kmeans should compile with new loading methid
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codetector
338
benchmarks/new_opencl/kmeans/read_input.c
Executable file
338
benchmarks/new_opencl/kmeans/read_input.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: example.c **/
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/** Description: Takes as input a file: **/
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/** ascii file: containing 1 data point per line **/
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/** binary file: first int is the number of objects **/
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/** 2nd int is the no. of features of each **/
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/** object **/
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/** This example 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: 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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* **/
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/** Edited by: Shuai Che, David Tarjan, Sang-Ha Lee
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* **/
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/** University of Virginia
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* **/
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/**
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* **/
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/** Description: No longer supports fuzzy c-means clustering;
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* **/
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/** only regular k-means clustering.
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* **/
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/** No longer performs "validity" function to
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* analyze **/
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/** compactness and separation crietria; instead
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* **/
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/** calculate root mean squared error.
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* **/
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/** **/
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/*************************************************************************/
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#define _CRT_SECURE_NO_DEPRECATE 1
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#include "kmeans.h"
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#include <fcntl.h>
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#include <limits.h>
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#include <math.h>
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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 <unistd.h>
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extern double wtime(void);
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/*---< usage() >------------------------------------------------------------*/
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void usage(char *argv0) {
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char *help = "\nUsage: %s [switches] -i filename\n\n"
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" -i filename :file containing data to be clustered\n"
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" -m max_nclusters :maximum number of clusters allowed "
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"[default=5]\n"
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" -n min_nclusters :minimum number of clusters allowed "
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"[default=5]\n"
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" -t threshold :threshold value "
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"[default=0.001]\n"
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" -l nloops :iteration for each number of clusters "
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"[default=1]\n"
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" -b :input file is in binary format\n"
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" -r :calculate RMSE "
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"[default=off]\n"
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" -o :output cluster center coordinates "
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"[default=off]\n";
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fprintf(stderr, help, argv0);
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exit(-1);
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}
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/*---< main() >-------------------------------------------------------------*/
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int setup(int argc, char **argv) {
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int opt;
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extern char *optarg;
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char *filename = 0;
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float *buf;
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char line[1024];
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int isBinaryFile = 0;
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float threshold = 0.001; /* default value */
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int max_nclusters = 5; /* default value */
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int min_nclusters = 5; /* default value */
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int best_nclusters = 0;
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int nfeatures = 0;
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int npoints = 0;
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float len;
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float **features;
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float **cluster_centres = NULL;
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int i, j, index;
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int nloops = 1; /* default value */
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int isRMSE = 0;
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float rmse;
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int isOutput = 0;
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// float cluster_timing, io_timing;
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/* obtain command line arguments and change appropriate options */
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while ((opt = getopt(argc, argv, "i:t:m:n:l:bro")) != EOF) {
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switch (opt) {
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case 'i':
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filename = optarg;
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break;
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case 'b':
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isBinaryFile = 1;
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break;
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case 't':
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threshold = atof(optarg);
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break;
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case 'm':
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max_nclusters = atoi(optarg);
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break;
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case 'n':
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min_nclusters = atoi(optarg);
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break;
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case 'r':
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isRMSE = 1;
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break;
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case 'o':
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isOutput = 1;
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break;
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case 'l':
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nloops = atoi(optarg);
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break;
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case '?':
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usage(argv[0]);
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break;
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default:
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usage(argv[0]);
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break;
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}
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}
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/* ============== I/O begin ==============*/
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/* get nfeatures and npoints */
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// io_timing = omp_get_wtime();
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/*if (isBinaryFile) { // Binary file input
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FILE *infile;
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if ((infile = fopen("100", "r")) == NULL) {
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fprintf(stderr, "Error: no such file (%s)\n", filename);
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exit(1);
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}
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fread(&npoints, 1, sizeof(int), infile);
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fread(&nfeatures, 1, sizeof(int), infile);
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// allocate space for features[][] and read attributes of all objects
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buf = (float *)malloc(npoints * nfeatures * sizeof(float));
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features = (float **)malloc(npoints * sizeof(float *));
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features[0] = (float *)malloc(npoints * nfeatures * sizeof(float));
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for (i = 1; i < npoints; i++) {
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features[i] = features[i - 1] + nfeatures;
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}
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fread(buf, 1, npoints * nfeatures * sizeof(float), infile);
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fclose(infile);
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} else {
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FILE *infile;
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if ((infile = fopen("100", "r")) == NULL) {
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fprintf(stderr, "Error: no such file (%s)\n", filename);
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exit(1);
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}
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while (fgets(line, 1024, infile) != NULL)
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if (strtok(line, " \t\n") != 0) {
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npoints++;
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}
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rewind(infile);
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while (fgets(line, 1024, infile) != NULL) {
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if (strtok(line, " \t\n") != 0) {
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// ignore the id (first attribute): nfeatures = 1;
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while (strtok(NULL, " ,\t\n") != NULL)
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nfeatures++;
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break;
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}
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}
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// allocate space for features[] and read attributes of all objects
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buf = (float *)malloc(npoints * nfeatures * sizeof(float));
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features = (float **)malloc(npoints * sizeof(float *));
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features[0] = (float *)malloc(npoints * nfeatures * sizeof(float));
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for (i = 1; i < npoints; i++)
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features[i] = features[i - 1] + nfeatures;
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rewind(infile);
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i = 0;
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while (fgets(line, 1024, infile) != NULL) {
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if (strtok(line, " \t\n") == NULL)
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continue;
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for (j = 0; j < nfeatures; j++) {
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buf[i] = atof(strtok(NULL, " ,\t\n"));
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i++;
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}
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}
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fclose(infile);
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}*/
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npoints = 100;
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nfeatures = 100;
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buf = (float *)malloc(npoints * nfeatures * sizeof(float));
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features = (float **)malloc(npoints * sizeof(float *));
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features[0] = (float *)malloc(npoints * nfeatures * sizeof(float));
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for (i = 1; i < npoints; i++) {
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features[i] = features[i - 1] + nfeatures;
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}
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for (i = 0; i < npoints * nfeatures; ++i) {
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buf[i] = (i % 64);
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}
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// io_timing = omp_get_wtime() - io_timing;
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printf("\nI/O completed\n");
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printf("\nNumber of objects: %d\n", npoints);
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printf("Number of features: %d\n", nfeatures);
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/* ============== I/O end ==============*/
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// error check for clusters
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if (npoints < min_nclusters) {
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printf("Error: min_nclusters(%d) > npoints(%d) -- cannot proceed\n",
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min_nclusters, npoints);
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exit(0);
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}
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srand(7); /* seed for future random number generator */
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memcpy(
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features[0], buf,
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npoints * nfeatures *
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sizeof(
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float)); /* now features holds 2-dimensional array of features */
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free(buf);
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/* ======================= core of the clustering ===================*/
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// cluster_timing = omp_get_wtime(); /* Total clustering time */
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cluster_centres = NULL;
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index = cluster(npoints, /* number of data points */
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nfeatures, /* number of features for each point */
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features, /* array: [npoints][nfeatures] */
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min_nclusters, /* range of min to max number of clusters */
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max_nclusters, threshold, /* loop termination factor */
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&best_nclusters, /* return: number between min and max */
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&cluster_centres, /* return: [best_nclusters][nfeatures] */
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&rmse, /* Root Mean Squared Error */
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isRMSE, /* calculate RMSE */
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nloops); /* number of iteration for each number of clusters */
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// cluster_timing = omp_get_wtime() - cluster_timing;
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/* =============== Command Line Output =============== */
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/* cluster center coordinates
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:displayed only for when k=1*/
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if ((min_nclusters == max_nclusters) && (isOutput == 1)) {
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printf("\n================= Centroid Coordinates =================\n");
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for (i = 0; i < max_nclusters; i++) {
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printf("%d:", i);
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for (j = 0; j < nfeatures; j++) {
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printf(" %.2f", cluster_centres[i][j]);
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}
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printf("\n\n");
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}
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}
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len = (float)((max_nclusters - min_nclusters + 1) * nloops);
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printf("Number of Iteration: %d\n", nloops);
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// printf("Time for I/O: %.5fsec\n", io_timing);
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// printf("Time for Entire Clustering: %.5fsec\n", cluster_timing);
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if (min_nclusters != max_nclusters) {
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if (nloops != 1) { // range of k, multiple iteration
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// printf("Average Clustering Time: %fsec\n",
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// cluster_timing / len);
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printf("Best number of clusters is %d\n", best_nclusters);
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} else { // range of k, single iteration
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// printf("Average Clustering Time: %fsec\n",
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// cluster_timing / len);
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printf("Best number of clusters is %d\n", best_nclusters);
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}
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} else {
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if (nloops != 1) { // single k, multiple iteration
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// printf("Average Clustering Time: %.5fsec\n",
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// cluster_timing / nloops);
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if (isRMSE) // if calculated RMSE
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printf("Number of trials to approach the best RMSE of %.3f is %d\n",
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rmse, index + 1);
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} else { // single k, single iteration
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if (isRMSE) // if calculated RMSE
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printf("Root Mean Squared Error: %.3f\n", rmse);
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}
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}
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/* free up memory */
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free(features[0]);
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free(features);
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return (0);
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}
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