Portability | portable |
---|---|
Stability | experimental |
Maintainer | amy@nualeargais.ie |
Safe Haskell | Safe-Inferred |
Tools for identifying patterns in data.
- class Pattern p where
- type Metric p
- difference :: p -> p -> Metric p
- makeSimilar :: p -> Metric p -> p -> p
- data NormalisedVector a
- normalise :: Floating a => [a] -> NormalisedVector a
- data ScaledVector a
- scale :: Fractional a => [(a, a)] -> [a] -> ScaledVector a
- scaleAll :: (Fractional a, Ord a) => [[a]] -> [ScaledVector a]
- adjustVector :: (Num a, Ord a, Eq a) => [a] -> a -> [a] -> [a]
- euclideanDistanceSquared :: Num a => [a] -> [a] -> a
- magnitudeSquared :: Num a => [a] -> a
Patterns
A pattern to be learned or classified.
difference :: p -> p -> Metric pSource
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 -> Metric p -> p -> pSource
returns a modified copy of
makeSimilar
target amount patternpattern
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
.
(Fractional a, Ord a, Eq a) => Pattern (ScaledVector a) | |
(Floating a, Fractional a, Ord a, Eq a) => Pattern (NormalisedVector a) |
Numeric vectors as patterns
Normalised vectors
data NormalisedVector a Source
A vector that has been normalised, i.e., the magnitude of the vector = 1.
Show a => Show (NormalisedVector a) | |
(Floating a, Fractional a, Ord a, Eq a) => Pattern (NormalisedVector a) |
normalise :: Floating a => [a] -> NormalisedVector aSource
Normalises a vector
Scaled vectors
data ScaledVector a Source
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
. Alternatively, if you can identify a maximum and
minimum value for each element in a vector, you can scale
individual vectors using scaleAll
.
scale
Show a => Show (ScaledVector a) | |
(Fractional a, Ord a, Eq a) => Pattern (ScaledVector a) |
scale :: Fractional a => [(a, a)] -> [a] -> ScaledVector aSource
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
,
scales the vector scale
qs xsxs
element by element,
mapping the maximum value expected at that index to one, and the
minimum value to zero.
scaleAll :: (Fractional a, Ord a) => [[a]] -> [ScaledVector a]Source
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.
Useful functions
If you wish to use raw numeric vectors as a pattern, use
no-warn-orphans
and add the following to your code:
instance (Floating a, Fractional a, Ord a, Eq a) ⇒ Pattern [a] a where difference = euclideanDistanceSquared makeSimilar = adjustVector
adjustVector :: (Num a, Ord a, Eq a) => [a] -> a -> [a] -> [a]Source
adjusts adjustVector
target amount vectorvector
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
.
euclideanDistanceSquared :: Num a => [a] -> [a] -> aSource
Calculates the square of the Euclidean distance between two vectors.
magnitudeSquared :: Num a => [a] -> aSource