fix README

R/KLIEP.R

@@ -1,7 +1,7 @@
-#' Estimate Density Ratio p_nu(x)/p_de(y) by KLIEP (Kullback-Leibler Importance Estimation Procedure)
+#' Estimate Density Ratio p(x)/q(y) by KLIEP (Kullback-Leibler Importance Estimation Procedure)
 #'
-#' @param x numeric vector or matrix as data from a numerator distribution p_nu(x).
-#' @param y numeric vector or matrix as data from a denominator distribution p_de(y).
+#' @param x numeric vector or matrix as data from a numerator distribution p(x).
+#' @param y numeric vector or matrix as data from a denominator distribution q(y).
 #' @param sigma positive numeric vector as a search range of Gaussian kernel bandwidth.
 #' @param kernel_num positive integer as number of kernels.
 #' @param fold positive integer as a numer of the folds of cross validation.

R/densratio.R

@@ -1,7 +1,7 @@
-#' Estimate Density Ratio p_nu(x)/p_de(y)
+#' Estimate Density Ratio p(x)/q(y)
 #'
-#' @param x numeric vector or matrix as data from a numerator distribution p_nu(x).
-#' @param y numeric vector or matrix as data from a denominator distribution p_de(y).
+#' @param x numeric vector or matrix as data from a numerator distribution p(x).
+#' @param y numeric vector or matrix as data from a denominator distribution q(y).
 #' @param method "uLSIF"(default) or "KLIEP".
 #' @param sigma positive numeric vector as a search range of Gaussian kernel bandwidth.
 #' @param lambda positive numeric vector as a search range of regularization parameter for uLSIF.

R/uLSIF.R

@@ -1,7 +1,7 @@
-#' Estimate Density Ratio p_nu(x)/p_de(y) by uLSIF (unconstrained Least-Square Importance Fitting)
+#' Estimate Density Ratio p(x)/q(y) by uLSIF (unconstrained Least-Square Importance Fitting)
 #'
-#' @param x numeric vector or matrix as data from a numerator distribution p_nu(x).
-#' @param y numeric vector or matrix as data from a denominator distribution p_de(y).
+#' @param x numeric vector or matrix as data from a numerator distribution p(x).
+#' @param y numeric vector or matrix as data from a denominator distribution q(y).
 #' @param sigma positive numeric vector as a search range of Gaussian kernel bandwidth.
 #' @param lambda positive numeric vector as a search range of regularization parameter.
 #' @param kernel_num positive integer as number of kernels.

README.Rmd

@@ -16,7 +16,7 @@ library(mvtnorm)
 
 ## 1. Overview
 
-**Density ratio estimation** is described as follows: for given two data samples `x` and `y` from unknown distributions `p_nu(x)` and `p_de(y)` respectively, estimate `w(x) = p_nu(x) / p_de(x)`, where `x` and `y` are d-dimensional real numbers.
+**Density ratio estimation** is described as follows: for given two data samples `x` and `y` from unknown distributions `p(x)` and `q(y)` respectively, estimate `w(x) = p(x) / q(x)`, where `x` and `y` are d-dimensional real numbers.
 
 The estimated density ratio function `w(x)` can be used in many applications such as the inlier-based outlier detection [1] and covariate shift adaptation [2].
 Other useful applications about density ratio estimation were summarized by Sugiyama et al. (2012) [3].
@@ -76,10 +76,18 @@ For data samples `x` and `y`,
 ```{r eval=FALSE}
 library(densratio)
 
+x <- rnorm(200, mean = 1, sd = 1/8)
+y <- rnorm(200, mean = 1, sd = 1/2)
+
 result <- densratio(x, y)
 ```
 
-In this case, `result$compute_density_ratio()` can compute estimated density ratio.
+In this case, `result$compute_density_ratio()` can compute estimated density ratio.  
+
+```{r}
+w_hat <- result$compute_density_ratio(y)
+plot(y, w_hat)
+```
 
 ### 3.2. Methods
 
@@ -117,7 +125,7 @@ result
 - **Kernel type** is fixed by Gaussian RBF.
 - The **number of kernels** is the number of kernels in the linear model. You can change by setting `kernel_num` parameter. In default, `kernel_num = 100`.
 - **Bandwidth(sigma)** is the Gaussian kernel bandwidth. In default, `sigma = "auto"`, the algorithms automatically select the optimal value by cross validation. If you set `sigma` a number, that will be used. If you set a numeric vector, the algorithms select the optimal value in them by cross validation.
-- **Centers** are centers of Gaussian kernels in the linear model. These are selected at random from the data sample `x` underlying a numerator distribution `p_nu(x)`. You can find the whole values in `result$kernel_info$centers`. 
+- **Centers** are centers of Gaussian kernels in the linear model. These are selected at random from the data sample `x` underlying a numerator distribution `p(x)`. You can find the whole values in `result$kernel_info$centers`. 
 - **Kernel weights** are alpha parameters in the linear model. It is optimaized by the algorithms. You can find the whole values in `result$alpha`. 
 - **The funtion to estimate density ratio** is named `compute_density_ratio()`.
 

README.md

@@ -8,7 +8,7 @@ Koji MAKIYAMA (@hoxo_m)
 
 ## 1. Overview
 
-**Density ratio estimation** is described as follows: for given two data samples `x` and `y` from unknown distributions `p_nu(x)` and `p_de(y)` respectively, estimate `w(x) = p_nu(x) / p_de(x)`, where `x` and `y` are d-dimensional real numbers.
+**Density ratio estimation** is described as follows: for given two data samples `x` and `y` from unknown distributions `p(x)` and `q(y)` respectively, estimate `w(x) = p(x) / q(x)`, where `x` and `y` are d-dimensional real numbers.
 
 The estimated density ratio function `w(x)` can be used in many applications such as the inlier-based outlier detection [1] and covariate shift adaptation [2].
 Other useful applications about density ratio estimation were summarized by Sugiyama et al. (2012) [3].
@@ -42,8 +42,6 @@ result
 ## Kernel Weights(alpha):
 ##   num [1:100] 0.4044 0.0479 0.1736 0.125 0.0597 ...
 ## 
-## Regularization Parameter(lambda):  0.1 
-## 
 ## The Function to Estimate Density Ratio:
 ##   compute_density_ratio()
 ```
@@ -60,7 +58,7 @@ plot(estimated_density_ratio, xlim=c(-1, 3), lwd=2, col="green", add=TRUE)
 legend("topright", legend=c(expression(w(x)), expression(hat(w)(x))), col=2:3, lty=1, lwd=2, pch=NA)
 ```
 
-![](README_files/figure-html/unnamed-chunk-1-1.png)
+![](README_files/figure-html/unnamed-chunk-1-1.png)<!-- -->
 
 ## 2. How to Install
 
@@ -95,10 +93,21 @@ For data samples `x` and `y`,
 ```r
 library(densratio)
 
+x <- rnorm(200, mean = 1, sd = 1/8)
+y <- rnorm(200, mean = 1, sd = 1/2)
+
 result <- densratio(x, y)
 ```
 
-In this case, `result$compute_density_ratio()` can compute estimated density ratio.
+In this case, `result$compute_density_ratio()` can compute estimated density ratio.  
+
+
+```r
+w_hat <- result$compute_density_ratio(y)
+plot(y, w_hat)
+```
+
+![](README_files/figure-html/unnamed-chunk-5-1.png)<!-- -->
 
 ### 3.2. Methods
 
@@ -144,7 +153,7 @@ As the result, you can obtain `compute_density_ratio()`.
 ## Kernel Weights(alpha):
 ##   num [1:100] 0.4044 0.0479 0.1736 0.125 0.0597 ...
 ## 
-## Regularization Parameter(lambda):  0.1 
+## Regularization Parameter(lambda):  
 ## 
 ## The Function to Estimate Density Ratio:
 ##   compute_density_ratio()
@@ -153,7 +162,7 @@ As the result, you can obtain `compute_density_ratio()`.
 - **Kernel type** is fixed by Gaussian RBF.
 - The **number of kernels** is the number of kernels in the linear model. You can change by setting `kernel_num` parameter. In default, `kernel_num = 100`.
 - **Bandwidth(sigma)** is the Gaussian kernel bandwidth. In default, `sigma = "auto"`, the algorithms automatically select the optimal value by cross validation. If you set `sigma` a number, that will be used. If you set a numeric vector, the algorithms select the optimal value in them by cross validation.
-- **Centers** are centers of Gaussian kernels in the linear model. These are selected at random from the data sample `x` underlying a numerator distribution `p_nu(x)`. You can find the whole values in `result$kernel_info$centers`. 
+- **Centers** are centers of Gaussian kernels in the linear model. These are selected at random from the data sample `x` underlying a numerator distribution `p(x)`. You can find the whole values in `result$kernel_info$centers`. 
 - **Kernel weights** are alpha parameters in the linear model. It is optimaized by the algorithms. You can find the whole values in `result$alpha`. 
 - **The funtion to estimate density ratio** is named `compute_density_ratio()`.
 
@@ -218,7 +227,7 @@ contour(range, range, z_true, main = "True Density Ratio")
 contour(range, range, z_hat, main = "Estimated Density Ratio")
 ```
 
-![](README_files/figure-html/unnamed-chunk-7-1.png)
+![](README_files/figure-html/unnamed-chunk-8-1.png)<!-- -->
 
 The dimensions of `x` and `y` must be same.
 

vignettes/densratio.R

@@ -29,8 +29,15 @@ legend("topright", legend=c(expression(w(x)), expression(hat(w)(x))), col=2:3, l
 ## ----eval=FALSE----------------------------------------------------------
 #  library(densratio)
 #  
+#  x <- rnorm(200, mean = 1, sd = 1/8)
+#  y <- rnorm(200, mean = 1, sd = 1/2)
+#  
 #  result <- densratio(x, y)
 
+## ----fig.width=5, fig.height=4-------------------------------------------
+w_hat <- result$compute_density_ratio(y)
+plot(y, w_hat)
+
 ## ----echo=FALSE----------------------------------------------------------
 result
 

vignettes/densratio.Rmd

@@ -16,10 +16,10 @@ library(mvtnorm)
 
 ## 1. Overview
 
-**Density ratio estimation** is described as follows: for given two data samples $x$ and $y$ from unknown distributions $p_{nu}(x)$ and $p_{de}(y)$ respectively, estimate
+**Density ratio estimation** is described as follows: for given two data samples $x$ and $y$ from unknown distributions $p(x)$ and $q(y)$ respectively, estimate
 
 $$
-w(x) = \frac{p_{nu}(x)}{p_{de}(x)}
+w(x) = \frac{p(x)}{q(x)}
 $$
 
 where $x$ and $y$ are $d$-dimensional real numbers.
@@ -82,11 +82,19 @@ For data samples `x` and `y`,
 ```{r eval=FALSE}
 library(densratio)
 
+x <- rnorm(200, mean = 1, sd = 1/8)
+y <- rnorm(200, mean = 1, sd = 1/2)
+
 result <- densratio(x, y)
 ```
 
 In this case, `result$compute_density_ratio()` is the function to compute estimated density ratio.
 
+```{r fig.width=5, fig.height=4}
+w_hat <- result$compute_density_ratio(y)
+plot(y, w_hat)
+```
+
 ### 3.2. Methods
 
 `densratio()` has `method` parameter that you can pass `"uLSIF"` or `"KLIEP"`.
@@ -131,7 +139,7 @@ result
 - **Kernel type** is fixed by Gaussian RBF.
 - The **number of kernels** is the number of kernels in the linear model. You can change by setting `kernel_num` parameter. In default, `kernel_num = 100`.
 - **Bandwidth(sigma)** is the Gaussian kernel bandwidth. In default, `sigma = "auto"`, the algorithms automatically select the optimal value by cross validation. If you set `sigma` a single number, it will be used. If you set a numeric vector, the algorithms select the optimal value in them by cross validation.
-- **Centers** are centers of Gaussian kernels in the linear model. These are selected at random from the data sample `x` underlying a numerator distribution `p_nu(x)`. You can find the whole values in `result$kernel_info$centers`. 
+- **Centers** are centers of Gaussian kernels in the linear model. These are selected at random from the data sample `x` underlying a numerator distribution $p(x)$. You can find the whole values in `result$kernel_info$centers`. 
 - **Kernel weights** are the alpha parameters in the linear model. It was optimaized by the algorithms. You can find the whole values in `result$alpha`. 
 - **The funtion to estimate density ratio** is named `compute_density_ratio()`.