"atomic bomb" commit. Reorganized OpenCV directory structure
This commit is contained in:
Vadim Pisarevsky
291
tests/cv/src/akmeans.cpp
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291
tests/cv/src/akmeans.cpp
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/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// Intel License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000, Intel Corporation, all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of Intel Corporation may not be used to endorse or promote products
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// derived 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 is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or 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 services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#include "cvtest.h"
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#if 0
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/* Testing parameters */
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static char test_desc[] = "KMeans clustering";
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static char* func_name[] =
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{
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"cvKMeans"
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};
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//based on Ara Nefian's implementation
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float distance(float* vector_1, float *vector_2, int VecSize)
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{
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int i;
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float dist;
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dist = 0.0;
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for (i = 0; i < VecSize; i++)
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{
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//printf ("%f, %f\n", vector_1[i], vector_2[i]);
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dist = dist + (vector_1[i] - vector_2[i])*(vector_1[i] - vector_2[i]);
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}
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return dist;
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}
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//returns number of made iterations
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int _real_kmeans( int numClusters, float **sample, int numSamples,
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int VecSize, int* a_class, double eps, int iter )
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{
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int i, k, n;
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int *counter;
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float minDist;
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float *dist;
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float **curr_cluster;
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float **prev_cluster;
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float error;
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//printf("* numSamples = %d, numClusters = %d, VecSize = %d\n", numSamples, numClusters, VecSize);
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//memory allocation
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dist = new float[numClusters];
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counter = new int[numClusters];
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//allocate memory for curr_cluster and prev_cluster
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curr_cluster = new float*[numClusters];
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prev_cluster = new float*[numClusters];
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for (k = 0; k < numClusters; k++){
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curr_cluster[k] = new float[VecSize];
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prev_cluster[k] = new float[VecSize];
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}
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//pick initial cluster centers
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for (k = 0; k < numClusters; k++)
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{
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for (n = 0; n < VecSize; n++)
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{
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curr_cluster[k][n] = sample[k*(numSamples/numClusters)][n];
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prev_cluster[k][n] = sample[k*(numSamples/numClusters)][n];
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}
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}
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int NumIter = 0;
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error = FLT_MAX;
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while ((error > eps) && (NumIter < iter))
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{
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NumIter++;
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//printf("NumIter = %d, error = %lf, \n", NumIter, error);
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//assign samples to clusters
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for (i = 0; i < numSamples; i++)
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{
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for (k = 0; k < numClusters; k++)
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{
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dist[k] = distance(sample[i], curr_cluster[k], VecSize);
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}
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minDist = dist[0];
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a_class[i] = 0;
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for (k = 1; k < numClusters; k++)
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{
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if (dist[k] < minDist)
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{
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minDist = dist[k];
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a_class[i] = k;
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}
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}
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}
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//reset clusters and counters
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for (k = 0; k < numClusters; k++){
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counter[k] = 0;
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for (n = 0; n < VecSize; n++){
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curr_cluster[k][n] = 0.0;
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}
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}
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for (i = 0; i < numSamples; i++){
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for (n = 0; n < VecSize; n++){
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curr_cluster[a_class[i]][n] = curr_cluster[a_class[i]][n] + sample[i][n];
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}
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counter[a_class[i]]++;
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}
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for (k = 0; k < numClusters; k++){
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for (n = 0; n < VecSize; n++){
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curr_cluster[k][n] = curr_cluster[k][n]/(float)counter[k];
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}
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}
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error = 0.0;
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for (k = 0; k < numClusters; k++){
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for (n = 0; n < VecSize; n++){
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error = error + (curr_cluster[k][n] - prev_cluster[k][n])*(curr_cluster[k][n] - prev_cluster[k][n]);
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}
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}
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//error = error/(double)(numClusters*VecSize);
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//copy curr_clusters to prev_clusters
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for (k = 0; k < numClusters; k++){
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for (n =0; n < VecSize; n++){
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prev_cluster[k][n] = curr_cluster[k][n];
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}
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}
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}
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//deallocate memory for curr_cluster and prev_cluster
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for (k = 0; k < numClusters; k++){
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delete curr_cluster[k];
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delete prev_cluster[k];
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}
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delete curr_cluster;
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delete prev_cluster;
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delete counter;
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delete dist;
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return NumIter;
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}
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static int fmaKMeans(void)
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{
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CvTermCriteria crit;
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float** vectors;
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int* output;
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int* etalon_output;
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int lErrors = 0;
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int lNumVect = 0;
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int lVectSize = 0;
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int lNumClust = 0;
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int lMaxNumIter = 0;
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float flEpsilon = 0;
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int i,j;
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static int read_param = 0;
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/* Initialization global parameters */
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if( !read_param )
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{
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read_param = 1;
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/* Read test-parameters */
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trsiRead( &lNumVect, "1000", "Number of vectors" );
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trsiRead( &lVectSize, "10", "Number of vectors" );
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trsiRead( &lNumClust, "20", "Number of clusters" );
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trsiRead( &lMaxNumIter,"100","Maximal number of iterations");
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trssRead( &flEpsilon, "0.5", "Accuracy" );
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}
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crit = cvTermCriteria( CV_TERMCRIT_EPS|CV_TERMCRIT_ITER, lMaxNumIter, flEpsilon );
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//allocate vectors
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vectors = (float**)cvAlloc( lNumVect * sizeof(float*) );
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for( i = 0; i < lNumVect; i++ )
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{
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vectors[i] = (float*)cvAlloc( lVectSize * sizeof( float ) );
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}
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output = (int*)cvAlloc( lNumVect * sizeof(int) );
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etalon_output = (int*)cvAlloc( lNumVect * sizeof(int) );
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//fill input vectors
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for( i = 0; i < lNumVect; i++ )
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{
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ats1flInitRandom( -2000, 2000, vectors[i], lVectSize );
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}
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/* run etalon kmeans */
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/* actually it is the simpliest realization of kmeans */
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int ni = _real_kmeans( lNumClust, vectors, lNumVect, lVectSize, etalon_output, crit.epsilon, crit.max_iter );
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trsWrite( ATS_CON, "%d iterations done\n", ni );
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/* Run OpenCV function */
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#define _KMEANS_TIME 0
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#if _KMEANS_TIME
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//timing section
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trsTimerStart(0);
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__int64 tics = atsGetTickCount();
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#endif
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cvKMeans( lNumClust, vectors, lNumVect, lVectSize,
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crit, output );
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#if _KMEANS_TIME
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tics = atsGetTickCount() - tics;
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trsTimerStop(0);
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//output result
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//double dbUsecs =ATS_TICS_TO_USECS((double)tics);
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trsWrite( ATS_CON, "Tics per iteration %d\n", tics/ni );
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#endif
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//compare results
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for( j = 0; j < lNumVect; j++ )
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{
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if ( output[j] != etalon_output[j] )
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{
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lErrors++;
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}
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}
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//free memory
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for( i = 0; i < lNumVect; i++ )
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{
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cvFree( &(vectors[i]) );
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}
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cvFree(&vectors);
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cvFree(&output);
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cvFree(&etalon_output);
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if( lErrors == 0 ) return trsResult( TRS_OK, "No errors fixed for this text" );
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else return trsResult( TRS_FAIL, "Detected %d errors", lErrors );
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}
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void InitAKMeans()
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{
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/* Register test function */
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trsReg( func_name[0], test_desc, atsAlgoClass, fmaKMeans );
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} /* InitAKMeans */
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#endif
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