Skip to content

Instantly share code, notes, and snippets.

View py_mqtt_1.py
# 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
oiehot / unreal_maya_art_rig.md
Created Dec 16, 2018
unreal_maya_art_rig.md
View unreal_maya_art_rig.md

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

View AAPawn.cpp
#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"));
View copyrighter.py
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
View alphaavantage.py
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
oiehot / tf_iris_classification.py
Created Nov 11, 2017
Tensorflow 심층신경망(DNN)을 이용하여 꽃 분류하기
View tf_iris_classification.py
'''
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
oiehot / tf_estimator_custom.py
Created Nov 10, 2017
Tensorflow Custom Estimator
View tf_estimator_custom.py
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
View tf_estimator_linear_regressor.py
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
@oiehot
oiehot / tf_ex1.py
Created Nov 10, 2017
Tensorflow GradientDescent
View tf_ex1.py
import random
import tensorflow as tf
# tf.constant
sess = tf.Session()
node1 = tf.constant(3.0, dtype=tf.float32) # 상수 노드
node2 = tf.constant(4.0)
node3 = tf.add(node1, node2) # 합 노드
View peewee_orm_ex1.py
import sys
import peewee as pw # http://peewee.readthedocs.io/en/latest/peewee/api.html
# CONNECT
try:
db = pw.MySQLDatabase("database_name", host="192.168.0.10", port=3306, user="oiehot", passwd="1234")
db.connect()
except:
print('DB 접속 실패')
sys.exit()
You can’t perform that action at this time.