A Single-layer Perceptron. More...

#include <yat/classifier/Perceptron.h>

Public Member Functions
const utility::Matrix &	covariance (void) const

double	oddsratio (size_t i) const

double	oddsratio_lower_CI (size_t i, double alpha=0.05) const

double	oddsratio_upper_CI (size_t i, double alpha=0.05) const

double	p_value (size_t i) const

double	predict (const utility::VectorBase &x) const

void	train (const utility::MatrixBase &x, const Target &target)
	train the model More...

const utility::Vector &	weight (void) const

Detailed Description

A Single-layer Perceptron.

Data are modeled as $\mu = \frac{1}{1 + \exp(-wx)}$

Since: New in yat 0.16

Member Function Documentation

◆ covariance()

const utility::Matrix& theplu::yat::classifier::Perceptron::covariance ( void ) const

Estimated covariance of weight vector.

Covariance is estimated as $\left(X'SX\right)^{-1}$ where $ S $ is a diagnoal matrix with $S_{ii} = \mu_i (1-\mu_i)$ where $\mu_i$ is the expected value of sample i.

◆ oddsratio()

double theplu::yat::classifier::Perceptron::oddsratio ( size_t i ) const

The odds ratio is defined as $\textrm{OR} = \exp(w_i)$

◆ oddsratio_lower_CI()

double theplu::yat::classifier::Perceptron::oddsratio_lower_CI	(	size_t	i,
		double	alpha = `0.05`
	)		const

The lower end of the confidence interval of estimation of oddsratio i with confidence 1 - alpha. The true value is estimated to be within confidence interval with probability 1 - alpha.

◆ oddsratio_upper_CI()

double theplu::yat::classifier::Perceptron::oddsratio_upper_CI	(	size_t	i,
		double	alpha = `0.05`
	)		const

The lower end of the confidence interval of estimation of oddsratio i with confidence 1 - alpha. The true value is estimated to be within confidence interval with probability 1 - alpha.

◆ p_value()

double theplu::yat::classifier::Perceptron::p_value ( size_t i ) const

Returns: p-value that for null hypothesis that ith weight is zero

◆ predict()

double theplu::yat::classifier::Perceptron::predict ( const utility::VectorBase & x ) const

Returns: $\frac{1}{1 + \exp(-wx)}$

◆ train()

void theplu::yat::classifier::Perceptron::train	(	const utility::MatrixBase &	x,
		const Target &	target
	)

train the model

Model parameters, $w$ , are calculated such that the log-likelihood, $\log \mathcal{L} = \sum y_i \log \left(\mu_i\right) + (1-y_i) \log \left(1 - \mu_i\right)$ , is maximized.

Parameters

x	each row corresponds to a data point and each column a feature.
target	describes the class label for each data point. Data that has binary set are trained to output 1.

◆ weight()

const utility::Vector& theplu::yat::classifier::Perceptron::weight ( void ) const

Returns: trained weight vector, .

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

yat/classifier/Perceptron.h

Public Member Functions