joopeed

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joopeed

Code is clean if it can be understood easily – by everyone on the team. Clean code can be read and enhanced by a developer other than its original author. With understandability comes readability, changeability, extensibility and maintainability.

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General rules

1. Follow standard conventions.
2. Keep it simple stupid. Simpler is always better. Reduce complexity as much as possible.
3. Boy scout rule. Leave the campground cleaner than you found it.
4. Always find root cause. Always look for the root cause of a problem.

Design rules

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#coding:utf-8
import csv
import nltk.classify.util
from nltk.classify import NaiveBayesClassifier
from nltk.corpus import stopwords

stopset = set(stopwords.words('english'))
data_pos = []
data_neg = []

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from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from sklearn.cluster import KMeans, MiniBatchKMeans, DBSCAN
from scipy.spatial.distance import cdist, pdist
import numpy as np
import json, nltk

stop_words = [] #nltk.corpus.stopwords.words('portuguese')
dados = json.loads(open("validos.json", "r").read())
vectorizer = TfidfVectorizer(max_df=0.5,

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#coding:utf-8
from bs4 import BeautifulSoup
import urllib2, sys, subprocess
from time import sleep
import codecs
from goose import Goose
g = Goose()

def fetch_news(term):
    results = []

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bestdados <- c()
for(i in 1:9) { 
  # funcao para melhorar a forma dos dados
  temp <- c()
  cols <- grep(toString(i), colnames(dados))
  temp <- dados[c(1, 2, 3, 4, 5, 6, cols)]
  temp$ano <- toString(i)
  names(temp) <- gsub(toString(i), "", names(temp))
  bestdados <- rbind(bestdados, temp)
}

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ci_by_group <- function(data=NULL, measurevar, groupvars=NULL, na.rm=FALSE,
                        conf.interval=.95, .drop=TRUE) {
  require(plyr)
  
  #Usando a funcao de raquel e adaptando
  ci = function(x, alpha = 0.01) {
    q = 1 - alpha/2
    s = sd(x)
    xbar = mean(x)
    n = length(x)

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ci_by_group <- function(data=NULL, measurevar, groupvars=NULL, na.rm=FALSE, .drop=TRUE) {
  require(plyr)
  
  #Usando a funcao de raquel
  ci = function(x, alpha = 0.01) {
    q = 1 - alpha/2
    s = sd(x)
    xbar = mean(x)
    n = length(x)
    error = qnorm(q)*s/sqrt(n)

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package sorting.simpleSorting;

import sorting.SortingImpl;
import sorting.Util;

/**
 * The selection sort algorithm chooses the smallest (greatest) element form the
 * array and puts it in the first (last) position. Then it repeats the same
 * process to put the second smallest (greatest) element in the second (second
 * from the end) position, and so on. The algorithm must sort the elements from

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package com.portaslogicas;

public class PortasLogicas {

	
	public int AND(int A, int B){
		if(A > B) return B;
		if(B > A) return A;
		return A;
	}