마야 언리얼 ART
설치하기
-
마켓플레이스에서 ARTv1을 구입하고 다운로드 한다.
-
C:/Program Files/Epic Games/UE_4.20/Engine/Plugins/Marketplace/ARTv1/MayaTools
-
ARTv1/MayaTools를 다른 곳으로 카피한다.] 예) D:/ARTv1/MayaTools
# 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' |
마켓플레이스에서 ARTv1을 구입하고 다운로드 한다.
C:/Program Files/Epic Games/UE_4.20/Engine/Plugins/Marketplace/ARTv1/MayaTools
ARTv1/MayaTools를 다른 곳으로 카피한다.] 예) D:/ARTv1/MayaTools
#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")); |
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 |
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' |
''' | |
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 |
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 |
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 |
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 |
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() |