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198 lines
8.2 KiB
C
198 lines
8.2 KiB
C
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/*****************************************************************************/
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/*IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. */
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/*By downloading, copying, installing or using the software you agree */
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/*to this license. If you do not agree to this license, do not download, */
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/*install, copy or use the software. */
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/* */
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/* */
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/*Copyright (c) 2005 Northwestern University */
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/*All rights reserved. */
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/*Redistribution of the software in source and binary forms, */
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/*with or without modification, is permitted provided that the */
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/*following conditions are met: */
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/* */
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/*1 Redistributions of source code must retain the above copyright */
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/* notice, this list of conditions and the following disclaimer. */
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/* */
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/*2 Redistributions in binary form must reproduce the above copyright */
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/* notice, this list of conditions and the following disclaimer in the */
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/* documentation and/or other materials provided with the distribution.*/
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/* */
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/*3 Neither the name of Northwestern University nor the names of its */
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/* contributors may be used to endorse or promote products derived */
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/* from this software without specific prior written permission. */
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/* */
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/*THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS ``AS */
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/*IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED */
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/*TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY, NON-INFRINGEMENT AND */
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/*FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL */
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/*NORTHWESTERN UNIVERSITY OR ITS CONTRIBUTORS BE LIABLE FOR ANY DIRECT, */
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/*INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES */
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/*(INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR */
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/*SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) */
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/*HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, */
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/*STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN */
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/*ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE */
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/*POSSIBILITY OF SUCH DAMAGE. */
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/******************************************************************************/
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/*************************************************************************/
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/** File: kmeans_clustering.c **/
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/** Description: Implementation of regular k-means clustering **/
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/** algorithm **/
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/** Author: Wei-keng Liao **/
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/** ECE Department, Northwestern University **/
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/** email: wkliao@ece.northwestern.edu **/
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/** **/
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/** Edited by: Jay Pisharath **/
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/** Northwestern University. **/
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/** **/
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/** ================================================================ **/
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/** **/
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/** Edited by: Sang-Ha Lee **/
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/** University of Virginia **/
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/** **/
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/** Description: No longer supports fuzzy c-means clustering; **/
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/** only regular k-means clustering. **/
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/** Simplified for main functionality: regular k-means **/
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/** clustering. **/
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/** **/
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/*************************************************************************/
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#include <stdio.h>
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#include <stdlib.h>
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#include <float.h>
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#include <math.h>
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#include "kmeans.h"
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#include <omp.h>
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#define RANDOM_MAX 2147483647
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#ifndef FLT_MAX
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#define FLT_MAX 3.40282347e+38
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#endif
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extern double wtime(void);
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int find_nearest_point(float *pt, /* [nfeatures] */
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int nfeatures,
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float **pts, /* [npts][nfeatures] */
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int npts)
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{
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int index, i;
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float min_dist = FLT_MAX;
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/* find the cluster center id with min distance to pt */
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for (i = 0; i < npts; i++)
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{
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float dist;
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dist = euclid_dist_2(pt, pts[i], nfeatures); /* no need square root */
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if (dist < min_dist)
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{
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min_dist = dist;
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index = i;
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}
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}
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return (index);
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}
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/*----< euclid_dist_2() >----------------------------------------------------*/
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/* multi-dimensional spatial Euclid distance square */
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__inline float euclid_dist_2(float *pt1,
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float *pt2,
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int numdims)
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{
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int i;
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float ans = 0.0;
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for (i = 0; i < numdims; i++)
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ans += (pt1[i] - pt2[i]) * (pt1[i] - pt2[i]);
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return (ans);
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}
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/*----< kmeans_clustering() >---------------------------------------------*/
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float **kmeans_clustering(float **feature, /* in: [npoints][nfeatures] */
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int nfeatures,
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int npoints,
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int nclusters,
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float threshold,
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int *membership) /* out: [npoints] */
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{
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int i, j, n = 0, index, loop = 0;
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int *new_centers_len; /* [nclusters]: no. of points in each cluster */
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float delta;
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float **clusters; /* out: [nclusters][nfeatures] */
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float **new_centers; /* [nclusters][nfeatures] */
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/* allocate space for returning variable clusters[] */
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clusters = (float **)malloc(nclusters * sizeof(float *));
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clusters[0] = (float *)malloc(nclusters * nfeatures * sizeof(float));
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for (i = 1; i < nclusters; i++)
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clusters[i] = clusters[i - 1] + nfeatures;
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/* randomly pick cluster centers */
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for (i = 0; i < nclusters; i++)
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{
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// n = (int)rand() % npoints;
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for (j = 0; j < nfeatures; j++)
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clusters[i][j] = feature[n][j];
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n++;
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}
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for (i = 0; i < npoints; i++)
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membership[i] = -1;
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/* need to initialize new_centers_len and new_centers[0] to all 0 */
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new_centers_len = (int *)calloc(nclusters, sizeof(int));
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new_centers = (float **)malloc(nclusters * sizeof(float *));
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new_centers[0] = (float *)calloc(nclusters * nfeatures, sizeof(float));
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for (i = 1; i < nclusters; i++)
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new_centers[i] = new_centers[i - 1] + nfeatures;
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do
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{
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delta = 0.0;
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for (i = 0; i < npoints; i++)
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{
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/* find the index of nestest cluster centers */
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index = find_nearest_point(feature[i], nfeatures, clusters, nclusters);
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/* if membership changes, increase delta by 1 */
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if (membership[i] != index)
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delta += 1.0;
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/* assign the membership to object i */
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membership[i] = index;
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/* update new cluster centers : sum of objects located within */
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new_centers_len[index]++;
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for (j = 0; j < nfeatures; j++)
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new_centers[index][j] += feature[i][j];
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}
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/* replace old cluster centers with new_centers */
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for (i = 0; i < nclusters; i++)
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{
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for (j = 0; j < nfeatures; j++)
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{
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if (new_centers_len[i] > 0)
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clusters[i][j] = new_centers[i][j] / new_centers_len[i];
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new_centers[i][j] = 0.0; /* set back to 0 */
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}
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new_centers_len[i] = 0; /* set back to 0 */
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}
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// delta /= npoints;
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} while (delta > threshold);
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free(new_centers[0]);
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free(new_centers);
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free(new_centers_len);
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return clusters;
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}
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