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