ulsif: Unconstrained Least Square Importance Fitting (ULSIF)

In hhau/densityratiosugiyama: Sugiyama, Suzuki and Kanamori (2012) density ratio estimation methods.

Description

Sugiyama, Suzuki and Kanamori (2012) density ratio estimation method with L2 penalty on the basis parameters.

Usage

ulsif(x.de, x.nu, lambda, sigma.chosen = 0.2, is.adaptive = FALSE,
  neigh.rank = 5, kernel.low = 0.5, kernel.high = 2, b = 50,
  fold = 6)

Arguments

x.de	A matrix with d rows, with one sample from p(x_de) per column.
x.nu	A matrix with d rows, with one sample from p(x_nu) per column.
lambda	Positive real number. Regularisation parameter, see Sugiyama, Suzuki and Kanamori (2012) Section 6.2.1 for details
sigma.chosen	Positive real number. Sigma for the Gaussian kernel radial basis functions. If this is set to zero, will be chosen via cross validation.
is.adaptive	Boolean. Adaptively choose location of basis functions.
neigh.rank	Positive integer. How many other kernels to use to compute distance metrics.
kernel.low	Real number. Lower bound for rescaled distances.
kernel.high	Real number. Upper bound for rescaled distances.
b	Positive integer. How many kernels to use.
fold	Positive integer. How many cross validation folds to use to select sigma.chosen

Details

x.de and x.nu should be the same dimension (same number of rows), but there can an uneven number of samples (number of rows).

hhau/densityratiosugiyama documentation built on May 14, 2019, 7:57 p.m.