Copyright | (c) Amy de Buitléir 2012-2014 |
---|---|
License | BSD-style |
Maintainer | amy@nualeargais.ie |
Stability | experimental |
Portability | portable |
Safe Haskell | Safe-Inferred |
Language | Haskell98 |
Tools for identifying patterns in data.
- class Classifier c k p where
- toList :: c k p -> [(k, p)]
- numModels :: c k p -> Int
- models :: c k p -> [p]
- differences :: (Pattern p, v ~ Metric p) => c k p -> p -> [(k, v)]
- classify :: (Pattern p, Ord v, v ~ Metric p) => c k p -> p -> k
- train :: (Ord v, v ~ Metric p) => c k p -> p -> c k p
- trainBatch :: c k p -> [p] -> c k p
- classifyAndTrain :: (Ord v, v ~ Metric p) => c k p -> p -> (k, c k p)
- diffAndTrain :: (Ord v, v ~ Metric p) => c k p -> p -> ([(k, v)], c k p)
- reportAndTrain :: (Ord v, v ~ Metric p) => c k p -> p -> (k, [(k, v)], c k p)
Documentation
class Classifier c k p where Source
A machine which learns to classify input patterns.
Minimal complete definition: trainBatch
, reportAndTrain
.
toList :: c k p -> [(k, p)] Source
Returns a list of index/model pairs.
numModels :: c k p -> Int Source
Returns the number of models this classifier can learn.
Returns the current models of the classifier.
differences :: (Pattern p, v ~ Metric p) => c k p -> p -> [(k, v)] Source
returns the indices of all nodes in
differences
c targetc
, paired with the difference between target
and the
node's model.
classify :: (Pattern p, Ord v, v ~ Metric p) => c k p -> p -> k Source
classify c target
returns the index of the node in c
whose model best matches the target
.
train :: (Ord v, v ~ Metric p) => c k p -> p -> c k p Source
returns a modified copy
of the classifier train
c targetc
that has partially learned the target
.
trainBatch :: c k p -> [p] -> c k p Source
returns a modified copy
of the classifier trainBatch
c targetsc
that has partially learned the targets
.
classifyAndTrain :: (Ord v, v ~ Metric p) => c k p -> p -> (k, c k p) Source
returns a tuple containing the
index of the node in classifyAndTrain
c targetc
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
, but
they
should give identical results.classify
c, train c p)
diffAndTrain :: (Ord v, v ~ Metric p) => c k p -> p -> ([(k, v)], c k p) Source
returns a tuple containing:
1. The indices of all nodes in diffAndTrain
c targetc
, 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
, but they should give identical
results.diff
c, train c p)
reportAndTrain :: (Ord v, v ~ Metric p) => c k p -> p -> (k, [(k, v)], c k p) Source
returns a tuple containing:
1. The index of the node in reportAndTrain
c f targetc
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
, but they should give identical
results.diff
c, train c p)
(GridMap gm p, (~) * k (Index (BaseGrid gm p)), Pattern p, FiniteGrid (gm p), GridMap gm (Metric p), (~) * k (Index (gm p)), (~) * k (Index (BaseGrid gm (Metric p))), Ord k, Ord (Metric p), Num (Metric p), Fractional (Metric p)) => Classifier (DSOM gm) k p | |
(Pattern p, Ord (Metric p), LearningFunction f, (~) * (Metric p) (LearningRate f), Num (LearningRate f), Ord k, Integral t) => Classifier (SSOM f t) k p | |
(GridMap gm p, (~) * k (Index (BaseGrid gm p)), Pattern p, Grid (gm p), GridMap gm (Metric p), (~) * k (Index (gm p)), (~) * k (Index (BaseGrid gm (Metric p))), Ord (Metric p), LearningFunction f, (~) * (Metric p) (LearningRate f), Num (LearningRate f), Integral t) => Classifier (SOM f t gm) k p |