Modified logistic regression module according to comments

- Reworked documentation to reflect actual code
- Removed some unused variables
- Removed unnecessary 'cv::' modifiers

modules/ml/doc/ml.rst

@@ -18,5 +18,5 @@ Most of the classification and regression algorithms are implemented as C++ clas
     random_trees
     expectation_maximization
     neural_networks
-    mldata
     logistic_regression
+    mldata

modules/ml/include/opencv2/ml.hpp

@@ -89,9 +89,6 @@ public:
     CV_PROP_RW double maxVal;
     CV_PROP_RW double logStep;
 };
-#define CV_TYPE_NAME_ML_LR          "opencv-ml-lr"
-
-
 
 class CV_EXPORTS TrainData
 {
@@ -590,7 +587,7 @@ public:
         int regularized;
         int train_method;
         int mini_batch_size;
-        cv::TermCriteria term_crit;
+        TermCriteria term_crit;
     };
 
     enum { REG_L1 = 0, REG_L2 = 1};

modules/ml/test/test_lr.cpp

@@ -74,7 +74,7 @@ static bool calculateError( const Mat& _p_labels, const Mat& _o_labels, float& e
     CV_Assert(_p_labels_temp.total() == _o_labels_temp.total());
     CV_Assert(_p_labels_temp.rows == _o_labels_temp.rows);
 
-    accuracy = (float)cv::countNonZero(_p_labels_temp == _o_labels_temp)/_p_labels_temp.rows;
+    accuracy = (float)countNonZero(_p_labels_temp == _o_labels_temp)/_p_labels_temp.rows;
     error = 1 - accuracy;
     return true;
 }
@@ -166,7 +166,7 @@ void CV_LRTest_SaveLoad::run( int /*start_from*/ )
     params1.mini_batch_size = 10;
 
     // train and save the classifier
-    String filename = cv::tempfile(".xml");
+    String filename = tempfile(".xml");
     try
     {
         // run LR classifier train classifier
@@ -208,8 +208,8 @@ void CV_LRTest_SaveLoad::run( int /*start_from*/ )
     // check if there is any difference between computed learnt mat and retreived mat
 
     float errorCount = 0.0;
-    errorCount += 1 - (float)cv::countNonZero(responses1 == responses2)/responses1.rows;
-    errorCount += 1 - (float)cv::sum(comp_learnt_mats)[0]/comp_learnt_mats.rows;
+    errorCount += 1 - (float)countNonZero(responses1 == responses2)/responses1.rows;
+    errorCount += 1 - (float)sum(comp_learnt_mats)[0]/comp_learnt_mats.rows;
 
     if(errorCount>0)
     {

samples/cpp/logistic_regression.cpp

@@ -58,9 +58,9 @@
 
 #include <iostream>
 
-#include <opencv2/core/core.hpp>
-#include <opencv2/ml/ml.hpp>
-#include <opencv2/highgui/highgui.hpp>
+#include <opencv2/core.hpp>
+#include <opencv2/ml.hpp>
+#include <opencv2/highgui.hpp>
 
 using namespace std;
 using namespace cv;
@@ -78,7 +78,7 @@ static void showImage(const Mat &data, int columns, const String &name)
 
 static float calculateAccuracyPercent(const Mat &original, const Mat &predicted)
 {
-    return 100 * (float)cv::countNonZero(original == predicted) / predicted.rows;
+    return 100 * (float)countNonZero(original == predicted) / predicted.rows;
 }
 
 int main()