/*************************************************************************/
/**   File:         example.c                                           **/
/**   Description:  Takes as input a file:                              **/
/**                 ascii  file: containing 1 data point per line       **/
/**                 binary file: first int is the number of objects     **/
/**                              2nd int is the no. of features of each **/
/**                              object                                 **/
/**                 This example performs a fuzzy c-means clustering    **/
/**                 on the data. Fuzzy clustering is performed using    **/
/**                 min to max clusters and the clustering that gets    **/
/**                 the best score according to a compactness and       **/
/**                 separation criterion are returned.                  **/
/**   Author:  Wei-keng Liao                                            **/
/**            ECE Department Northwestern University                   **/
/**            email: wkliao@ece.northwestern.edu                       **/
/**                                                                     **/
/**   Edited by: Jay Pisharath                                          **/
/**              Northwestern University.                               **/
/**                                                                     **/
/**   ================================================================  **/
/**																		**/
/**   Edited by: Shuai Che, David Tarjan, Sang-Ha Lee					**/
/**				 University of Virginia									**/
/**																		**/
/**   Description:	No longer supports fuzzy c-means clustering;	 	**/
/**					only regular k-means clustering.					**/
/**					No longer performs "validity" function to analyze	**/
/**					compactness and separation crietria; instead		**/
/**					calculate root mean squared error.					**/
/**                                                                     **/
/*************************************************************************/
#define _CRT_SECURE_NO_DEPRECATE 1

#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <limits.h>
#include <math.h>
#include  <fcntl.h>

#include "kmeans.h"
#include <getopt.h>
extern double wtime(void);

/*---< usage() >------------------------------------------------------------*/
void usage(char *argv0) {
    char *help =
        "\nUsage: %s [switches] -i filename\n\n"
		"    -i filename      :file containing data to be clustered\n"		
		"    -m max_nclusters :maximum number of clusters allowed    [default=5]\n"
        "    -n min_nclusters :minimum number of clusters allowed    [default=5]\n"
		"    -t threshold     :threshold value                       [default=0.001]\n"
		"    -l nloops        :iteration for each number of clusters [default=1]\n"
		"    -b               :input file is in binary format\n"
        "    -r               :calculate RMSE                        [default=off]\n"
		"    -o               :output cluster center coordinates     [default=off]\n";
    fprintf(stderr, help, argv0);
    exit(-1);
}

/*---< main() >-------------------------------------------------------------*/
int setup(int argc, char **argv) {
		int		opt;
 extern char   *optarg;
		char   *filename = 0;
		float  *buf;
		char	line[1024];
		int		isBinaryFile = 0;

		float	threshold = 0.001;		/* default value */
		int		max_nclusters=5;		/* default value */
		int		min_nclusters=5;		/* default value */
		int		best_nclusters = 0;
		int		nfeatures = 0;
		int		npoints = 0;
		float	len;
		         
		float **features;
		float **cluster_centres=NULL;
		int		i, j, index;
		int		nloops = 1;				/* default value */
				
		int		isRMSE = 0;		
		float	rmse;
		
		int		isOutput = 0;
		float	cluster_timing, io_timing;		

		/* obtain command line arguments and change appropriate options */
		while ( (opt=getopt(argc,argv,"i:t:m:n:l:bro"))!= EOF) {
        switch (opt) {
            case 'i': filename=optarg;
                      break;
            case 'b': isBinaryFile = 1;
                      break;            
            case 't': threshold=atof(optarg);
                      break;
            case 'm': max_nclusters = atoi(optarg);
                      break;
            case 'n': min_nclusters = atoi(optarg);
                      break;
			case 'r': isRMSE = 1;
                      break;
			case 'o': isOutput = 1;
					  break;
		    case 'l': nloops = atoi(optarg);
					  break;
            case '?': usage(argv[0]);
                      break;
            default: usage(argv[0]);
                      break;
        }
    }

    if (filename == 0) usage(argv[0]);
		
	/* ============== I/O begin ==============*/
    /* get nfeatures and npoints */
    if (isBinaryFile) {		//Binary file input
        int infile;
        if ((infile = open(filename, O_RDONLY, "0600")) == -1) {
            fprintf(stderr, "Error: no such file (%s)\n", filename);
            exit(1);
        }
        read(infile, &npoints,   sizeof(int));
        read(infile, &nfeatures, sizeof(int));        

        /* allocate space for features[][] and read attributes of all objects */
        buf         = (float*) malloc(npoints*nfeatures*sizeof(float));
        features    = (float**)malloc(npoints*          sizeof(float*));
        features[0] = (float*) malloc(npoints*nfeatures*sizeof(float));
        for (i=1; i<npoints; i++)
            features[i] = features[i-1] + nfeatures;

        read(infile, buf, npoints*nfeatures*sizeof(float));

        close(infile);
    }
    else {
        FILE *infile;
        if ((infile = fopen(filename, "r")) == NULL) {
            fprintf(stderr, "Error: no such file (%s)\n", filename);
            exit(1);
		}		
        while (fgets(line, 1024, infile) != NULL)
			if (strtok(line, " \t\n") != 0)
                npoints++;			
        rewind(infile);
        while (fgets(line, 1024, infile) != NULL) {
            if (strtok(line, " \t\n") != 0) {
                /* ignore the id (first attribute): nfeatures = 1; */
                while (strtok(NULL, " ,\t\n") != NULL) nfeatures++;
                break;
            }
        }        

        /* allocate space for features[] and read attributes of all objects */
        buf         = (float*) malloc(npoints*nfeatures*sizeof(float));
        features    = (float**)malloc(npoints*          sizeof(float*));
        features[0] = (float*) malloc(npoints*nfeatures*sizeof(float));
        for (i=1; i<npoints; i++)
            features[i] = features[i-1] + nfeatures;
        rewind(infile);
        i = 0;
        while (fgets(line, 1024, infile) != NULL) {
            if (strtok(line, " \t\n") == NULL) continue;            
            for (j=0; j<nfeatures; j++) {
                buf[i] = atof(strtok(NULL, " ,\t\n"));             
                i++;
            }            
        }
        fclose(infile);
    }
	
	printf("\nI/O completed\n");
	printf("\nNumber of objects: %d\n", npoints);
	printf("Number of features: %d\n", nfeatures);	
	/* ============== I/O end ==============*/

	// error check for clusters
	if (npoints < min_nclusters)
	{
		printf("Error: min_nclusters(%d) > npoints(%d) -- cannot proceed\n", min_nclusters, npoints);
		exit(0);
	}

	memcpy(features[0], buf, npoints*nfeatures*sizeof(float)); /* now features holds 2-dimensional array of features */
	free(buf);

	/* ======================= core of the clustering ===================*/

	cluster_centres = NULL;
    index = cluster(npoints,				/* number of data points */
					nfeatures,				/* number of features for each point */
					features,				/* array: [npoints][nfeatures] */
					min_nclusters,			/* range of min to max number of clusters */
					max_nclusters,
					threshold,				/* loop termination factor */
				   &best_nclusters,			/* return: number between min and max */
				   &cluster_centres,		/* return: [best_nclusters][nfeatures] */  
				   &rmse,					/* Root Mean Squared Error */
					isRMSE,					/* calculate RMSE */
					nloops);				/* number of iteration for each number of clusters */		


	/* =============== Command Line Output =============== */

	/* cluster center coordinates
	   :displayed only for when k=1*/
	if((min_nclusters == max_nclusters) && (isOutput == 1)) {
		printf("\n================= Centroid Coordinates =================\n");
		for(i = 0; i < max_nclusters; i++){
			printf("%d:", i);
			for(j = 0; j < nfeatures; j++){
				printf(" %.2f", cluster_centres[i][j]);
			}
			printf("\n\n");
		}
	}
	
	len = (float) ((max_nclusters - min_nclusters + 1)*nloops);

	printf("Number of Iteration: %d\n", nloops);
	printf("Time for I/O: %.5fsec\n", io_timing);
	printf("Time for Entire Clustering: %.5fsec\n", cluster_timing);
	
	if(min_nclusters != max_nclusters){
		if(nloops != 1){									//range of k, multiple iteration
			printf("Average Clustering Time: %fsec\n",
					cluster_timing / len);
			printf("Best number of clusters is %d\n", best_nclusters);				
		}
		else{												//range of k, single iteration
			printf("Average Clustering Time: %fsec\n",
					cluster_timing / len);
			printf("Best number of clusters is %d\n", best_nclusters);				
		}
	}
	else{
		if(nloops != 1){									// single k, multiple iteration
			printf("Average Clustering Time: %.5fsec\n",
					cluster_timing / nloops);
			if(isRMSE)										// if calculated RMSE
				printf("Number of trials to approach the best RMSE of %.3f is %d\n", rmse, index + 1);
		}
		else{												// single k, single iteration				
			if(isRMSE)										// if calculated RMSE
				printf("Root Mean Squared Error: %.3f\n", rmse);
		}
	}
	

	/* free up memory */
	free(features[0]);
	free(features);    
    return(0);
}

