Cloned library GKlib with extra build files for internal package management.
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/*!
\file evaluate.c
\brief Various routines to evaluate classification performance
\author George
\date 9/23/2008
\version\verbatim $Id: evaluate.c 13328 2012-12-31 14:57:40Z karypis $ \endverbatim
*/
#include <GKlib.h>
/**********************************************************************
* This function computes the max accuracy score of a ranked list,
* given +1/-1 class list
**********************************************************************/
float ComputeAccuracy(int n, gk_fkv_t *list)
{
int i, P, N, TP, FN = 0;
float bAccuracy = 0.0;
float acc;
for (P=0, i=0;i<n;i++)
P += (list[i].val == 1? 1 : 0);
N = n - P;
TP = FN = 0;
for(i=0; i<n; i++){
if (list[i].val == 1)
TP++;
else
FN++;
acc = (TP + N - FN) * 100.0/ (P + N) ;
if (acc > bAccuracy)
bAccuracy = acc;
}
return bAccuracy;
}
/*****************************************************************************
* This function computes the ROC score of a ranked list, given a +1/-1 class
* list.
******************************************************************************/
float ComputeROCn(int n, int maxN, gk_fkv_t *list)
{
int i, P, TP, FP, TPprev, FPprev, AUC;
float prev;
FP = TP = FPprev = TPprev = AUC = 0;
prev = list[0].key -1;
for (P=0, i=0; i<n; i++)
P += (list[i].val == 1 ? 1 : 0);
for (i=0; i<n && FP < maxN; i++) {
if (list[i].key != prev) {
AUC += (TP+TPprev)*(FP-FPprev)/2;
prev = list[i].key;
FPprev = FP;
TPprev = TP;
}
if (list[i].val == 1)
TP++;
else {
FP++;
}
}
AUC += (TP+TPprev)*(FP-FPprev)/2;
return (TP*FP > 0 ? (float)(1.0*AUC/(P*FP)) : 0.0);
}
/*****************************************************************************
* This function computes the median rate of false positive for each positive
* instance.
******************************************************************************/
float ComputeMedianRFP(int n, gk_fkv_t *list)
{
int i, P, N, TP, FP;
P = N = 0;
for (i=0; i<n; i++) {
if (list[i].val == 1)
P++;
else
N++;
}
FP = TP = 0;
for (i=0; i<n && TP < (P+1)/2; i++) {
if (list[i].val == 1)
TP++;
else
FP++;
}
return 1.0*FP/N;
}
/*********************************************************
* Compute the mean
********************************************************/
float ComputeMean (int n, float *values)
{
int i;
float mean = 0.0;
for(i=0; i < n; i++)
mean += values[i];
return 1.0 * mean/ n;
}
/********************************************************
* Compute the standard deviation
********************************************************/
float ComputeStdDev(int n, float *values)
{
int i;
float mean = ComputeMean(n, values);
float stdDev = 0;
for(i=0;i<n;i++){
stdDev += (values[i] - mean)* (values[i] - mean);
}
return sqrt(1.0 * stdDev/n);
}