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updated documentation to reflect new api changes for logistic regression

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This commit is contained in:
Rahul Kavi
2013-11-05 05:31:49 -05:00
committed by Maksim Shabunin
parent 3cdd2b2761
commit 3a6deb3ed3
1 changed files with 37 additions and 18 deletions
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55
modules/ml/doc/logistic_regression.rst
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@@ -88,6 +88,9 @@ LogisticRegressionParams
If the training method is set to LogisticRegression::MINI_BATCH, it has to be set to positive integer. It can range from 1 to number of training samples.
.. ocv:member:: cv::TermCriteria term_crit
Sets termination criteria for training algorithm.
LogisticRegressionParams::LogisticRegressionParams
--------------------------------------------------
@@ -95,19 +98,34 @@ The constructors.
.. ocv:function:: LogisticRegressionParams::LogisticRegressionParams()
.. ocv:function:: LogisticRegressionParams::LogisticRegressionParams(double alpha, int num_iters, int norm, int regularized, int train_method, int minbatchsize)
.. ocv:function:: LogisticRegressionParams::LogisticRegressionParams(double learning_rate, int iters, int train_method, int normlization, int reg, int mini_batch_size)
:param alpha: Specifies the learning rate.
:param learning_rate: Specifies the learning rate.
:param num_iters: Specifies the number of iterations.
:param norm: Specifies the kind of regularization to be applied. ``LogisticRegression::REG_L1`` or ``LogisticRegression::REG_L2``. To use this, set ``LogisticRegressionParams.regularized`` to a integer greater than zero.
:param: regularized: To enable or disable regularization. Set to positive integer (greater than zero) to enable and to 0 to disable.
:param iters: Specifies the number of iterations.
:param: train_method: Specifies the kind of training method used. It should be set to either ``LogisticRegression::BATCH`` or ``LogisticRegression::MINI_BATCH``. If using ``LogisticRegression::MINI_BATCH``, set ``LogisticRegressionParams.mini_batch_size`` to a positive integer.
:param: mini_batch_size: Specifies the number of training samples taken in each step of Mini-Batch Gradient Descent.
:param normalization: Specifies the kind of regularization to be applied. ``LogisticRegression::REG_L1`` or ``LogisticRegression::REG_L2`` (L1 norm or L2 norm). To use this, set ``LogisticRegressionParams.regularized`` to a integer greater than zero.
:param: reg: To enable or disable regularization. Set to positive integer (greater than zero) to enable and to 0 to disable.
:param: mini_batch_size: Specifies the number of training samples taken in each step of Mini-Batch Gradient Descent. Will only be used if using ``LogisticRegression::MINI_BATCH`` training algorithm.
The full constructor initializes corresponding members. The default constructor creates an object with dummy parameters.
::
LogisticRegressionParams::LogisticRegressionParams()
{
term_crit = cv::TermCriteria(TermCriteria::COUNT + TermCriteria::EPS, 1000, 0.001);
alpha = 0.001;
num_iters = 1000;
norm = LogisticRegression::REG_L2;
regularized = 1;
train_method = LogisticRegression::BATCH;
mini_batch_size = 1;
}
By initializing this structure, one can set all the parameters required for Logistic Regression classifier.
@@ -121,7 +139,9 @@ LogisticRegression::LogisticRegression
--------------------------------------
The constructors.
.. ocv:function:: LogisticRegression::LogisticRegression()
.. ocv:function:: LogisticRegression( const LogisticRegressionParams& params)
:param params: The training parameters for the classifier of type ``LogisticRegressionParams``.
.. ocv:function:: LogisticRegression::LogisticRegression(cv::InputArray data_ip, cv::InputArray labels_ip, const LogisticRegressionParams& params)
@@ -154,23 +174,22 @@ Predicts responses for input samples and returns a float type.
:param predicted_labels: Predicted labels as a column matrix and of type ``CV_32S``.
LogisticRegression::get_learnt_thetas
---------------------------------------
-------------------------------------
This function returns the trained paramters arranged across rows. For a two class classifcation problem, it returns a row matrix.
.. ocv:function:: cv::Mat LogisticRegression::get_learnt_thetas()
It returns learnt paramters of the Logistic Regression as a matrix of type ``CV_32F``.
LogisticRegression::save
LogisticRegression::read
------------------------
This function saves the trained LogisticRegression clasifier to disk.
This function reads the trained LogisticRegression clasifier from disk.
.. ocv:function:: void LogisticRegression::save(string filepath) const
.. ocv:function:: void LogisticRegression::read(const FileNode& fn)
LogisticRegression::load
------------------------
This function loads the trained LogisticRegression clasifier from disk.
LogisticRegression::write
-------------------------
This function writes the trained LogisticRegression clasifier to disk.
.. ocv:function:: void LogisticRegression::load(const string filepath)
.. ocv:function:: void LogisticRegression::write(FileStorage& fs) const