KERREG_Driver.f90 File Reference

This example generates a RBF kernel using training and testing data from disk, compress it using entry-valuation-based APIs, and evaluate the prediction error. More...

Data Types
type	application_module::quant_app

Modules
module	application_module

Functions/Subroutines
real(kind=8) function	application_module::arg_thresh_zmn (quant)
subroutine	application_module::zelem_rbf (m, n, value_e, quant)
program	butterflypack_krr
subroutine	rbf_solve (bmat, option, msh, quant, ptree, stats)

Detailed Description

This example generates a RBF kernel using training and testing data from disk, compress it using entry-valuation-based APIs, and evaluate the prediction error.

Note that instead of the use of precision dependent subroutine/module/type names "d_", one can also use the following
#define DAT 1
#include "dButterflyPACK_config.fi"
which will macro replace precision-independent subroutine/module/type names "X" with "d_X" defined in SRC_DOUBLE with double precision

Function/Subroutine Documentation

butterflypack_krr()

program butterflypack_krr

(

)

Here is the call graph for this function:

rbf_solve()

subroutine rbf_solve	(	type(d_bmatrix)	bmat,
		type(d_hoption)	option,
		type(d_mesh)	msh,
		type(quant_app)	quant,
		type(d_proctree)	ptree,
		type(d_hstat)	stats
	)

Here is the call graph for this function: