rahul8590

#!/bin/python

from __future__ import division
import operator


class Slack:

    def __init__(self):
        self.items = []

rahul8590

#!/bin/python
import sys

def splitstr(n,cut_list):
    if len(cut_list) == 0: 
        return [0,[]]
    min_positions = []
    min_cost = sys.maxint
    for k in cut_list:
        left_split = [ x for x in cut_list if x < k]

rahul8590

#!/bin/bash

wget --no-check-certificate --no-cookies --header "Cookie: oraclelicense=accept-securebackup-cookie" http://download.oracle.com/otn-pub/java/jdk/7u55-b13/jdk-7u55-linux-i586.tar.gz

wget http://apache.mirrors.pair.com//cassandra/2.0.7/apache-cassandra-2.0.7-bin.tar.gz

tar -xvf jdk-7u55-linux-i586.tar.gz\?AuthParam\=1397971823_52e8ace10923c9f8a5705faca17b4719

gunzip apache-cassandra-2.0.7-bin.tar.gz

rahul8590

import javax.script.ScriptEngine;
import javax.script.ScriptEngineFactory;
import javax.script.ScriptEngineManager;
import org.luaj.vm2.*;
import org.luaj.vm2.lib.jse.*;
import javax.script.CompiledScript;
import javax.script.Bindings;
import javax.script.Compilable;

public class ScriptEngineSample {

rahul8590

'''
divmod() function is a handy tool in such cases. 
>>> divmod(10,2)
(5, 0)
'''
r = set()
def factorize(n):
  for i in range(1,int(n ** 0.5) + 1 ):
    div,mod = divmod(n,i)
    if mod == 0:

rahul8590

1. General Background and Overview

• Probabilistic Data Structures for Web Analytics and Data Mining : A great overview of the space of probabilistic data structures and how they are used in approximation algorithm implementation.
• Models and Issues in Data Stream Systems
• Philippe Flajolet’s contribution to streaming algorithms : A presentation by Jérémie Lumbroso that visits some of the hostorical perspectives and how it all began with Flajolet
• Approximate Frequency Counts over Data Streams by Gurmeet Singh Manku & Rajeev Motwani : One of the early papers on the subject.
• [Methods for Finding Frequent Items in Data Streams](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.187.9800&rep

rahul8590

xl = {0:0.329,4:0.632,6:0.795,8:0.917,10:0.981}
sum = []
for n in range(1,21):
 a1 = xl[4]**n - xl[0]**n
 a2 = xl[6]**n - xl[4]**n
 a3 = xl[8]**n - xl[6]**n
 a4 = xl[10]**n - xl[8]**n
 a5 =  1 - xl[10]**n
 print "the intermediate values are " ,a1,a2,a3,a4,a5
 sum.append(a1*0 + a2*1 + a3*2 + a4*5.5 + a5*11)

rahul8590

// Use Gists to store code you would like to remember later on
console.log(window); // log the "window" object to the console

rahul8590

var Hapi = require('hapi');

// Create a server with a host and port
var server = Hapi.createServer('localhost', 8000);

server.route([
    
    {
	method: 'GET',
    path: '/{path*}',

rahul8590

#include<iostream>
#include <cmath>
  int main()
  {
    const int size = 256;
    double sinTable[size];
   
    #pragma omp parallel 
   {
	printf("hello\n");