test<-0
install.packages("combinat")
w=4
to_sum=rep(0,w)
for (b in 1:w){
to_sum(b)=b^{w-b+1}*combn(365,b)
}
for (b in 1:w){
to_sum[b]=b^{w-b+1}*combn(365,b)
}
b^{w-b+1}*combn(365,b)
b
combn(365,b)
combn(365,4)
choose(365,4)
w=4
to_sum=rep(0,w)
for (b in 1:w){
to_sum[b]=b^{w-b+1}*choose(365,b)
}
the_sum=sum(to_sum)
E=1/365^w*the_sum
val=w*(365-E)
1/365^w
obj<-function(w){
to_sum=rep(0,w)
for (b in 1:w){
to_sum[b]=b^{w-b+1}*choose(365,b)
}
the_sum=sum(to_sum)
E=1/365^w*the_sum
val=w*(365-E)
return(val)
}
hrs=rep(0,365)
for (w in 1:365){
hrs[w]=obj(w)
}
plot(hrs)
hrs
obj(200)
obj2<-function(w){
to_sum=rep(0,w)
for (b in 1:w){
to_sum[b]=b^{w-b+1}*choose(365,b)/365^w
}
E=sum(to_sum)
val=w*(365-E)
return(val)
}
hrs=rep(0,365)
for (w in 1:365){
hrs[w]=obj2(w)
}
plot(hrs)
hrs=rep(0,365)
for (w in 1:365){
hrs[w]=obj(w)
}
plot(hrs)
hrs[1]
w=1
to_sum=rep(0,w)
for (b in 1:w){
to_sum[b]=b^{w-b+1}*choose(365,b)/365^w
}
E=sum(to_sum)
val=w*(365-E)
return(val)
to_sum=rep(0,w)
for (b in 1:w){
to_sum[b]=b^{w-b+1}*choose(365,b)/365^w
}
E=sum(to_sum)
val=w*(365-E)
w-365
w=365
to_sum=rep(0,w)
for (b in 1:w){
to_sum[b]=b^{w-b+1}*choose(365,b)/365^w
}
to_sum
w=200
b=200
b^{w-b+1}*choose(365,b)/365^w
b
w
choose(365,b)
365^b
365^b1/365^200
1/365^200
b
w
to_sum=rep(0,w)
for (b in 1:w){
to_sum[b]=(w-b+1)*log(b)+log(choose(365,b))-w*log(365)
}
to_sum
exp(to_sum)
sum(exp(to_sum))
obj3<-function(w){
to_sum=rep(0,w)
for (b in 1:w){
to_sum[b]=(w-b+1)*log(b)+log(choose(365,b))-w*log(365)
}
E=sum(exp(to_sum))
val=w*(365-E)
return(val)
}
hrs=rep(0,365)
for (w in 1:365){
hrs[w]=obj3(w)
}
plot(hrs)
hrs
obj3(365)
w=365
to_sum=rep(0,w)
for (b in 1:w){
to_sum[b]=(w-b+1)*log(b)+log(choose(365,b))-w*log(365)
}
E=sum(exp(to_sum))
E
hrs=rep(0,10000)
for (w in 1:365){
hrs[w]=obj3(w)
}
plot(hrs)
hrs
hrs=rep(0,1000)
for (w in 1:365){
hrs[w]=obj3(w)
}
plot(hrs)
hrs
obj3(365)
obj3(366)
obj3(400)
obj3(400)
obj3(500)
obj3<-function(w){
to_sum=rep(0,w)
for (b in 1:w){
to_sum[b]=(w-b+1)*log(b)+log(choose(365,b))-w*log(365)
}
E=sum(exp(to_sum))
val=w*(365-E)
return(val)
}
obj3(500)
hrs=rep(0,10000)
for (w in 1:length(hrs)){
hrs[w]=obj3(w)
}
hrs=rep(0,1000)
for (w in 1:length(hrs)){
hrs[w]=obj3(w)
}
plot(hrs)
w<-c(70,95,120,145,175)
(w-45)/2/2.2
w<-c(70,95,125,150,180)
(w-45)/2/2.2
w<-c(70,95,125,150,175)
(w-45)/2/2.2
w<-c(70,95,125,150,180)
(w-45)/2/2.2
w<-c(70,100,125,155,185)
(w-45)/2/2.2
w<-c(75,100,130,160,190)
(w-45)/2/2.2
w<-c(70,95,125,150,180)
(w-45)/2/2.2
w<-c(70,100,125,155,185)
(w-45)/2/2.2
print0('works')
print('works')
library(np)
library(tictoc)
setwd("/Users/rahul/Documents/kiv2019/kernelsmooth2")
df<-read.csv("df_in.csv",sep=",",header=FALSE)
names(df)<-c('p','t','s','y','z')
x_vis<-as.matrix(read.csv("df_vis.csv",sep=",",header=FALSE))
View(x_vis)
library(np)
library(tictoc)
setwd("/Users/rahul/Documents/kiv2019/kernelsmooth2")
df<-read.csv("df_in.csv",sep=",",header=FALSE)
names(df)<-c('p','t','s','y','z')
df <- df[order(df$s),]
df <- df[order(df$t),]
df <- df[order(df$p),]
View(df)
library(np)
library(tictoc)
setwd("/Users/rahul/Documents/kiv2019/kernelsmooth2")
df<-read.csv("df_in.csv",sep=",",header=FALSE)
names(df)<-c('p','t','s','y','z')
df <- df[order(df$t),]
df <- df[order(df$p),]
df <- df[order(df$s),]
View(df)
p<-df[[1]]
t<-df[[2]]
s<-df[[3]]
y<-df[[4]]
z<-df[[5]]
ts<-data.frame(t,s)
View(ts)
library(np)
library(tictoc)
setwd("/Users/rahul/Documents/kiv2019/kernelsmooth2")
df<-read.csv("df_in.csv",sep=",",header=FALSE)
names(df)<-c('p','t','s','y','z')
#match order of sorting to x_vis from get_design.m
df <- df[order(df$t),]
df <- df[order(df$p),]
df <- df[order(df$s),]
p<-df[[1]]
t<-df[[2]]
s<-df[[3]]
y<-df[[4]]
z<-df[[5]]
ts<-data.frame(t,s)
df_vis<-read.csv(read.csv("df_vis.csv",sep=",",header=FALSE))
p_vis<-df_vis[[1]]
t_vis<-df_vis[[2]]
s_vis<-df_vis[[3]]
df_vis<-read.matrix(read.csv("df_vis.csv",sep=",",header=FALSE))
library(np)
library(tictoc)
setwd("/Users/rahul/Documents/kiv2019/kernelsmooth2")
df<-read.csv("df_in.csv",sep=",",header=FALSE)
names(df)<-c('p','t','s','y','z')
#match order of sorting to x_vis from get_design.m
df <- df[order(df$t),]
df <- df[order(df$p),]
df <- df[order(df$s),]
p<-df[[1]]
t<-df[[2]]
s<-df[[3]]
y<-df[[4]]
z<-df[[5]]
ts<-data.frame(t,s)
df_vis<-read.csv("df_vis.csv",sep=",",header=FALSE)
p_vis<-df_vis[[1]]
t_vis<-df_vis[[2]]
s_vis<-df_vis[[3]]
ts_vis<-data.frame(t_vis,s_vis)
View(ts_vis)
library(np)
library(tictoc)
setwd("/Users/rahul/Documents/kiv2019/kernelsmooth")
df<-read.csv("df_in.csv",sep=",",header=FALSE)
names(df)<-c('x','y','z')
df <- df[order(df$x),]
x<-df[[1]]
y<-df[[2]]
z<-df[[3]]
x_vis<-as.matrix(read.csv("df_vis.csv",sep=",",header=FALSE))
#tic('kernel smoothing IV')
model.iv <- npregiv(y=y,
z=x,
w=z,
x=NULL,
zeval=x_vis, #comment out for x
xeval=NULL,
method="Tikhonov")
#toc()
y_vis <- model.iv$phi
plot(x_vis,y_vis)
library(np)
library(tictoc)
setwd("/Users/rahul/Documents/kiv2019/kernelsmooth2")
df<-read.csv("df_in.csv",sep=",",header=FALSE)
names(df)<-c('p','t','s','y','z')
#match order of sorting to x_vis from get_design.m
df <- df[order(df$t),]
df <- df[order(df$p),]
df <- df[order(df$s),]
p<-df[[1]]
t<-df[[2]]
s<-df[[3]]
y<-df[[4]]
z<-df[[5]]
ts<-data.frame(t,s)
df_vis<-read.csv("df_vis.csv",sep=",",header=FALSE)
p_vis<-df_vis[[1]]
t_vis<-df_vis[[2]]
s_vis<-df_vis[[3]]
ts_vis<-data.frame(t_vis,s_vis)
#tic('kernel smoothing IV')
model.iv <- npregiv(y=y,
z=p,
w=z,
x=ts,
zeval=p_vis, #comment out for x
xeval=ts_vis,
method="Tikhonov")
#toc()
y_vis <- model.iv$phi
library(np)
library(tictoc)
setwd("/Users/rahul/Documents/kiv2019/kernelsmooth2")
df<-read.csv("df_in.csv",sep=",",header=FALSE)
names(df)<-c('p','t','s','y','z')
#match order of sorting to x_vis from get_design.m
df <- df[order(df$t),]
df <- df[order(df$p),]
df <- df[order(df$s),]
p<-df[[1]]
t<-df[[2]]
s<-df[[3]]
y<-df[[4]]
z<-df[[5]]
ts<-data.frame(t,s)
df_vis<-read.csv("df_vis.csv",sep=",",header=FALSE)
p_vis<-df_vis[[1]]
t_vis<-df_vis[[2]]
s_vis<-df_vis[[3]]
ts_vis<-data.frame(t_vis,s_vis)
model.iv <- npregiv(y=y,
z=p,
w=z,
x=t, #should be ts
zeval=p_vis, #comment out for x
xeval=t_vis, #should be ts_vis
method="Tikhonov")
model.iv <- npregiv(y=y,
z=p,
w=z,
x=NULL, #should be ts
zeval=p_vis, #comment out for x
xeval=NULL, #should be ts_vis
method="Tikhonov")
View(ts)
#tic('kernel smoothing IV')
model.iv <- npregiv(y=y,
z=p,
w=z,
x=s, #should be ts
zeval=p_vis, #comment out for x
xeval=s_vis, #should be ts_vis
method="Tikhonov")
model.iv <- npregiv(y=y,
z=p,
w=z,
x=ts, #should be ts
zeval=p_vis, #comment out for x
xeval=ts_vis, #should be ts_vis
method="Tikhonov",
p=0)
y_vis <- model.iv$phi
write.csv(y_vis,file = "df_out.csv")
library(np)
library(tictoc)
setwd("/Users/rahul/Documents/kiv2019/kernelsmooth")
df<-read.csv("df_in.csv",sep=",",header=FALSE)
names(df)<-c('x','y','z')
df <- df[order(df$x),]
x<-df[[1]]
y<-df[[2]]
z<-df[[3]]
x_vis<-as.matrix(read.csv("df_vis.csv",sep=",",header=FALSE))
#tic('kernel smoothing IV')
model.iv <- npregiv(y=y,
z=x,
w=z,
x=NULL,
zeval=x_vis, #comment out for x
xeval=NULL,
method="Tikhonov",
p=0)
#toc()
y_vis <- model.iv$phi
y_vis <- model.iv$phi
plot(x_vis,y_vis)
