plusml::MulticlassLogisticRegression Class Reference

Class implementing multiclassification logistic regression. More...

#include <multiclass_logistic_regression.h>

Public Member Functions
	MulticlassLogisticRegression (uint64_t features, uint64_t classes, bool bias_enabled=true)
	Constructor for MulticlassLogisticRegression.
uint64_t	Features () const
	Get number of features in each sample for the model.
void	FitSGD (const Eigen::MatrixXf &samples, const Eigen::MatrixXi &targets, float learning_rate, uint64_t batch_size, uint64_t epochs)
	Fit model using stochastic gradient descent with given hyperparameters.
Eigen::MatrixXf	Predict (Eigen::MatrixXf samples)
	Get predictions for a given matrix of samples.

Detailed Description

Class implementing multiclassification logistic regression.

Constructor & Destructor Documentation

MulticlassLogisticRegression()

plusml::MulticlassLogisticRegression::MulticlassLogisticRegression	(	uint64_t	features,
		uint64_t	classes,
		bool	bias_enabled = true )

Constructor for MulticlassLogisticRegression.

Parameters

features	Number of features in each sample
classes	Number of classes in classification problem
bias_enabled	Specifies whether to use bias or not

Member Function Documentation

Features()

uint64_t plusml::MulticlassLogisticRegression::Features

(

)

const

Get number of features in each sample for the model.

Returns

Number of features in each sample for the model

FitSGD()

void plusml::MulticlassLogisticRegression::FitSGD	(	const Eigen::MatrixXf &	samples,
		const Eigen::MatrixXi &	targets,
		float	learning_rate,
		uint64_t	batch_size,
		uint64_t	epochs )

Fit model using stochastic gradient descent with given hyperparameters.

Parameters

samples	Matrix of samples (MxN where M is number of samples and N is number of features in each sample)
targets	Matrix of targets (Mx1 where M is number of samples)
learning_rate	Learning rate for SGD
batch_size	Batch size for SGD
epochs	Number of epochs for SGD

Predict()

Eigen::MatrixXf plusml::MulticlassLogisticRegression::Predict

(

Eigen::MatrixXf

samples

)

Get predictions for a given matrix of samples.

Parameters

samples

Matrix of samples (MxN where M is number of samples and N is number of features in each sample)

Returns

Matrix of predicted probabilities (MxN where M is number of samples and N is number of classes)

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The documentation for this class was generated from the following files:

• include/PlusML/multiclass_logistic_regression.h
• src/multiclass_logistic_regression.cpp