Header file for gensvm_cv_util.c. More...

#include "gensvm_base.h"

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Functions
void	gensvm_make_cv_split (long N, long folds, long *cv_idx)
	Create a cross validation split vector. More...

void	gensvm_get_tt_split (struct GenData full_data, struct GenData train_data, struct GenData test_data, long cv_idx, long fold_idx)
	Wrapper around sparse/dense versions of this function. More...

void	gensvm_get_tt_split_dense (struct GenData full_data, struct GenData train_data, struct GenData test_data, long cv_idx, long fold_idx)
	Create train and test datasets for a CV split with dense data. More...

void	gensvm_get_tt_split_sparse (struct GenData full_data, struct GenData train_data, struct GenData test_data, long cv_idx, long fold_idx)
	Create train and test dataset for a CV split with sparse data. More...

Detailed Description

Header file for gensvm_cv_util.c.

Author: G.J.J. van den Burg

Date: 2014-01-07

Contains function declarations for functions needed for performing cross validation on GenData structures.

Copyright: Copyright 2016, G.J.J. van den Burg.

This file is part of GenSVM.

GenSVM is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

GenSVM is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with GenSVM. If not, see http://www.gnu.org/licenses/.

Definition in file gensvm_cv_util.h.

Function Documentation

◆ gensvm_get_tt_split()

void gensvm_get_tt_split	(	struct GenData *	full_data,
		struct GenData *	train_data,
		struct GenData *	test_data,
		long *	cv_idx,
		long	fold_idx
	)

Wrapper around sparse/dense versions of this function.

This function tests if the data in the full_data structure is stored in a dense matrix format or not, and calls gensvm_get_tt_split_dense() or gensvm_get_tt_split_sparse() accordingly.

See also: gensvm_get_tt_split_dense(), gensvm_get_tt_split_sparse()

Parameters

[in]	full_data	a GenData structure for the entire dataset
[in,out]	train_data	an initialized GenData structure which on exit contains the training dataset
[in,out]	test_data	an initialized GenData structure which on exit contains the test dataset
[in]	cv_idx	a vector of cv partitions created by gensvm_make_cv_split()
[in]	fold_idx	index of the fold which becomes the test dataset

Definition at line 107 of file gensvm_cv_util.c.

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◆ gensvm_get_tt_split_dense()

void gensvm_get_tt_split_dense	(	struct GenData *	full_data,
		struct GenData *	train_data,
		struct GenData *	test_data,
		long *	cv_idx,
		long	fold_idx
	)

Create train and test datasets for a CV split with dense data.

Given a GenData structure for the full dataset, a previously created cross validation split vector and a fold index, a training and test dataset are created. It is assumed here that the data is stored as a dense matrix, and that the train and test data should also be stored as a dense matrix.

See also: gensvm_get_tt_split_sparse(), gensvm_get_tt_split()

Parameters

[in]	full_data	a GenData structure for the entire dataset
[in,out]	train_data	an initialized GenData structure which on exit contains the training dataset
[in,out]	test_data	an initialized GenData structure which on exit contains the test dataset
[in]	cv_idx	a vector of cv partitions created by gensvm_make_cv_split()
[in]	fold_idx	index of the fold which becomes the test dataset

Definition at line 142 of file gensvm_cv_util.c.

◆ gensvm_get_tt_split_sparse()

void gensvm_get_tt_split_sparse	(	struct GenData *	full_data,
		struct GenData *	train_data,
		struct GenData *	test_data,
		long *	cv_idx,
		long	fold_idx
	)

Create train and test dataset for a CV split with sparse data.

Given a GenData structure for the full dataset, a previously created cross validation split vector and a fold index, a training and test dataset are created. It is assumed here that the data is stored as a sparse matrix, and that the train and test data should also be stored as a sparse matrix.

See also: gensvm_get_tt_split_dense(), gensvm_get_tt_split()

Parameters

[in]	full_data	a GenData structure for the entire dataset
[in,out]	train_data	an initialized GenData structure which on exit contains the training dataset
[in,out]	test_data	an initialized GenData structure which on exit contains the test dataset
[in]	cv_idx	a vector of cv partitions created by gensvm_make_cv_split()
[in]	fold_idx	index of the fold which becomes the test dataset

Definition at line 223 of file gensvm_cv_util.c.

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◆ gensvm_make_cv_split()

void gensvm_make_cv_split	(	long	N,
		long	folds,
		long *	cv_idx
	)

Create a cross validation split vector.

A pre-allocated vector of length N is created which can be used to define cross validation splits. The folds are contain between $\lfloor N / folds \rfloor$ and $\lceil N / folds \rceil$ instances. An instance is mapped to a partition randomly until all folds contain $N \% folds$ instances. The zero fold then contains $N / folds + N \% folds$ instances. These remaining $N \% folds$ instances are then distributed over the first $N \% folds$ folds.

Parameters

[in]	N	number of instances
[in]	folds	number of folds
[in,out]	cv_idx	array of size N which contains the fold index for each observation on exit

Definition at line 54 of file gensvm_cv_util.c.

Functions

Detailed Description

Function Documentation

◆ gensvm_get_tt_split()

◆ gensvm_get_tt_split_dense()

◆ gensvm_get_tt_split_sparse()

◆ gensvm_make_cv_split()