-- Hoogle documentation, generated by Haddock -- See Hoogle, http://www.haskell.org/hoogle/ -- | Self-Organising Maps. -- -- A Kohonen Self-organising Map (SOM) maps input patterns onto a regular -- grid (usually two-dimensional) where each node in the grid is a model -- of the input data, and does so using a method which ensures that any -- topological relationships within the input data are also represented -- in the grid. This implementation supports the use of non-numeric -- patterns. -- -- In layman's terms, a SOM can be useful when you you want to discover -- the underlying structure of some data. -- -- The userguide is available at -- https://github.com/mhwombat/som/wiki. @package som @version 8.0.2 -- | Tools for identifying patterns in data. module Data.Datamining.Pattern adjustNum :: (Num a, Ord a, Eq a) => a -> a -> a -> a absDifference :: Num a => a -> a -> a -- | adjustVector target amount vector adjusts each element -- of vector to move it closer to the corresponding element of -- target. The amount of adjustment is controlled by the -- learning rate amount, which is a number between 0 and 1. -- Larger values of amount permit more adjustment. If -- amount=1, the result will be identical to the -- target. If amount=0, the result will be the -- unmodified pattern. If target is shorter than -- vector, the result will be the same length as -- target. If target is longer than vector, -- the result will be the same length as vector. adjustVector :: (Num a, Ord a, Eq a) => [a] -> a -> [a] -> [a] -- | Same as adjustVector, except that the result will -- always be the same length as vector. This means that if -- target is shorter than vector, the "leftover" -- elements of vector will be copied the result, unmodified. adjustVectorPreserveLength :: (Num a, Ord a, Eq a) => [a] -> a -> [a] -> [a] -- | Calculates the square of the Euclidean distance between two vectors. euclideanDistanceSquared :: Num a => [a] -> [a] -> a magnitudeSquared :: Num a => [a] -> a -- | A vector that has been normalised, i.e., the magnitude of the vector = -- 1. data NormalisedVector a -- | Normalises a vector normalise :: Floating a => [a] -> NormalisedVector 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. data ScaledVector a -- | Given a vector qs of pairs of numbers, where each pair -- represents the maximum and minimum value to be expected at each index -- in xs, scale qs xs scales the vector -- xs element by element, mapping the maximum value expected at -- that index to one, and the minimum value to zero. scale :: Fractional a => [(a, a)] -> [a] -> ScaledVector a -- | Scales a set of vectors by determining the maximum and minimum values -- at each index in the vector, and mapping the maximum value to one, and -- the minimum value to zero. scaleAll :: (Fractional a, Ord a) => [[a]] -> [ScaledVector a] instance Show a => Show (ScaledVector a) instance Show a => Show (NormalisedVector a) -- | Tools for identifying patterns in data. module Data.Datamining.Clustering.Classifier -- | A machine which learns to classify input patterns. Minimal complete -- definition: trainBatch, reportAndTrain. class Classifier (c :: * -> * -> * -> *) v k p where classify c p = f $ differences c p where f [] = error "classifier has no models" f xs = fst $ minimumBy (comparing snd) xs train c p = c' where (_, _, c') = reportAndTrain c p classifyAndTrain c p = (bmu, c') where (bmu, _, c') = reportAndTrain c p diffAndTrain c p = (ds, c') where (_, ds, c') = reportAndTrain c p toList :: Classifier c v k p => c v k p -> [(k, p)] numModels :: Classifier c v k p => c v k p -> Int models :: Classifier c v k p => c v k p -> [p] differences :: Classifier c v k p => c v k p -> p -> [(k, v)] classify :: (Classifier c v k p, Ord v) => c v k p -> p -> k train :: Classifier c v k p => c v k p -> p -> c v k p trainBatch :: Classifier c v k p => c v k p -> [p] -> c v k p classifyAndTrain :: Classifier c v k p => c v k p -> p -> (k, c v k p) diffAndTrain :: Classifier c v k p => c v k p -> p -> ([(k, v)], c v k p) reportAndTrain :: Classifier c v k p => c v k p -> p -> (k, [(k, v)], c v k p) -- | A module containing private DSOM internals. Most developers -- should use DSOM instead. This module is subject to change -- without notice. module Data.Datamining.Clustering.DSOMInternal -- | A Self-Organising Map (DSOM). -- -- Although DSOM implements GridMap, most users will -- only need the interface provided by -- Data.Datamining.Clustering.Classifier. If you chose to use -- the GridMap functions, please note: -- --
    --
  1. The functions adjust, and adjustWithKey do not -- increment the counter. You can do so manually with -- incrementCounter.
    2. --
  2. The functions map and mapWithKey are not -- implemented (they just return an error). It would be -- problematic to implement them because the input DSOM and the output -- DSOM would have to have the same Metric type.
    3. --
data DSOM gm x k p [DSOM] :: gm p -> (x -> x -> x -> x) -> (p -> p -> x) -> (p -> x -> p -> p) -> DSOM gm x k p -- | Maps patterns to tiles in a regular grid. In the context of a SOM, the -- tiles are called "nodes" [gridMap] :: DSOM gm x k p -> gm p -- | A function which determines the how quickly the SOM learns. [learningRate] :: DSOM gm x k p -> (x -> x -> x -> x) -- | A function which 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. [difference] :: DSOM gm x k p -> p -> p -> x -- | A function which updates models. If this function is f, then -- f 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. [makeSimilar] :: DSOM gm x k p -> p -> x -> p -> p withGridMap :: (gm p -> gm p) -> DSOM gm x k p -> DSOM gm x k p -- | Extracts the grid and current models from the DSOM. toGridMap :: GridMap gm p => DSOM gm x k p -> gm p adjustNode :: (FiniteGrid (gm p), GridMap gm p, k ~ Index (gm p), k ~ Index (BaseGrid gm p), Ord k, Num x, Fractional x) => gm p -> (p -> x -> p -> p) -> (p -> p -> x) -> (x -> x -> x) -> p -> k -> k -> (p -> p) scaleDistance :: (Num a, Fractional a) => Int -> Int -> a -- | Trains the specified node and the neighbourood around it to better -- match a target. Most users should use train, which -- automatically determines the BMU and trains it and its neighbourhood. trainNeighbourhood :: (FiniteGrid (gm p), GridMap gm p, k ~ Index (gm p), k ~ Index (BaseGrid gm p), Ord k, Num x, Fractional x) => DSOM gm x t p -> k -> p -> DSOM gm x k p justTrain :: (FiniteGrid (gm p), GridMap gm p, GridMap gm x, k ~ Index (gm p), k ~ Index (gm x), k ~ Index (BaseGrid gm p), k ~ Index (BaseGrid gm x), Ord k, Ord x, Num x, Fractional x) => DSOM gm x t p -> p -> DSOM gm x k p -- | Configures a learning function that depends not on the time, but on -- how good a model we already have for the target. If the BMU is an -- exact match for the target, no learning occurs. Usage is -- rougierLearningFunction r p, where r is the -- maximal learning rate (0 <= r <= 1), and p is the -- elasticity. -- -- NOTE: When using this learning function, ensure that abs . -- difference is always between 0 and 1, inclusive. Otherwise you -- may get invalid learning rates. rougierLearningFunction :: (Eq a, Ord a, Floating a) => a -> a -> (a -> a -> a -> a) instance Foldable gm => Foldable (DSOM gm x k) instance Grid (gm p) => Grid (DSOM gm x k p) instance (Foldable gm, GridMap gm p, FiniteGrid (BaseGrid gm p)) => GridMap (DSOM gm x k) p instance (GridMap gm p, k ~ Index (BaseGrid gm p), FiniteGrid (gm p), GridMap gm x, k ~ Index (gm p), k ~ Index (gm x), k ~ Index (BaseGrid gm x), Ord k, Ord x, Num x, Fractional x) => Classifier (DSOM gm) x k p -- | A modified Kohonen Self-organising Map (SOM) which supports a -- time-independent learning function. (See SOM for a -- description of a SOM.) -- -- References: -- -- module Data.Datamining.Clustering.DSOM -- | A Self-Organising Map (DSOM). -- -- Although DSOM implements GridMap, most users will -- only need the interface provided by -- Data.Datamining.Clustering.Classifier. If you chose to use -- the GridMap functions, please note: -- --
    --
  1. The functions adjust, and adjustWithKey do not -- increment the counter. You can do so manually with -- incrementCounter.
    2. --
  2. The functions map and mapWithKey are not -- implemented (they just return an error). It would be -- problematic to implement them because the input DSOM and the output -- DSOM would have to have the same Metric type.
    3. --
data DSOM gm x k p [DSOM] :: gm p -> (x -> x -> x -> x) -> (p -> p -> x) -> (p -> x -> p -> p) -> DSOM gm x k p -- | Maps patterns to tiles in a regular grid. In the context of a SOM, the -- tiles are called "nodes" [gridMap] :: DSOM gm x k p -> gm p -- | A function which determines the how quickly the SOM learns. [learningRate] :: DSOM gm x k p -> (x -> x -> x -> x) -- | A function which 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. [difference] :: DSOM gm x k p -> p -> p -> x -- | A function which updates models. If this function is f, then -- f 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. [makeSimilar] :: DSOM gm x k p -> p -> x -> p -> p -- | Extracts the grid and current models from the DSOM. toGridMap :: GridMap gm p => DSOM gm x k p -> gm p -- | Configures a learning function that depends not on the time, but on -- how good a model we already have for the target. If the BMU is an -- exact match for the target, no learning occurs. Usage is -- rougierLearningFunction r p, where r is the -- maximal learning rate (0 <= r <= 1), and p is the -- elasticity. -- -- NOTE: When using this learning function, ensure that abs . -- difference is always between 0 and 1, inclusive. Otherwise you -- may get invalid learning rates. rougierLearningFunction :: (Eq a, Ord a, Floating a) => a -> a -> (a -> a -> a -> a) -- | Trains the specified node and the neighbourood around it to better -- match a target. Most users should use train, which -- automatically determines the BMU and trains it and its neighbourhood. trainNeighbourhood :: (FiniteGrid (gm p), GridMap gm p, k ~ Index (gm p), k ~ Index (BaseGrid gm p), Ord k, Num x, Fractional x) => DSOM gm x t p -> k -> p -> DSOM gm x k p -- | A module containing private SSOM internals. Most developers -- should use SSOM instead. This module is subject to change -- without notice. module Data.Datamining.Clustering.SSOMInternal -- | A typical learning function for classifiers. exponential r0 -- d t returns the learning rate at time t. 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: -- -- -- -- where << means "is much smaller than" (not the Haskell -- << operator!) exponential :: Floating a => a -> a -> a -> a -- | A Simplified Self-Organising Map (SSOM). x is the type of the -- learning rate and the difference metric. t is the type of the -- counter. k is the type of the model indices. p is -- the type of the input patterns and models. data SSOM t x k p [SSOM] :: Map k p -> (t -> x) -> (p -> p -> x) -> (p -> x -> p -> p) -> t -> SSOM t x k p -- | Maps patterns to nodes. [sMap] :: SSOM t x k p -> Map k p -- | A function which determines the learning rate for a node. The input -- parameter indicates how many patterns (or pattern batches) have -- previously been presented to the classifier. Typically this is used to -- make the learning rate decay over time. The output is the learning -- rate for that node (the amount by which the node's model should be -- updated to match the target). The learning rate should be between zero -- and one. [learningRate] :: SSOM t x k p -> t -> x -- | A function which 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. [difference] :: SSOM t x k p -> p -> p -> x -- | A function which updates models. For example, if this function is -- f, then f 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. [makeSimilar] :: SSOM t x k p -> p -> x -> p -> p -- | A counter used as a "time" parameter. If you create the SSOM with a -- counter value 0, and don't directly modify it, then the -- counter will represent the number of patterns that this SSOM has -- classified. [counter] :: SSOM t x k p -> t -- | Extracts the current models from the SSOM. A synonym for -- sMap. toMap :: SSOM t x k p -> Map k p -- | Trains the specified node to better match a target. Most users should -- use train, which automatically determines the BMU and -- trains it. trainNode :: (Num t, Ord k) => SSOM t x k p -> k -> p -> SSOM t x k p incrementCounter :: Num t => SSOM t x k p -> SSOM t x k p justTrain :: (Num t, Ord k, Ord x) => SSOM t x k p -> p -> SSOM t x k p instance Selector S1_0_4SSOM instance Selector S1_0_3SSOM instance Selector S1_0_2SSOM instance Selector S1_0_1SSOM instance Selector S1_0_0SSOM instance Constructor C1_0SSOM instance Datatype D1SSOM instance Generic (SSOM t x k p) instance (Num t, Ord x, Num x, Ord k) => Classifier (SSOM t) x k p -- | A Simplified Self-organising Map (SSOM). An SSOM maps input patterns -- onto a set, where each element in the set is a model of the input -- data. An SSOM is like a Kohonen Self-organising Map (SOM), except that -- instead of a grid, it uses a simple set of unconnected models. Since -- the models are unconnected, only the model that best matches the input -- is ever updated. This makes it faster, however, topological -- relationships within the input data are not preserved. This -- implementation supports the use of non-numeric patterns. -- -- In layman's terms, a SSOM can be useful when you you want to build a -- set of models on some data. A tutorial is available at -- https://github.com/mhwombat/som/wiki. -- -- References: -- -- module Data.Datamining.Clustering.SSOM -- | A Simplified Self-Organising Map (SSOM). x is the type of the -- learning rate and the difference metric. t is the type of the -- counter. k is the type of the model indices. p is -- the type of the input patterns and models. data SSOM t x k p [SSOM] :: Map k p -> (t -> x) -> (p -> p -> x) -> (p -> x -> p -> p) -> t -> SSOM t x k p -- | Maps patterns to nodes. [sMap] :: SSOM t x k p -> Map k p -- | A function which determines the learning rate for a node. The input -- parameter indicates how many patterns (or pattern batches) have -- previously been presented to the classifier. Typically this is used to -- make the learning rate decay over time. The output is the learning -- rate for that node (the amount by which the node's model should be -- updated to match the target). The learning rate should be between zero -- and one. [learningRate] :: SSOM t x k p -> t -> x -- | A function which 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. [difference] :: SSOM t x k p -> p -> p -> x -- | A function which updates models. For example, if this function is -- f, then f 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. [makeSimilar] :: SSOM t x k p -> p -> x -> p -> p -- | A counter used as a "time" parameter. If you create the SSOM with a -- counter value 0, and don't directly modify it, then the -- counter will represent the number of patterns that this SSOM has -- classified. [counter] :: SSOM t x k p -> t -- | Extracts the current models from the SSOM. A synonym for -- sMap. toMap :: SSOM t x k p -> Map k p -- | A typical learning function for classifiers. exponential r0 -- d t returns the learning rate at time t. 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: -- -- -- -- where << means "is much smaller than" (not the Haskell -- << operator!) exponential :: Floating a => a -> a -> a -> a -- | Trains the specified node to better match a target. Most users should -- use train, which automatically determines the BMU and -- trains it. trainNode :: (Num t, Ord k) => SSOM t x k p -> k -> p -> SSOM t x k p -- | A module containing private SOM internals. Most developers -- should use SOM instead. This module is subject to change -- without notice. module Data.Datamining.Clustering.SOMInternal -- | A typical learning function for classifiers. -- decayingGaussian r0 rf w0 wf tf returns a bell -- curve-shaped function. At time zero, the maximum learning rate -- (applied to the BMU) is r0, and the neighbourhood width is -- w0. Over time the bell curve shrinks and the learning rate -- tapers off, until at time tf, the maximum learning rate -- (applied to the BMU) is rf, and the neighbourhood width is -- wf. Normally the parameters should be chosen such that: -- -- -- -- where << means "is much smaller than" (not the Haskell -- << operator!) decayingGaussian :: Floating x => x -> x -> x -> x -> x -> x -> x -> x -- | A learning function that only updates the BMU and has a constant -- learning rate. stepFunction :: (Num d, Fractional x, Eq d) => x -> t -> d -> x -- | A learning function that updates all nodes with the same, constant -- learning rate. This can be useful for testing. constantFunction :: x -> t -> d -> x -- | A Self-Organising Map (SOM). -- -- Although SOM implements GridMap, most users will -- only need the interface provided by -- Data.Datamining.Clustering.Classifier. If you chose to use -- the GridMap functions, please note: -- --
    --
  1. The functions adjust, and adjustWithKey do not -- increment the counter. You can do so manually with -- incrementCounter.
    2. --
  2. The functions map and mapWithKey are not -- implemented (they just return an error). It would be -- problematic to implement them because the input SOM and the output SOM -- would have to have the same Metric type.
    3. --
data SOM t d gm x k p [SOM] :: gm p -> (t -> d -> x) -> (p -> p -> x) -> (p -> x -> p -> p) -> t -> SOM t d gm x k p -- | Maps patterns to tiles in a regular grid. In the context of a SOM, the -- tiles are called "nodes" [gridMap] :: SOM t d gm x k p -> gm p -- | A function which determines the how quickly the SOM learns. For -- example, if the function is f, then f t d returns -- the learning rate for a node. The parameter t indicates how -- many patterns (or pattern batches) have previously been presented to -- the classifier. Typically this is used to make the learning rate decay -- over time. The parameter d is the grid distance from the node -- being updated to the BMU (Best Matching Unit). The output is the -- learning rate for that node (the amount by which the node's model -- should be updated to match the target). The learning rate should be -- between zero and one. [learningRate] :: SOM t d gm x k p -> t -> d -> x -- | A function which 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. [difference] :: SOM t d gm x k p -> p -> p -> x -- | A function which updates models. If this function is f, then -- f 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. [makeSimilar] :: SOM t d gm x k p -> p -> x -> p -> p -- | A counter used as a "time" parameter. If you create the SOM with a -- counter value 0, and don't directly modify it, then the -- counter will represent the number of patterns that this SOM has -- classified. [counter] :: SOM t d gm x k p -> t withGridMap :: (gm p -> gm p) -> SOM t d gm x k p -> SOM t d gm x k p currentLearningFunction :: (Num t) => SOM t d gm x k p -> (d -> x) -- | Extracts the grid and current models from the SOM. A synonym for -- gridMap. toGridMap :: GridMap gm p => SOM t d gm x k p -> gm p adjustNode :: (Grid g, k ~ Index g, Num t) => g -> (t -> x) -> (p -> x -> p -> p) -> p -> k -> k -> p -> p -- | Trains the specified node and the neighbourood around it to better -- match a target. Most users should use train, which -- automatically determines the BMU and trains it and its neighbourhood. trainNeighbourhood :: (Grid (gm p), GridMap gm p, Index (BaseGrid gm p) ~ Index (gm p), Num t, Num x, Num d) => SOM t d gm x k p -> Index (gm p) -> p -> SOM t d gm x k p incrementCounter :: Num t => SOM t d gm x k p -> SOM t d gm x k p justTrain :: (Ord x, Grid (gm p), GridMap gm x, GridMap gm p, Index (BaseGrid gm x) ~ Index (gm p), Index (BaseGrid gm p) ~ Index (gm p), Num t, Num x, Num d) => SOM t d gm x k p -> p -> SOM t d gm x k p instance Selector S1_0_4SOM instance Selector S1_0_3SOM instance Selector S1_0_2SOM instance Selector S1_0_1SOM instance Selector S1_0_0SOM instance Constructor C1_0SOM instance Datatype D1SOM instance Generic (SOM t d gm x k p) instance Foldable gm => Foldable (SOM t d gm x k) instance Grid (gm p) => Grid (SOM t d gm x k p) instance (Foldable gm, GridMap gm p, Grid (BaseGrid gm p)) => GridMap (SOM t d gm x k) p instance (GridMap gm p, k ~ Index (BaseGrid gm p), Grid (gm p), GridMap gm x, k ~ Index (gm p), k ~ Index (BaseGrid gm x), Num t, Ord x, Num x, Num d) => Classifier (SOM t d gm) x k p -- | A Kohonen Self-organising Map (SOM). A SOM maps input patterns onto a -- regular grid (usually two-dimensional) where each node in the grid is -- a model of the input data, and does so using a method which ensures -- that any topological relationships within the input data are also -- represented in the grid. This implementation supports the use of -- non-numeric patterns. -- -- In layman's terms, a SOM can be useful when you you want to discover -- the underlying structure of some data. A tutorial is available at -- https://github.com/mhwombat/som/wiki. -- -- NOTES: -- -- -- -- References: -- -- module Data.Datamining.Clustering.SOM -- | A Self-Organising Map (SOM). -- -- Although SOM implements GridMap, most users will -- only need the interface provided by -- Data.Datamining.Clustering.Classifier. If you chose to use -- the GridMap functions, please note: -- --
    --
  1. The functions adjust, and adjustWithKey do not -- increment the counter. You can do so manually with -- incrementCounter.
    2. --
  2. The functions map and mapWithKey are not -- implemented (they just return an error). It would be -- problematic to implement them because the input SOM and the output SOM -- would have to have the same Metric type.
    3. --
data SOM t d gm x k p [SOM] :: gm p -> (t -> d -> x) -> (p -> p -> x) -> (p -> x -> p -> p) -> t -> SOM t d gm x k p -- | Maps patterns to tiles in a regular grid. In the context of a SOM, the -- tiles are called "nodes" [gridMap] :: SOM t d gm x k p -> gm p -- | A function which determines the how quickly the SOM learns. For -- example, if the function is f, then f t d returns -- the learning rate for a node. The parameter t indicates how -- many patterns (or pattern batches) have previously been presented to -- the classifier. Typically this is used to make the learning rate decay -- over time. The parameter d is the grid distance from the node -- being updated to the BMU (Best Matching Unit). The output is the -- learning rate for that node (the amount by which the node's model -- should be updated to match the target). The learning rate should be -- between zero and one. [learningRate] :: SOM t d gm x k p -> t -> d -> x -- | A function which 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. [difference] :: SOM t d gm x k p -> p -> p -> x -- | A function which updates models. If this function is f, then -- f 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. [makeSimilar] :: SOM t d gm x k p -> p -> x -> p -> p -- | A counter used as a "time" parameter. If you create the SOM with a -- counter value 0, and don't directly modify it, then the -- counter will represent the number of patterns that this SOM has -- classified. [counter] :: SOM t d gm x k p -> t -- | Extracts the grid and current models from the SOM. A synonym for -- gridMap. toGridMap :: GridMap gm p => SOM t d gm x k p -> gm p -- | A typical learning function for classifiers. -- decayingGaussian r0 rf w0 wf tf returns a bell -- curve-shaped function. At time zero, the maximum learning rate -- (applied to the BMU) is r0, and the neighbourhood width is -- w0. Over time the bell curve shrinks and the learning rate -- tapers off, until at time tf, the maximum learning rate -- (applied to the BMU) is rf, and the neighbourhood width is -- wf. Normally the parameters should be chosen such that: -- -- -- -- where << means "is much smaller than" (not the Haskell -- << operator!) decayingGaussian :: Floating x => x -> x -> x -> x -> x -> x -> x -> x -- | A learning function that only updates the BMU and has a constant -- learning rate. stepFunction :: (Num d, Fractional x, Eq d) => x -> t -> d -> x -- | A learning function that updates all nodes with the same, constant -- learning rate. This can be useful for testing. constantFunction :: x -> t -> d -> x -- | Trains the specified node and the neighbourood around it to better -- match a target. Most users should use train, which -- automatically determines the BMU and trains it and its neighbourhood. trainNeighbourhood :: (Grid (gm p), GridMap gm p, Index (BaseGrid gm p) ~ Index (gm p), Num t, Num x, Num d) => SOM t d gm x k p -> Index (gm p) -> p -> SOM t d gm x k p