/*M/////////////////////////////////////////////////////////////////////////////////////// // // 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. // // // Intel License Agreement // For Open Source Computer Vision Library // // Copyright (C) 2000, Intel Corporation, all rights reserved. // Third party copyrights are property of their respective owners. // // Redistribution and use in source and binary forms, with or without modification, // are permitted provided that the following conditions are met: // // * Redistribution's of source code must retain the above copyright notice, // this list of conditions and the following disclaimer. // // * Redistribution's 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. // // * The name of Intel Corporation may not 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 and fitness for a particular purpose are disclaimed. // In no event shall the Intel Corporation or 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. // //M*/ #include "cvtest.h" #if 0 /* Testing parameters */ static char test_desc[] = "KMeans clustering"; static char* func_name[] = { "cvKMeans" }; //based on Ara Nefian's implementation float distance(float* vector_1, float *vector_2, int VecSize) { int i; float dist; dist = 0.0; for (i = 0; i < VecSize; i++) { //printf ("%f, %f\n", vector_1[i], vector_2[i]); dist = dist + (vector_1[i] - vector_2[i])*(vector_1[i] - vector_2[i]); } return dist; } //returns number of made iterations int _real_kmeans( int numClusters, float **sample, int numSamples, int VecSize, int* a_class, double eps, int iter ) { int i, k, n; int *counter; float minDist; float *dist; float **curr_cluster; float **prev_cluster; float error; //printf("* numSamples = %d, numClusters = %d, VecSize = %d\n", numSamples, numClusters, VecSize); //memory allocation dist = new float[numClusters]; counter = new int[numClusters]; //allocate memory for curr_cluster and prev_cluster curr_cluster = new float*[numClusters]; prev_cluster = new float*[numClusters]; for (k = 0; k < numClusters; k++){ curr_cluster[k] = new float[VecSize]; prev_cluster[k] = new float[VecSize]; } //pick initial cluster centers for (k = 0; k < numClusters; k++) { for (n = 0; n < VecSize; n++) { curr_cluster[k][n] = sample[k*(numSamples/numClusters)][n]; prev_cluster[k][n] = sample[k*(numSamples/numClusters)][n]; } } int NumIter = 0; error = FLT_MAX; while ((error > eps) && (NumIter < iter)) { NumIter++; //printf("NumIter = %d, error = %lf, \n", NumIter, error); //assign samples to clusters for (i = 0; i < numSamples; i++) { for (k = 0; k < numClusters; k++) { dist[k] = distance(sample[i], curr_cluster[k], VecSize); } minDist = dist[0]; a_class[i] = 0; for (k = 1; k < numClusters; k++) { if (dist[k] < minDist) { minDist = dist[k]; a_class[i] = k; } } } //reset clusters and counters for (k = 0; k < numClusters; k++){ counter[k] = 0; for (n = 0; n < VecSize; n++){ curr_cluster[k][n] = 0.0; } } for (i = 0; i < numSamples; i++){ for (n = 0; n < VecSize; n++){ curr_cluster[a_class[i]][n] = curr_cluster[a_class[i]][n] + sample[i][n]; } counter[a_class[i]]++; } for (k = 0; k < numClusters; k++){ for (n = 0; n < VecSize; n++){ curr_cluster[k][n] = curr_cluster[k][n]/(float)counter[k]; } } error = 0.0; for (k = 0; k < numClusters; k++){ for (n = 0; n < VecSize; n++){ error = error + (curr_cluster[k][n] - prev_cluster[k][n])*(curr_cluster[k][n] - prev_cluster[k][n]); } } //error = error/(double)(numClusters*VecSize); //copy curr_clusters to prev_clusters for (k = 0; k < numClusters; k++){ for (n =0; n < VecSize; n++){ prev_cluster[k][n] = curr_cluster[k][n]; } } } //deallocate memory for curr_cluster and prev_cluster for (k = 0; k < numClusters; k++){ delete curr_cluster[k]; delete prev_cluster[k]; } delete curr_cluster; delete prev_cluster; delete counter; delete dist; return NumIter; } static int fmaKMeans(void) { CvTermCriteria crit; float** vectors; int* output; int* etalon_output; int lErrors = 0; int lNumVect = 0; int lVectSize = 0; int lNumClust = 0; int lMaxNumIter = 0; float flEpsilon = 0; int i,j; static int read_param = 0; /* Initialization global parameters */ if( !read_param ) { read_param = 1; /* Read test-parameters */ trsiRead( &lNumVect, "1000", "Number of vectors" ); trsiRead( &lVectSize, "10", "Number of vectors" ); trsiRead( &lNumClust, "20", "Number of clusters" ); trsiRead( &lMaxNumIter,"100","Maximal number of iterations"); trssRead( &flEpsilon, "0.5", "Accuracy" ); } crit = cvTermCriteria( CV_TERMCRIT_EPS|CV_TERMCRIT_ITER, lMaxNumIter, flEpsilon ); //allocate vectors vectors = (float**)cvAlloc( lNumVect * sizeof(float*) ); for( i = 0; i < lNumVect; i++ ) { vectors[i] = (float*)cvAlloc( lVectSize * sizeof( float ) ); } output = (int*)cvAlloc( lNumVect * sizeof(int) ); etalon_output = (int*)cvAlloc( lNumVect * sizeof(int) ); //fill input vectors for( i = 0; i < lNumVect; i++ ) { ats1flInitRandom( -2000, 2000, vectors[i], lVectSize ); } /* run etalon kmeans */ /* actually it is the simpliest realization of kmeans */ int ni = _real_kmeans( lNumClust, vectors, lNumVect, lVectSize, etalon_output, crit.epsilon, crit.max_iter ); trsWrite( ATS_CON, "%d iterations done\n", ni ); /* Run OpenCV function */ #define _KMEANS_TIME 0 #if _KMEANS_TIME //timing section trsTimerStart(0); __int64 tics = atsGetTickCount(); #endif cvKMeans( lNumClust, vectors, lNumVect, lVectSize, crit, output ); #if _KMEANS_TIME tics = atsGetTickCount() - tics; trsTimerStop(0); //output result //double dbUsecs =ATS_TICS_TO_USECS((double)tics); trsWrite( ATS_CON, "Tics per iteration %d\n", tics/ni ); #endif //compare results for( j = 0; j < lNumVect; j++ ) { if ( output[j] != etalon_output[j] ) { lErrors++; } } //free memory for( i = 0; i < lNumVect; i++ ) { cvFree( &(vectors[i]) ); } cvFree(&vectors); cvFree(&output); cvFree(&etalon_output); if( lErrors == 0 ) return trsResult( TRS_OK, "No errors fixed for this text" ); else return trsResult( TRS_FAIL, "Detected %d errors", lErrors ); } void InitAKMeans() { /* Register test function */ trsReg( func_name[0], test_desc, atsAlgoClass, fmaKMeans ); } /* InitAKMeans */ #endif