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# 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){ |
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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) + |
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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){ |
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#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{ |
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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))+ |
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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) |
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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){ |
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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 |
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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) |
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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") |