mirror of
https://github.com/Steffo99/unimore-hpc-assignments.git
synced 2024-11-23 08:34:23 +00:00
198 lines
8.2 KiB
C
198 lines
8.2 KiB
C
|
/*****************************************************************************/
|
||
|
|
/*IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. */
|
||
|
|
/*By downloading, copying, installing or using the software you agree */
|
||
|
|
/*to this license. If you do not agree to this license, do not download, */
|
||
|
|
/*install, copy or use the software. */
|
||
|
|
/* */
|
||
|
|
/* */
|
||
|
|
/*Copyright (c) 2005 Northwestern University */
|
||
|
|
/*All rights reserved. */
|
||
|
|
|
||
|
|
/*Redistribution of the software in source and binary forms, */
|
||
|
|
/*with or without modification, is permitted provided that the */
|
||
|
|
/*following conditions are met: */
|
||
|
|
/* */
|
||
|
|
/*1 Redistributions of source code must retain the above copyright */
|
||
|
|
/* notice, this list of conditions and the following disclaimer. */
|
||
|
|
/* */
|
||
|
|
/*2 Redistributions in binary form must reproduce the above copyright */
|
||
|
|
/* notice, this list of conditions and the following disclaimer in the */
|
||
|
|
/* documentation and/or other materials provided with the distribution.*/
|
||
|
|
/* */
|
||
|
|
/*3 Neither the name of Northwestern University nor the names of its */
|
||
|
|
/* contributors may be used to endorse or promote products derived */
|
||
|
|
/* from this software without specific prior written permission. */
|
||
|
|
/* */
|
||
|
|
/*THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS ``AS */
|
||
|
|
/*IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED */
|
||
|
|
/*TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY, NON-INFRINGEMENT AND */
|
||
|
|
/*FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL */
|
||
|
|
/*NORTHWESTERN UNIVERSITY OR ITS CONTRIBUTORS BE LIABLE FOR ANY DIRECT, */
|
||
|
|
/*INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES */
|
||
|
|
/*(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR */
|
||
|
|
/*SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) */
|
||
|
|
/*HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, */
|
||
|
|
/*STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN */
|
||
|
|
/*ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE */
|
||
|
|
/*POSSIBILITY OF SUCH DAMAGE. */
|
||
|
|
/******************************************************************************/
|
||
|
|
/*************************************************************************/
|
||
|
|
/** File: kmeans_clustering.c **/
|
||
|
|
/** Description: Implementation of regular k-means clustering **/
|
||
|
|
/** algorithm **/
|
||
|
|
/** Author: Wei-keng Liao **/
|
||
|
|
/** ECE Department, Northwestern University **/
|
||
|
|
/** email: wkliao@ece.northwestern.edu **/
|
||
|
|
/** **/
|
||
|
|
/** Edited by: Jay Pisharath **/
|
||
|
|
/** Northwestern University. **/
|
||
|
|
/** **/
|
||
|
|
/** ================================================================ **/
|
||
|
|
/** **/
|
||
|
|
/** Edited by: Sang-Ha Lee **/
|
||
|
|
/** University of Virginia **/
|
||
|
|
/** **/
|
||
|
|
/** Description: No longer supports fuzzy c-means clustering; **/
|
||
|
|
/** only regular k-means clustering. **/
|
||
|
|
/** Simplified for main functionality: regular k-means **/
|
||
|
|
/** clustering. **/
|
||
|
|
/** **/
|
||
|
|
/*************************************************************************/
|
||
|
|
|
||
|
|
#include <stdio.h>
|
||
|
|
#include <stdlib.h>
|
||
|
|
#include <float.h>
|
||
|
|
#include <math.h>
|
||
|
|
#include "kmeans.h"
|
||
|
|
#include <omp.h>
|
||
|
|
|
||
|
|
#define RANDOM_MAX 2147483647
|
||
|
|
|
||
|
|
#ifndef FLT_MAX
|
||
|
|
#define FLT_MAX 3.40282347e+38
|
||
|
|
#endif
|
||
|
|
|
||
|
|
extern double wtime(void);
|
||
|
|
|
||
|
|
int find_nearest_point(float *pt, /* [nfeatures] */
|
||
|
|
int nfeatures,
|
||
|
|
float **pts, /* [npts][nfeatures] */
|
||
|
|
int npts)
|
||
|
|
{
|
||
|
|
int index, i;
|
||
|
|
float min_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 < min_dist)
|
||
|
|
{
|
||
|
|
min_dist = dist;
|
||
|
|
index = i;
|
||
|
|
}
|
||
|
|
}
|
||
|
|
return (index);
|
||
|
|
}
|
||
|
|
|
||
|
|
/*----< 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);
|
||
|
|
}
|
||
|
|
|
||
|
|
/*----< kmeans_clustering() >---------------------------------------------*/
|
||
|
|
float **kmeans_clustering(float **feature, /* in: [npoints][nfeatures] */
|
||
|
|
int nfeatures,
|
||
|
|
int npoints,
|
||
|
|
int nclusters,
|
||
|
|
float threshold,
|
||
|
|
int *membership) /* out: [npoints] */
|
||
|
|
{
|
||
|
|
|
||
|
|
int i, j, n = 0, index, loop = 0;
|
||
|
|
int *new_centers_len; /* [nclusters]: no. of points in each cluster */
|
||
|
|
float delta;
|
||
|
|
float **clusters; /* out: [nclusters][nfeatures] */
|
||
|
|
float **new_centers; /* [nclusters][nfeatures] */
|
||
|
|
|
||
|
|
/* allocate space for returning variable clusters[] */
|
||
|
|
clusters = (float **)malloc(nclusters * sizeof(float *));
|
||
|
|
clusters[0] = (float *)malloc(nclusters * nfeatures * sizeof(float));
|
||
|
|
for (i = 1; i < nclusters; i++)
|
||
|
|
clusters[i] = clusters[i - 1] + nfeatures;
|
||
|
|
|
||
|
|
/* randomly pick cluster centers */
|
||
|
|
for (i = 0; i < nclusters; i++)
|
||
|
|
{
|
||
|
|
// n = (int)rand() % npoints;
|
||
|
|
for (j = 0; j < nfeatures; j++)
|
||
|
|
clusters[i][j] = feature[n][j];
|
||
|
|
n++;
|
||
|
|
}
|
||
|
|
|
||
|
|
for (i = 0; i < npoints; i++)
|
||
|
|
membership[i] = -1;
|
||
|
|
|
||
|
|
/* need to initialize new_centers_len and new_centers[0] to all 0 */
|
||
|
|
new_centers_len = (int *)calloc(nclusters, sizeof(int));
|
||
|
|
|
||
|
|
new_centers = (float **)malloc(nclusters * sizeof(float *));
|
||
|
|
new_centers[0] = (float *)calloc(nclusters * nfeatures, sizeof(float));
|
||
|
|
for (i = 1; i < nclusters; i++)
|
||
|
|
new_centers[i] = new_centers[i - 1] + nfeatures;
|
||
|
|
|
||
|
|
do
|
||
|
|
{
|
||
|
|
|
||
|
|
delta = 0.0;
|
||
|
|
|
||
|
|
for (i = 0; i < npoints; i++)
|
||
|
|
{
|
||
|
|
/* find the index of nestest cluster centers */
|
||
|
|
index = find_nearest_point(feature[i], nfeatures, clusters, nclusters);
|
||
|
|
/* if membership changes, increase delta by 1 */
|
||
|
|
if (membership[i] != index)
|
||
|
|
delta += 1.0;
|
||
|
|
|
||
|
|
/* assign the membership to object i */
|
||
|
|
membership[i] = index;
|
||
|
|
|
||
|
|
/* update new cluster centers : sum of objects located within */
|
||
|
|
new_centers_len[index]++;
|
||
|
|
for (j = 0; j < nfeatures; j++)
|
||
|
|
new_centers[index][j] += feature[i][j];
|
||
|
|
}
|
||
|
|
|
||
|
|
/* replace old cluster centers with new_centers */
|
||
|
|
for (i = 0; i < nclusters; i++)
|
||
|
|
{
|
||
|
|
for (j = 0; j < nfeatures; j++)
|
||
|
|
{
|
||
|
|
if (new_centers_len[i] > 0)
|
||
|
|
clusters[i][j] = new_centers[i][j] / new_centers_len[i];
|
||
|
|
new_centers[i][j] = 0.0; /* set back to 0 */
|
||
|
|
}
|
||
|
|
new_centers_len[i] = 0; /* set back to 0 */
|
||
|
|
}
|
||
|
|
|
||
|
|
// delta /= npoints;
|
||
|
|
} while (delta > threshold);
|
||
|
|
|
||
|
|
free(new_centers[0]);
|
||
|
|
free(new_centers);
|
||
|
|
free(new_centers_len);
|
||
|
|
|
||
|
|
return clusters;
|
||
|
|
}
|