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| public class ManualBallsGenerator { | |
| Scanner scanner; | |
| public ManualBallsGenerator() { | |
| this.scanner = new Scanner(System.in); | |
| } | |
| public BaseBalls generate() { | |
| System.out.println("์ซ์ 3๊ฐ ์ ๋ ฅ"); |
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| from flask import Flask | |
| from sqlalchemy import create_engine | |
| from sqlalchemy.orm import sessionmaker | |
| import time | |
| app = Flask(__name__) | |
| engine = create_engine('mysql+pymysql://root:22380476@localhost/pool?charset=UTF8MB4', pool_recycle=300) | |
| SessionMaker = sessionmaker(bind=engine) | |
| engine.execute('SELECT NOW();') |
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| def localProperties = new Properties() | |
| def localPropertiesFile = rootProject.file('local.properties') | |
| if (localPropertiesFile.exists()) { | |
| localPropertiesFile.withReader('UTF-8') { reader -> | |
| localProperties.load(reader) | |
| } | |
| } | |
| def flutterRoot = localProperties.getProperty('flutter.sdk') | |
| if (flutterRoot == null) { |
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| apiVersion: kibana.k8s.elastic.co/v1 | |
| kind: Kibana | |
| metadata: | |
| name: quickstart | |
| spec: | |
| version: 7.6.2 | |
| podTemplate: | |
| spec: | |
| containers: | |
| - name: kibana |
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| apiVersion: elasticsearch.k8s.elastic.co/v1 | |
| kind: Elasticsearch | |
| metadata: | |
| name: quickstart | |
| spec: | |
| version: 7.6.2 | |
| nodeSets: | |
| - name: default | |
| count: 1 | |
| config: |
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| import winsound | |
| import time | |
| winsound.Beep(293, 200) # D | |
| winsound.Beep(293, 200) # D | |
| winsound.Beep(293, 200) # D | |
| winsound.Beep(293, 600) # D | |
| winsound.Beep(246, 600) # B | |
| time.sleep(0.1) |
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| class G: | |
| pending = set() | |
| bombed = 0 | |
| board = [] | |
| def bomb(): | |
| for i, j in reversed(sorted(G.pending)): | |
| G.board[i].pop(j) | |
| G.bombed += 1 |
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| #!/usr/bin/env python | |
| import flask | |
| from flask import request, jsonify | |
| from os import path, environ | |
| from raven.contrib.flask import Sentry | |
| import cv2 | |
| import numpy as np | |
| import requests |
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| import sys , os | |
| sys.path.append(os.pardir) | |
| from dataset.mnist import load_mnist | |
| from common.functions import sigmoid, softmax, np | |
| def get_data(): | |
| (x_train, t_train), (x_test, t_test)=\ | |
| load_mnist(normalize=True, flatten=True, one_hot_label=True) | |
| return x_train, t_train |
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| # ํ ์ค ์ง๋ฆฌ ์๋ฃจ์ ํด์ | |
| def solution(t, l = []): | |
| if not t: # ํ์ถ ์กฐ๊ฑด. ์ผ๊ฐํ์ ์ ๋ถ ์๋ชจํ๋ค. | |
| return max(l) | |
| result = [] | |
| for x, y, z in zip([0]+l, l+[0], t[0]): # [0] + l = 0 ๋ํ๊ธฐ ๋ฆฌ์คํธ | |
| # zip ์ ๋ฆฌ์คํธ๋ฅผ n ๊ฐ ๋ฐ์์ ๊ฐ ๋ฆฌ์คํธ ๋ง๋ค foreach ๋ฅผ ๋์์ค์ | |
| # for a, b, c in zip(A, B, C) ์ด๋ฉด a ๋ A์ ์์, b ๋ B์ ์์, c ๋ C์ ์์ | |
| result.append(max(x, y) + z) # result ์ ํ๋์ฉ ๋ํจ |