Multivariable (FSharp.Stats) | FSharp.Stats

FSharp.Stats

Multivariable Module

Namespace: FSharp.Stats.Fitting
Assembly: FSharp.Stats.dll
Parent Module: Linear

Multivariable handles multi dimensional data where a vector of independent x values should be used to predict a single y value.

Functions and values

Function or value	Description
`fit xData yData` Full Usage: `fit xData yData` Parameters: xData : `Matrix<float>` - matrix of x vectors yData : `Vector<float>` - vector of y values Returns: `Coefficients` vector of linear coefficients	Calculates the coefficients for a straight line fitting the data. Linear regression minimizes the sum of squared residuals. xData : `Matrix<float>` matrix of x vectors yData : `Vector<float>` vector of y values Returns: `Coefficients` vector of linear coefficients Example `let xVectorMulti = [ [1.; 1. ;2. ] [2.; 0.5;6. ] [3.; 0.8;10. ] [4.; 2. ;14. ] [5.; 4. ;18. ] [6.; 3. ;22. ] ] \|> Matrix.ofJaggedSeq // Here the x values are transformed. In most cases the y values are just provided. let yVectorMulti = let transformX (x) = x \|> Matrix.mapiRows (fun _ v -> 100. + (v.[0] * 2.5) + (v.[1] * 4.) + (v.[2] * 0.5)) xVectorMulti \|> transformX \|> vector let coefficientsMV = Multivariable.fit xVectorMulti yVectorMulti` val xVectorMulti: obj val yVectorMulti: obj val transformX: x: 'a -> 'b val x: 'a val coefficientsMV: obj
`fitCholesky xData yData` Full Usage: `fitCholesky xData yData` Parameters: xData : `Matrix<float>` - matrix of x vectors yData : `Vector<float>` - vector of y values Returns: `Coefficients` vector of linear coefficients	Calculates the coefficients for a straight line fitting the data using Cholesky Decomposition. Linear regression minimizes the sum of squared residuals. xData : `Matrix<float>` matrix of x vectors yData : `Vector<float>` vector of y values Returns: `Coefficients` vector of linear coefficients Example `let xVectorMulti = [ [1.; 1. ;2. ] [2.; 0.5;6. ] [3.; 0.8;10. ] [4.; 2. ;14. ] [5.; 4. ;18. ] [6.; 3. ;22. ] ] \|> Matrix.ofJaggedSeq // Here the x values are transformed. In most cases the y values are just provided. let yVectorMulti = let transformX (x) = x \|> Matrix.mapiRows (fun _ v -> 100. + (v.[0] * 2.5) + (v.[1] * 4.) + (v.[2] * 0.5)) xVectorMulti \|> transformX \|> vector let coefficientsMV = Multivariable.fitCholesky xVectorMulti yVectorMulti` val xVectorMulti: obj val yVectorMulti: obj val transformX: x: 'a -> 'b val x: 'a val coefficientsMV: obj
`predict coef x` Full Usage: `predict coef x` Parameters: coef : `Coefficients` - Coefficients from linear regression. x : `Vector<float>` - x vector for which the y value should be predicted Returns: `float` predicted y value with given coefficients at X=x	Takes linear coefficients and x vector to predict the corresponding y value. coef : `Coefficients` Coefficients from linear regression. x : `Vector<float>` x vector for which the y value should be predicted Returns: `float` predicted y value with given coefficients at X=x Example let xVectorMulti = [ [1.; 1. ;2. ] [2.; 0.5;6. ] [3.; 0.8;10. ] [4.; 2. ;14. ] [5.; 4. ;18. ] [6.; 3. ;22. ] ] \|> Matrix.ofJaggedSeq // Here the x values are transformed. In most cases the y values are just provided. let yVectorMulti = let transformX (x) = x \|> Matrix.mapiRows (fun _ v -> 100. + (v.[0] * 2.5) + (v.[1] * 4.) + (v.[2] * 0.5)) xVectorMulti \|> transformX \|> vector let coefficientsMV = Multivariable.fit xVectorMulti yVectorMulti let fittingFunctionMV x = Multivariable.predict coefficientsMV x val xVectorMulti: obj val yVectorMulti: obj val transformX: x: 'a -> 'b val x: 'a val coefficientsMV: obj val fittingFunctionMV: x: 'a -> 'b