NAME¶

mlpack::regression::LogisticRegressionFunction - The log-likelihood function for the logistic regression objective function.

SYNOPSIS¶

Public Member Functions¶

LogisticRegressionFunction (const arma::mat &predictors, const arma::vec & responses, const double lambda=0) LogisticRegressionFunction (const arma::mat &predictors, const arma::vec & responses, const arma::mat &initialPoint, const double lambda=0) double Evaluate (const arma::mat &parameters) const Evaluate the logistic regression log-likelihood function with the given parameters. double Evaluate (const arma::mat &parameters, const size_t i) const Evaluate the logistic regression log-likelihood function with the given parameters, but using only one data point. const arma::mat & GetInitialPoint () const Return the initial point for the optimization. void Gradient (const arma::mat &parameters, arma::mat &gradient) const Evaluate the gradient of the logistic regression log-likelihood function with the given parameters. void Gradient (const arma::mat &parameters, const size_t i, arma::mat &gradient) const Evaluate the gradient of the logistic regression log-likelihood function with the given parameters, and with respect to only one point in the dataset. const arma::mat & InitialPoint () const Return the initial point for the optimization. arma::mat & InitialPoint () Modify the initial point for the optimization. const double & Lambda () const Return the regularization parameter (lambda). double & Lambda () Modify the regularization parameter (lambda). size_t NumFunctions () const Return the number of separable functions (the number of predictor points). const arma::mat & Predictors () const Return the matrix of predictors. const arma::vec & Responses () const Return the vector of responses.

Private Attributes¶

arma::mat initialPoint The initial point, from which to start the optimization. double lambda The regularization parameter for L2-regularization. const arma::mat & predictors The matrix of data points (predictors). const arma::vec & responses The vector of responses to the input data points.

Detailed Description¶

The log-likelihood function for the logistic regression objective function. This is used by various mlpack optimizers to train a logistic regression model. Definition at line 37 of file logistic_regression_function.hpp.

Constructor & Destructor Documentation¶

mlpack::regression::LogisticRegressionFunction::LogisticRegressionFunction (const arma::mat &predictors, const arma::vec &responses, const doublelambda = 0)¶

mlpack::regression::LogisticRegressionFunction::LogisticRegressionFunction (const arma::mat &predictors, const arma::vec &responses, const arma::mat &initialPoint, const doublelambda = 0)¶

Member Function Documentation¶

double mlpack::regression::LogisticRegressionFunction::Evaluate (const arma::mat &parameters) const¶

Evaluate the logistic regression log-likelihood function with the given 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). The optimum (minimum) of this function is 0.0, and occurs when each point is classified correctly with very high probability. Parameters:

double mlpack::regression::LogisticRegressionFunction::Evaluate (const arma::mat &parameters, const size_ti) const¶

Evaluate the logistic regression log-likelihood function with the given parameters, but using only one data point. This is useful for optimizers such as SGD, which require a separable objective function. Note that if the point has 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). The optimum (minimum) of this function is 0.0, and occurs when the point is classified correctly with very high probability. Parameters:

const arma::mat& mlpack::regression::LogisticRegressionFunction::GetInitialPoint () const [inline]¶

Return the initial point for the optimization. Definition at line 117 of file logistic_regression_function.hpp. References initialPoint.

void mlpack::regression::LogisticRegressionFunction::Gradient (const arma::mat &parameters, arma::mat &gradient) const¶

Evaluate the gradient of the logistic regression log-likelihood function with the given parameters. Parameters:

void mlpack::regression::LogisticRegressionFunction::Gradient (const arma::mat &parameters, const size_ti, arma::mat &gradient) const¶

Evaluate the gradient of the logistic regression log-likelihood function with the given parameters, and with respect to only one point in the dataset. This is useful for optimizers such as SGD, which require a separable objective function. Parameters:

const arma::mat& mlpack::regression::LogisticRegressionFunction::InitialPoint () const [inline]¶

Return the initial point for the optimization. Definition at line 50 of file logistic_regression_function.hpp. References initialPoint.

arma::mat& mlpack::regression::LogisticRegressionFunction::InitialPoint () [inline]¶

Modify the initial point for the optimization. Definition at line 52 of file logistic_regression_function.hpp. References initialPoint.

const double& mlpack::regression::LogisticRegressionFunction::Lambda () const [inline]¶

Return the regularization parameter (lambda). Definition at line 55 of file logistic_regression_function.hpp. References lambda.

double& mlpack::regression::LogisticRegressionFunction::Lambda () [inline]¶

Modify the regularization parameter (lambda). Definition at line 57 of file logistic_regression_function.hpp. References lambda.

size_t mlpack::regression::LogisticRegressionFunction::NumFunctions () const [inline]¶

Return the number of separable functions (the number of predictor points). Definition at line 120 of file logistic_regression_function.hpp.

const arma::mat& mlpack::regression::LogisticRegressionFunction::Predictors () const [inline]¶

Return the matrix of predictors. Definition at line 60 of file logistic_regression_function.hpp. References predictors.

const arma::vec& mlpack::regression::LogisticRegressionFunction::Responses () const [inline]¶

Return the vector of responses. Definition at line 62 of file logistic_regression_function.hpp. References responses.

Member Data Documentation¶

arma::mat mlpack::regression::LogisticRegressionFunction::initialPoint [private]¶

The initial point, from which to start the optimization. Definition at line 124 of file logistic_regression_function.hpp. Referenced by GetInitialPoint(), and InitialPoint().

double mlpack::regression::LogisticRegressionFunction::lambda [private]¶

The regularization parameter for L2-regularization. Definition at line 130 of file logistic_regression_function.hpp. Referenced by Lambda().

const arma::mat& mlpack::regression::LogisticRegressionFunction::predictors [private]¶

The matrix of data points (predictors). Definition at line 126 of file logistic_regression_function.hpp. Referenced by Predictors().

const arma::vec& mlpack::regression::LogisticRegressionFunction::responses [private]¶

The vector of responses to the input data points. Definition at line 128 of file logistic_regression_function.hpp. Referenced by Responses().

Author¶

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