gistfile1.txt

#define GENERATE_TESTDATA
TEST(ML_ANN, Method)
{
    String folder = string(cvtest::TS::ptr()->get_data_path());
    String original_path = folder + "waveform.data";
    String dataname = folder + "waveform";

    Ptr<TrainData> tdata2 = TrainData::loadFromCSV(original_path, 0);
    Mat responses(tdata2->getResponses().rows, 3, CV_32FC1, Scalar(0));
    for (int i = 0; i<tdata2->getResponses().rows; i++)
        responses.at<float>(i, static_cast<int>(tdata2->getResponses().at<float>(i, 0))) = 1;
    Ptr<TrainData> tdata = TrainData::create(tdata2->getSamples(), ml::ROW_SAMPLE, responses);

    ASSERT_FALSE(tdata.empty()) << "Could not find test data file : " << original_path;
    RNG& rng = theRNG();
    rng.state = 1027401484159173092;
    tdata->setTrainTestSplitRatio(0.8);

    vector<int> methodType;
    methodType.push_back(ml::ANN_MLP::BACKPROP);
    methodType.push_back(ml::ANN_MLP::RPROP);
    methodType.push_back(ml::ANN_MLP::ANNEAL);
    vector<String> methodName;
    methodName.push_back("_backprop");
    methodName.push_back("_rprop");
    methodName.push_back("_anneal");
#ifdef GENERATE_TESTDATA
    rng.state = 1027401484159173092;
    Ptr<ml::ANN_MLP> xx = ml::ANN_MLP::create();
    Mat_<int> layerSizesXX(1, 3);
    layerSizesXX(0, 0) = tdata->getNVars();
    layerSizesXX(0, 1) = 30;
    layerSizesXX(0, 2) = tdata->getResponses().cols;
    xx->setLayerSizes(layerSizesXX);
    xx->setActivationFunction(ml::ANN_MLP::SIGMOID_SYM);
    xx->setTrainMethod(ml::ANN_MLP::RPROP);
    xx->setTermCriteria(TermCriteria(TermCriteria::COUNT, 1, 0.01));
    xx->train(tdata, ml::ANN_MLP::NO_OUTPUT_SCALE);
    xx->save(dataname + "_init_weight.yml");
#endif
cout<<" BEGIN\n";
    for (size_t i = 0; i < methodType.size(); i++)
    {
        rng.state = 1027401484159173092;

        Ptr<ml::ANN_MLP> x = Algorithm::load<ANN_MLP>(dataname + "_init_weight.yml");
        //        Ptr<ml::ANN_MLP> x = ml::ANN_MLP::create();
        ASSERT_TRUE(x!=NULL) << "Could not load init weigths\n  " ;
        x->setTrainMethod(methodType[i]);
        x->setTermCriteria(TermCriteria(TermCriteria::COUNT, 1, 0.01)); // ONLY ONE ITERATION
for (int jj=0;jj<10;jj++)
{        
x->train(tdata, ml::ANN_MLP::NO_OUTPUT_SCALE + ml::ANN_MLP::UPDATE_WEIGHTS);
x->save(format("%s%s%d.yml",dataname.c_str() , methodName[i].c_str(),jj ));// SAVE RESULTS AT ITERATION #jj

}
        ASSERT_TRUE(x->isTrained()) << "Could not train networks with  " << methodName[i];
#ifdef GENERATE_TESTDATA
        x->save(dataname + methodName[i] + ".yml");
#else
        Ptr<ml::ANN_MLP> y = Algorithm::load<ANN_MLP>(dataname + methodName[i] + ".yml");
        ASSERT_TRUE(y != NULL) << "Could not load   " << dataname + methodName[i] + ".yml";
        Mat testSamples = tdata->getTestSamples();
        Mat rx, ry, dst;
        for (int j = 0; j < 4; j++)
        {
            rx = x->getWeights(j);
            ry = y->getWeights(j);
            double n = cvtest::norm(rx, ry, NORM_INF);
            EXPECT_LT(n, FLT_EPSILON) << "Weights are not equal for " << dataname + methodName[i] + ".yml and " << methodName[i] << " layer : " << j;
    }
        x->predict(testSamples, rx);
        y->predict(testSamples, ry);
        double n = cvtest::norm(rx, ry, NORM_INF);
        EXPECT_LT(n, FLT_EPSILON) << "Predict are not equal for " << dataname + methodName[i] + ".yml and " << methodName[i];
#endif
}
}