid gm x), Ord k, Ord x, Num x, Fractional x) => Classifier (DSOM gm) x k p Source #
Instance details Defined in Data.Datamining.Clustering.DSOMInternal Methods toList :: DSOM gm x k p -> [(k, p)] Source # numModels :: DSOM gm x k p -> Int Source # models :: DSOM gm x k p -> [p] Source # differences :: DSOM gm x k p -> p -> [(k, x)] Source # classify :: DSOM gm x k p -> p -> k Source # train :: DSOM gm x k p -> p -> DSOM gm x k p Source # trainBatch :: DSOM gm x k p -> [p] -> DSOM gm x k p Source # classifyAndTrain :: DSOM gm x k p -> p -> (k, DSOM gm x k p) Source # diffAndTrain :: DSOM gm x k p -> p -> ([(k, x)], DSOM gm x k p) Source # reportAndTrain :: DSOM gm x k p -> p -> (k, [(k, x)], DSOM gm x k p) Source #
(GridMap gm p, k ~ Index (BaseGrid gm p), Grid (gm p), GridMap gm x, k ~ Index (gm p), k ~ Index (BaseGrid gm x), Num t, Ord x, Num x, Num d) => Classifier (SOM t d gm) x k p Source #
Instance details Defined in Data.Datamining.Clustering.SOMInternal Methods toList :: SOM t d gm x k p -> [(k, p)] Source # numModels :: SOM t d gm x k p -> Int Source # models :: SOM t d gm x k p -> [p] Source # differences :: SOM t d gm x k p -> p -> [(k, x)] Source # classify :: SOM t d gm x k p -> p -> k Source # train :: SOM t d gm x k p -> p -> SOM t d gm x k p Source # trainBatch :: SOM t d gm x k p -> [p] -> SOM t d gm x k p Source # classifyAndTrain :: SOM t d gm x k p -> p -> (k, SOM t d gm x k p) Source # diffAndTrain :: SOM t d gm x k p -> p -> ([(k, x)], SOM t d gm x k p) Source # reportAndTrain :: SOM t d gm x k p -> p -> (k, [(k, x)], SOM t d gm x k p) Source #