gelnet_blr_opt: GELnet optimizer for binary logistic regression

In ArtemSokolov/gelnet: Generalized Elastic Nets

Description

Constructs a GELnet model for logistic regression using the Newton method.

Usage

gelnet_blr_opt(X, y, l1, l2, max_iter = 100L, eps = 1e-05,
  silent = FALSE, verbose = FALSE, balanced = FALSE,
  nonneg = FALSE, w_init = NULL, b_init = NULL, d = NULL,
  P = NULL, m = NULL)

Arguments

X	n-by-p matrix of n samples in p dimensions
y	n-by-1 vector of binary response labels (must be in 0,1)
l1	coefficient for the L1-norm penalty
l2	coefficient for the L2-norm penalty
max_iter	maximum number of iterations
eps	convergence precision
silent	set to TRUE to suppress run-time output to stdout (default: FALSE)
balanced	boolean specifying whether the balanced model is being trained
nonneg	set to TRUE to enforce non-negativity constraints on the weights (default: FALSE )
w_init	initial parameter estimate for the weights
b_init	initial parameter estimate for the bias term
d	p-by-1 vector of feature weights
P	p-by-p feature association penalty matrix
m	p-by-1 vector of translation coefficients

Details

The method operates by constructing iteratively re-weighted least squares approximations of the log-likelihood loss function and then calling the linear regression routine to solve those approximations. The least squares approximations are obtained via the Taylor series expansion about the current parameter estimates.

Value

A list with two elements:

w

p-by-1 vector of p model weights

b

scalar, bias term for the linear model

See Also

gelnet.lin

ArtemSokolov/gelnet documentation built on Sept. 13, 2019, 4:01 a.m.