yujuwon

#!/usr/bin/env python
# coding: utf8
"""Example of multi-processing with Joblib. Here, we're exporting
part-of-speech-tagged, true-cased, (very roughly) sentence-separated text, with
each "sentence" on a newline, and spaces between tokens. Data is loaded from
the IMDB movie reviews dataset and will be loaded automatically via Thinc's
built-in dataset loader.

Compatible with: spaCy v2.0.0+
"""

yujuwon

package com.test

import org.apache.spark._
import org.apache.spark.SparkContext._

object WordCount {
  def main(args: Array[String]): Unit = {
    val master = args.length match {
      case x: Int if x > 0 => args(0)
      case _ => "local"

yujuwon

from sklearn.naive_bayes import MultinomialNB
from sklearn.pipeline import Pipeline
from sklearn.feature_extraction.text import TfidfVectorizer, HashingVectorizer, CountVectorizer

# scikit learn으로 다양한 종류의 모델을 만들어 보자.
clf_1 = Pipeline([('vect', CountVectorizer()),
                   ('clf', MultinomialNB())])
clf_2 = Pipeline([('vect', HashingVectorizer(non_negative=True)),
                  ('clf', MultinomialNB())])
clf_3 = Pipeline([('vect', TfidfVectorizer()),

yujuwon

$(document).ready(function(){
  GetData(1);
});

function GetData(_page){
    var _offset = _page;
    var _data_list = "{{ data_list|join('|') }}";
    if(_data_list != ''){
      $.ajax({
        url : '/getData',

yujuwon

public static void main(String[] args) throws Exception {
  Configuration hconf = HBaseConfiguration.create();
  hconf.set("hbase.rootdir", "hbase root 경로");
  hconf.set("hbase.zookeeper.quorum", "hbase zookeeper 주소");
  hconf.set("hbase.zookeeper.property.clientPort", "zookeeper port");
  
  HBaseAdmin ha = new HBaseAdmin(hconf);
  for (int i = 0; i < ha.listTableNames().length; i++){
    System.out.println(ha.listTableNames()[i]);
  }

yujuwon

import tensorflow as tf
import numpy as np
from cell.lstm import BN_LSTMCell
import sys
  
class Seq2Seq(object):
 
  def __init__(self, xseq_len, yseq_len,
               xvocab_size, yvocab_size,
               emb_dim, num_layers, ckpt_path,

yujuwon

def dual_encoder_model(
  hparams,
  mode,
  context,
  context_len,
  utterance,
  utterance_len,
  targets):
  
  # Initialize embedidngs randomly or with pre-trained vectors if available

yujuwon

from telegram.ext import Updater, CommandHandler, MessageHandler, Filters
import logging
import os
import random
 
# Enable logging
logging.basicConfig(format='%(asctime)s - %(name)s - %(levelname)s - %(message)s',
                    level=logging.INFO)
 
logger = logging.getLogger(__name__)

yujuwon

import csv
import numpy as np
import tensorflow as tf
from tensorflow.contrib import rnn
import matplotlib.pyplot as plt
%matplotlib inline

def load_series(filename ,series_dix=1):
    try:
        with open(filename) as csvfile:

yujuwon

import numpy as np
import pickle
import tensorflow as tf
import matplotlib.pyplot as plt
%matplotlib inline

def unpickle(file):
    fo = open(file, 'rb')
    dict = pickle.load(fo)
    fo.close()