CV_Assert(_p_labels_temp.total() == _o_labels_temp.total());
CV_Assert(_p_labels_temp.rows == _o_labels_temp.rows);
- Mat result = (_p_labels_temp == _o_labels_temp)/255;
- accuracy = (float)cv::sum(result)[0]/result.rows;
+ accuracy = (float)cv::countNonZero(_p_labels_temp == _o_labels_temp)/_p_labels_temp.rows;
error = 1 - accuracy;
return true;
}
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3);
- CvLR_TrainParams params = CvLR_TrainParams();
Mat responses1, responses2;
float error = 0.0f;
- CvLR_TrainParams params1 = CvLR_TrainParams();
- CvLR_TrainParams params2 = CvLR_TrainParams();
+ LogisticRegressionParams params1 = LogisticRegressionParams();
+ LogisticRegressionParams params2 = LogisticRegressionParams();
params1.alpha = 1.0;
params1.num_iters = 10001;
- params1.norm = CvLR::REG_L2;
- // params1.debug = 1;
+ params1.norm = LogisticRegression::REG_L2;
params1.regularized = 1;
- params1.train_method = CvLR::BATCH;
- params1.minibatchsize = 10;
+ params1.train_method = LogisticRegression::BATCH;
+ params1.mini_batch_size = 10;
// run LR classifier train classifier
data.convertTo(data, CV_32FC1);
labels.convertTo(labels, CV_32FC1);
- CvLR lr1(data, labels, params1);
+ LogisticRegression lr1(data, labels, params1);
// predict using the same data
lr1.predict(data, responses1);
ts->printf(cvtest::TS::LOG, "Bad prediction labels\n" );
test_code = cvtest::TS::FAIL_INVALID_OUTPUT;
}
-
else if(error > 0.05f)
{
ts->printf(cvtest::TS::LOG, "Bad accuracy of (%f)\n", error);
params2.alpha = 1.0;
params2.num_iters = 9000;
- params2.norm = CvLR::REG_L2;
- // params2.debug = 1;
+ params2.norm = LogisticRegression::REG_L2;
params2.regularized = 1;
- params2.train_method = CvLR::MINI_BATCH;
- params2.minibatchsize = 10;
+ params2.train_method = LogisticRegression::MINI_BATCH;
+ params2.mini_batch_size = 10;
// now train using mini batch gradient descent
- CvLR lr2(data, labels, params2);
+ LogisticRegression lr2(data, labels, params2);
lr2.predict(data, responses2);
responses2.convertTo(responses2, CV_32S);
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);
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3);
- CvLR_TrainParams params = CvLR_TrainParams();
+ // LogisticRegressionParams params = LogisticRegressionParams();
Mat responses1, responses2;
Mat learnt_mat1, learnt_mat2;
float errorCount = 0.0;
- CvLR_TrainParams params1 = CvLR_TrainParams();
- CvLR_TrainParams params2 = CvLR_TrainParams();
+ LogisticRegressionParams params1 = LogisticRegressionParams();
params1.alpha = 1.0;
params1.num_iters = 10001;
- params1.norm = CvLR::REG_L2;
- // params1.debug = 1;
+ params1.norm = LogisticRegression::REG_L2;
params1.regularized = 1;
- params1.train_method = CvLR::BATCH;
- params1.minibatchsize = 10;
+ params1.train_method = LogisticRegression::BATCH;
+ params1.mini_batch_size = 10;
data.convertTo(data, CV_32FC1);
labels.convertTo(labels, CV_32FC1);
// run LR classifier train classifier
- CvLR lr1(data, labels, params1);
- CvLR lr2;
- learnt_mat1 = lr1.get_learnt_mat();
+ LogisticRegression lr1(data, labels, params1);
+ LogisticRegression lr2;
+ learnt_mat1 = lr1.get_learnt_thetas();
+
lr1.predict(data, responses1);
// now save the classifier
- // Write out
string filename = cv::tempfile(".xml");
try
{
lr2.predict(data, responses2);
- learnt_mat2 = lr2.get_learnt_mat();
+ learnt_mat2 = lr2.get_learnt_thetas();
- // compare difference in prediction outputs before and after loading from disk
- pred_result1 = (responses1 == responses2)/255;
+ CV_Assert(responses1.rows == responses2.rows);
// compare difference in learnt matrices before and after loading from disk
comp_learnt_mats = (learnt_mat1 == learnt_mat2);
// compare difference in prediction outputs and stored inputs
// check if there is any difference between computed learnt mat and retreived mat
- errorCount += 1 - (float)cv::sum(pred_result1)[0]/pred_result1.rows;
+ errorCount += 1 - (float)cv::countNonZero(responses1 == responses2)/responses1.rows;
errorCount += 1 - (float)cv::sum(comp_learnt_mats)[0]/comp_learnt_mats.rows;
-
if(errorCount>0)
{
ts->printf( cvtest::TS::LOG, "Different prediction results before writing and after reading (errorCount=%d).\n", errorCount );