project directories reorganization

2020-04-14 06:35:20 -04:00
parent 1de06fd9c0
commit fc155e1223
1056 changed files with 8120 additions and 8120 deletions
--- a/benchmarks/old_opencl/kmeans/rmse.c
+++ b/benchmarks/old_opencl/kmeans/rmse.c
@@ -0,0 +1,94 @@
+/*************************************************************************/
+/**   File:         rmse.c												**/
+/**   Description:  calculate root mean squared error of particular     **/
+/**                 clustering.											**/
+/**   Author:  Sang-Ha Lee												**/
+/**            University of Virginia.									**/
+/**																		**/
+/**   Note: euclid_dist_2() and find_nearest_point() adopted from       **/
+/**			Minebench code.												**/
+/**                                                                     **/
+/*************************************************************************/
+
+#include <stdio.h>
+#include <stdlib.h>
+#include <float.h>
+#include <math.h>
+
+#include "kmeans.h"
+
+extern double wtime(void);
+
+/*----< euclid_dist_2() >----------------------------------------------------*/
+/* multi-dimensional spatial Euclid distance square */
+__inline
+float euclid_dist_2(float *pt1,
+                    float *pt2,
+                    int    numdims)
+{
+    int i;
+    float ans=0.0;
+
+    for (i=0; i<numdims; i++)
+        ans += (pt1[i]-pt2[i]) * (pt1[i]-pt2[i]);
+
+    return(ans);
+}
+
+/*----< find_nearest_point() >-----------------------------------------------*/
+__inline
+int find_nearest_point(float  *pt,          /* [nfeatures] */
+                       int     nfeatures,
+                       float  **pts,         /* [npts][nfeatures] */
+                       int     npts)
+{
+    int index, i;
+    float max_dist=FLT_MAX;
+
+    /* find the cluster center id with min distance to pt */
+    for (i=0; i<npts; i++) {
+        float dist;
+        dist = euclid_dist_2(pt, pts[i], nfeatures);  /* no need square root */
+        if (dist < max_dist) {
+            max_dist = dist;
+            index    = i;
+        }
+    }
+    return(index);
+}
+
+/*----< rms_err(): calculates RMSE of clustering >-------------------------------------*/
+float rms_err	(float **feature,         /* [npoints][nfeatures] */
+                 int     nfeatures,
+                 int     npoints,
+                 float **cluster_centres, /* [nclusters][nfeatures] */
+                 int     nclusters)
+{
+    int    i;
+	int	   nearest_cluster_index;	/* cluster center id with min distance to pt */
+    float  sum_euclid = 0.0;		/* sum of Euclidean distance squares */
+    float  ret;						/* return value */
+    
+    /* calculate and sum the sqaure of euclidean distance*/	
+    #pragma omp parallel for \
+                shared(feature,cluster_centres) \
+                firstprivate(npoints,nfeatures,nclusters) \
+                private(i, nearest_cluster_index) \
+                schedule (static)	
+    for (i=0; i<npoints; i++) {
+        nearest_cluster_index = find_nearest_point(feature[i], 
+													nfeatures, 
+													cluster_centres, 
+													nclusters);
+
+		sum_euclid += euclid_dist_2(feature[i],
+									cluster_centres[nearest_cluster_index],
+									nfeatures);
+		
+    }	
+	/* divide by n, then take sqrt */
+	ret = sqrt(sum_euclid / npoints);
+
+    return(ret);
+}
+