project directories reorganization
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Blaise Tine
155
benchmarks/old_opencl/kmeans/cluster.c
Executable file
155
benchmarks/old_opencl/kmeans/cluster.c
Executable file
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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: cluster.c **/
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/** Description: Takes as input a file, containing 1 data point per **/
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/** per line, and performs a fuzzy c-means clustering **/
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/** on the data. Fuzzy clustering is performed using **/
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/** min to max clusters and the clustering that gets **/
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/** the best score according to a compactness and **/
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/** separation criterion are returned. **/
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/** Author: Brendan McCane **/
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/** James Cook University of North Queensland. **/
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/** Australia. email: mccane@cs.jcu.edu.au **/
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/** **/
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/** Edited by: Jay Pisharath, Wei-keng Liao **/
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/** Northwestern University. **/
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/** **/
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/** ================================================================ **/
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/** **/
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/** Edited by: Shuai Che, David Tarjan, 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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/** No longer performs "validity" function to analyze **/
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/** compactness and separation crietria; instead **/
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/** calculate root mean squared error. **/
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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 <string.h>
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#include <limits.h>
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#include <math.h>
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#include <float.h>
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#include "kmeans.h"
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float min_rmse_ref = FLT_MAX;
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extern double wtime(void);
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/* reference min_rmse value */
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/*---< cluster() >-----------------------------------------------------------*/
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int cluster(int npoints, /* number of data points */
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int nfeatures, /* number of attributes for each point */
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float **features, /* array: [npoints][nfeatures] */
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int min_nclusters, /* range of min to max number of clusters */
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int max_nclusters,
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float threshold, /* loop terminating factor */
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int *best_nclusters, /* out: number between min and max with lowest RMSE */
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float ***cluster_centres, /* out: [best_nclusters][nfeatures] */
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float *min_rmse, /* out: minimum RMSE */
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int isRMSE, /* calculate RMSE */
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int nloops /* number of iteration for each number of clusters */
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)
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{
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int nclusters; /* number of clusters k */
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int index =0; /* number of iteration to reach the best RMSE */
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int rmse; /* RMSE for each clustering */
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int *membership; /* which cluster a data point belongs to */
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float **tmp_cluster_centres; /* hold coordinates of cluster centers */
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int i;
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/* allocate memory for membership */
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membership = (int*) malloc(npoints * sizeof(int));
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/* sweep k from min to max_nclusters to find the best number of clusters */
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for(nclusters = min_nclusters; nclusters <= max_nclusters; nclusters++)
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{
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if (nclusters > npoints) break; /* cannot have more clusters than points */
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/* allocate device memory, invert data array (@ kmeans_cuda.cu) */
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allocate(npoints, nfeatures, nclusters, features);
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/* iterate nloops times for each number of clusters */
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for(i = 0; i < nloops; i++)
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{
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/* initialize initial cluster centers, CUDA calls (@ kmeans_cuda.cu) */
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tmp_cluster_centres = kmeans_clustering(features,
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nfeatures,
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npoints,
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nclusters,
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threshold,
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membership);
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if (*cluster_centres) {
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free((*cluster_centres)[0]);
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free(*cluster_centres);
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}
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*cluster_centres = tmp_cluster_centres;
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/* find the number of clusters with the best RMSE */
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if(isRMSE)
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{
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rmse = rms_err(features,
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nfeatures,
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npoints,
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tmp_cluster_centres,
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nclusters);
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if(rmse < min_rmse_ref){
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min_rmse_ref = rmse; //update reference min RMSE
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*min_rmse = min_rmse_ref; //update return min RMSE
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*best_nclusters = nclusters; //update optimum number of clusters
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index = i; //update number of iteration to reach best RMSE
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}
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}
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}
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deallocateMemory(); /* free device memory (@ kmeans_cuda.cu) */
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}
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free(membership);
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return index;
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}
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