tests/example.R

In LuXiaoEei/image-denosing-by-single-sided-local-linear-kernel-estimation: image-denosing-by-single-sided-local-linear-kernel-estimation

require(data.table)
require(EBImage)
require(SSLKE)
f1 <- function(x,y){
  z <- -2*(x-0.5)^2-2*(y-0.5)^2+sign((x-0.5)^2+(y-0.5)^2<0.25^2)
return(z)
  }
test <- data.table(x=rep(c(0:99),100),y=rep(c(0:99),each=100))
test$z <-(f1(test[["x"]]/100,test[["y"]]/100)+1)/2
#set.seed(111)
Test <- copy(test)
Test$z <- test[['z']]+rnorm(10000,0,0.2^2)

display(matrix(test[['z']],100))#原图

display(matrix(Test[['z']],100))#加噪

Dist <- as.matrix(dist(test[,c('x','y')]))
time1 <- Sys.time()
hat_z <- c()
Test <- as.data.frame(Test)
SearchPoints0 <- SearchPoints0(h=5)
for (i in 1:nrow(Test)){
  print(Sys.time())
  hat_z[i] <- SSLKE:::LKE(m=Test[i,1],n=Test[i,2],h=5,
      data=Test,
      SearchPoints0,
      xmin = 0,
      xmax = 100,
      ymin = 0,
      ymax = 100,
      scale=100,
      Dist=Dist,
      index=i)[1,1]
}
Sys.time()-time1

display(matrix(hat_z,100))#局部线性核光滑




Rprof("Rprof-mem.out", memory.profiling=TRUE)
time1 <- Sys.time()
hat_Z <- data.frame(matrix(numeric(0),ncol=6,nrow = nrow(Test)))
Test <- as.data.frame(Test)
SearchPoints0 <- SearchPoints0(h=5)
for (i in 1:nrow(Test)){
  print(i)
  hat_Z[i,] <- LKE_side(m=Test[i,1],n=Test[i,2],h=5,
                  data=Test,
                  SearchPoints0,
                  xmin = 0,
                  xmax = 100,
                  ymin = 0,
                  ymax = 100,
                  scale=100)
}#论文中单边的局部线性核光滑
Sys.time()-time1

result <- judge_u(data=hat_Z,u=0.0002599*4) #选择估计值
Rprof(NULL)
summaryRprof("Rprof-mem.out", memory="both")


display(matrix(result[,1],100))#局部线性核光滑
display(matrix(result[,2],100))#大于0部分局部线性核光滑
display(matrix(result[,3],100))#小于0部分局部线性核光滑
display(matrix(result[,7],100))#论文中单边局部线性核光滑

MISE(result[,7],test[,3]) #计算积分均方误差


Sys.time()
LKE(m=2,n=2,h=5,data=Test,SearchPoints0(h=5),xmin = 0,xmax = 99,ymin = 0,ymax = 99,scale=100)
Sys.time()
LKE_side(m=2,n=0,h=5,data=Test,SearchPoints0(h=5),xmin = 0,xmax = 99,ymin = 0,ymax = 99,scale=100)
Sys.time()

m=2
n=0
h=5
data=Test
SearchPoints0<- SearchPoints0(h=5)
xmin = 0
xmax = 99
ymin = 0
ymax = 99
scale=100

Z = Area2$z
a=beta2[1,1]
x_grad = beta2[2,1]
X = Area2$x
y_grad = beta2[3,1]
Y = Area2$y
W = Area2$K


data=hat_Z
u=0.0002599