linearRegressionCG

Functions
template<domain D :shared3p >
D float32	linearRegressionCG (D int32[[2]] variables, D int32[[1]] dependent, uint iterations)
template<domain D :shared3p >
D float64	linearRegressionCG (D int64[[2]] variables, D int64[[1]] dependent, uint iterations)
template<domain D :shared3p >
D float32	linearRegressionCG (D float32[[2]] variables, D float32[[1]] dependent, uint iterations)
template<domain D :shared3p >
D float64	linearRegressionCG (D float64[[2]] variables, D float64[[1]] dependent, uint iterations)

Detailed Description

Fitting of linear models with multiple explanatory variables using the conjugate gradient method.

Note

D - shared3p protection domain

Supported types - int32 / int64 / float32 / float64

You can pass an empty matrix as the variables argument to specify a null model with just intercept.

Parameters

variables	- a matrix where each column is a sample of an explanatory variable
dependent	- sample vector of dependent variable
iterations	- number of iterations to use. Empirical testing showed that 10 iterations provides better accuracy than LU decomposition and Gaussian elimination and with a high number of variables it is also faster.

Returns

returns vector {β_1, β_1, …, β_n} such that y ≈ β_1 * x_1 + β_2 * x_2 + … + β_(n-1) * x_(n-1) + β_n where y is the dependent variable and x_i are the explanatory variables.

Leakage

None

Function Documentation

linearRegressionCG() [1/4]

D float32 linearRegressionCG	(	D float32[[2]]	variables,
		D float32[[1]]	dependent,
		uint	iterations
	)

linearRegressionCG() [2/4]

D float64 linearRegressionCG	(	D float64[[2]]	variables,
		D float64[[1]]	dependent,
		uint	iterations
	)

linearRegressionCG() [3/4]

D float32 linearRegressionCG	(	D int32[[2]]	variables,
		D int32[[1]]	dependent,
		uint	iterations
	)

linearRegressionCG() [4/4]

D float64 linearRegressionCG	(	D int64[[2]]	variables,
		D int64[[1]]	dependent,
		uint	iterations
	)