Array (FSharp.Stats)

Array Module

Namespace: FSharp.Stats
Assembly: FSharp.Stats.dll

Module to compute common statistical measure on array

Functions and values

Function or value	Description
`Array.cov array1 array2` Full Usage: `Array.cov array1 array2` Parameters: array1 : `^T array` - The first input array. array2 : `^T array` - The second input array. Returns: `^U` sample covariance estimator (Bessel's correction by N-1) Modifiers: inline Type parameters: ^T, ^U, ^a	Computes the sample covariance of two random variables Returns NaN if data is empty or if any entry is NaN. array1 : `^T array` The first input array. array2 : `^T array` The second input array. Returns: `^U` sample covariance estimator (Bessel's correction by N-1)
`Array.covBy f array` Full Usage: `Array.covBy f array` Parameters: f : `'T -> ^a * ^a` - A function applied to transform each element of the input array into a tuple of paired observations. array : `'T array` - The input array. Returns: `^U` sample covariance estimator (Bessel's correction by N-1) Modifiers: inline Type parameters: 'T, ^a, ^U, ^b	Computes the sample covariance of two random variables generated by applying a function to the input array. Returns NaN if data is empty or if any entry is NaN. f : `'T -> ^a * ^a` A function applied to transform each element of the input array into a tuple of paired observations. array : `'T array` The input array. Returns: `^U` sample covariance estimator (Bessel's correction by N-1) Example `// To get the sample covariance between x and y observations: let xy = [\| {\| x = 5.; y = 2. \|} {\| x = 12.; y = 8. \|} {\| x = 18.; y = 18. \|} {\| x = -23.; y = -20. \|} {\| x = 45.; y = 28. \|} \|] xy \|> Array.covBy (fun x -> x.x, x.y) // evaluates to 434.90` val xy: {\| x: float; y: float \|} array anonymous record field x: float anonymous record field y: float module Array from Microsoft.FSharp.Collections
`Array.covOfPairs array` Full Usage: `Array.covOfPairs array` Parameters: array : `(^T * ^T) array` - The input array. Returns: `^U` sample covariance estimator (Bessel's correction by N-1) Modifiers: inline Type parameters: ^T, ^U, ^a	Computes the sample covariance of two random variables. The covariance will be calculated between the paired observations. Returns NaN if data is empty or if any entry is NaN. array : `(^T * ^T) array` The input array. Returns: `^U` sample covariance estimator (Bessel's correction by N-1) Example `// Consider an array of paired x and y values: // [\| (x1, y1); (x2, y2); (x3, y3); (x4, y4); ... \|] let xy = [\| (5., 2.); (12., 8.); (18., 18.); (-23., -20.); (45., 28.) \|] // To get the sample covariance between x and y: xy \|> Array.covOfPairs // evaluates to 434.90` val xy: (float * float) array module Array from Microsoft.FSharp.Collections
`Array.covPopulation array1 array2` Full Usage: `Array.covPopulation array1 array2` Parameters: array1 : `^T array` - The first input array. array2 : `^T array` - The second input array. Returns: `^U` population covariance estimator (denominator N) Modifiers: inline Type parameters: ^T, ^U, ^a	Computes the population covariance of two random variables Returns NaN if data is empty or if any entry is NaN. array1 : `^T array` The first input array. array2 : `^T array` The second input array. Returns: `^U` population covariance estimator (denominator N)
`Array.covPopulationBy f array` Full Usage: `Array.covPopulationBy f array` Parameters: f : `'T -> ^a * ^a` - A function applied to transform each element of the input array into a tuple of paired observations. array : `'T array` - The input array. Returns: `^U` population covariance estimator (denominator N) Modifiers: inline Type parameters: 'T, ^a, ^U, ^b	Computes the population covariance of two random variables generated by applying a function to the input array. Returns NaN if data is empty or if any entry is NaN. f : `'T -> ^a * ^a` A function applied to transform each element of the input array into a tuple of paired observations. array : `'T array` The input array. Returns: `^U` population covariance estimator (denominator N) Example `// To get the population covariance between x and y observations: let xy = [\| {\| x = 5.; y = 2. \|} {\| x = 12.; y = 8. \|} {\| x = 18.; y = 18. \|} {\| x = -23.; y = -20. \|} {\| x = 45.; y = 28. \|} \|] xy \|> Array.covPopulationBy (fun x -> x.x, x.y) // evaluates to 347.92` val xy: {\| x: float; y: float \|} array anonymous record field x: float anonymous record field y: float module Array from Microsoft.FSharp.Collections
`Array.covPopulationOfPairs array` Full Usage: `Array.covPopulationOfPairs array` Parameters: array : `(^T * ^T) array` - The input array. Returns: `^U` population covariance estimator (denominator N) Modifiers: inline Type parameters: ^T, ^U, ^a	Computes the population covariance of two random variables. The covariance will be calculated between the paired observations. Returns NaN if data is empty or if any entry is NaN. array : `(^T * ^T) array` The input array. Returns: `^U` population covariance estimator (denominator N) Example `// Consider an array of paired x and y values: // [\| (x1, y1); (x2, y2); (x3, y3); (x4, y4); ... \|] let xy = [\| (5., 2.); (12., 8.); (18., 18.); (-23., -20.); (45., 28.) \|] // To get the population covariance between x and y: xy \|> Array.covPopulationOfPairs // evaluates to 347.92` val xy: (float * float) array module Array from Microsoft.FSharp.Collections
`Array.dropNaN array` Full Usage: `Array.dropNaN array` Parameters: array : `float array` - Returns: `float array`	Filters out all nan values from an array array : `float array` Returns: `float array` Example
`Array.median items` Full Usage: `Array.median items` Parameters: items : `^T array` - Returns: `^T` Modifiers: inline Type parameters: ^T, ^a	Computes the sample median items : `^T array` Returns: `^T` Example
`Array.medianAbsoluteDev data` Full Usage: `Array.medianAbsoluteDev data` Parameters: data : `float[]` - Returns: `float`	Median absolute deviation (MAD) data : `float[]` Returns: `float` Example
`Array.partitionSortInPlace left right items` Full Usage: `Array.partitionSortInPlace left right items` Parameters: left : `int` - right : `int` - items : `'T array` - Returns: `int` Modifiers: inline Type parameters: 'T	Arranges the items between the left and right border, that all items left of the pivot element are smaller and bigger on the right. Function works in place and returns the index of the pivote element (using Lomuto's partitioning algorithm) left : `int` right : `int` items : `'T array` Returns: `int` Example
`Array.quickSelect k items` Full Usage: `Array.quickSelect k items` Parameters: k : `int` - items : `'T array` - Returns: `'T` Modifiers: inline Type parameters: 'T	Finds the kth smallest element in an unordered array (note that k is ONE-based) k : `int` items : `'T array` Returns: `'T` Example
`Array.quickSelectInPlace k items` Full Usage: `Array.quickSelectInPlace k items` Parameters: k : `int` - items : `'T array` - Returns: `'T` Modifiers: inline Type parameters: 'T	Finds the kth smallest element in an unordered array (note that k is ONE-based) Works in place and can change the order of the elements in the input array k : `int` items : `'T array` Returns: `'T` Example
`Array.quickSelectInPlaceWith left right k arr` Full Usage: `Array.quickSelectInPlaceWith left right k arr` Parameters: left : `int` - right : `int` - k : `int` - arr : `'T array` - Returns: `'T` Modifiers: inline Type parameters: 'T	Finds the kth smallest element in an unordered array (note that k is ONE-based) Works in place and can change the order of the elements in the input array left : `int` right : `int` k : `int` arr : `'T array` Returns: `'T` Example
`Array.range a` Full Usage: `Array.range a` Parameters: a : `'a array` Returns: `Interval<'a>`	a : `'a array` Returns: `Interval<'a>`
`Array.sampleWithOutReplacement rnd source k` Full Usage: `Array.sampleWithOutReplacement rnd source k` Parameters: rnd : `Random` - source : `'a array` - k : `int` - Returns: `'a array`	Samples from an array of obj without replacement (without putting back) Implementation according to: http://krkadev.blogspot.de/2010/08/random-numbers-without-repetition.html rnd : `Random` source : `'a array` k : `int` Returns: `'a array` Example
`Array.sampleWithReplacement rnd source k` Full Usage: `Array.sampleWithReplacement rnd source k` Parameters: rnd : `Random` - source : `'a array` - k : `int` - Returns: `'a array`	Samples from an array of obj wit replacement (with putting back) When we sample with replacement, the two sample values are independent.Practically, this means that what we get on the first one doesn't affect what we get on the second.Mathematically, this means that the covariance between the two is zero rnd : `Random` source : `'a array` k : `int` Returns: `'a array` Example
`Array.seqInit from tto length` Full Usage: `Array.seqInit from tto length` Parameters: from : `float` - tto : `float` - length : `int` - Returns: `float array`	Generates array sequence (like R! seq.int) from : `float` tto : `float` length : `int` Returns: `float array` Example
`Array.shuffleFisherYates arr` Full Usage: `Array.shuffleFisherYates arr` Parameters: arr : `'a[]` - Returns: `'a array`	Shuffels the input array (method: Fisher-Yates) arr : `'a[]` Returns: `'a array` Example
`Array.shuffleFisherYatesInPlace arr` Full Usage: `Array.shuffleFisherYatesInPlace arr` Parameters: arr : `'a[]` - Returns: `'a[]`	Shuffels the input array (method: Fisher-Yates) in place arr : `'a[]` Returns: `'a[]` Example
`Array.sort2InPlaceByKeys from count keys items` Full Usage: `Array.sort2InPlaceByKeys from count keys items` Parameters: from : `int` count : `int` keys : `'T array` items : `'T array`	from : `int` count : `int` keys : `'T array` items : `'T array`
`Array.swapInPlace left right items` Full Usage: `Array.swapInPlace left right items` Parameters: left : `int` right : `int` items : `'T array` Modifiers: inline Type parameters: 'T	left : `int` right : `int` items : `'T array`
`Array.varOf mean items` Full Usage: `Array.varOf mean items` Parameters: mean : `^a` - items : `^T[]` - Returns: `^T` Modifiers: inline Type parameters: ^a, ^T, ^b, ^c, ^d	Computes the Variance N-1 mean : `^a` items : `^T[]` Returns: `^T` Example
`Array.weightedMean weights items` Full Usage: `Array.weightedMean weights items` Parameters: weights : `^T array` - items : `^T array` - Returns: `'b` Modifiers: inline Type parameters: ^T, ^a, 'b	Computes the Weighted Mean weights : `^T array` items : `^T array` Returns: `'b` Example
`Array.weightedVariance mean weights items` Full Usage: `Array.weightedVariance mean weights items` Parameters: mean : `^a` - weights : `^T array` - items : `^T array` - Returns: `'f` Modifiers: inline Type parameters: ^a, ^T, ^b, ^c, ^d, ^e, 'f	Computes the Weighted Variance mean : `^a` weights : `^T array` items : `^T array` Returns: `'f` Example

Array Module

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