Copyright	David Johnson (c) 2019-2020
License	BSD3
Maintainer	David Johnson <djohnson.m@gmail.com>
Stability	Experimental
Portability	GHC
Safe Haskell	None
Language	Haskell2010

ArrayFire.Statistics

Description

Statistics API. Example of finding the top k elements along with their indices from an Array

>>> let (vals,indexes) = topk ( vector @Double 10 [1..] ) 3 TopKDefault
>>> print vals
>>> print indexes

ArrayFire Array
[3 1 1 1]
  10.0000     9.0000     8.0000

ArrayFire Array
[3 1 1 1]
   9          8          7

Synopsis

mean :: AFType a => Array a -> Int -> Array a
meanWeighted :: AFType a => Array a -> Array a -> Int -> Array a
var :: AFType a => Array a -> Bool -> Int -> Array a
varWeighted :: AFType a => Array a -> Array a -> Int -> Array a
stdev :: AFType a => Array a -> Int -> Array a
cov :: AFType a => Array a -> Array a -> Bool -> Array a
median :: AFType a => Array a -> Int -> Array a
meanAll :: AFType a => Array a -> (Double, Double)
meanAllWeighted :: AFType a => Array a -> Array a -> (Double, Double)
varAll :: AFType a => Array a -> Bool -> (Double, Double)
varAllWeighted :: AFType a => Array a -> Array a -> (Double, Double)
stdevAll :: AFType a => Array a -> (Double, Double)
medianAll :: (AFType a, Fractional a) => Array a -> (Double, Double)
corrCoef :: AFType a => Array a -> Array a -> (Double, Double)
topk :: AFType a => Array a -> Int -> TopK -> (Array a, Array a)

Documentation

mean Source #

Arguments

:: AFType a
=> Array a	Input `Array`
-> Int	The dimension along which the mean is extracted
-> Array a	Will contain the mean of the input `Array` along dimension dim

Calculates mean of Array along user-specified dimension.

>>> mean ( vector @Int 10 [1..] ) 0
ArrayFire Array
  [1 1 1 1]
     5.5000

meanWeighted Source #

Arguments

:: AFType a
=> Array a	Input `Array`
-> Array a	Weights `Array`
-> Int	The dimension along which the mean is extracted
-> Array a	Will contain the mean of the input `Array` along dimension dim

Calculates meanWeighted of Array along user-specified dimension.

>>> meanWeighted (vector @Double 10 [1..10]) (vector @Double 10 [1..10]) 0
ArrayFire Array
  [1 1 1 1]
     7.0000

var Source #

Arguments

:: AFType a
=> Array a	Input `Array`
-> Bool	boolean denoting Population variance (false) or Sample Variance (true)
-> Int	The dimension along which the variance is extracted
-> Array a	will contain the variance of the input array along dimension dim

Calculates variance of Array along user-specified dimension.

>>> var (vector @Double 8 [1..8]) False 0
ArrayFire Array
  [1 1 1 1]
     6.0

varWeighted Source #

Arguments

:: AFType a
=> Array a	Input `Array`
-> Array a	Weights `Array` used to scale input in before getting variance
-> Int	The dimension along which the variance is extracted
-> Array a	Contains the variance of the input array along dimension dim

Calculates varWeighted of Array along user-specified dimension.

>>> varWeighted ( vector @Int 10 [1..] ) ( vector @Int 10 [1..] ) 0
ArrayFire Array
  [1 1 1 1]
     5.5000

stdev Source #

Arguments

:: AFType a
=> Array a	Input `Array`
-> Int	The dimension along which the standard deviation is extracted
-> Array a	Contains the standard deviation of the input array along dimension dim

Calculates stdev of Array along user-specified dimension.

>>> stdev (vector @Double 10 (cycle [1,-1])) 0
ArrayFire Array
  [1 1 1 1]
     1.0

cov Source #

Arguments

:: AFType a
=> Array a	First input `Array`
-> Array a	Second input `Array`
-> Bool	A boolean specifying if biased estimate should be taken (default: `False`)
-> Array a	Contains will the covariance of the input `Array`s

Calculates covariance of two Arrays with a bias specifier.

>>> cov (vector @Double 10 (repeat 1)) (vector @Double 10 (repeat 1)) False
ArrayFire Array
  [1 1 1 1]
     0.0

median Source #

Arguments

:: AFType a
=> Array a	Input `Array`
-> Int	Dimension along which to calculate `median`
-> Array a	Array containing `median`

Calculates median of Array along user-specified dimension.

>>> print $ median ( vector @Int 10 [1..] ) 0
ArrayFire Array
  [1 1 1 1]
     5.5000

meanAll Source #

Arguments

:: AFType a
=> Array a	Input `Array`
-> (Double, Double)	Mean result (real and imaginary part)

Calculates mean of all elements in an Array

>>> fst (meanAll (matrix @Double (2,2) (repeat 10)))
10.0

meanAllWeighted Source #

Arguments

:: AFType a
=> Array a	Input `Array`
-> Array a	`Array` of weights
-> (Double, Double)	Weighted mean (real and imaginary part)

Calculates weighted mean of all elements in an Array

>>> print $ fst (meanAllWeighted (matrix @Double (2,2) (repeat 10)) (matrix @Double (2,2) (repeat 0)))
10

varAll Source #

Arguments

:: AFType a
=> Array a	Input `Array`
-> Bool	Input `Array`
-> (Double, Double)	Variance (real and imaginary part)

Calculates variance of all elements in an Array

>>> fst (varAll (vector @Double 10 (repeat 10)) False)
0

varAllWeighted Source #

Arguments

:: AFType a
=> Array a	Input `Array`
-> Array a	`Array` of weights
-> (Double, Double)	Variance weighted result, (real and imaginary part)

Calculates weighted variance of all elements in an Array

>>> varAllWeighted ( vector @Int 10 [1..] ) ( vector @Int 10 [1..] )
0

stdevAll Source #

Arguments

:: AFType a
=> Array a	Input `Array`
-> (Double, Double)	Standard deviation result, (real and imaginary part)

Calculates standard deviation of all elements in an Array

>>> fst (stdevAll (vector @Double 10 (repeat 10)))
10

medianAll Source #

Arguments

:: (AFType a, Fractional a)
=> Array a	Input `Array`
-> (Double, Double)	Median result, real and imaginary part

Calculates median of all elements in an Array

>>> fst (medianAll (vector @Double 10 (repeat 10)))
10

corrCoef Source #

Arguments

:: AFType a
=> Array a	First input `Array`
-> Array a	Second input `Array`
-> (Double, Double)	Correlation coefficient result, real and imaginary part

This algorithm returns Pearson product-moment correlation coefficient. https://en.wikipedia.org/wiki/Pearson_correlation_coefficient

>>> fst (corrCoef ( vector @Int 10 [1..] ) ( vector @Int 10 [10,9..] ))
-1

topk Source #

Arguments

:: AFType a
=> Array a	First input `Array`, with at least k elements along dim
-> Int	The number of elements to be retrieved along the dim dimension
-> TopK	If descending, the highest values are returned. Otherwise, the lowest values are returned
-> (Array a, Array a)	Returns The values of the top k elements along the dim dimension along with the indices of the top k elements along the dim dimension

This function returns the top k values along a given dimension of the input array.

>>> let (vals,indexes) = topk ( vector @Double 10 [1..] ) 3 TopKDefault
>>> print indexes

ArrayFire Array
[3 1 1 1]
  10.0000     9.0000     8.0000

>>> print vals
ArrayFire Array
[3 1 1 1]
   9          8          7

The indices along with their values are returned. If the input is a multi-dimensional array, the indices will be the index of the value in that dimension. Order of duplicate values are not preserved. This function is optimized for small values of k. This function performs the operation across all dimensions of the input array. This function is optimized for small values of k. The order of the returned keys may not be in the same order as the appear in the input array