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#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; }

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[[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

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# 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 --------------------------

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"""
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

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"""
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

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My favorites

Help

:help - main help portal (with links to more help pages)
:viusage - show a huge cheat sheet listing every command

Edit

0 - (zero) start of line

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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/#"

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# 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");

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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

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# -*- 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