Normalized file endings.

modules/ml/doc/decision_trees.rst

@@ -318,4 +318,3 @@ decision tree.
 
 
 .. [Breiman84] Breiman, L., Friedman, J. Olshen, R. and Stone, C. (1984), *Classification and Regression Trees*, Wadsworth.
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modules/ml/doc/k_nearest_neighbors.rst

@@ -194,5 +194,3 @@ The sample below (currently using the obsolete ``CvMat`` structures) demonstrate
         cvReleaseMat( &trainData );
         return 0;
     }
-
-

modules/ml/doc/neural_networks.rst

@@ -278,4 +278,3 @@ Returns neurons weights of the particular layer.
 .. ocv:function:: double* CvANN_MLP::get_weights(int layer)
 
     :param layer: Index of the particular layer.
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modules/ml/doc/statistical_models.rst

@@ -161,4 +161,3 @@ Predicts the response for a sample.
 The method is used to predict the response for a new sample. In case of a classification, the method returns the class label. In case of a regression, the method returns the output function value. The input sample must have as many components as the ``train_data`` passed to ``train`` contains. If the ``var_idx`` parameter is passed to ``train``, it is remembered and then is used to extract only the necessary components from the input sample in the method ``predict``.
 
 The suffix ``const`` means that prediction does not affect the internal model state, so the method can be safely called from within different threads.
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modules/ml/src/ertrees.cpp

@@ -1847,4 +1847,3 @@ bool CvERTrees::train( const Mat& _train_data, int _tflag,
 }
 
 // End of file.
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modules/ml/src/knearest.cpp

@@ -480,4 +480,3 @@ float CvKNearest::find_nearest( const cv::Mat& _samples, int k, CV_OUT cv::Mat&
 }
 
 /* End of file */
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modules/ml/src/nbayes.cpp

@@ -623,4 +623,3 @@ float CvNormalBayesClassifier::predict( const Mat& _samples, Mat* _results ) con
 }
 
 /* End of file. */
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modules/ml/src/svm.cpp

@@ -2949,4 +2949,3 @@ cvTrainSVM_CrossValidation( const CvMat* train_data, int tflag,
 #endif
 
 /* End of file. */
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modules/ml/test/test_emknearestkmeans.cpp

@@ -678,4 +678,3 @@ TEST(ML_KNearest, accuracy) { CV_KNearestTest test; test.safe_run(); }
 TEST(ML_EM, accuracy) { CV_EMTest test; test.safe_run(); }
 TEST(ML_EM, save_load) { CV_EMTest_SaveLoad test; test.safe_run(); }
 TEST(ML_EM, classification) { CV_EMTest_Classification test; test.safe_run(); }
-