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//Mongo Installation

// Generate mongod with parameters
echo "mongod --dbpath=~/data --nojournal" > mongod
chmod a+x mongod


// 1. Run the mongo deamon (mongod)
cd ~/data
./mondog

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function mit18086_poisson
%MIT18086_POISSON
%    Sets up and solves a 1d, 2d and 3d Poisson problem
%    on domain [0 1]^dim, with homogeneous Dirichlet b.c.
%    everywhere and constant right hand side.
%    Uses successive Kronecker products to construct
%    sparse system matrices

% 03/2007 by Benjamin Seibold
%            http://www-math.mit.edu/~seibold/

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// Asyncronous for each

async.forEach(messageIds, function(messageId, callback) {
    // Do stuff
    callback();
}, function(err) {
    if (err) return next(err);
    // Success
});

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let promiseToCleanTheRoom = new Promise(function(resolve, reject) {

  //cleaning the room

  let isClean = false;

  if (isClean) {
    resolve('Clean');
  } else {
    reject('not Clean');

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// Implementation
function sleep(ms){
    return new Promise(resolve=>{
        setTimeout(resolve,ms)
    })
}

// Usage. Sleep two seconds
await sleep(2000)

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#include <vector>
#include <iostream>
using namespace std;

// Requires C++11 compiler
int main(int argc, char const *argv[])
{
   vector<int> vec{5,7,2,32,9,45};

   for_each(vec.begin(), vec.end(),

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---
BasedOnStyle: LLVM
AccessModifierOffset: -2
AlignAfterOpenBracket: true
AlignEscapedNewlinesLeft: false
AlignOperands: true
AlignTrailingComments: true
AllowAllParametersOfDeclarationOnNextLine: true
AllowShortBlocksOnASingleLine: false
AllowShortCaseLabelsOnASingleLine: false

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sysctl -a hw

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# module load python/3.6-anaconda-4.4

import csv
with open("susy_full_normed_train.csv","rt", encoding="utf8") as source:
    rdr= csv.reader( source )
    with open("susy_full_normed_train_low_features.csv","wt") as result:
        wtr= csv.writer( result )
        for r in rdr:
            wtr.writerow( (r[0], r[1], r[2], r[3], r[4], r[5], r[6], r[7] ) )

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if [[ $1 == "r" ]];
then
rm -rf build
mkdir build
fi

cd build

# -DCMAKE_BUILD_TYPE=Release \