logisCost: Cost function for a logistic classifier

In MarcoDVisser/rML: Machine Learning routines for R

Description

calculates the cost and derivatives for a simple logistic classifier

Gradient function for a logistic classifier

Usage

logisCost(theta, X, y, lambda)

logisGrad(theta, X, y, lambda)

Arguments

theta	numeric vector of parameters
X	design matrix: an array of numeric variables or features including intercepts
y	binary classification vector
lambda	regularization coefficient

Author(s)

Marco D. Visser

Examples

X<-array(rnorm(200),dim=c(100,2))
y<-sqrt(X[,2]^2+X[,1]^2)<.5
designX<-mapFeat(X)
designX<-cbind(rep(1,100),designX)
theta<-rep(0,ncol(designX))
lambda<-1
logisCost(theta,designX,y,lambda)
logisGrad(theta,designX,y,lambda)
optim(theta,logisCost,logisGrad,X=designX,y=y,lambda=1,method="BFGS")

MarcoDVisser/rML documentation built on May 7, 2019, 2:49 p.m.