| Portability | portable |
|---|---|
| Stability | experimental |
| Maintainer | amy@nualeargais.ie |
| Safe Haskell | Safe-Inferred |
Data.Datamining.Clustering.Classifier
Description
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 whereSource
A machine which learns to classify input patterns.
Minimal complete definition: trainBatch, reportAndTrain.
Methods
toList :: c k p -> [(k, p)]Source
Returns a list of index/model pairs.
numModels :: c k p -> IntSource
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
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 -> kSource
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 pSource
train c targetc that has partially learned the target.
trainBatch :: c k p -> [p] -> c k pSource
trainBatch c targetsc that has partially learned the targets.
classifyAndTrain :: (Ord v, v ~ Metric p) => c k p -> p -> (k, c k p)Source
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
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
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)
Instances
| (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 | |
| (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 |