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/**
* @file crossval.c
* @author Gertjan van den Burg
* @date January 7, 2014
* @brief Functions for cross validation
*
* @details
* This file contains functions for performing cross validation. The funtion
* msvmmaj_make_cv_split() creates a cross validation vector for non-stratified
* cross validation. The function msvmmaj_get_tt_split() creates a train and
* test dataset from a given dataset and a pre-determined CV partition vector.
* See individual function documentation for details.
*
*/
#include "crossval.h"
#include "msvmmaj.h"
#include "msvmmaj_matrix.h"
/**
* @brief Create a cross validation split vector
*
* @details
* A pre-allocated vector of length N is created which can be used to define
* cross validation splits. The folds are contain between
* @f$ \lfloor N / folds \rfloor @f$ and @f$ \lceil N / folds \rceil @f$
* instances. An instance is mapped to a partition randomly until all folds
* contain @f$ N \% folds @f$ instances. The zero fold then contains
* @f$ N / folds + N \% folds @f$ instances. These remaining @f$ N \% folds @f$
* instances are then distributed over the first @f$ N \% folds @f$ folds.
*
* @param[in] N number of instances
* @param[in] folds number of folds
* @param[in,out] cv_idx array of size N which contains the fold index
* for each observation on exit
*
*/
void msvmmaj_make_cv_split(long N, long folds, long *cv_idx)
{
long i, j, idx;
long big_folds = N%folds;
long small_fold_size = N/folds;
j = 0;
for (i=0; i<small_fold_size*folds; i++)
while (1) {
idx = rand()%N;
if (cv_idx[idx] == 0) {
cv_idx[idx] = j;
j++;
j%=folds;
break;
}
}
j = 0;
i = 0;
while (i < big_folds) {
if (cv_idx[j] == 0) {
cv_idx[j] = i++;
}
j++;
}
}
/**
* @brief Create train and test datasets for a CV split
*
* @details
* Given a MajData structure for the full dataset, a previously created
* cross validation split vector and a fold index, a training and test dataset
* are created.
*
* @param[in] full_data a MajData structure for the entire
* dataset
* @param[in,out] train_data an initialized MajData structure which
* on exit contains the training dataset
* @param[in,out] test_data an initialized MajData structure which
* on exit contains the test dataset
* @param[in] cv_idx a vector of cv partitions created by
* msvmmaj_make_cv_split()
* @param[in] fold_idx index of the fold which becomes the
* test dataset
*/
void msvmmaj_get_tt_split(struct MajData *full_data, struct MajData *train_data,
struct MajData *test_data, long *cv_idx, long fold_idx)
{
long i, j, k, l, test_n, train_n;
long n = full_data->n;
long m = full_data->m;
long K = full_data->K;
test_n = 0;
for (i=0; i<n; i++)
if (cv_idx[i] == fold_idx)
test_n++;
train_n = n - test_n;
test_data->n = test_n;
train_data->n = train_n;
train_data->K = K;
test_data->K = K;
train_data->m = m;
test_data->m = m;
train_data->y = Calloc(long, train_n);
test_data->y = Calloc(long, test_n);
train_data->Z = Calloc(double, train_n*(m+1));
test_data->Z = Calloc(double, test_n*(m+1));
k = 0;
l = 0;
for (i=0; i<n; i++) {
if (cv_idx[i] == fold_idx) {
test_data->y[k] = full_data->y[i];
for (j=0; j<m+1; j++)
matrix_set(test_data->Z, m+1, k, j,
matrix_get(full_data->Z, m+1,
i, j));
k++;
} else {
train_data->y[l] = full_data->y[i];
for (j=0; j<m+1; j++)
matrix_set(train_data->Z, m+1, l, j,
matrix_get(full_data->Z, m+1,
i, j));
l++;
}
}
}
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