CrossValidation (FSharp.Stats) | FSharp.Stats

FSharp.Stats

CrossValidation Module

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

Types and nested modules

Type/Module	Description
Error
CrossValidationResult<'a>

Functions and values

Function or value	Description
`createCrossValidationResult error errorStDev` Full Usage: `createCrossValidationResult error errorStDev` Parameters: error : `'a` errorStDev : `'a` Returns: `CrossValidationResult<'a>`	error : `'a` errorStDev : `'a` Returns: `CrossValidationResult<'a>`
`kFold k xData yData fit error` Full Usage: `kFold k xData yData fit error` Parameters: k : `^a` - xData : `Matrix<^T>` - yData : `Vector<^T>` - fit : `Matrix<^T> -> Vector<^T> -> Vector<^T> -> ^T` - error : `^T -> ^T -> ^T` - Returns: `^T` Modifiers: inline Type parameters: ^a, ^T	Computes a k fold cross-validation, k: training set size (and number of iterations), xData: rowwise x-coordinate matrix, yData: yData vector fit: x and y data lead to function that maps a rowwise matrix of xCoordinates to a y-coordinate, error: defines the error of the fitted y-coordinate and the actual y-coordinate k : `^a` xData : `Matrix<^T>` yData : `Vector<^T>` fit : `Matrix<^T> -> Vector<^T> -> Vector<^T> -> ^T` error : `^T -> ^T -> ^T` Returns: `^T` Example
`loocv xData yData fitFunc error` Full Usage: `loocv xData yData fitFunc error` Parameters: xData : `Matrix<^T>` - yData : `Vector<^T>` - fitFunc : `Matrix<^T> -> Vector<^T> -> Vector<^T> -> ^T` - error : `^T -> ^T -> ^T` - Returns: `^T` Modifiers: inline Type parameters: ^T	Computes a leave one out cross-validation xData: rowwise x-coordinate matrix, yData: yData vector fit: x and y data lead to function that maps an xData row vector to a y-coordinate, error: defines the error of the fitted y-coordinate and the actual y-coordinate xData : `Matrix<^T>` yData : `Vector<^T>` fitFunc : `Matrix<^T> -> Vector<^T> -> Vector<^T> -> ^T` error : `^T -> ^T -> ^T` Returns: `^T` Example
`repeatedKFold k iterations xData yData fit error getStDev` Full Usage: `repeatedKFold k iterations xData yData fit error getStDev` Parameters: k : `^a` - iterations : `int` - xData : `Matrix<^T>` - yData : `Vector<^T>` - fit : `Matrix<^T> -> Vector<^T> -> Vector<^T> -> ^T` - error : `^T -> ^T -> ^T` - getStDev : `^T seq -> ^T` - Returns: `CrossValidationResult<^T>` Modifiers: inline Type parameters: ^a, ^T	Computes a repeated k fold cross-validation, k: training set size (and number of iterations), iterations: number of random subset creation, xData: rowwise x-coordinate matrix, yData: yData vector fit: x and y data lead to function that maps a xData row vector to a y-coordinate, error: defines the error of the fitted y-coordinate and the actual y-coordinate, getStDev: function that calculates the standard deviation from a seq<^T>. (Seq.stDev) k : `^a` iterations : `int` xData : `Matrix<^T>` yData : `Vector<^T>` fit : `Matrix<^T> -> Vector<^T> -> Vector<^T> -> ^T` error : `^T -> ^T -> ^T` getStDev : `^T seq -> ^T` Returns: `CrossValidationResult<^T>` Example
`shuffleAndSplit p iterations xData yData fit error getStDev` Full Usage: `shuffleAndSplit p iterations xData yData fit error getStDev` Parameters: p : `float` - iterations : `int` - xData : `Matrix<^T>` - yData : `Vector<^T>` - fit : `Matrix<^T> -> Vector<^T> -> Vector<^T> -> ^T` - error : `^T -> ^T -> ^T` - getStDev : `^T seq -> ^T` - Returns: `CrossValidationResult<^T>` Modifiers: inline Type parameters: ^T	Computes a repeated shuffel-and-split cross validation p: percentage of training set size from original size, iterations: number of random subset creation, xData: rowwise x-coordinate matrix, yData: yData vector fit: x and y data lead to function that maps a xData row vector to a y-coordinate, error: defines the error of the fitted y-coordinate and the actual y-coordinate, getStDev: function that calculates the standard deviation from a seq<^T>. (Seq.stDev) p : `float` iterations : `int` xData : `Matrix<^T>` yData : `Vector<^T>` fit : `Matrix<^T> -> Vector<^T> -> Vector<^T> -> ^T` error : `^T -> ^T -> ^T` getStDev : `^T seq -> ^T` Returns: `CrossValidationResult<^T>` Example