HLearn.Models.Classifiers.Common

Documentation

indicator :: Num a => Bool -> a

bool2num :: Num a => Bool -> a

num2bool :: (Ord a, Num a) => a -> Bool

class Labeled dp where

Associated Types

type Label dp

type Attributes dp

Methods

getLabel :: dp -> Label dp

getAttributes :: dp -> Attributes dp

Instances

Labeled (Coord ring)

Labeled (label, attr)

class Labeled (Datapoint model) => ProbabilityClassifier model where

Associated Types

type ResultDistribution model

Methods

probabilityClassify :: model -> Attributes (Datapoint model) -> ResultDistribution model

Instances

(ProbabilityClassifier (BaseModel params), ~ * (Ring (BaseModel params)) (Ring (ResultDistribution (FiniteBoost params))), Module (ResultDistribution (FiniteBoost params)), FiniteBoostParams params) => ProbabilityClassifier (FiniteBoost params)

(~ * (Margin labelLens dist) (Categorical label prob), Ord label, Ord prob, Fractional prob, ~ * label (Label (Datapoint dist)), ~ * prob (Probability (MarginalizeOut labelLens dist)), Labeled (Datapoint dist), ~ * (Datapoint (MarginalizeOut labelLens dist)) (Attributes (Datapoint dist)), PDF (MarginalizeOut labelLens dist), PDF (Margin labelLens dist), Marginalize labelLens dist) => ProbabilityClassifier (Bayes labelLens dist)

(Ord label, Ord (Ring dp), MetricSpace (Centroid dp), Monoid dp, HasRing dp) => ProbabilityClassifier (Perceptron label dp)

(ProbabilityClassifier basemodel, Monoid (ResultDistribution basemodel)) => ProbabilityClassifier (MonoidBoost k basemodel)

(Ord label, ~ * label (Label (label, dp)), Foldable container, MetricSpace dp, Ord (Ring dp)) => ProbabilityClassifier (NaiveNN container label dp)

class MarginClassifier model where

Methods

margin :: model -> Attributes (Datapoint model) -> (Ring model, Label (Datapoint model))

class Labeled (Datapoint model) => Classifier model where

Methods

classify :: model -> Attributes (Datapoint model) -> Label (Datapoint model)

Instances

Floating ring => Classifier (PowerLaw ring)

(ProbabilityClassifier (Bayes labelLens dist), ~ * (Label (Datapoint (Bayes labelLens dist))) (Datapoint (Margin labelLens dist)), Mean (Margin labelLens dist)) => Classifier (Bayes labelLens dist)

class (Classifier model, Ring model ~ Label (Datapoint model)) => Regression model

this is a default instance that any instance of Classifier should satisfy if it is also an instance of ProbabilityClassifier instance ( Label (Datapoint model) ~ Datapoint (ResultDistribution model) , Mean (ResultDistribution model) , ProbabilityClassifier model ) => Classifier model where classify model dp = mean $ probabilityClassify model dp

Regression is classification where the class labels are (isomorphic to) real numbers. The constraints could probably be better specified, but they're close enough for now.

Instances

(Classifier model, ~ * (Ring model) (Label (Datapoint model))) => Regression model