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updated test for logistic regression

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Rahul Kavi 2013-11-05 05:35:21 -05:00 committed by Maksim Shabunin
parent b3b4e83aed
commit a9df50eefc
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modules/ml/test/test_lr.cpp
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@ -94,35 +94,43 @@ void CV_LRTest::run( int /*start_from*/ )
// initialize varibles from the popular Iris Dataset
Mat data = (Mat_<double>(150, 4)<<
5.1,3.5,1.4,0.2, 4.9,3.0,1.4,0.2, 4.7,3.2,1.3,0.2, 4.6,3.1,1.5,0.2,
5.0,3.6,1.4,0.2, 5.4,3.9,1.7,0.4, 4.6,3.4,1.4,0.3, 5.0,3.4,1.5,0.2, 4.4,2.9,1.4,0.2, 4.9,3.1,1.5,0.1,
5.4,3.7,1.5,0.2, 4.8,3.4,1.6,0.2, 4.8,3.0,1.4,0.1, 4.3,3.0,1.1,0.1, 5.8,4.0,1.2,0.2, 5.7,4.4,1.5,0.4,
5.4,3.9,1.3,0.4, 5.1,3.5,1.4,0.3, 5.7,3.8,1.7,0.3, 5.1,3.8,1.5,0.3, 5.4,3.4,1.7,0.2, 5.1,3.7,1.5,0.4,
4.6,3.6,1.0,0.2, 5.1,3.3,1.7,0.5, 4.8,3.4,1.9,0.2, 5.0,3.0,1.6,0.2, 5.0,3.4,1.6,0.4,
5.2,3.5,1.5,0.2, 5.2,3.4,1.4,0.2, 4.7,3.2,1.6,0.2, 4.8,3.1,1.6,0.2, 5.4,3.4,1.5,0.4,
5.2,4.1,1.5,0.1, 5.5,4.2,1.4,0.2, 4.9,3.1,1.5,0.1, 5.0,3.2,1.2,0.2, 5.5,3.5,1.3,0.2,
4.9,3.1,1.5,0.1, 4.4,3.0,1.3,0.2, 5.1,3.4,1.5,0.2, 5.0,3.5,1.3,0.3, 4.5,2.3,1.3,0.3,
4.4,3.2,1.3,0.2, 5.0,3.5,1.6,0.6, 5.1,3.8,1.9,0.4, 4.8,3.0,1.4,0.3, 5.1,3.8,1.6,0.2,
4.6,3.2,1.4,0.2, 5.3,3.7,1.5,0.2, 5.0,3.3,1.4,0.2, 7.0,3.2,4.7,1.4, 6.4,3.2,4.5,1.5,
6.9,3.1,4.9,1.5, 5.5,2.3,4.0,1.3, 6.5,2.8,4.6,1.5, 5.7,2.8,4.5,1.3, 6.3,3.3,4.7,1.6,
4.9,2.4,3.3,1.0, 6.6,2.9,4.6,1.3, 5.2,2.7,3.9,1.4, 5.0,2.0,3.5,1.0, 5.9,3.0,4.2,1.5,
6.0,2.2,4.0,1.0, 6.1,2.9,4.7,1.4, 5.6,2.9,3.6,1.3, 6.7,3.1,4.4,1.4, 5.6,3.0,4.5,1.5,
5.8,2.7,4.1,1.0, 6.2,2.2,4.5,1.5, 5.6,2.5,3.9,1.1, 5.9,3.2,4.8,1.8, 6.1,2.8,4.0,1.3,
6.3,2.5,4.9,1.5, 6.1,2.8,4.7,1.2, 6.4,2.9,4.3,1.3, 6.6,3.0,4.4,1.4, 6.8,2.8,4.8,1.4,
6.7,3.0,5.0,1.7, 6.0,2.9,4.5,1.5, 5.7,2.6,3.5,1.0, 5.5,2.4,3.8,1.1, 5.5,2.4,3.7,1.0,
5.8,2.7,3.9,1.2, 6.0,2.7,5.1,1.6, 5.4,3.0,4.5,1.5, 6.0,3.4,4.5,1.6, 6.7,3.1,4.7,1.5,
6.3,2.3,4.4,1.3, 5.6,3.0,4.1,1.3, 5.5,2.5,4.0,1.3, 5.5,2.6,4.4,1.2, 6.1,3.0,4.6,1.4,
5.8,2.6,4.0,1.2, 5.0,2.3,3.3,1.0, 5.6,2.7,4.2,1.3, 5.7,3.0,4.2,1.2, 5.7,2.9,4.2,1.3,
6.2,2.9,4.3,1.3, 5.1,2.5,3.0,1.1, 5.7,2.8,4.1,1.3, 6.3,3.3,6.0,2.5, 5.8,2.7,5.1,1.9,
7.1,3.0,5.9,2.1, 6.3,2.9,5.6,1.8, 6.5,3.0,5.8,2.2, 7.6,3.0,6.6,2.1, 4.9,2.5,4.5,1.7,
7.3,2.9,6.3,1.8, 6.7,2.5,5.8,1.8, 7.2,3.6,6.1,2.5, 6.5,3.2,5.1,2.0, 6.4,2.7,5.3,1.9,
6.8,3.0,5.5,2.1, 5.7,2.5,5.0,2.0, 5.8,2.8,5.1,2.4, 6.4,3.2,5.3,2.3, 6.5,3.0,5.5,1.8,
7.7,3.8,6.7,2.2, 7.7,2.6,6.9,2.3, 6.0,2.2,5.0,1.5, 6.9,3.2,5.7,2.3, 5.6,2.8,4.9,2.0,
7.7,2.8,6.7,2.0, 6.3,2.7,4.9,1.8, 6.7,3.3,5.7,2.1, 7.2,3.2,6.0,1.8, 6.2,2.8,4.8,1.8,
6.1,3.0,4.9,1.8, 6.4,2.8,5.6,2.1, 7.2,3.0,5.8,1.6, 7.4,2.8,6.1,1.9, 7.9,3.8,6.4,2.0,
6.4,2.8,5.6,2.2, 6.3,2.8,5.1,1.5, 6.1,2.6,5.6,1.4, 7.7,3.0,6.1,2.3, 6.3,3.4,5.6,2.4,
6.4,3.1,5.5,1.8, 6.0,3.0,4.8,1.8, 6.9,3.1,5.4,2.1, 6.7,3.1,5.6,2.4, 6.9,3.1,5.1,2.3,
5.8,2.7,5.1,1.9, 6.8,3.2,5.9,2.3, 6.7,3.3,5.7,2.5, 6.7,3.0,5.2,2.3, 6.3,2.5,5.0,1.9,
6.5,3.0,5.2,2.0, 6.2,3.4,5.4,2.3, 5.9,3.0,5.1,1.8);
5.0,3.6,1.4,0.2, 5.4,3.9,1.7,0.4, 4.6,3.4,1.4,0.3, 5.0,3.4,1.5,0.2,
4.4,2.9,1.4,0.2, 4.9,3.1,1.5,0.1, 5.4,3.7,1.5,0.2, 4.8,3.4,1.6,0.2,
4.8,3.0,1.4,0.1, 4.3,3.0,1.1,0.1, 5.8,4.0,1.2,0.2, 5.7,4.4,1.5,0.4,
5.4,3.9,1.3,0.4, 5.1,3.5,1.4,0.3, 5.7,3.8,1.7,0.3, 5.1,3.8,1.5,0.3,
5.4,3.4,1.7,0.2, 5.1,3.7,1.5,0.4, 4.6,3.6,1.0,0.2, 5.1,3.3,1.7,0.5,
4.8,3.4,1.9,0.2, 5.0,3.0,1.6,0.2, 5.0,3.4,1.6,0.4, 5.2,3.5,1.5,0.2,
5.2,3.4,1.4,0.2, 4.7,3.2,1.6,0.2, 4.8,3.1,1.6,0.2, 5.4,3.4,1.5,0.4,
5.2,4.1,1.5,0.1, 5.5,4.2,1.4,0.2, 4.9,3.1,1.5,0.1, 5.0,3.2,1.2,0.2,
5.5,3.5,1.3,0.2, 4.9,3.1,1.5,0.1, 4.4,3.0,1.3,0.2, 5.1,3.4,1.5,0.2,
5.0,3.5,1.3,0.3, 4.5,2.3,1.3,0.3, 4.4,3.2,1.3,0.2, 5.0,3.5,1.6,0.6,
5.1,3.8,1.9,0.4, 4.8,3.0,1.4,0.3, 5.1,3.8,1.6,0.2, 4.6,3.2,1.4,0.2,
5.3,3.7,1.5,0.2, 5.0,3.3,1.4,0.2, 7.0,3.2,4.7,1.4, 6.4,3.2,4.5,1.5,
6.9,3.1,4.9,1.5, 5.5,2.3,4.0,1.3, 6.5,2.8,4.6,1.5, 5.7,2.8,4.5,1.3,
6.3,3.3,4.7,1.6, 4.9,2.4,3.3,1.0, 6.6,2.9,4.6,1.3, 5.2,2.7,3.9,1.4,
5.0,2.0,3.5,1.0, 5.9,3.0,4.2,1.5, 6.0,2.2,4.0,1.0, 6.1,2.9,4.7,1.4,
5.6,2.9,3.6,1.3, 6.7,3.1,4.4,1.4, 5.6,3.0,4.5,1.5, 5.8,2.7,4.1,1.0,
6.2,2.2,4.5,1.5, 5.6,2.5,3.9,1.1, 5.9,3.2,4.8,1.8, 6.1,2.8,4.0,1.3,
6.3,2.5,4.9,1.5, 6.1,2.8,4.7,1.2, 6.4,2.9,4.3,1.3, 6.6,3.0,4.4,1.4,
6.8,2.8,4.8,1.4, 6.7,3.0,5.0,1.7, 6.0,2.9,4.5,1.5, 5.7,2.6,3.5,1.0,
5.5,2.4,3.8,1.1, 5.5,2.4,3.7,1.0, 5.8,2.7,3.9,1.2, 6.0,2.7,5.1,1.6,
5.4,3.0,4.5,1.5, 6.0,3.4,4.5,1.6, 6.7,3.1,4.7,1.5, 6.3,2.3,4.4,1.3,
5.6,3.0,4.1,1.3, 5.5,2.5,4.0,1.3, 5.5,2.6,4.4,1.2, 6.1,3.0,4.6,1.4,
5.8,2.6,4.0,1.2, 5.0,2.3,3.3,1.0, 5.6,2.7,4.2,1.3, 5.7,3.0,4.2,1.2,
5.7,2.9,4.2,1.3, 6.2,2.9,4.3,1.3, 5.1,2.5,3.0,1.1, 5.7,2.8,4.1,1.3,
6.3,3.3,6.0,2.5, 5.8,2.7,5.1,1.9, 7.1,3.0,5.9,2.1, 6.3,2.9,5.6,1.8,
6.5,3.0,5.8,2.2, 7.6,3.0,6.6,2.1, 4.9,2.5,4.5,1.7, 7.3,2.9,6.3,1.8,
6.7,2.5,5.8,1.8, 7.2,3.6,6.1,2.5, 6.5,3.2,5.1,2.0, 6.4,2.7,5.3,1.9,
6.8,3.0,5.5,2.1, 5.7,2.5,5.0,2.0, 5.8,2.8,5.1,2.4, 6.4,3.2,5.3,2.3,
6.5,3.0,5.5,1.8, 7.7,3.8,6.7,2.2, 7.7,2.6,6.9,2.3, 6.0,2.2,5.0,1.5,
6.9,3.2,5.7,2.3, 5.6,2.8,4.9,2.0, 7.7,2.8,6.7,2.0, 6.3,2.7,4.9,1.8,
6.7,3.3,5.7,2.1, 7.2,3.2,6.0,1.8, 6.2,2.8,4.8,1.8, 6.1,3.0,4.9,1.8,
6.4,2.8,5.6,2.1, 7.2,3.0,5.8,1.6, 7.4,2.8,6.1,1.9, 7.9,3.8,6.4,2.0,
6.4,2.8,5.6,2.2, 6.3,2.8,5.1,1.5, 6.1,2.6,5.6,1.4, 7.7,3.0,6.1,2.3,
6.3,3.4,5.6,2.4, 6.4,3.1,5.5,1.8, 6.0,3.0,4.8,1.8, 6.9,3.1,5.4,2.1,
6.7,3.1,5.6,2.4, 6.9,3.1,5.1,2.3, 5.8,2.7,5.1,1.9, 6.8,3.2,5.9,2.3,
6.7,3.3,5.7,2.5, 6.7,3.0,5.2,2.3, 6.3,2.5,5.0,1.9, 6.5,3.0,5.2,2.0,
6.2,3.4,5.4,2.3, 5.9,3.0,5.1,1.8);
Mat labels = (Mat_<int>(150, 1)<< 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
@ -136,7 +144,6 @@ void CV_LRTest::run( int /*start_from*/ )
float error = 0.0f;
LogisticRegressionParams params1 = LogisticRegressionParams();
LogisticRegressionParams params2 = LogisticRegressionParams();
params1.alpha = 1.0;
params1.num_iters = 10001;
@ -167,31 +174,6 @@ void CV_LRTest::run( int /*start_from*/ )
test_code = cvtest::TS::FAIL_BAD_ACCURACY;
}
params2.alpha = 1.0;
params2.num_iters = 9000;
params2.norm = LogisticRegression::REG_L2;
params2.regularized = 1;
params2.train_method = LogisticRegression::MINI_BATCH;
params2.mini_batch_size = 10;
// now train using mini batch gradient descent
LogisticRegression lr2(data, labels, params2);
lr2.predict(data, responses2);
responses2.convertTo(responses2, CV_32S);
//calculate error
if(!calculateError(responses2, labels, error))
{
ts->printf(cvtest::TS::LOG, "Bad prediction labels\n" );
test_code = cvtest::TS::FAIL_INVALID_OUTPUT;
}
else if(error > 0.06f)
{
ts->printf(cvtest::TS::LOG, "Bad accuracy of (%f)\n", error);
test_code = cvtest::TS::FAIL_BAD_ACCURACY;
}
ts->set_failed_test_info(test_code);
}
@ -213,35 +195,43 @@ void CV_LRTest_SaveLoad::run( int /*start_from*/ )
// initialize varibles from the popular Iris Dataset
Mat data = (Mat_<double>(150, 4)<<
5.1,3.5,1.4,0.2, 4.9,3.0,1.4,0.2, 4.7,3.2,1.3,0.2, 4.6,3.1,1.5,0.2,
5.0,3.6,1.4,0.2, 5.4,3.9,1.7,0.4, 4.6,3.4,1.4,0.3, 5.0,3.4,1.5,0.2, 4.4,2.9,1.4,0.2, 4.9,3.1,1.5,0.1,
5.4,3.7,1.5,0.2, 4.8,3.4,1.6,0.2, 4.8,3.0,1.4,0.1, 4.3,3.0,1.1,0.1, 5.8,4.0,1.2,0.2, 5.7,4.4,1.5,0.4,
5.4,3.9,1.3,0.4, 5.1,3.5,1.4,0.3, 5.7,3.8,1.7,0.3, 5.1,3.8,1.5,0.3, 5.4,3.4,1.7,0.2, 5.1,3.7,1.5,0.4,
4.6,3.6,1.0,0.2, 5.1,3.3,1.7,0.5, 4.8,3.4,1.9,0.2, 5.0,3.0,1.6,0.2, 5.0,3.4,1.6,0.4,
5.2,3.5,1.5,0.2, 5.2,3.4,1.4,0.2, 4.7,3.2,1.6,0.2, 4.8,3.1,1.6,0.2, 5.4,3.4,1.5,0.4,
5.2,4.1,1.5,0.1, 5.5,4.2,1.4,0.2, 4.9,3.1,1.5,0.1, 5.0,3.2,1.2,0.2, 5.5,3.5,1.3,0.2,
4.9,3.1,1.5,0.1, 4.4,3.0,1.3,0.2, 5.1,3.4,1.5,0.2, 5.0,3.5,1.3,0.3, 4.5,2.3,1.3,0.3,
4.4,3.2,1.3,0.2, 5.0,3.5,1.6,0.6, 5.1,3.8,1.9,0.4, 4.8,3.0,1.4,0.3, 5.1,3.8,1.6,0.2,
4.6,3.2,1.4,0.2, 5.3,3.7,1.5,0.2, 5.0,3.3,1.4,0.2, 7.0,3.2,4.7,1.4, 6.4,3.2,4.5,1.5,
6.9,3.1,4.9,1.5, 5.5,2.3,4.0,1.3, 6.5,2.8,4.6,1.5, 5.7,2.8,4.5,1.3, 6.3,3.3,4.7,1.6,
4.9,2.4,3.3,1.0, 6.6,2.9,4.6,1.3, 5.2,2.7,3.9,1.4, 5.0,2.0,3.5,1.0, 5.9,3.0,4.2,1.5,
6.0,2.2,4.0,1.0, 6.1,2.9,4.7,1.4, 5.6,2.9,3.6,1.3, 6.7,3.1,4.4,1.4, 5.6,3.0,4.5,1.5,
5.8,2.7,4.1,1.0, 6.2,2.2,4.5,1.5, 5.6,2.5,3.9,1.1, 5.9,3.2,4.8,1.8, 6.1,2.8,4.0,1.3,
6.3,2.5,4.9,1.5, 6.1,2.8,4.7,1.2, 6.4,2.9,4.3,1.3, 6.6,3.0,4.4,1.4, 6.8,2.8,4.8,1.4,
6.7,3.0,5.0,1.7, 6.0,2.9,4.5,1.5, 5.7,2.6,3.5,1.0, 5.5,2.4,3.8,1.1, 5.5,2.4,3.7,1.0,
5.8,2.7,3.9,1.2, 6.0,2.7,5.1,1.6, 5.4,3.0,4.5,1.5, 6.0,3.4,4.5,1.6, 6.7,3.1,4.7,1.5,
6.3,2.3,4.4,1.3, 5.6,3.0,4.1,1.3, 5.5,2.5,4.0,1.3, 5.5,2.6,4.4,1.2, 6.1,3.0,4.6,1.4,
5.8,2.6,4.0,1.2, 5.0,2.3,3.3,1.0, 5.6,2.7,4.2,1.3, 5.7,3.0,4.2,1.2, 5.7,2.9,4.2,1.3,
6.2,2.9,4.3,1.3, 5.1,2.5,3.0,1.1, 5.7,2.8,4.1,1.3, 6.3,3.3,6.0,2.5, 5.8,2.7,5.1,1.9,
7.1,3.0,5.9,2.1, 6.3,2.9,5.6,1.8, 6.5,3.0,5.8,2.2, 7.6,3.0,6.6,2.1, 4.9,2.5,4.5,1.7,
7.3,2.9,6.3,1.8, 6.7,2.5,5.8,1.8, 7.2,3.6,6.1,2.5, 6.5,3.2,5.1,2.0, 6.4,2.7,5.3,1.9,
6.8,3.0,5.5,2.1, 5.7,2.5,5.0,2.0, 5.8,2.8,5.1,2.4, 6.4,3.2,5.3,2.3, 6.5,3.0,5.5,1.8,
7.7,3.8,6.7,2.2, 7.7,2.6,6.9,2.3, 6.0,2.2,5.0,1.5, 6.9,3.2,5.7,2.3, 5.6,2.8,4.9,2.0,
7.7,2.8,6.7,2.0, 6.3,2.7,4.9,1.8, 6.7,3.3,5.7,2.1, 7.2,3.2,6.0,1.8, 6.2,2.8,4.8,1.8,
6.1,3.0,4.9,1.8, 6.4,2.8,5.6,2.1, 7.2,3.0,5.8,1.6, 7.4,2.8,6.1,1.9, 7.9,3.8,6.4,2.0,
6.4,2.8,5.6,2.2, 6.3,2.8,5.1,1.5, 6.1,2.6,5.6,1.4, 7.7,3.0,6.1,2.3, 6.3,3.4,5.6,2.4,
6.4,3.1,5.5,1.8, 6.0,3.0,4.8,1.8, 6.9,3.1,5.4,2.1, 6.7,3.1,5.6,2.4, 6.9,3.1,5.1,2.3,
5.8,2.7,5.1,1.9, 6.8,3.2,5.9,2.3, 6.7,3.3,5.7,2.5, 6.7,3.0,5.2,2.3, 6.3,2.5,5.0,1.9,
6.5,3.0,5.2,2.0, 6.2,3.4,5.4,2.3, 5.9,3.0,5.1,1.8);
5.0,3.6,1.4,0.2, 5.4,3.9,1.7,0.4, 4.6,3.4,1.4,0.3, 5.0,3.4,1.5,0.2,
4.4,2.9,1.4,0.2, 4.9,3.1,1.5,0.1, 5.4,3.7,1.5,0.2, 4.8,3.4,1.6,0.2,
4.8,3.0,1.4,0.1, 4.3,3.0,1.1,0.1, 5.8,4.0,1.2,0.2, 5.7,4.4,1.5,0.4,
5.4,3.9,1.3,0.4, 5.1,3.5,1.4,0.3, 5.7,3.8,1.7,0.3, 5.1,3.8,1.5,0.3,
5.4,3.4,1.7,0.2, 5.1,3.7,1.5,0.4, 4.6,3.6,1.0,0.2, 5.1,3.3,1.7,0.5,
4.8,3.4,1.9,0.2, 5.0,3.0,1.6,0.2, 5.0,3.4,1.6,0.4, 5.2,3.5,1.5,0.2,
5.2,3.4,1.4,0.2, 4.7,3.2,1.6,0.2, 4.8,3.1,1.6,0.2, 5.4,3.4,1.5,0.4,
5.2,4.1,1.5,0.1, 5.5,4.2,1.4,0.2, 4.9,3.1,1.5,0.1, 5.0,3.2,1.2,0.2,
5.5,3.5,1.3,0.2, 4.9,3.1,1.5,0.1, 4.4,3.0,1.3,0.2, 5.1,3.4,1.5,0.2,
5.0,3.5,1.3,0.3, 4.5,2.3,1.3,0.3, 4.4,3.2,1.3,0.2, 5.0,3.5,1.6,0.6,
5.1,3.8,1.9,0.4, 4.8,3.0,1.4,0.3, 5.1,3.8,1.6,0.2, 4.6,3.2,1.4,0.2,
5.3,3.7,1.5,0.2, 5.0,3.3,1.4,0.2, 7.0,3.2,4.7,1.4, 6.4,3.2,4.5,1.5,
6.9,3.1,4.9,1.5, 5.5,2.3,4.0,1.3, 6.5,2.8,4.6,1.5, 5.7,2.8,4.5,1.3,
6.3,3.3,4.7,1.6, 4.9,2.4,3.3,1.0, 6.6,2.9,4.6,1.3, 5.2,2.7,3.9,1.4,
5.0,2.0,3.5,1.0, 5.9,3.0,4.2,1.5, 6.0,2.2,4.0,1.0, 6.1,2.9,4.7,1.4,
5.6,2.9,3.6,1.3, 6.7,3.1,4.4,1.4, 5.6,3.0,4.5,1.5, 5.8,2.7,4.1,1.0,
6.2,2.2,4.5,1.5, 5.6,2.5,3.9,1.1, 5.9,3.2,4.8,1.8, 6.1,2.8,4.0,1.3,
6.3,2.5,4.9,1.5, 6.1,2.8,4.7,1.2, 6.4,2.9,4.3,1.3, 6.6,3.0,4.4,1.4,
6.8,2.8,4.8,1.4, 6.7,3.0,5.0,1.7, 6.0,2.9,4.5,1.5, 5.7,2.6,3.5,1.0,
5.5,2.4,3.8,1.1, 5.5,2.4,3.7,1.0, 5.8,2.7,3.9,1.2, 6.0,2.7,5.1,1.6,
5.4,3.0,4.5,1.5, 6.0,3.4,4.5,1.6, 6.7,3.1,4.7,1.5, 6.3,2.3,4.4,1.3,
5.6,3.0,4.1,1.3, 5.5,2.5,4.0,1.3, 5.5,2.6,4.4,1.2, 6.1,3.0,4.6,1.4,
5.8,2.6,4.0,1.2, 5.0,2.3,3.3,1.0, 5.6,2.7,4.2,1.3, 5.7,3.0,4.2,1.2,
5.7,2.9,4.2,1.3, 6.2,2.9,4.3,1.3, 5.1,2.5,3.0,1.1, 5.7,2.8,4.1,1.3,
6.3,3.3,6.0,2.5, 5.8,2.7,5.1,1.9, 7.1,3.0,5.9,2.1, 6.3,2.9,5.6,1.8,
6.5,3.0,5.8,2.2, 7.6,3.0,6.6,2.1, 4.9,2.5,4.5,1.7, 7.3,2.9,6.3,1.8,
6.7,2.5,5.8,1.8, 7.2,3.6,6.1,2.5, 6.5,3.2,5.1,2.0, 6.4,2.7,5.3,1.9,
6.8,3.0,5.5,2.1, 5.7,2.5,5.0,2.0, 5.8,2.8,5.1,2.4, 6.4,3.2,5.3,2.3,
6.5,3.0,5.5,1.8, 7.7,3.8,6.7,2.2, 7.7,2.6,6.9,2.3, 6.0,2.2,5.0,1.5,
6.9,3.2,5.7,2.3, 5.6,2.8,4.9,2.0, 7.7,2.8,6.7,2.0, 6.3,2.7,4.9,1.8,
6.7,3.3,5.7,2.1, 7.2,3.2,6.0,1.8, 6.2,2.8,4.8,1.8, 6.1,3.0,4.9,1.8,
6.4,2.8,5.6,2.1, 7.2,3.0,5.8,1.6, 7.4,2.8,6.1,1.9, 7.9,3.8,6.4,2.0,
6.4,2.8,5.6,2.2, 6.3,2.8,5.1,1.5, 6.1,2.6,5.6,1.4, 7.7,3.0,6.1,2.3,
6.3,3.4,5.6,2.4, 6.4,3.1,5.5,1.8, 6.0,3.0,4.8,1.8, 6.9,3.1,5.4,2.1,
6.7,3.1,5.6,2.4, 6.9,3.1,5.1,2.3, 5.8,2.7,5.1,1.9, 6.8,3.2,5.9,2.3,
6.7,3.3,5.7,2.5, 6.7,3.0,5.2,2.3, 6.3,2.5,5.0,1.9, 6.5,3.0,5.2,2.0,
6.2,3.4,5.4,2.3, 5.9,3.0,5.1,1.8);
Mat labels = (Mat_<int>(150, 1)<< 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
@ -260,6 +250,7 @@ void CV_LRTest_SaveLoad::run( int /*start_from*/ )
float errorCount = 0.0;
LogisticRegressionParams params1 = LogisticRegressionParams();
LogisticRegressionParams params2 = LogisticRegressionParams();
params1.alpha = 1.0;
params1.num_iters = 10001;
@ -273,7 +264,7 @@ void CV_LRTest_SaveLoad::run( int /*start_from*/ )
// run LR classifier train classifier
LogisticRegression lr1(data, labels, params1);
LogisticRegression lr2;
LogisticRegression lr2(params2);
learnt_mat1 = lr1.get_learnt_thetas();
lr1.predict(data, responses1);
@ -282,7 +273,11 @@ void CV_LRTest_SaveLoad::run( int /*start_from*/ )
string filename = cv::tempfile(".xml");
try
{
lr1.save(filename.c_str());
//lr1.save(filename.c_str());
FileStorage fs;
fs.open(filename.c_str(),FileStorage::WRITE);
lr1.write(fs);
fs.release();
}
catch(...)
@ -293,7 +288,12 @@ void CV_LRTest_SaveLoad::run( int /*start_from*/ )
try
{
lr2.load(filename.c_str());
//lr2.load(filename.c_str());
FileStorage fs;
fs.open(filename.c_str(),FileStorage::READ);
FileNode fn = fs.root();
lr2.read(fn);
fs.release();
}
catch(...)