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)

abikoushi

library(ggplot2)
library(reshape2)
library(patchwork)

diffeq <- function(r, u_ini, m,
                   sys_noise=0,
                   obs_noise=0,
                   implicit=FALSE){ 
  n <- length(u_ini)
  if(implicit){

abikoushi

difueq <- function(u_ini, timestep){
  Deltat <-1/400
  Deltax <-1/10
  Deltay <-1/10
  r <- Deltat/(Deltax^2+Deltay^2)
  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

abikoushi

module pelt

using Distributions
using Statistics

function costlpnorm(y, sta, en) 
  suby = y[sta:en]
  muhat = mean(suby)
  sigmahat = std(suby)
  return -sum(x -> logpdf(Normal(muhat, sigmahat), x), suby)

abikoushi

library(ggplot2)
library(tidyr)
library(tibble)
library(dplyr)
#library(pheatmap)
head(iris)

Y <- log(as.matrix(iris[,1:4]))
d <- dist(Y)
h <- hclust(d,method = "ward.D")

abikoushi

library(ggplot2)
library(tidyr)
library(tibble)
library(dplyr)
library(pheatmap)
Y <- log(as.matrix(iris[,-5]))
drow <- rowMeans(sweep(Y,2,Y[1,])^2)
dcol <- colMeans(sweep(Y,1,Y[,1])^2)
df <- rowid_to_column(iris) %>% 
  mutate(rrow = order(drow)) %>%