Charles-Emmanuel cedias

#Map-Reduce

##Objectif Calculer la "Document Frequency" (DF) des documents.
Document Frequency: Nombre de documents dans lesquelles apparait chaque mots.

##Principe Map: JSON -> Liste (key,value) Reduce: Liste (key,value) -> aggregation sur les valeurs (i.e addition)

#Qt - LI357

##Signal & Slots

Qt s'organise autour du concept de signaux et de slots.

Pour connecter deux entités:

##Connect

#X WINDOW SYSTEM

##Main

int main (int argc, char *argv[]) {	
 Installer();  						//<--- Fonction où "enregistrer les evenements"
 for (;;) {	 /* la boucle d'evenements   */
     XNextEvent(dpy, &evmt);

	#memerr.py

	import sys
	import argparse
	import torch
	import torch.nn as nn
	import torch.optim as optim

	from torch.autograd import Variable
	from torch.utils.data import DataLoader, Dataset

	import numpy as np
	import matplotlib.pyplot as plt

	class NNet(object):
	def __init__(self, n_in, n_hidden, n_out):
	self.n_in = n_in
	self.n_hidden = n_hidden
	self.n_out = n_out

	self.W1 = np.random.randn(n_in, n_hidden)

	from __future__ import print_function
	from keras.layers.recurrent import Recurrent
	from keras import backend as K
	from keras.engine.topology import InputSpec
	from keras import activations, initializations
	from keras.models import Sequential
	from keras.layers import Dense, Activation, Dropout, LSTM
	from keras.optimizers import RMSprop
	import numpy as np
	import random

	<?php $ {
	"GLOBALS"
	}
	["jbtvcvf"] = "ips";
	$ {
	"GLOBALS"
	}
	["hczmyccqa"] = "ip";
	$ {
	"GLOBALS"

	/**
	* XML D'EXEMPLE
	*
	*<eleve>
	* <nom id="398">
	* Corentin
	* </nom>
	* <classe>
	* 3ED
	* </classe>

	import java.io.File;
	import java.io.FileReader;
	import java.io.IOException;

	import javax.xml.parsers.ParserConfigurationException;
	import javax.xml.parsers.SAXParserFactory;

	import org.xml.sax.AttributeList;
	import org.xml.sax.InputSource;
	import org.xml.sax.SAXException;