Statistics 4240 - Data Mining

Here is my code for homework 1. D.


# read the data

myData <- read.table("/Users/dbmad/Documents/DM08/fatality.txt",header=TRUE)
x <- myData[,1]
y <- myData[,2]



# constants

N <- dim(myData)[1]
MAXWINDOWSIZE <- 250
MINWINDOWSIZE <- 5
NUMITER <- 100

# initialization

pValue <- 0;
smallestLambdaArray <- rep(0,NUMITER);

count <- 1 #first iteration of the algorithm is to find actual clusters, all other permutations are for comparison
compareprob <- rep(0, NUMITER)

while(count <= NUMITER) {
cat("iteration: ",count,"\n");
smallestLambda <- 100000
windowSize <- MINWINDOWSIZE
while(windowSize < MAXWINDOWSIZE){
windowStart <- 1
while (windowStart <= (N - windowSize + 1)) {
window <- y[windowStart:(windowStart+windowSize-1)]
positives <- sum(window)
negatives <- windowSize - positives
outWindowSize <- N - windowSize
outPositives <- sum(y) - positives
outNegatives <- sum(1-y) - negatives
if ((positives > 0) && (negatives > 0) && ((positives/windowSize)>(sum(y)/N))) {
windowLambda <- -1 * (log((positives/windowSize)^positives) + log((negatives/windowSize)^negatives) + log((outPositives/outWindowSize)^outPositives) + log((outNegatives/outWindowSize)^outNegatives))
if (windowLambda < smallestLambda) {
smallestLambda <- windowLambda
# cat(x[windowStart],"-",x[windowStart+windowSize-1],"\t",windowLambda,"\t",windowSize,"\t",positives,"\n");
if(count == 1) {
windowSizeOfCluster <- windowSize
clusterWindowStart <- windowStart
}
}
}
windowStart <- windowStart + 1
}
windowSize <- windowSize + 1
}
y <- sample(y, N, replace = FALSE)
smallestLambdaArray[count] <- smallestLambda
if (smallestLambdaArray[count] <= smallestLambdaArray[1]) {
pValue <- pValue + 1;

}

count <- count + 1

}

cat("Size: ", windowSizeOfCluster,"\n")

cat("Window: ",x[clusterWindowStart],"-",x[clusterWindowStart+windowSizeOfCluster-1],"\n")

cat("pValue: ",pValue/NUMITER,"\n")