From 993c503ce1b440be6947bc91fbf1fa6098569b51 Mon Sep 17 00:00:00 2001
From: Gertjan van den Burg <gertjanvandenburg@gmail.com>
Date: Mon, 9 May 2016 20:45:06 +0200
Subject: use gensvm namespace for all crossval/timer/util

---
 src/crossval.c | 143 ---------------------------------------------------------
 1 file changed, 143 deletions(-)
 delete mode 100644 src/crossval.c

(limited to 'src/crossval.c')

diff --git a/src/crossval.c b/src/crossval.c
deleted file mode 100644
index 85f9341..0000000
--- a/src/crossval.c
+++ /dev/null
@@ -1,143 +0,0 @@
-/**
- * @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
- * gensvm_make_cv_split() creates a cross validation vector for non-stratified
- * cross validation. The function gensvm_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 "gensvm.h"
-#include "gensvm_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 gensvm_make_cv_split(long N, long folds, long *cv_idx)
-{
-	long i, j, idx;
-
-	for (i=0; i<N; i++)
-		cv_idx[i] = 0;
-
-	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 GenData 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 GenData structure for the entire 
- * 					dataset
- * @param[in,out] 	train_data 	an initialized GenData structure which
- * 					on exit contains the training dataset
- * @param[in,out] 	test_data 	an initialized GenData structure which
- * 					on exit contains the test dataset
- * @param[in] 		cv_idx 		a vector of cv partitions created by
- * 					gensvm_make_cv_split()
- * @param[in] 		fold_idx 	index of the fold which becomes the 
- * 					test dataset
- */
-void gensvm_get_tt_split(struct GenData *full_data, struct GenData *train_data,
-		struct GenData *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;
-
-	double value;
-
-	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->RAW = Calloc(double, train_n*(m+1));
-	test_data->RAW = 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++) {
-				value = matrix_get(full_data->RAW, m+1, i, j);
-				matrix_set(test_data->RAW, m+1, k, j, value);
-			}
-			k++;
-		} else {
-			train_data->y[l] = full_data->y[i];
-			for (j=0; j<m+1; j++) {
-				value = matrix_get(full_data->RAW, m+1, i, j);
-				matrix_set(train_data->RAW, m+1, l, j, value);
-			}
-			l++;
-		}
-	}
-
-	train_data->Z = train_data->RAW;
-	test_data->Z = test_data->RAW;
-}
-- 
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