mirror of
https://github.com/opencv/opencv.git
synced 2024-11-24 03:00:14 +08:00
82 lines
2.8 KiB
C++
82 lines
2.8 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.
|
|
//
|
|
// AUTHOR: Rahul Kavi rahulkavi[at]live[at]com
|
|
|
|
//
|
|
// Test data uses subset of data from the popular Iris Dataset (1936):
|
|
// - http://archive.ics.uci.edu/ml/datasets/Iris
|
|
// - https://en.wikipedia.org/wiki/Iris_flower_data_set
|
|
//
|
|
|
|
#include "test_precomp.hpp"
|
|
|
|
namespace opencv_test { namespace {
|
|
|
|
TEST(ML_LR, accuracy)
|
|
{
|
|
std::string dataFileName = findDataFile("iris.data");
|
|
Ptr<TrainData> tdata = TrainData::loadFromCSV(dataFileName, 0);
|
|
ASSERT_FALSE(tdata.empty());
|
|
|
|
Ptr<LogisticRegression> p = LogisticRegression::create();
|
|
p->setLearningRate(1.0);
|
|
p->setIterations(10001);
|
|
p->setRegularization(LogisticRegression::REG_L2);
|
|
p->setTrainMethod(LogisticRegression::BATCH);
|
|
p->setMiniBatchSize(10);
|
|
p->train(tdata);
|
|
|
|
Mat responses;
|
|
p->predict(tdata->getSamples(), responses);
|
|
|
|
float error = 1000;
|
|
EXPECT_TRUE(calculateError(responses, tdata->getResponses(), error));
|
|
EXPECT_LE(error, 0.05f);
|
|
}
|
|
|
|
//==================================================================================================
|
|
|
|
TEST(ML_LR, save_load)
|
|
{
|
|
string dataFileName = findDataFile("iris.data");
|
|
Ptr<TrainData> tdata = TrainData::loadFromCSV(dataFileName, 0);
|
|
ASSERT_FALSE(tdata.empty());
|
|
Mat responses1, responses2;
|
|
Mat learnt_mat1, learnt_mat2;
|
|
String filename = tempfile(".xml");
|
|
{
|
|
Ptr<LogisticRegression> lr1 = LogisticRegression::create();
|
|
lr1->setLearningRate(1.0);
|
|
lr1->setIterations(10001);
|
|
lr1->setRegularization(LogisticRegression::REG_L2);
|
|
lr1->setTrainMethod(LogisticRegression::BATCH);
|
|
lr1->setMiniBatchSize(10);
|
|
ASSERT_NO_THROW(lr1->train(tdata));
|
|
ASSERT_NO_THROW(lr1->predict(tdata->getSamples(), responses1));
|
|
ASSERT_NO_THROW(lr1->save(filename));
|
|
learnt_mat1 = lr1->get_learnt_thetas();
|
|
}
|
|
{
|
|
Ptr<LogisticRegression> lr2;
|
|
ASSERT_NO_THROW(lr2 = Algorithm::load<LogisticRegression>(filename));
|
|
ASSERT_NO_THROW(lr2->predict(tdata->getSamples(), responses2));
|
|
learnt_mat2 = lr2->get_learnt_thetas();
|
|
}
|
|
// compare difference in prediction outputs and stored inputs
|
|
EXPECT_MAT_NEAR(responses1, responses2, 0.f);
|
|
|
|
Mat comp_learnt_mats;
|
|
comp_learnt_mats = (learnt_mat1 == learnt_mat2);
|
|
comp_learnt_mats = comp_learnt_mats.reshape(1, comp_learnt_mats.rows*comp_learnt_mats.cols);
|
|
comp_learnt_mats.convertTo(comp_learnt_mats, CV_32S);
|
|
comp_learnt_mats = comp_learnt_mats/255;
|
|
// check if there is any difference between computed learnt mat and retrieved mat
|
|
EXPECT_EQ(comp_learnt_mats.rows, sum(comp_learnt_mats)[0]);
|
|
|
|
remove( filename.c_str() );
|
|
}
|
|
|
|
}} // namespace
|