Marianne Monteiro hereismari

## catalogo_de_livros.cpp
#include <bits/stdc++.h>
#define MAX 100
#define INF -1
#define ll long long int
using namespace std;

vector<ll> livros[MAX];
vector<ll> answer;

bool comp(int a, int b) { return a > b; }

## local.conf
[[local|localrc]]
ADMIN_PASSWORD=nova
MYSQL_PASSWORD=nova
RABBIT_PASSWORD=nova
SERVICE_PASSWORD=$ADMIN_PASSWORD
SERVICE_TOKEN=nova

# Enable Swift
enable_service s-proxy s-object s-container s-account

## union-find.py
# n : numero de nos
# q : numero de operacoes
#     operacoes:
#         1, u, v : conecta conjuntos de u e v
#         2, u    : impreme tamanho do conjunto de u
#         3, u    : imprime par de u
#
n, q = map(int, raw_input().split())

# -------------------- UNION FIND --------------------------

## min-char-rnn.py
"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License

Original code from: Andrej Karpathy (@karpathy)
Modified by: Marianne Linhares (@mari-linhares)

"""

import numpy as np

## min-char-rnn-tensorflow.py
"""
Vanilla Char-RNN using TensorFlow by Vinh Khuc (@knvinh).
Adapted from Karpathy's min-char-rnn.py
https://gist.github.com/karpathy/d4dee566867f8291f086
Requires tensorflow>=1.0
BSD License
"""
import random
import numpy as np
import tensorflow as tf

## My Favorites.md

      
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## check_scoreboard.py
import Tkinter as tk
import tkMessageBox

import requests
import pandas as pd
import numpy as np

import time

url = "http://www.bombonera.org/score2017f2/score/#"

## eda-metodologia.R
# bibliotecas
library('dplyr');
library('ggplot2');
library('caret');

# importando os dados
dados <- read.csv('~/Documents/eda.csv')

# renomear as colunas
colnames(dados) <- c("matricula", "id_reclamacao", "insatisfacao");

## estimators-pseudocodigo.py
import tensorflow as tf

# Cria-se uma model function para implementar nosso modelo
def model_fn():
  pass

# Cria-se uma input function que deve ser passada para o Estimator na maior parte
# de suas operacoes

# input function de treino

## mnist_input_fn.py
# -*- coding: utf-8 -*-

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import tensorflow as tf
# MNIST
from tensorflow.examples.tutorials.mnist import input_data
	#include <bits/stdc++.h>
	#define MAX 100
	#define INF -1
	#define ll long long int
	using namespace std;

	vector<ll> livros[MAX];
	vector<ll> answer;

	bool comp(int a, int b) { return a > b; }
	[[local\|localrc]]
	ADMIN_PASSWORD=nova
	MYSQL_PASSWORD=nova
	RABBIT_PASSWORD=nova
	SERVICE_PASSWORD=$ADMIN_PASSWORD
	SERVICE_TOKEN=nova

	# Enable Swift
	enable_service s-proxy s-object s-container s-account
	# n : numero de nos
	# q : numero de operacoes
	# operacoes:
	# 1, u, v : conecta conjuntos de u e v
	# 2, u : impreme tamanho do conjunto de u
	# 3, u : imprime par de u
	#
	n, q = map(int, raw_input().split())

	# -------------------- UNION FIND --------------------------
	"""
	Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
	BSD License

	Original code from: Andrej Karpathy (@karpathy)
	Modified by: Marianne Linhares (@mari-linhares)

	"""

	import numpy as np
	"""
	Vanilla Char-RNN using TensorFlow by Vinh Khuc (@knvinh).
	Adapted from Karpathy's min-char-rnn.py
	https://gist.github.com/karpathy/d4dee566867f8291f086
	Requires tensorflow>=1.0
	BSD License
	"""
	import random
	import numpy as np
	import tensorflow as tf
	import Tkinter as tk
	import tkMessageBox

	import requests
	import pandas as pd
	import numpy as np

	import time

	url = "http://www.bombonera.org/score2017f2/score/#"
	# bibliotecas
	library('dplyr');
	library('ggplot2');
	library('caret');

	# importando os dados
	dados <- read.csv('~/Documents/eda.csv')

	# renomear as colunas
	colnames(dados) <- c("matricula", "id_reclamacao", "insatisfacao");
	import tensorflow as tf

	# Cria-se uma model function para implementar nosso modelo
	def model_fn():
	pass

	# Cria-se uma input function que deve ser passada para o Estimator na maior parte
	# de suas operacoes

	# input function de treino
	# -- coding: utf-8 --

	from __future__ import absolute_import
	from __future__ import division
	from __future__ import print_function

	import tensorflow as tf
	# MNIST
	from tensorflow.examples.tutorials.mnist import input_data