LMLogisticLossIterations: Linear model iteration with logistic loss

In SixianZhang/CS499-Coding-Project-2: Linear model for regression and binary classification

Description

Training by using gradient descent on a linear model with logistic loss . Return a matrix of weight vector for each iteration to the max iteration.

Usage

LMLogisticLossIterations(X.mat, y.vec, max.iterations, step.size)

Arguments

X.mat	train feature matrix of size [n x p]
y.vec	train label vector of size [n x 1]
max.iterations	integer scalar greater than 1
step.size	a numeric scalar greater than zero

Value

W.mat matrix of weight vectors of size [(p + 1) x max.iterations]

Examples

data(spam, package = "ElemStatLearn")
X.mat <- as.matrix(spam[, 1:57])
y.vec <- ifelse(spam$spam == "spam", 1, -1)
W.mat <- LMLogisticLossIterations(X.mat, y.vec, max.iterations = 100L, step.size = 0.5)
(W.mat)

SixianZhang/CS499-Coding-Project-2 documentation built on May 26, 2019, 3:31 p.m.