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nginx.conf


user www-data;
worker_processes auto;
pid /run/nginx.pid;
include /etc/nginx/modules-enabled/*.conf;

events {
worker_connections 768;
# multi_accept on;
}

http {

##
# Basic Settings
##

sendfile on;
tcp_nopush on;
types_hash_max_size 2048;
client_max_body_size 10M; # Set client upload size = 10Mbyte
# server_tokens off;

# server_names_hash_bucket_size 64;
# server_name_in_redirect off;

include /etc/nginx/mime.types;
default_type application/octet-stream;

##
# SSL Settings
##

ssl_protocols TLSv1 TLSv1.1 TLSv1.2 TLSv1.3; # Dropping SSLv3, ref: POODLE
ssl_prefer_server_ciphers on;

##
# Logging Settings
##

access_log /var/log/nginx/access.log;
error_log /var/log/nginx/error.log;

##
# Gzip Settings
##

gzip on;

# gzip_vary on;
# gzip_proxied any;
# gzip_comp_level 6;
# gzip_buffers 16 8k;
# gzip_http_version 1.1;
# gzip_types text/plain text/css application/json application/javascript text/xml application/xml application/xml+rss text/javascript;

##
# Virtual Host Configs
##

include /etc/nginx/conf.d/*.conf;
include /etc/nginx/sites-enabled/*;
}

#mail {
# # See sample authentication script at:
# # http://wiki.nginx.org/ImapAuthenticateWithApachePhpScript
#
# # auth_http localhost/auth.php;
# # pop3_capabilities "TOP" "USER";
# # imap_capabilities "IMAP4rev1" "UIDPLUS";
#
# server {
# listen localhost:110;
# protocol pop3;
# proxy on;
# }
#
# server {
# listen localhost:143;
# protocol imap;
# proxy on;
# }
#}

이 블로그의 인기 게시물

Python Sklearn make_regression

from sklearn.datasets import make_regression import matplotlib.pyplot as plt X, y = make_regression(n_samples=250, n_features=1, noise=50, random_state=2) plt.scatter(X,y, s=2) plt.show() from sklearn.linear_model import LinearRegression from sklearn.model_selection import train_test_split # 한글 깨짐 없이 나오게 설정 from matplotlib import rcParams # 인코딩 폰트 설정 rcParams['font.family'] = 'New Gulim' rcParams['font.size'] = 10 x_train, x_test, y_train, y_test = train_test_split(X,y, test_size=.20, random_state=0) x_train.shape, x_test.shape, y_train.shape, y_test.shape # 모델 생성 model = LinearRegression() # 학습하기 model.fit(x_train, y_train) # 가중치, 편향치 구하기 model.coef_, model.intercept_ # (array([90.11061494]), 2.4224269924448585) # 결정 계수 model.score(x_train, y_train) # 0.789267454050733 # 추정 pred = model.predict(x_test) # 산점도 plt.scatter(x_test,y_test) plt.plot(x_test, pred, 'r-') plt.show() # 추정 model.predict([[3.0]]) # 학습할 때 주는 데이터의 형식을 따른다 # x의 최소값, 최대값을 계수와 절편을 사용하여 ...

Javascript on 함수

엔터키 감지하기 <input type="password" onkeypress="func(event)" /> function func(event) {      if(event.keyCode == 13) { // keyCode 13은 엔터이다           alert("엔터를 입력했습니다.");     }     if (event.tartget.value == 13) {          alert("엔터를 입력했습니다.");     } }

Grid 정렬

  .container { display : grid ; gap : 22px ; width : 1000px ; grid-template-columns : repeat ( auto-fit , 150px ); margin : auto ; justify-content : center ; } .container {      display : grid ; gap : 22px ; grid-template-columns : repeat ( auto-fit , minmax ( 250px , 1fr )); }