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kernel.Rmd
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kernel.Rmd
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---
title: "kernel"
output: pdf_document
---
```{r}
library(RLT)
load("./data/kernel_9004_500.Rdata")
load("./data/y.Rdata")
X <- kernel_90004
y <- as.factor(mortality)
n = nrow(X)
trainn = 50
testn = n - trainn
ntrees = 1
ncores = 1
nmin = 10
mtry = 10
sampleprob = 0.85
rule = "best"
nsplit = ifelse(rule == "best", 0, 3)
importance = FALSE
trainid = sample(1:n, trainn,replace = TRUE)
trainX = X[trainid,trainid]
trainY = y[trainid]
testX = X[-trainid,trainid]
testY = y[-trainid]
```
```{r}
n <- 1000
BM <- matrix(NA, nrow = n, ncol = n)
Y <- as.factor(rep(c(0,1), each = n/2))
for (i in 1:n) {
for (j in 1:n) {
if(i == j){
BM[i,j] <- 1
}else{
if(Y[i] == Y[j]){
p = 0.6
}else{
p=0.4
}
BM[i,j] <- sample(c(1,0), size = 1, prob = c(p,1-p) )
}
}
}
```
```{r}
X <- BM
y <- Y
n = nrow(X)
trainn = 0.1 * n
testn = n - trainn
ntrees = 1000
ncores = 1
nmin = 5
mtry = 3
sampleprob = 0.85
rule = "best"
nsplit = ifelse(rule == "best", 0, 3)
importance = FALSE
trainid = sample(1:n, trainn,replace = TRUE)
trainX = X[trainid,trainid]
trainY = y[trainid]
testX = X[-trainid,trainid]
testY = y[-trainid]
```
```{r,message=FALSE}
RLTfit <- RLT(trainX, trainY, ntrees = ntrees, ncores = ncores, nmin = nmin/2, mtry = mtry, split.gen = rule, nsplit = nsplit, resample.prob = sampleprob, importance = importance)
RLTPred <- predict.RLT(RLTfit, testX, ncores = ncores)
#treePred <- apply(RLTPred$Prediction , 1, function(x) mean(x,na.rm = TRUE))
treePred <- RLTPred$Prediction
which(!complete.cases(treePred))
apply(treePred, 1, mean)
Ktrain <- graph.kernel(RLTfit,trainX)
Ktest <- graph.kernel(RLTfit, testX, trainX)
```
```{r}
trainX
trainY
```
```{r}
#library('plot.matrix')
RLTKtrain <- Ktrain$Kernel/ntrees
RLTKtest <- Ktest$Kernel/ntrees
trainK <- exp(-trainX/1000)
pred <- RLTKtest %*% as.numeric(trainY)/rowSums(RLTKtest)
table(as.numeric(pred > 0.5), testY)
mean(as.numeric(pred > 0.5) == testY)
mean(as.numeric(treePred > 0.5) == testY)
```
```{r}
sample_id <- sample(1:10,n,replace = TRUE)
per <- matrix(NA, nrow = 2, ncol = 10)
for (i in 1:10) {
trainid <- which(sample_id == i)
trainX = X[trainid,trainid]
trainY = y[trainid]
testX = X[-trainid,trainid]
testY = y[-trainid]
RLTfit <- RLT(trainX, as.factor(trainY), ntrees = 100, ncores = ncores, nmin = nmin/2, mtry = mtry, split.gen = rule, nsplit = nsplit, resample.prob = sampleprob, importance = importance)
RLTPred <- predict.RLT(RLTfit, testX, ncores = ncores)
treePred <- apply(RLTPred$Prediction , 1, function(x) mean(x,na.rm = TRUE))
Ktest <- graph.kernel(RLTfit, testX, trainX)
RLTKtest <- Ktest$Kernel/ntrees
pred <- RLTKtest %*% trainY/rowSums(RLTKtest)
per[1,i] <- mean(as.numeric(pred > 0.5) == testY)
per[2,i] <- mean(as.numeric(treePred > 0.5) == testY)
}
```
```{r}
```