logisCost: Cost function for a logistic classifier

Description Usage Arguments Author(s) Examples

View source: R/logisClass.R

Description

calculates the cost and derivatives for a simple logistic classifier

Gradient function for a logistic classifier

Usage

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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

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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.