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Merge pull request #1828 from greatsharma/documentationFixup

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Complete Documentation
This commit is contained in:
Shikhar Jaiswal
2019-04-05 19:01:27 +05:30
committed by GitHub
parent a3eafd63a9 f89a04fc9e
commit c2b8e4d8a1
1 changed files with 26 additions and 6 deletions
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src/mlpack/methods/logistic_regression/logistic_regression_function.hpp
+26 -6
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@@ -28,10 +28,25 @@ template<typename MatType = arma::mat>
class LogisticRegressionFunction
{
public:
/**
* Creates the LogisticRegressionFunction.
*
* @param predictors The matrix of data points.
* @param responses The measured data for each point in predictors.
* @param lambda Regularization constant for ridge regression.
*/
LogisticRegressionFunction(const MatType& predictors,
const arma::Row<size_t>& responses,
const double lambda = 0);
/**
* Creates the LogisticRegressionFunction with initialPoint.
*
* @param predictors The matrix of data points.
* @param responses The measured data for each point in predictors.
* @param initialPoint Point from which to start the optimization.
* @param lambda Regularization constant for ridge regression.
*/
LogisticRegressionFunction(const MatType& predictors,
const arma::Row<size_t>& responses,
const arma::vec& initialPoint,
@@ -59,7 +74,7 @@ class LogisticRegressionFunction
/**
* Evaluate the logistic regression log-likelihood function with the given
* parameters. Note that if a point has 0 probability of being classified
* parameters. Note that if a point has 0 probability of being classified
* directly with the given parameters, then Evaluate() will return nan (this
* is kind of a corner case and should not happen for reasonable models).
*
@@ -72,9 +87,9 @@ class LogisticRegressionFunction
/**
* Evaluate the logistic regression log-likelihood function with the given
* parameters using the given batch size from the given point index. This is
* parameters using the given batch size from the given point index. This is
* useful for optimizers such as SGD, which require a separable objective
* function. Note that if the points have 0 probability of being classified
* function. Note that if the points have 0 probability of being classified
* correctly with the given parameters, then Evaluate() will return nan (this
* is kind of a corner case and should not happen for reasonable models).
*
@@ -102,8 +117,8 @@ class LogisticRegressionFunction
/**
* Evaluate the gradient of the logistic regression log-likelihood function
* with the given parameters, for the given batch size from a given point the
* in dataset. This is useful for optimizers such as SGD, which require a
* with the given parameters, for the given batch size from a given point in
* the dataset. This is useful for optimizers such as SGD, which require a
* separable objective function.
*
* @param parameters Vector of logistic regression parameters.
@@ -122,7 +137,7 @@ class LogisticRegressionFunction
/**
* Evaluate the gradient of the logistic regression log-likelihood function
* with the given parameters, and with respect to only one feature in the
* dataset. This is useful for optimizers such as SCD, which require
* dataset. This is useful for optimizers such as SCD, which require
* partial gradients.
*
* @param parameters Vector of logistic regression parameters.
@@ -142,6 +157,11 @@ class LogisticRegressionFunction
double EvaluateWithGradient(const arma::mat& parameters,
GradType& gradient) const;
/**
* Evaluate the objective function and gradient of the logistic regression
* log-likelihood function simultaneously with the given parameters, for
* the given batch size from a given point in the dataset.
*/
template<typename GradType>
double EvaluateWithGradient(const arma::mat& parameters,
const size_t begin,