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hoxo-m committed Jun 30, 2019
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2 changes: 1 addition & 1 deletion DESCRIPTION
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Package: densratio
Type: Package
Version: 0.2.0
Version: 0.2.1
Title: Density Ratio Estimation
Description: Density ratio estimation.
The estimated density ratio function can be used in many applications such as
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4 changes: 2 additions & 2 deletions R/KLIEP.R
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Expand Up @@ -4,8 +4,8 @@
#' @param x2 numeric vector or matrix. Data from a denominator distribution q(x).
#' @param sigma positive numeric vector. Search range of Gaussian kernel bandwidth.
#' @param kernel_num positive integer. Number of kernels.
#' @param fold positive integer. Numer of the folds of cross validation.
#' @param verbose logical(default TRUE).
#' @param fold positive integer. Number of the folds of cross validation.
#' @param verbose logical (default TRUE).
#'
#' @return KLIEP object that contains a function to compute estimated density ratio.
#'
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2 changes: 1 addition & 1 deletion R/compute_kernel_Gaussian.R
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Expand Up @@ -15,7 +15,7 @@ kernel_Gaussian <- function(x, y, sigma) {
#' @param x a numeric vector.
#' @param y a numeric vector.
#'
#' @return squared euclid distance
#' @return squared Euclid distance
squared_euclid_distance <- function(x, y) {
sum((x - y) ^ 2)
}
4 changes: 2 additions & 2 deletions README.Rmd
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Expand Up @@ -24,8 +24,8 @@ library(mvtnorm)

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4 changes: 2 additions & 2 deletions README.md
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[![Travis-CI Build
Status](https://travis-ci.org/hoxo-m/densratio.svg?branch=master)](https://travis-ci.org/hoxo-m/densratio)
[![CRAN
Version](http://www.r-pkg.org/badges/version/densratio)](https://CRAN.R-project.org/package=densratio)
Version](https://www.r-pkg.org/badges/version/densratio)](https://CRAN.R-project.org/package=densratio)
[![CRAN
Downloads](http://cranlogs.r-pkg.org/badges/densratio)](http://cranlogs.r-pkg.org/badges/densratio/)
Downloads](https://cranlogs.r-pkg.org/badges/densratio)](https://CRAN.R-project.org/package=densratio)
[![Coverage
Status](https://coveralls.io/repos/github/hoxo-m/densratio/badge.svg?branch=master)](https://coveralls.io/github/hoxo-m/densratio?branch=master)
[![Say
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4 changes: 2 additions & 2 deletions man/KLIEP.Rd

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2 changes: 1 addition & 1 deletion man/squared_euclid_distance.Rd

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1 change: 1 addition & 0 deletions tests/testthat/test-KLIEP.R
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Expand Up @@ -14,6 +14,7 @@ test_that("KLIEP", {
expected_kernel_weights <- c(0.0885607375, 0.0178664639, 0.0240389107,
0.0000000000, 0.0810753470, 0.0001353598)

testthat::skip_on_cran()
expect_equal(head(kernel_weights), expected_kernel_weights)
expect_equal(sigma, 0.09)
})
1 change: 1 addition & 0 deletions tests/testthat/test-RuLSIF.R
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Expand Up @@ -14,6 +14,7 @@ test_that("RuLSIF", {
expected_kernel_weights <- c(0.070454411, 0.028303149, 0.003146211,
0.010641579, 0.055200243, 0.012069721)

testthat::skip_on_cran()
expect_equal(head(kernel_weights), expected_kernel_weights)
expect_equal(sigma, 0.1)
expect_equal(lambda, 0.1)
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1 change: 1 addition & 0 deletions tests/testthat/test-uLSIF.R
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Expand Up @@ -14,6 +14,7 @@ test_that("uLSIF", {
expected_kernel_weights <- c(0.0674550859, 0.0400446153, 0.0004589047,
0.0168489465, 0.0670843163, 0.0189929309)

testthat::skip_on_cran()
expect_equal(head(kernel_weights), expected_kernel_weights)
expect_equal(sigma, 0.1)
expect_equal(lambda, 1)
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2 changes: 1 addition & 1 deletion vignettes/densratio.Rmd
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Expand Up @@ -25,7 +25,7 @@ $$

where $x1$ and $x2$ are $d$-dimensional real numbers.

The estimated density ratio function $w(x)$ can be used in many applications such as **anomaly detection** [Hido et al. 2011], **changepoint detection** [Liu et al. 2013], and **covariate shift adaptation** [Sugiyama et al. 2007].
The estimated density ratio function $w(x)$ can be used in many applications such as **anomaly detection** [Hido et al. 2011], **change-point detection** [Liu et al. 2013], and **covariate shift adaptation** [Sugiyama et al. 2007].
Other useful applications about density ratio estimation were summarized by [Sugiyama et al. 2012].

The package **densratio** provides a function `densratio()` that returns an object with a method to estimate density ratio as `compute_density_ratio()`.
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