skyer9

# -*- coding: utf-8 -*-
import pyspark
from pyspark.sql import SQLContext


# +--------------+---------+------------------+
# |        doc_id|    token|             tfidf|
# +--------------+---------+------------------+
# |R13E0LFYMKV62T|         |12.613611576441143|
# |R1QYQ8ZJ9LS8L9|     high|11.634883586257445|

skyer9

skyer9

{
  "AWSTemplateFormatVersion" : "2010-09-09",

  "Description" : "ElasticSearch Cluster.",

  "Parameters" : {

    "KeyName": {
      "Description" : "The name of an existing key pair to enable SSH access to Amazon EC2 instances",
      "Type": "String",

skyer9

$ tzselect
Please identify a location so that time zone rules can be set correctly.
Please select a continent or ocean.
 1) Africa
 2) Americas
 3) Antarctica
 4) Arctic Ocean
 5) Asia
 6) Atlantic Ocean

skyer9

1. 마켓 플레이스에서 SFTP 다운받기

2. ctrl+shift+p 를 누른 후, sftp: config 를 입력한다.

3. 설정을 아래와 같이 수정한다.

{
    "context": "./sftp/searver_a",
    "protocol": "sftp",
    "host": "13.XXX.42.109",

skyer9

package kr.co.episode.muglangguide.data.remote;

import android.content.Context;

import org.apache.http.conn.ssl.SSLSocketFactory;

import java.io.IOException;
import java.io.InputStream;
import java.net.URL;
import java.security.KeyManagementException;

skyer9

# https://github.com/fchollet/keras/blob/master/examples/mnist_cnn.py
'''Trains a simple convnet on the MNIST dataset.
Gets to 99.25% test accuracy after 12 epochs
(there is still a lot of margin for parameter tuning).
16 seconds per epoch on a GRID K520 GPU.
'''

from __future__ import print_function
import numpy as np

skyer9

# https://github.com/fchollet/keras/blob/master/examples/mnist_cnn.py
'''Trains a simple convnet on the MNIST dataset.
Gets to 99.25% test accuracy after 12 epochs
(there is still a lot of margin for parameter tuning).
16 seconds per epoch on a GRID K520 GPU.
'''

from __future__ import print_function
import numpy as np

skyer9

from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import SGD
import numpy as np

x_data = [[0, 0, 0],
          [1, 0, 0],
          [0, 1, 0],
          [0, 0, 1],
          [1, 1, 0],

skyer9

x_train = [1., 2., 3., 4.]
y_train = [1.1, 2.1, 3.1, 4.1]

W, b = 123, 45
learning_rate = 0.05

for epoch in range(0, 200):
    for j in range(0, len(x_train)):
        cal_y = W * x_train[j] + b