generic-library/opencv
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

Mentioned in doc if a function is parallelized with the TBB library (issue #421)

Browse Source
This commit is contained in:
Ilya Lysenkov
2012-04-13 15:31:18 +00:00
parent 74cc370c74
commit 2f44610ef3
13 changed files with 28 additions and 11 deletions
Download Patch File Download Diff File Expand all files Collapse all files

2
modules/ml/doc/decision_trees.rst
View File

@@ -239,6 +239,8 @@ There are four ``train`` methods in :ocv:class:`CvDTree`:
* The **last** method ``train`` is mostly used for building tree ensembles. It takes the pre-constructed :ocv:class:`CvDTreeTrainData` instance and an optional subset of the training set. The indices in ``subsampleIdx`` are counted relatively to the ``_sample_idx`` , passed to the ``CvDTreeTrainData`` constructor. For example, if ``_sample_idx=[1, 5, 7, 100]`` , then ``subsampleIdx=[0,3]`` means that the samples ``[1, 100]`` of the original training set are used.
The function is parallelized with the TBB library.
CvDTree::predict

2
modules/ml/doc/k_nearest_neighbors.rst
View File

@@ -79,6 +79,8 @@ In case of C++ interface you can use output pointers to empty matrices and the f
If only a single input vector is passed, all output matrices are optional and the predicted value is returned by the method.
The function is parallelized with the TBB library.
CvKNearest::get_max_k
---------------------
Returns the number of maximum neighbors that may be passed to the method :ocv:func:`CvKNearest::find_nearest`.

5
modules/ml/doc/neural_networks.rst
View File

@@ -238,6 +238,9 @@ Trains/updates MLP.
This method applies the specified training algorithm to computing/adjusting the network weights. It returns the number of done iterations.
The RPROP training algorithm is parallelized with the TBB library.
CvANN_MLP::predict
------------------
Predicts responses for input samples.
@@ -275,4 +278,4 @@ Returns neurons weights of the particular layer.
.. ocv:function:: double* CvANN_MLP::get_weights(int layer)
:param layer: Index of the particular layer.

1
modules/ml/doc/normal_bayes_classifier.rst
View File

@@ -60,3 +60,4 @@ Predicts the response for sample(s).
The method estimates the most probable classes for input vectors. Input vectors (one or more) are stored as rows of the matrix ``samples``. In case of multiple input vectors, there should be one output vector ``results``. The predicted class for a single input vector is returned by the method.
The function is parallelized with the TBB library.

2
modules/ml/doc/random_trees.rst
View File

@@ -112,6 +112,8 @@ Trains the Random Trees model.
The method :ocv:func:`CvRTrees::train` is very similar to the method :ocv:func:`CvDTree::train` and follows the generic method :ocv:func:`CvStatModel::train` conventions. All the parameters specific to the algorithm training are passed as a :ocv:class:`CvRTParams` instance. The estimate of the training error (``oob-error``) is stored in the protected class member ``oob_error``.
The function is parallelized with the TBB library.
CvRTrees::predict
-----------------
Predicts the output for an input sample.

3
modules/ml/doc/support_vector_machines.rst
View File

@@ -242,6 +242,9 @@ Predicts the response for input sample(s).
If you pass one sample then prediction result is returned. If you want to get responses for several samples then you should pass the ``results`` matrix where prediction results will be stored.
The function is parallelized with the TBB library.
CvSVM::get_default_grid
-----------------------
Generates a grid for SVM parameters.