#1205 fixed ~100 of ~700 parameters

modules/ml/doc/decision_trees.rst

@@ -267,7 +267,7 @@ Returns error of the decision tree.
 
 .. ocv:function:: float CvDTree::calc_error( CvMLData* trainData, int type, std::vector<float> *resp = 0 )
 
-    :param data: Data for the decision tree.
+    :param trainData: Data for the decision tree.
     
     :param type: Type of error. Possible values are:
 

modules/ml/doc/gradient_boosted_trees.rst

@@ -213,7 +213,7 @@ Predicts a response for an input sample.
     in the same position in the ``sample`` vector. If there are no missing values
     in the feature vector, an empty matrix can be passed instead of the missing mask.
     
-   :param weak_responses: Matrix used to obtain predictions of all the trees.
+   :param weakResponses: Matrix used to obtain predictions of all the trees.
     The matrix has :math:`K` rows,
     where :math:`K` is the count of output classes (1 for the regression case).
     The matrix has as many columns as the ``slice`` length.

modules/ml/doc/neural_networks.rst

@@ -202,7 +202,9 @@ Constructs MLP with the specified topology.
 
     :param activateFunc: Parameter specifying the activation function for each neuron: one of  ``CvANN_MLP::IDENTITY``, ``CvANN_MLP::SIGMOID_SYM``, and ``CvANN_MLP::GAUSSIAN``.
 
-    :param fparam1/fparam2: Free parameters of the activation function, :math:`\alpha` and :math:`\beta`, respectively. See the formulas in the introduction section.
+    :param fparam1: Free parameter of the activation function, :math:`\alpha`. See the formulas in the introduction section.
+    
+    :param fparam2: Free parameter of the activation function, :math:`\beta`. See the formulas in the introduction section.
 
 The method creates an MLP network with the specified topology and assigns the same activation function to all the neurons.
 

modules/ml/doc/random_trees.rst

@@ -165,9 +165,9 @@ Retrieves the proximity measure between two training samples.
 
 .. ocv:function:: float CvRTrees::get_proximity( const CvMat* sample1, const CvMat* sample2, const CvMat* missing1 = 0, const CvMat* missing2 = 0 ) const
 
-    :param sample_1: The first sample.
+    :param sample1: The first sample.
 
-    :param sample_2: The second sample.
+    :param sample2: The second sample.
 
     :param missing1: Optional missing measurement mask of the first sample.
 

modules/ml/doc/support_vector_machines.rst

@@ -232,7 +232,9 @@ Predicts the response for input sample(s).
 
 .. ocv:pyfunction:: cv2.SVM.predict(sample[, returnDFVal]) -> retval
 
-    :param sample(s): Input sample(s) for prediction.
+    :param sample: Input sample for prediction.
+
+    :param samples: Input samples for prediction.
 
     :param returnDFVal: Specifies a type of the return value. If ``true`` and the problem is 2-class classification then the method returns the decision function value that is signed distance to the margin, else the function returns a class label (classification) or estimated function value (regression).