mirror of
https://github.com/opencv/opencv.git
synced 2024-12-05 09:49:12 +08:00
78 lines
2.5 KiB
C++
78 lines
2.5 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 { namespace {
|
||
|
|
||
|
using cv::ml::TrainData;
|
||
|
using cv::ml::EM;
|
||
|
using cv::ml::KNearest;
|
||
|
|
||
|
TEST(ML_KNearest, accuracy)
|
||
|
{
|
||
|
int sizesArr[] = { 500, 700, 800 };
|
||
|
int pointsCount = sizesArr[0]+ sizesArr[1] + sizesArr[2];
|
||
|
|
||
|
Mat trainData( pointsCount, 2, CV_32FC1 ), trainLabels;
|
||
|
vector<int> sizes( sizesArr, sizesArr + sizeof(sizesArr) / sizeof(sizesArr[0]) );
|
||
|
Mat means;
|
||
|
vector<Mat> covs;
|
||
|
defaultDistribs( means, covs );
|
||
|
generateData( trainData, trainLabels, sizes, means, covs, CV_32FC1, CV_32FC1 );
|
||
|
|
||
|
Mat testData( pointsCount, 2, CV_32FC1 );
|
||
|
Mat testLabels;
|
||
|
generateData( testData, testLabels, sizes, means, covs, CV_32FC1, CV_32FC1 );
|
||
|
|
||
|
{
|
||
|
SCOPED_TRACE("Default");
|
||
|
Mat bestLabels;
|
||
|
float err = 1000;
|
||
|
Ptr<KNearest> knn = KNearest::create();
|
||
|
knn->train(trainData, ml::ROW_SAMPLE, trainLabels);
|
||
|
knn->findNearest(testData, 4, bestLabels);
|
||
|
EXPECT_TRUE(calcErr( bestLabels, testLabels, sizes, err, true ));
|
||
|
EXPECT_LE(err, 0.01f);
|
||
|
}
|
||
|
{
|
||
|
// TODO: broken
|
||
|
#if 0
|
||
|
SCOPED_TRACE("KDTree");
|
||
|
Mat bestLabels;
|
||
|
float err = 1000;
|
||
|
Ptr<KNearest> knn = KNearest::create();
|
||
|
knn->setAlgorithmType(KNearest::KDTREE);
|
||
|
knn->train(trainData, ml::ROW_SAMPLE, trainLabels);
|
||
|
knn->findNearest(testData, 4, bestLabels);
|
||
|
EXPECT_TRUE(calcErr( bestLabels, testLabels, sizes, err, true ));
|
||
|
EXPECT_LE(err, 0.01f);
|
||
|
#endif
|
||
|
}
|
||
|
}
|
||
|
|
||
|
TEST(ML_KNearest, regression_12347)
|
||
|
{
|
||
|
Mat xTrainData = (Mat_<float>(5,2) << 1, 1.1, 1.1, 1, 2, 2, 2.1, 2, 2.1, 2.1);
|
||
|
Mat yTrainLabels = (Mat_<float>(5,1) << 1, 1, 2, 2, 2);
|
||
|
Ptr<KNearest> knn = KNearest::create();
|
||
|
knn->train(xTrainData, ml::ROW_SAMPLE, yTrainLabels);
|
||
|
|
||
|
Mat xTestData = (Mat_<float>(2,2) << 1.1, 1.1, 2, 2.2);
|
||
|
Mat zBestLabels, neighbours, dist;
|
||
|
// check output shapes:
|
||
|
int K = 16, Kexp = std::min(K, xTrainData.rows);
|
||
|
knn->findNearest(xTestData, K, zBestLabels, neighbours, dist);
|
||
|
EXPECT_EQ(xTestData.rows, zBestLabels.rows);
|
||
|
EXPECT_EQ(neighbours.cols, Kexp);
|
||
|
EXPECT_EQ(dist.cols, Kexp);
|
||
|
// see if the result is still correct:
|
||
|
K = 2;
|
||
|
knn->findNearest(xTestData, K, zBestLabels, neighbours, dist);
|
||
|
EXPECT_EQ(1, zBestLabels.at<float>(0,0));
|
||
|
EXPECT_EQ(2, zBestLabels.at<float>(1,0));
|
||
|
}
|
||
|
|
||
|
}} // namespace
|