initialize git depository in the current directory
git init .
display the git remote/origin
cat .git/config
initialize git depository in the current directory
git init .
display the git remote/origin
cat .git/config
# Bookstack | |
mysqldump -u root bookstack > /bkp/mysql/mysql-`hostname`-`date +%Y-%m-%d-`bookstack.sql |
#/bin/bash | |
find /bkp/postgres/ -mtime +2 -exec rm {} \; | |
exit 0 |
version: "3" | |
services: | |
selenium-hub1: | |
image: selenium/hub:3.141.59-20200409 | |
container_name: selenium-hub1 | |
ports: | |
- "172.16.1.25:4444:4444" | |
environment: | |
- GRID_MAX_SESSION=10 | |
networks: |
awk -v l=500000 '(NR==1){header=$0;next} | |
(NR%l==2) { | |
close(file); | |
file=sprintf("%s.%0.5d.csv",FILENAME,++c) | |
sub(/csv[.]/,"",file) | |
print header > file | |
} | |
{print > file}' empresas.csv |
[program:echo-worker] | |
process_name=%(program_name)s_%(process_num)02d | |
directory=/home/$USER/www/portal | |
command=/usr/bin/laravel-echo-server start | |
autostart=true | |
autorestart=true | |
user=$USER | |
numprocs=1 | |
redirect_stderr=true | |
stdout_logfile=/home/$USER/www/echo-worker.log |
# To execute this docker-compose yml file use `docker-compose -f <file_name> up` | |
# Add the `-d` flag at the end for detached execution | |
version: "3" | |
services: | |
selenium-hub1: | |
image: selenium/hub:3.141.59-20200409 | |
container_name: selenium-hub1 | |
ports: | |
- "4444:4444" | |
environment: |
$.ajax({ | |
url: 'http://localhost/item/1/delete', | |
type: 'DELETE', | |
dataType: "JSON", | |
data: { | |
"id": 1, | |
}, | |
headers: { | |
'X-CSRF-TOKEN': $('meta[name="csrf-token"]').attr('content') | |
}, |
train_male = train_dataset[train_dataset['Sex'] == 'male']['Age'].median() | |
train_female = train_dataset[train_dataset['Sex'] == 'female']['Age'].median() | |
train_dataset.loc[train_dataset['Sex'] == 'male', ['Age']] = train_male; | |
train_dataset.loc[train_dataset['Sex'] == 'female', ['Age']] = train_female; | |
# Keep test dataset in sync | |
test_male = train_dataset[train_dataset['Sex'] == 'male']['Age'].median() | |
test_female = train_dataset[train_dataset['Sex'] == 'female']['Age'].median() | |
test_dataset.loc[test_dataset['Sex'] == 'male', ['Age']] = test_male; |
Gradiente Estocástico = encontrar, de forma iterativa, os valores dos parâmetros que minimizam determinada função de interesse. | |
Regressão Linear = processo de traçar uma reta através dos dados em um diagrama de dispersão. | |
Correção = como duas ou mais variáveis estão relacionadas uma com a outra | |
K-Means | |
Least Squares = Método dos mínimos quadrados | |
Algoritmo Apriori = sistema de recomendaçes |