ssisaias

public class ClassicSingleton {
   private static ClassicSingleton instance = null;
   protected ClassicSingleton() {
      // Exists only to defeat instantiation.
   }
   public static ClassicSingleton getInstance() {
      if(instance == null) {
         instance = new ClassicSingleton();
      }
      return instance;

ssisaias

  //Login config
	@Autowired
	public void configAuthentication(AuthenticationManagerBuilder auth_1, AuthenticationManagerBuilder auth_2) throws Exception {
		
	  auth_1.jdbcAuthentication().dataSource(dataSource)
		.usersByUsernameQuery(
			"select login,password,1 from table1 where login=?")
		.authoritiesByUsernameQuery(
			"select login,role from table1 where login=?");
	  

ssisaias

var fs = require('fs');

var obj = JSON.parse(fs.readFileSync('fileURI', 'utf8'));

total = 0;
// obj is the root element; you can change <items> for whatever array you like
obj.items.forEach(element => {
    if(element.snippet.totalReplyCount>0){
        element.replies.comments.forEach(reply => {
            total++;

ssisaias

# 2018 - Isaias S. Silva
# This script converts the sentistrength output to a CSV one with zeros and ones, so we can use in a two class classifier.

import requests
import time
import datetime
import sys
import json

#Attention to the file names bellow

ssisaias

import nltk
import csv
import pickle
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
from sklearn.naive_bayes import MultinomialNB 
from myclassify import Analise # this should be found here: https://gist.github.com/ssisaias/fc49e7983a244b8c29b8f069f263216a
from pandas import DataFrame
import numpy

ssisaias

import nltk
import csv
import pickle
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
from sklearn.naive_bayes import MultinomialNB 
from myclassify import Analise
from pandas import DataFrame
import numpy
from sklearn.feature_extraction.text import CountVectorizer

ssisaias

import requests
import time
import datetime
import sys
import json
import re

#change the file name here. the file is generate by ytCommentMiner
dumpFile = open('videoei2-RjJDBHc2017-11-20-1200.json')
json_data = json.load(dumpFile)

ssisaias

import nltk
import csv
import pickle
from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords

class Analise (object):
    def __init__(self):
        self.stop_words = set(stopwords.words("portuguese"))
        self.dataPath_treino = 'output.txt'

ssisaias

public boolean mediate(MessageContext context) { 
try {
	String test = context.getProperty("mock_property").toString();
	OMElement xmlPayload = AXIOMUtil.stringToOM(test);
	//log.info(xmlPayload);
	OMElement envelope = context.getEnvelope().getBody().getFirstElement();
	//log.info(envelope);
	OMElement body = envelope.getFirstElement();
	//log.info(body);
	OMElement items = body.getFirstElement();

ssisaias

[Rainmeter]
Update=5000
Background=#@#Background.png
; #@# is equal to Rainmeter\Skins\illustro\@Resources
BackgroundMode=3
BackgroundMargins=0,34,0,14

[Metadata]
; Contains basic information of the skin.
Name=System