abikoushi

\documentclass[tikz,border=14pt]{standalone}
\usepackage{tikz}
\usetikzlibrary{matrix,shapes,decorations.pathreplacing, backgrounds, positioning}

\begin{document}
    \begin{tikzpicture}[
        %Styles
        Matrix/.style={
            matrix of nodes,
            text height=2.5ex,

abikoushi

library(pheatmap)
library(grid)

mid0scale <- function(test, paletteLength=120){
  myColor <- colorRampPalette(c("orange", "white", "royalblue"))(paletteLength)
  myBreaks <- c(seq(min(test), 0, length.out=ceiling(paletteLength/2) + 1), 
                seq(max(test)/paletteLength, max(test), length.out=floor(paletteLength/2)))
  return(list(color=myColor, breaks=myBreaks))
}
set.seed(123)

abikoushi

library(pheatmap)

# length(breaks) == length(paletteLength) + 1
# use floor and ceiling to deal with even/odd length pallettelengths
mid0scale <- function(test, paletteLength=120){
  myColor <- colorRampPalette(c("orange", "white", "royalblue"))(paletteLength)
  myBreaks <- c(seq(min(test), 0, length.out=ceiling(paletteLength/2) + 1), 
                seq(max(test)/paletteLength, max(test), length.out=floor(paletteLength/2)))
  return(list(color=myColor, breaks=myBreaks))
}

abikoushi

\documentclass[tikz,border=14pt]{standalone}
\usepackage{tikz}
\usetikzlibrary{shapes,positioning}
%\usepackage{xcolor}
\begin{document}
 \begin{tikzpicture}
\node[draw, rounded corners, fill=gray!10](dna) at (0,0){DNA};
\node[draw, rounded corners, fill=gray!10, right = of dna, xshift = +15pt](rna){RNA};
\node[draw, rounded corners, fill=gray!10, right = of rna, xshift = +15pt](protein) {protein};
\node[draw, rounded corners, fill=gray!10, right = of protein](phenotype) {phenotype};

abikoushi

# 須山『ベイズ推論による機械学習入門』（講談社）の実装例です

softmax <- function(x){
  maxx <- max(x)
  exp(x-maxx)/sum(exp(x-maxx))
}
logp_x <-function(x,lambda,loglambda){
  x*loglambda-lambda
}
logsumexp <- function(x){

abikoushi

library(ggplot2)
library(dplyr)

Andrews <- function(t,
                    Sepal.Length, Sepal.Width,
                    Petal.Length, Petal.Width,...){
  root2 <- sqrt(2)
  sapply(t, function(t){Sepal.Length/root2 +
                               Sepal.Width*sin(t) +
                               Petal.Length*cos(t) +

abikoushi

library(dplyr)
library(ggplot2)

set.seed(123)
fdf <- expand_grid(year=1:4, generation=1:4) %>% 
  mutate(mu=rnorm(16),sigma=abs(rnorm(16)))


layerfun <- split(fdf, interaction(fdf$year, fdf$generation)) %>% 
  lapply(function(x){

abikoushi

#1D
simLWR <- function(r, m, u0,
                   sys_noise=0, 
                   obs_noise=0,
                   explicit=FALSE){
  n <- length(u0)
  if(explicit){
    A <- diag(1-r,n)
    A[cbind(2:n,1:(n-1))] <- r    
  }else{

abikoushi

library(gganimate)
library(dplyr)

th <- seq(-pi,pi,by=0.01)
a <- seq(1,6, by=0.1)
df <- data.frame(x=c(cos(outer(th,a))),
                 y=rep(sin(th), length(a)),
                 a=rep(a, each=length(th)))

ggplot(df, aes(x=x, y=y, group=a, colour=a))+

abikoushi

library(gganimate)
library(ggplot2)
#install.packages("gganimate")
diffeq <- function(u_ini, r, timestep){
  M <- nrow(u_ini)
  N <- ncol(u_ini)
  u_old <- u_ini
  U <- array(0, dim=c(M,N,timestep+1))
  U[,,1] <- u_ini
  tmp <- matrix(0,M,N)