The LogisticRegression class implements an L2regularized logistic regression model, and supports training with multiple optimizers and classification. More...
Public Member Functions  
LogisticRegression (const MatType &predictors, const arma::Row< size_t > &responses, const double lambda=0)  
Construct the LogisticRegression class with the given labeled training data. More...  
LogisticRegression (const MatType &predictors, const arma::Row< size_t > &responses, const arma::rowvec &initialPoint, const double lambda=0)  
Construct the LogisticRegression class with the given labeled training data. More...  
LogisticRegression (const size_t dimensionality=0, const double lambda=0)  
Construct the LogisticRegression class without performing any training. More...  
template < typename OptimizerType >  
LogisticRegression (const MatType &predictors, const arma::Row< size_t > &responses, OptimizerType &optimizer, const double lambda)  
Construct the LogisticRegression class with the given labeled training data. More...  
template < typename VecType >  
size_t  Classify (const VecType &point, const double decisionBoundary=0.5) const 
Classify the given point. More...  
void  Classify (const MatType &dataset, arma::Row< size_t > &labels, const double decisionBoundary=0.5) const 
Classify the given points, returning the predicted labels for each point. More...  
void  Classify (const MatType &dataset, arma::mat &probabilities) const 
Classify the given points, returning class probabilities for each point. More...  
double  ComputeAccuracy (const MatType &predictors, const arma::Row< size_t > &responses, const double decisionBoundary=0.5) const 
Compute the accuracy of the model on the given predictors and responses, optionally using the given decision boundary. More...  
double  ComputeError (const MatType &predictors, const arma::Row< size_t > &responses) const 
Compute the error of the model. More...  
const double &  Lambda () const 
Return the lambda value for L2regularization. More...  
double &  Lambda () 
Modify the lambda value for L2regularization. More...  
const arma::rowvec &  Parameters () const 
Return the parameters (the b vector). More...  
arma::rowvec &  Parameters () 
Modify the parameters (the b vector). More...  
template < typename Archive >  
void  serialize (Archive &ar, const uint32_t) 
Serialize the model. More...  
template<typename OptimizerType = ens::L_BFGS, typename... CallbackTypes>  
double  Train (const MatType &predictors, const arma::Row< size_t > &responses, CallbackTypes &&... callbacks) 
Train the LogisticRegression model on the given input data. More...  
template<typename OptimizerType , typename... CallbackTypes>  
double  Train (const MatType &predictors, const arma::Row< size_t > &responses, OptimizerType &optimizer, CallbackTypes &&... callbacks) 
Train the LogisticRegression model with the given instantiated optimizer. More...  
The LogisticRegression class implements an L2regularized logistic regression model, and supports training with multiple optimizers and classification.
The class supports different observation types via the MatType template parameter; for instance, logistic regression can be performed on sparse datasets by specifying arma::sp_mat as the MatType parameter.
LogisticRegression can be used for general classification tasks, but the class is restricted to support only two classes. For multiclass logistic regression, see mlpack::regression::SoftmaxRegression.
MatType  Type of data matrix. 
Definition at line 39 of file logistic_regression.hpp.
LogisticRegression  (  const MatType &  predictors, 
const arma::Row< size_t > &  responses,  
const double  lambda = 0 

) 
Construct the LogisticRegression class with the given labeled training data.
This will train the model. Optionally, specify lambda, which is the penalty parameter for L2regularization. If not specified, it is set to 0, which results in standard (unregularized) logistic regression.
It is not possible to set a custom optimizer with this constructor. Either use a constructor that does not train and call Train() with a custom optimizer type, or use the constructor that takes an instantiated optimizer. (This unfortunate situation is a language restriction of C++.)
predictors  Input training variables. 
responses  Outputs resulting from input training variables. 
lambda  L2regularization parameter. 
LogisticRegression  (  const MatType &  predictors, 
const arma::Row< size_t > &  responses,  
const arma::rowvec &  initialPoint,  
const double  lambda = 0 

) 
Construct the LogisticRegression class with the given labeled training data.
This will train the model. Optionally, specify lambda, which is the penalty parameter for L2regularization. If not specified, it is set to 0, which results in standard (unregularized) logistic regression.
It is not possible to set a custom optimizer with this constructor. Either use a constructor that does not train and call Train() with a custom optimizer type, or use the constructor that takes an instantiated optimizer. (This unfortunate situation is a language restriction of C++.)
predictors  Input training variables. 
responses  Outputs results from input training variables. 
initialPoint  Initial model to train with. 
lambda  L2regularization parameter. 
LogisticRegression  (  const size_t  dimensionality = 0 , 
const double  lambda = 0 

) 
Construct the LogisticRegression class without performing any training.
The dimensionality of the data (which will be used to set the size of the parameters vector) must be specified, and all of the parameters in the model will be set to 0. Note that the dimensionality may be changed later by directly modifying the parameters vector (using Parameters()).
dimensionality  Dimensionality of the data. 
lambda  L2regularization parameter. 
LogisticRegression  (  const MatType &  predictors, 
const arma::Row< size_t > &  responses,  
OptimizerType &  optimizer,  
const double  lambda  
) 
Construct the LogisticRegression class with the given labeled training data.
This will train the model. This overload takes an already instantiated optimizer (which holds the LogisticRegressionFunction error function, which must also be instantiated), so that the optimizer can be configured before the training is run by this constructor. The update policy of the optimizer can be set through the policy argument. The predictors and responses and initial point are all taken from the error function contained in the optimizer.
predictors  Input training variables. 
responses  Outputs results from input training variables. 
optimizer  Instantiated optimizer with instantiated error function. 
lambda  L2regularization parameter. 
size_t Classify  (  const VecType &  point, 
const double  decisionBoundary = 0.5 

)  const 
Classify the given point.
The predicted label is returned. Optionally, specify the decision boundary; logistic regression returns a value between 0 and 1. If the value is greater than the decision boundary, the response is taken to be 1; otherwise, it is 0. By default the decision boundary is 0.5.
point  Point to classify. 
decisionBoundary  Decision boundary (default 0.5). 
Referenced by LogisticRegression< MatType >::Lambda().
void Classify  (  const MatType &  dataset, 
arma::Row< size_t > &  labels,  
const double  decisionBoundary = 0.5 

)  const 
Classify the given points, returning the predicted labels for each point.
Optionally, specify the decision boundary; logistic regression returns a value between 0 and 1. If the value is greater than the decision boundary, the response is taken to be 1; otherwise, it is 0. By default the decision boundary is 0.5.
dataset  Set of points to classify. 
labels  Predicted labels for each point. 
decisionBoundary  Decision boundary (default 0.5). 
void Classify  (  const MatType &  dataset, 
arma::mat &  probabilities  
)  const 
Classify the given points, returning class probabilities for each point.
dataset  Set of points to classify. 
probabilities  Class probabilities for each point (output). 
double ComputeAccuracy  (  const MatType &  predictors, 
const arma::Row< size_t > &  responses,  
const double  decisionBoundary = 0.5 

)  const 
Compute the accuracy of the model on the given predictors and responses, optionally using the given decision boundary.
The responses should be either 0 or 1. Logistic regression returns a value between 0 and 1. If the value is greater than the decision boundary, the response is taken to be 1; otherwise, it is 0. By default, the decision boundary is 0.5.
The accuracy is returned as a percentage, between 0 and 100.
predictors  Input predictors. 
responses  Vector of responses. 
decisionBoundary  Decision boundary (default 0.5). 
Referenced by LogisticRegression< MatType >::Lambda().
double ComputeError  (  const MatType &  predictors, 
const arma::Row< size_t > &  responses  
)  const 
Compute the error of the model.
This returns the negative objective function of the logistic regression loglikelihood function. For the model to be optimal, the negative loglikelihood function should be minimized.
predictors  Input predictors. 
responses  Vector of responses. 
Referenced by LogisticRegression< MatType >::Lambda().

inline 
Return the lambda value for L2regularization.
Definition at line 168 of file logistic_regression.hpp.

inline 
Modify the lambda value for L2regularization.
Definition at line 170 of file logistic_regression.hpp.
References LogisticRegression< MatType >::Classify(), LogisticRegression< MatType >::ComputeAccuracy(), LogisticRegression< MatType >::ComputeError(), and LogisticRegression< MatType >::serialize().

inline 
Return the parameters (the b vector).
Definition at line 163 of file logistic_regression.hpp.

inline 
Modify the parameters (the b vector).
Definition at line 165 of file logistic_regression.hpp.
void serialize  (  Archive &  ar, 
const uint32_t  
) 
Serialize the model.
Referenced by LogisticRegression< MatType >::Lambda().
double Train  (  const MatType &  predictors, 
const arma::Row< size_t > &  responses,  
CallbackTypes &&...  callbacks  
) 
Train the LogisticRegression model on the given input data.
By default, the LBFGS optimization algorithm is used, but others can be specified (such as ens::SGD).
This will use the existing model parameters as a starting point for the optimization. If this is not what you want, then you should access the parameters vector directly with Parameters() and modify it as desired.
OptimizerType  Type of optimizer to use to train the model. 
CallbackTypes  Types of Callback Functions. 
predictors  Input training variables. 
responses  Outputs results from input training variables. 
callbacks  Callback function for ensmallen optimizer OptimizerType . See https://www.ensmallen.org/docs.html#callbackdocumentation. 
double Train  (  const MatType &  predictors, 
const arma::Row< size_t > &  responses,  
OptimizerType &  optimizer,  
CallbackTypes &&...  callbacks  
) 
Train the LogisticRegression model with the given instantiated optimizer.
Using this overload allows configuring the instantiated optimizer before training is performed.
This will use the existing model parameters as a starting point for the optimization. If this is not what you want, then you should access the parameters vector directly with Parameters() and modify it as desired.
OptimizerType  Type of optimizer to use to train the model. 
CallbackTypes  Types of Callback Functions. 
predictors  Input training variables. 
responses  Outputs results from input training variables. 
optimizer  Instantiated optimizer with instantiated error function. 
callbacks  Callback function for ensmallen optimizer OptimizerType . See https://www.ensmallen.org/docs.html#callbackdocumentation. 