saraswatmks

pos_w = 1/mean(train_tmp$target)
w = ifelse(train_tmp$target == 0, 1, pos_w)
w = w/sum(w)

saraswatmks

insertion_sort <- function(line)
{
  
  for(i in 2:length(line))
  {
    
    k <- i-1
    tmp <- line[i]
    while(k > 0 && line[k] > tmp)
    {

saraswatmks

N, D = map(int, input().split())

rate = [int(x) for x in input().split()]

for _ in range(D):
    prof1 = int(input())
    d = {} # this has indexes
    d1 = {} ## this has index difference

    for i in range(len(rate)):

saraswatmks

## https://www.hackerrank.com/contests/morgan-stanley-codeathon-2017/challenges/shell-sort-command

n = int(input())
strs = [input().split() for x in range(n)]

count_space = len([item for sublist in strs for item in sublist])/ len(strs)

key, reverse_flag, c_type = input().split()

## set type first

saraswatmks

damn <- function(x)  ## x should be a vector
{
  p <- list()
  
  for(j in x)
  {
  out <- tryCatch({
      
      if(j %% 2 == 0) p <- append(p, j)
      else p <- append(p, 101)

saraswatmks

# Enter your code here. Read input from STDIN. Print output to STDOUT
T = int(raw_input())
sell = [[] for i in range(100)]
product_sell = [[[] for j in range(10)] for i in range(100)]
state_sell = [[[] for j in range(7)] for i in range(100)]
p_s_sell = [[[[] for j in range(7)] for j in range(10)] for i in range(100)]
for t in range(T):
    query = 0
    data = raw_input().split()
    if data[0] == 'S':

saraswatmks

#include <iostream>
#include <cstring>

using namespace std;

void parseDot(char s[100], int &first, int &second) {
    int length = strlen(s);
    int temp = 0, dotFound = 0, negative = 1;
    for (int i = 0; i < length; i++) {
        if (s[i] == '.') {

saraswatmks

path <- "/home/manish/Desktop/temp"
setwd(path)

library(data.table)
library(lubridate)
library(tidytext)

saleData <- fread("new_data/final_model_data.txt")

head(saleData)

saraswatmks

def calculate_ndcg(test_movies, predicted_movies, already_watched):
	partial_dcgs = []
	predicted_event_flags = {}
	actual_events = test_movies
	for pred_index, predicted_event in enumerate(predicted_movies):
		try:
			last_occ = predicted_event_flags.get(predicted_event, -1)
			actual_index = actual_events[last_occ+1:].index(predicted_event)
			effective_index = actual_index + last_occ + 1
			predicted_event_flags[predicted_event] = effective_index

saraswatmks

strn = 'abcbaabbcc'
d = {}
k = 2
prev_word = strn[0]
c = 1

for s in strn[1:]:
    if prev_word == s:
        c+=1
        print('this is c = {} in first loop'.format(c))