mirror of
https://github.com/opencv/opencv.git
synced 2024-12-13 16:09:23 +08:00
5ff1fababc
ml: refactored tests * use parametrized tests where appropriate * use stable theRNG in most tests * use modern style with EXPECT_/ASSERT_ checks
190 lines
6.0 KiB
C++
190 lines
6.0 KiB
C++
// This file is part of OpenCV project.
|
|
// It is subject to the license terms in the LICENSE file found in the top-level directory
|
|
// of this distribution and at http://opencv.org/license.html.
|
|
#include "test_precomp.hpp"
|
|
|
|
namespace opencv_test {
|
|
|
|
void defaultDistribs( Mat& means, vector<Mat>& covs, int type)
|
|
{
|
|
float mp0[] = {0.0f, 0.0f}, cp0[] = {0.67f, 0.0f, 0.0f, 0.67f};
|
|
float mp1[] = {5.0f, 0.0f}, cp1[] = {1.0f, 0.0f, 0.0f, 1.0f};
|
|
float mp2[] = {1.0f, 5.0f}, cp2[] = {1.0f, 0.0f, 0.0f, 1.0f};
|
|
means.create(3, 2, type);
|
|
Mat m0( 1, 2, CV_32FC1, mp0 ), c0( 2, 2, CV_32FC1, cp0 );
|
|
Mat m1( 1, 2, CV_32FC1, mp1 ), c1( 2, 2, CV_32FC1, cp1 );
|
|
Mat m2( 1, 2, CV_32FC1, mp2 ), c2( 2, 2, CV_32FC1, cp2 );
|
|
means.resize(3), covs.resize(3);
|
|
|
|
Mat mr0 = means.row(0);
|
|
m0.convertTo(mr0, type);
|
|
c0.convertTo(covs[0], type);
|
|
|
|
Mat mr1 = means.row(1);
|
|
m1.convertTo(mr1, type);
|
|
c1.convertTo(covs[1], type);
|
|
|
|
Mat mr2 = means.row(2);
|
|
m2.convertTo(mr2, type);
|
|
c2.convertTo(covs[2], type);
|
|
}
|
|
|
|
// generate points sets by normal distributions
|
|
void generateData( Mat& data, Mat& labels, const vector<int>& sizes, const Mat& _means, const vector<Mat>& covs, int dataType, int labelType )
|
|
{
|
|
vector<int>::const_iterator sit = sizes.begin();
|
|
int total = 0;
|
|
for( ; sit != sizes.end(); ++sit )
|
|
total += *sit;
|
|
CV_Assert( _means.rows == (int)sizes.size() && covs.size() == sizes.size() );
|
|
CV_Assert( !data.empty() && data.rows == total );
|
|
CV_Assert( data.type() == dataType );
|
|
|
|
labels.create( data.rows, 1, labelType );
|
|
|
|
randn( data, Scalar::all(-1.0), Scalar::all(1.0) );
|
|
vector<Mat> means(sizes.size());
|
|
for(int i = 0; i < _means.rows; i++)
|
|
means[i] = _means.row(i);
|
|
vector<Mat>::const_iterator mit = means.begin(), cit = covs.begin();
|
|
int bi, ei = 0;
|
|
sit = sizes.begin();
|
|
for( int p = 0, l = 0; sit != sizes.end(); ++sit, ++mit, ++cit, l++ )
|
|
{
|
|
bi = ei;
|
|
ei = bi + *sit;
|
|
CV_Assert( mit->rows == 1 && mit->cols == data.cols );
|
|
CV_Assert( cit->rows == data.cols && cit->cols == data.cols );
|
|
for( int i = bi; i < ei; i++, p++ )
|
|
{
|
|
Mat r = data.row(i);
|
|
r = r * (*cit) + *mit;
|
|
if( labelType == CV_32FC1 )
|
|
labels.at<float>(p, 0) = (float)l;
|
|
else if( labelType == CV_32SC1 )
|
|
labels.at<int>(p, 0) = l;
|
|
else
|
|
{
|
|
CV_DbgAssert(0);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
int maxIdx( const vector<int>& count )
|
|
{
|
|
int idx = -1;
|
|
int maxVal = -1;
|
|
vector<int>::const_iterator it = count.begin();
|
|
for( int i = 0; it != count.end(); ++it, i++ )
|
|
{
|
|
if( *it > maxVal)
|
|
{
|
|
maxVal = *it;
|
|
idx = i;
|
|
}
|
|
}
|
|
CV_Assert( idx >= 0);
|
|
return idx;
|
|
}
|
|
|
|
bool getLabelsMap( const Mat& labels, const vector<int>& sizes, vector<int>& labelsMap, bool checkClusterUniq)
|
|
{
|
|
size_t total = 0, nclusters = sizes.size();
|
|
for(size_t i = 0; i < sizes.size(); i++)
|
|
total += sizes[i];
|
|
|
|
CV_Assert( !labels.empty() );
|
|
CV_Assert( labels.total() == total && (labels.cols == 1 || labels.rows == 1));
|
|
CV_Assert( labels.type() == CV_32SC1 || labels.type() == CV_32FC1 );
|
|
|
|
bool isFlt = labels.type() == CV_32FC1;
|
|
|
|
labelsMap.resize(nclusters);
|
|
|
|
vector<bool> buzy(nclusters, false);
|
|
int startIndex = 0;
|
|
for( size_t clusterIndex = 0; clusterIndex < sizes.size(); clusterIndex++ )
|
|
{
|
|
vector<int> count( nclusters, 0 );
|
|
for( int i = startIndex; i < startIndex + sizes[clusterIndex]; i++)
|
|
{
|
|
int lbl = isFlt ? (int)labels.at<float>(i) : labels.at<int>(i);
|
|
CV_Assert(lbl < (int)nclusters);
|
|
count[lbl]++;
|
|
CV_Assert(count[lbl] < (int)total);
|
|
}
|
|
startIndex += sizes[clusterIndex];
|
|
|
|
int cls = maxIdx( count );
|
|
CV_Assert( !checkClusterUniq || !buzy[cls] );
|
|
|
|
labelsMap[clusterIndex] = cls;
|
|
|
|
buzy[cls] = true;
|
|
}
|
|
|
|
if(checkClusterUniq)
|
|
{
|
|
for(size_t i = 0; i < buzy.size(); i++)
|
|
if(!buzy[i])
|
|
return false;
|
|
}
|
|
|
|
return true;
|
|
}
|
|
|
|
bool calcErr( const Mat& labels, const Mat& origLabels, const vector<int>& sizes, float& err, bool labelsEquivalent, bool checkClusterUniq)
|
|
{
|
|
err = 0;
|
|
CV_Assert( !labels.empty() && !origLabels.empty() );
|
|
CV_Assert( labels.rows == 1 || labels.cols == 1 );
|
|
CV_Assert( origLabels.rows == 1 || origLabels.cols == 1 );
|
|
CV_Assert( labels.total() == origLabels.total() );
|
|
CV_Assert( labels.type() == CV_32SC1 || labels.type() == CV_32FC1 );
|
|
CV_Assert( origLabels.type() == labels.type() );
|
|
|
|
vector<int> labelsMap;
|
|
bool isFlt = labels.type() == CV_32FC1;
|
|
if( !labelsEquivalent )
|
|
{
|
|
if( !getLabelsMap( labels, sizes, labelsMap, checkClusterUniq ) )
|
|
return false;
|
|
|
|
for( int i = 0; i < labels.rows; i++ )
|
|
if( isFlt )
|
|
err += labels.at<float>(i) != labelsMap[(int)origLabels.at<float>(i)] ? 1.f : 0.f;
|
|
else
|
|
err += labels.at<int>(i) != labelsMap[origLabels.at<int>(i)] ? 1.f : 0.f;
|
|
}
|
|
else
|
|
{
|
|
for( int i = 0; i < labels.rows; i++ )
|
|
if( isFlt )
|
|
err += labels.at<float>(i) != origLabels.at<float>(i) ? 1.f : 0.f;
|
|
else
|
|
err += labels.at<int>(i) != origLabels.at<int>(i) ? 1.f : 0.f;
|
|
}
|
|
err /= (float)labels.rows;
|
|
return true;
|
|
}
|
|
|
|
bool calculateError( const Mat& _p_labels, const Mat& _o_labels, float& error)
|
|
{
|
|
error = 0.0f;
|
|
float accuracy = 0.0f;
|
|
Mat _p_labels_temp;
|
|
Mat _o_labels_temp;
|
|
_p_labels.convertTo(_p_labels_temp, CV_32S);
|
|
_o_labels.convertTo(_o_labels_temp, CV_32S);
|
|
|
|
CV_Assert(_p_labels_temp.total() == _o_labels_temp.total());
|
|
CV_Assert(_p_labels_temp.rows == _o_labels_temp.rows);
|
|
|
|
accuracy = (float)countNonZero(_p_labels_temp == _o_labels_temp)/_p_labels_temp.rows;
|
|
error = 1 - accuracy;
|
|
return true;
|
|
}
|
|
|
|
} // namespace
|