Data.Datamining.Clustering.SOMInternal

A module containing private SOM internals. Most developers should use SOM instead. This module is subject to change without notice.

Documentation

adjustNode :: Pattern p v => p -> (v, p) -> p

adjustVector :: (Num a, Ord a, Eq a) => [a] -> a -> [a] -> [a]

adjustVector target amount vector adjusts vector to move it closer to target. The amount of adjustment is controlled by the learning rate r, which is a number between 0 and 1. Larger values of r permit more adjustment. If r=1, the result will be identical to the target. If amount=0, the result will be the unmodified pattern.

classify :: (Ord v, Pattern p v) => GridMap g k p -> p -> k

classify c pattern returns the position of the node in c whose pattern best matches the input pattern.

classifyAndTrain :: (Eq k, Ord v, Pattern p v, Grid g s k) => (Int -> v) -> GridMap g k p -> p -> (k, GridMap g k p)

If f is a function that returns the learning rate to apply to a node based on its distance from the node that best matches the target, then classifyAndTrain f c target returns a tuple containing the position of the node in c whose pattern best matches the input target, and a modified copy of the classifier c that has partially learned the target. Invoking classifyAndTrain f c p may be faster than invoking (p classify c, train f c p), but they should give identical results.

differences :: Pattern p v => p -> GridMap g k p -> GridMap g k v

Deprecated: Use diffs instead

pattern `'differences'\` c returns the positions of all nodes in c, paired with the difference between pattern and the node's pattern. This function has been replaced with diffs, which swaps the parameter order to be consistent with classify.

diffs :: Pattern p v => GridMap g k p -> p -> GridMap g k v

diffs c pattern returns the positions of all nodes in c, paired with the difference between pattern and the node's pattern.

diffAndTrain :: (Eq k, Ord v, Pattern p v, Grid g s k) => (Int -> v) -> GridMap g k p -> p -> (GridMap g k v, GridMap g k p)

If f is a function that returns the learning rate to apply to a node based on its distance from the node that best matches the target, then diffAndTrain f c target returns a tuple containing: 1. The positions of all nodes in c, paired with the difference between pattern and the node's pattern 2. A modified copy of the classifier c that has partially learned the target. Invoking diffAndTrain f c p may be faster than invoking (p differences c, train f c p), but they should give identical results.

euclideanDistanceSquared :: Num a => [a] -> [a] -> a

Calculates the square of the Euclidean distance between two vectors.

magnitudeSquared :: Num a => [a] -> a

normalise :: Floating a => [a] -> NormalisedVector a

Normalises a vector

data NormalisedVector a

A vector that has been normalised, i.e., the magnitude of the vector = 1.

Instances

Show a => Show (NormalisedVector a)

(Floating a, Fractional a, Ord a, Eq a) => Pattern (NormalisedVector a) a

scale :: Fractional a => [(a, a)] -> [a] -> ScaledVector a

Given a vector qs of pairs of numbers, where each pair represents the maximum and minimum value to be expected at each position in xs, scale qs xs scales the vector xs element by element, mapping the maximum value expected at that position to one, and the minimum value to zero.

scaleAll :: (Fractional a, Ord a) => [[a]] -> [ScaledVector a]

Scales a set of vectors by determining the maximum and minimum values at each position in the vector, and mapping the maximum value to one, and the minimum value to zero.

data ScaledVector a

A vector that has been scaled so that all elements in the vector are between zero and one. To scale a set of vectors, use scaleAll. Alternatively, if you can identify a maximum and minimum value for each element in a vector, you can scale individual vectors using scale.

Instances

Show a => Show (ScaledVector a)

(Fractional a, Ord a, Eq a) => Pattern (ScaledVector a) a

train :: (Ord v, Pattern p v, Grid g s k) => (Int -> v) -> GridMap g k p -> p -> GridMap g k p

If f d is a function that returns the learning rate to apply to a node based on its distance dfrom the node that best matches the input pattern, then train f c pattern returns a modified copy of the classifier c that has partially learned the target.

trainBatch :: (Ord v, Grid g s k, Pattern p v) => (Int -> v) -> GridMap g k p -> [p] -> GridMap g k p

Same as train, but applied to multiple patterns.

class Pattern p v | p -> v where

A pattern to be learned or classified by a self-organising map.

Methods

difference :: p -> p -> v

Compares two patterns and returns a non-negative number representing how different the patterns are. A result of 0 indicates that the patterns are identical.

makeSimilar :: p -> v -> p -> p

makeSimilar target amount pattern returns a modified copy of pattern that is more similar to target than pattern is. The magnitude of the adjustment is controlled by the amount parameter, which should be a number between 0 and 1. Larger values for amount permit greater adjustments. If amount=1, the result should be identical to the target. If amount=0, the result should be the unmodified pattern.

Instances

(Fractional a, Ord a, Eq a) => Pattern (ScaledVector a) a

(Floating a, Fractional a, Ord a, Eq a) => Pattern (NormalisedVector a) a