mwacc

# read dataset from local file
data <- read.csv("/Users/kostya/Downloads/abalone.data.csv", header=F)

# set names for dataframe columns
colnames(data) <- c('Sex', 'Length', 'Diameter', 'Height', 'WholeWeight', 'ShuckedWeight',
                    'VisceraWeight', 'ShellWeight', 'Rings')

# split dataset into train and test seta
train.size <- floor(0.9 * nrow(data))

mwacc

set.seed(100)
N <- 10000000
USER_NUM <- 25000
MOVIE_NUM <- 10000
dayOfWeek = rnorm(N, 6, 2)
partOfDay = c(1,2,3,3,4,4,4)
isRainOrSnow = c(0,0,0,1)
isRainOfSnowBefore = c(0,0,0,1)
temperature = c(0,1,2,3,4)
cameFrom = c(0,1,2,3,4,5,6,7)

mwacc

#install.packages("randomForest")
library(randomForest) 
train = read.csv(file="D:\\work\\R\\train.csv",head=TRUE,sep=",")
test = read.csv(file="D:\\work\\R\\test.csv",head=TRUE,sep=",")

rf = randomForest(ACTION ~ RESOURCE+MGR_ID+ROLE_DEPTNAME+ROLE_FAMILY_DESC+ROLE_ROLLUP_2+ROLE_ROLLUP_1+ROLE_TITLE+ROLE_CODE, data=train, importance=TRUE, ntree=250, mtry=3)

prediction = predict(rf, test)
write.csv(prediction, "D:\\work\\R\\out.csv", row.names=FALSE)

mwacc

def toSort  = [5, 6, 7, 1, 4, 3, 2, 9, 0, 7, 8]

def partition(toSort) {
	if(toSort.size == 1) return toSort

	int midle = (int)(toSort.size/2)
	def left = new ArrayList(), right = new ArrayList();
	for(int i = 0; i < midle; i++) left << toSort[i]
	for(int i = midle; i < toSort.size; i++) right << toSort[i]
	

mwacc

#Install the R package arules if required
#install.packages("arules");
#load the arules package
library("arules");
 
# read the transaction file as a Transaction class
txn = read.transactions(file="d:\\work\\R\\items.csv", rm.duplicates= FALSE, format="basket",sep=",");
 
#To visualize the item frequency in txn file
itemFrequencyPlot(txn);

mwacc

class Constants {
    static final goods = [
        'Apple', 'Orange', 'Pineaple', 'Cherry', 'Beef', 'Sugar', 'Milk',
        'Pear', 'Limon', 'Blubbery', 'IceCream', 'Cake', 'Orange', 'Milk',
        'Cola', 'Bread', 'Coffe', 'Cookies', 'Beer', 'Tea', 'Salmon', 'Steal Water', 'Nuts']
    
}

rand = new Random()

mwacc

/*
<dependencies>
        <dependency>
            <groupId>com.rabbitmq</groupId>
            <artifactId>amqp-client</artifactId>
            <version>3.1.1</version>
        </dependency>
    </dependencies>

*/

mwacc

def levenshtain(String s1, String s2, int i, int j) {
  if(i == -1 && j == -1) {
		return 0
	} else if( j == -1 && i > -1 ) {
		return i
	} else if(i == -1 && j > -1) {
		return j
	} else {
		return min(
				levenshtain(s1, s2, i, j-1)+1,

mwacc

class Node {
  def val
	def left
	def right
}


/*
     root
   A       B

mwacc

int i = 0;
File file = new File("out2.csv")
file << "Id,Action\n"

new File('out.csv').eachLine { line ->
  if(i > 0) {
    file.append i
  	file.append ','
  	file.append Double.valueOf(line) > 0.6 ? 1 : 0;
  	file.append '\n'