oiehot

from selenium import webdriver

class Article():
	def __init__(self, id, title, author, date, view_count=0, like_count=0, contents=''):
		self.id = id
		self.title = title
		self.author = author
		self.date = date
		self.view_count = view_count
		self.like_count = like_count

oiehot

import os
import comtypes.client
app = comtypes.client.CreateObject('Photoshop.Application.60') # CS6

# maya config
project_path = 'd:/project/alice/maya'
log_path     = project_path + '/log'
images_path  = project_path + '/images'
layers       = ['master', 'prop_matte', 'shadow', 'wall_matte']
passes       = ['beauty', 'N']

oiehot

import numpy as np
import tensorflow as tf

# 커스텀 Estimator 모델(func)
def model_fn(features, labels, mode):
	W = tf.get_variable('W', [1], dtype=tf.float64) # tf.get_variable(): Gets an existing variable with parameter or create a new one.
	b = tf.get_variable('b', [1], dtype=tf.float64)
	y = W * features['x'] + b
	
	# loss(오차) 서브 그래프

oiehot

# https://mosquitto.org
# https://www.vultr.com/docs/how-to-install-mosquitto-mqtt-broker-server-on-ubuntu-16-04
# https://pypi.org/project/paho-mqtt/
# http://www.steves-internet-guide.com/into-mqtt-python-client/
# $ mosquitto_pub -t "house/main-light" -m "message from mosquitto_pub client" -u "oiehot" -P "*******"

import time
import paho.mqtt.client as mqtt

ID = 'oiehot'

oiehot

마야 언리얼 ART

설치하기

1. 마켓플레이스에서 ARTv1을 구입하고 다운로드 한다.

2. C:/Program Files/Epic Games/UE_4.20/Engine/Plugins/Marketplace/ARTv1/MayaTools

3. ARTv1/MayaTools를 다른 곳으로 카피한다.] 예) D:/ARTv1/MayaTools

oiehot

#include "ABPawn.h"

AABPawn::AABPawn()
{
	PrimaryActorTick.bCanEverTick = true;
	
	Capsule = CreateDefaultSubobject<UCapsuleComponent>(TEXT("CAPSULE"));
	Mesh = CreateDefaultSubobject<USkeletalMeshComponent>(TEXT("MESH"));
	Movement = CreateDefaultSubobject<UFloatingPawnMovement>(TEXT("MOVEMENT"));
	SpringArm = CreateDefaultSubobject<USpringArmComponent>(TEXT("SPRINGARM"));

oiehot

import os
import sys
import re
from PIL import Image, ImageDraw, ImageFont
from PyQt5.QtWidgets import *
from PyQt5.QtCore import pyqtSignal
from PyQt5.QtCore import QEvent

from datetime import datetime
from os.path import getmtime

oiehot

import io
import requests
import pandas as pd
import json

# https://www.alphavantage.co/query?function=TIME_SERIES_DAILY&symbol=MSFT&apikey=demo
# https://www.alphavantage.co/query?function=TIME_SERIES_DAILY&symbol=MSFT&outputsize=full&apikey=demo
# https://www.alphavantage.co/query?function=TIME_SERIES_DAILY&symbol=MSFT&apikey=demo&datatype=csv

apikey = 'API_KEY_HERE'

oiehot

'''
Tensorflow 심층신경망(DNN)을 이용하여 꽃 분류하기 (Classification)

순서:
1. CSV로 부터 Iris 훈련/시험 데이터를 읽는다.
2. 분류하는 신경망을 만든다.
3. 데이터를 통한 훈련.
4. 새로운 샘플을 통해 판별하기.

	SL		SW		PL		PW		species

oiehot

import numpy as np
import tensorflow as tf

x = tf.feature_column.numeric_column('x', shape=[1]) # 랭크 1 텐서, 1차원 배열
feature_columns = [x]

# LinearRegressor < tf.estimator.Estimator
estimator = tf.estimator.LinearRegressor(feature_columns=feature_columns)

# 데이터 세트