| Copyright | (c) 2012-2021 Amy de Buitléir |
|---|---|
| License | BSD-style |
| Maintainer | amy@nualeargais.ie |
| Stability | experimental |
| Portability | portable |
| Safe Haskell | Safe-Inferred |
| Language | Haskell2010 |
Data.Datamining.Clustering.SGMInternal
Description
A module containing private SGM internals. Most developers should
use SGM instead. This module is subject to change without notice.
Synopsis
- exponential :: (Floating a, Integral t) => a -> a -> t -> a
- data SGM t x k p = SGM {
- toMap :: Map k (p, t)
- learningRate :: t -> x
- maxSize :: Int
- diffThreshold :: x
- allowDeletion :: Bool
- difference :: p -> p -> x
- makeSimilar :: p -> x -> p -> p
- nextIndex :: k
- makeSGM :: Bounded k => (t -> x) -> Int -> x -> Bool -> (p -> p -> x) -> (p -> x -> p -> p) -> SGM t x k p
- isEmpty :: SGM t x k p -> Bool
- numModels :: SGM t x k p -> Int
- modelMap :: SGM t x k p -> Map k p
- counterMap :: SGM t x k p -> Map k t
- modelAt :: Ord k => SGM t x k p -> k -> p
- labels :: SGM t x k p -> [k]
- models :: SGM t x k p -> [p]
- counters :: SGM t x k p -> [t]
- time :: Num t => SGM t x k p -> t
- addNode :: (Num t, Enum k, Ord k) => p -> SGM t x k p -> SGM t x k p
- deleteNode :: Ord k => k -> SGM t x k p -> SGM t x k p
- incrementCounter :: (Num t, Ord k) => k -> SGM t x k p -> SGM t x k p
- trainNode :: (Num t, Ord k) => SGM t x k p -> k -> p -> SGM t x k p
- leastUsefulNode :: Ord t => SGM t x k p -> k
- deleteLeastUsefulNode :: (Ord t, Ord k) => SGM t x k p -> SGM t x k p
- addModel :: (Num t, Ord t, Enum k, Ord k) => p -> SGM t x k p -> SGM t x k p
- classify :: (Num t, Ord t, Num x, Ord x, Enum k, Ord k) => SGM t x k p -> p -> (k, x, Map k (p, x))
- classify' :: (Num t, Ord t, Num x, Ord x, Enum k, Ord k) => SGM t x k p -> p -> (k, x, Map k (p, x), SGM t x k p)
- matchOrder :: (Ord a, Ord b) => (a, b) -> (a, b) -> Ordering
- trainAndClassify :: (Num t, Ord t, Num x, Ord x, Enum k, Ord k) => SGM t x k p -> p -> (k, x, Map k (p, x), SGM t x k p)
- train :: (Num t, Ord t, Num x, Ord x, Enum k, Ord k) => SGM t x k p -> p -> SGM t x k p
- trainBatch :: (Num t, Ord t, Num x, Ord x, Enum k, Ord k) => SGM t x k p -> [p] -> SGM t x k p
Documentation
exponential :: (Floating a, Integral t) => a -> a -> t -> a Source #
A typical learning function for classifiers.
exponential r0 d tt.
When t = 0, the learning rate is r0.
Over time the learning rate decays exponentially; the decay rate is
d.
Normally the parameters are chosen such that:
- 0 < r0 < 1
- 0 < d
A Simplified Self-Organising Map (SGM).
t is the type of the counter.
x is the type of the learning rate and the difference metric.
k is the type of the model indices.
p is the type of the input patterns and models.
Constructors
| SGM | |
Fields
| |
Instances
makeSGM :: Bounded k => (t -> x) -> Int -> x -> Bool -> (p -> p -> x) -> (p -> x -> p -> p) -> SGM t x k p Source #
makeSGM lr n dt diff mslr,
and will be able to hold up to n models.
It will create a new model based on a pattern presented to it when
(1) the SGM contains no models, or
(2) the difference between the pattern and the closest matching
model exceeds the threshold dt.
It will use the function diff to measure the similarity between
an input pattern and a model.
It will use the function ms to adjust models as needed to make
them more similar to input patterns.
counterMap :: SGM t x k p -> Map k t Source #
Returns a map from node ID to counter (number of times the node's model has been the closest match to an input pattern).
counters :: SGM t x k p -> [t] Source #
Returns the current counters (number of times the node's model has been the closest match to an input pattern).
time :: Num t => SGM t x k p -> t Source #
The current "time" (number of times the SGM has been trained).
addNode :: (Num t, Enum k, Ord k) => p -> SGM t x k p -> SGM t x k p Source #
Adds a new node to the SGM.
deleteNode :: Ord k => k -> SGM t x k p -> SGM t x k p Source #
Removes a node from the SGM. Deleted nodes are never re-used.
incrementCounter :: (Num t, Ord k) => k -> SGM t x k p -> SGM t x k p Source #
Increments the match counter.
trainNode :: (Num t, Ord k) => SGM t x k p -> k -> p -> SGM t x k p Source #
Trains the specified node to better match a target.
Most users should use train
leastUsefulNode :: Ord t => SGM t x k p -> k Source #
Returns the node that has been the BMU least often.
deleteLeastUsefulNode :: (Ord t, Ord k) => SGM t x k p -> SGM t x k p Source #
Deletes the node that has been the BMU least often.
addModel :: (Num t, Ord t, Enum k, Ord k) => p -> SGM t x k p -> SGM t x k p Source #
Adds a new node to the SGM, deleting the least useful node/model if necessary to make room.
classify :: (Num t, Ord t, Num x, Ord x, Enum k, Ord k) => SGM t x k p -> p -> (k, x, Map k (p, x)) Source #
classify s ps that most closely
matches the pattern p.
It will not make any changes to the classifier.
Returns the ID of the node with the best matching model,
the difference between the best matching model and the pattern,
and the SGM labels paired with the model and the difference
between the input and the corresponding model.
The final paired list is sorted in decreasing order of similarity.
classify' :: (Num t, Ord t, Num x, Ord x, Enum k, Ord k) => SGM t x k p -> p -> (k, x, Map k (p, x), SGM t x k p) Source #
Internal method. NOTE: This function may create a new model, but it does not modify existing models.
matchOrder :: (Ord a, Ord b) => (a, b) -> (a, b) -> Ordering Source #
Order models by ascending difference from the input pattern, then by creation order (label number).
trainAndClassify :: (Num t, Ord t, Num x, Ord x, Enum k, Ord k) => SGM t x k p -> p -> (k, x, Map k (p, x), SGM t x k p) Source #
trainAndClassify s ps that most
closely matches p, and updates it to be a somewhat better match.
If necessary, it will create a new node and model.
Returns the ID of the node with the best matching model,
the difference between the best matching model and the pattern,
the differences between the input and each model in the SGM,
and the updated SGM.