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Code Packages Releases Wiki Activity

Partially back-port #25075 to 4.x

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This commit is contained in:
Alexander Smorkalov
2024-03-04 15:51:05 +03:00
parent ef611df09b
commit daa8f7dfc6
111 changed files with 1124 additions and 1124 deletions
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modules/ml/src/lr.cpp
+14 -14
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@@ -109,15 +109,15 @@ bool LogisticRegressionImpl::train(const Ptr<TrainData>& trainData, int)
CV_Assert( !_labels_i.empty() && !_data_i.empty());
if(_labels_i.cols != 1)
{
CV_Error( CV_StsBadArg, "labels should be a column matrix" );
CV_Error( cv::Error::StsBadArg, "labels should be a column matrix" );
}
if(_data_i.type() != CV_32FC1 || _labels_i.type() != CV_32FC1)
{
CV_Error( CV_StsBadArg, "data and labels must be a floating point matrix" );
CV_Error( cv::Error::StsBadArg, "data and labels must be a floating point matrix" );
}
if(_labels_i.rows != _data_i.rows)
{
CV_Error( CV_StsBadArg, "number of rows in data and labels should be equal" );
CV_Error( cv::Error::StsBadArg, "number of rows in data and labels should be equal" );
}
// class labels
@@ -126,7 +126,7 @@ bool LogisticRegressionImpl::train(const Ptr<TrainData>& trainData, int)
int num_classes = (int) this->forward_mapper.size();
if(num_classes < 2)
{
CV_Error( CV_StsBadArg, "data should have at least 2 classes" );
CV_Error( cv::Error::StsBadArg, "data should have at least 2 classes" );
}
// add a column of ones to the data (bias/intercept term)
@@ -174,7 +174,7 @@ bool LogisticRegressionImpl::train(const Ptr<TrainData>& trainData, int)
this->learnt_thetas = thetas.clone();
if( cvIsNaN( (double)sum(this->learnt_thetas)[0] ) )
{
CV_Error( CV_StsBadArg, "check training parameters. Invalid training classifier" );
CV_Error( cv::Error::StsBadArg, "check training parameters. Invalid training classifier" );
}
// success
@@ -187,7 +187,7 @@ float LogisticRegressionImpl::predict(InputArray samples, OutputArray results, i
// check if learnt_mats array is populated
if(!this->isTrained())
{
CV_Error( CV_StsBadArg, "classifier should be trained first" );
CV_Error( cv::Error::StsBadArg, "classifier should be trained first" );
}
// coefficient matrix
@@ -206,7 +206,7 @@ float LogisticRegressionImpl::predict(InputArray samples, OutputArray results, i
Mat data = samples.getMat();
if(data.type() != CV_32F)
{
CV_Error( CV_StsBadArg, "data must be of floating type" );
CV_Error( cv::Error::StsBadArg, "data must be of floating type" );
}
// add a column of ones to the data (bias/intercept term)
@@ -327,7 +327,7 @@ double LogisticRegressionImpl::compute_cost(const Mat& _data, const Mat& _labels
if(cvIsNaN( cost ) == 1)
{
CV_Error( CV_StsBadArg, "check training parameters. Invalid training classifier" );
CV_Error( cv::Error::StsBadArg, "check training parameters. Invalid training classifier" );
}
return cost;
@@ -398,12 +398,12 @@ Mat LogisticRegressionImpl::batch_gradient_descent(const Mat& _data, const Mat&
// implements batch gradient descent
if(this->params.alpha<=0)
{
CV_Error( CV_StsBadArg, "check training parameters (learning rate) for the classifier" );
CV_Error( cv::Error::StsBadArg, "check training parameters (learning rate) for the classifier" );
}
if(this->params.num_iters <= 0)
{
CV_Error( CV_StsBadArg, "number of iterations cannot be zero or a negative number" );
CV_Error( cv::Error::StsBadArg, "number of iterations cannot be zero or a negative number" );
}
int llambda = 0;
@@ -439,12 +439,12 @@ Mat LogisticRegressionImpl::mini_batch_gradient_descent(const Mat& _data, const
if(this->params.mini_batch_size <= 0 || this->params.alpha == 0)
{
CV_Error( CV_StsBadArg, "check training parameters for the classifier" );
CV_Error( cv::Error::StsBadArg, "check training parameters for the classifier" );
}
if(this->params.num_iters <= 0)
{
CV_Error( CV_StsBadArg, "number of iterations cannot be zero or a negative number" );
CV_Error( cv::Error::StsBadArg, "number of iterations cannot be zero or a negative number" );
}
Mat theta_p = _init_theta.clone();
@@ -551,7 +551,7 @@ void LogisticRegressionImpl::write(FileStorage& fs) const
// check if open
if(fs.isOpened() == 0)
{
CV_Error(CV_StsBadArg,"file can't open. Check file path");
CV_Error(cv::Error::StsBadArg,"file can't open. Check file path");
}
writeFormat(fs);
string desc = "Logistic Regression Classifier";
@@ -574,7 +574,7 @@ void LogisticRegressionImpl::read(const FileNode& fn)
// check if empty
if(fn.empty())
{
CV_Error( CV_StsBadArg, "empty FileNode object" );
CV_Error( cv::Error::StsBadArg, "empty FileNode object" );
}
this->params.alpha = (double)fn["alpha"];