---
name: "mlpack_regression_LogisticRegressionFunction"
section: "3"
platform: "linux"
package: "mlpack-doc"
version: "1.0.10-1"
release: "Debian 8 (Jessie)"
platforms: ["linux"]
url: "https://new.carta.tech/mlpack_regression_LogisticRegressionFunction"
---

# mlpack\_regression\_LogisticRegressionFunction(3)

The log-likelihood function for the logistic regression objective function.

## 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:**

*parameters* Vector of logistic regression 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:**

*parameters* Vector of logistic regression parameters. \
 *i* Index of point to use for objective function evaluation.

### 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:**

*parameters* Vector of logistic regression parameters. \
 *gradient* Vector to output gradient into.

### 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:**

*parameters* Vector of logistic regression parameters. \
 *i* Index of points to use for objective function gradient evaluation. \
 *gradient* Vector to output gradient into.

### 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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