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@gilyes
gilyes / Backup, restore postgres in docker container
Last active January 18, 2025 00:57
Backup/restore postgres in docker container
Backup:
docker exec -t -u postgres your-db-container pg_dumpall -c > dump_`date +%d-%m-%Y"_"%H_%M_%S`.sql
Restore:
cat your_dump.sql | docker exec -i your-db-container psql -Upostgres
@joshdover
joshdover / README.md
Last active September 28, 2023 21:38
Idiomatic React Testing Patterns

Idiomatic React Testing Patterns

Testing React components seems simple at first. Then you need to test something that isn't a pure interaction and things seem to break down. These 4 patterns should help you write readable, flexible tests for the type of component you are testing.

Setup

I recommend doing all setup in the most functional way possible. If you can avoid it, don't set variables in a beforeEach. This will help ensure tests are isolated and make things a bit easier to reason about. I use a pattern that gives great defaults for each test example but allows every example to override props when needed:

@noeljackson
noeljackson / docker-compose-swarm.sh
Last active September 27, 2020 17:56
Run docker-compose with docker-machine and docker-swarm on digital ocean
#from https://www.linux.com/learn/how-use-docker-machine-create-swarm-cluster
#export digital ocean token
export DO_TOKEN="abcdefghijklmnopqrstuvwxyz"
#make keystore
docker-machine create -d digitalocean \
-digitalocean-access-token ${DO_TOKEN} \
--digitalocean-region "nyc1" \
--digitalocean-image="ubuntu-14-04-x64" \
--digitalocean-size "512mb" \
@giuseppebonaccorso
giuseppebonaccorso / cartpole.py
Last active March 3, 2023 06:13
OpenAI Gym Cartpole-v0 LSTM experiment
'''
OpenAI-Gym Cartpole-v0 LSTM experiment
Giuseppe Bonaccorso (http://www.bonaccorso.eu)
'''
import gym
import numpy as np
import time
from keras.models import Sequential
@mbollmann
mbollmann / attention_lstm.py
Last active August 22, 2024 07:06
My attempt at creating an LSTM with attention in Keras
class AttentionLSTM(LSTM):
"""LSTM with attention mechanism
This is an LSTM incorporating an attention mechanism into its hidden states.
Currently, the context vector calculated from the attended vector is fed
into the model's internal states, closely following the model by Xu et al.
(2016, Sec. 3.1.2), using a soft attention model following
Bahdanau et al. (2014).
The layer expects two inputs instead of the usual one:
@acdlite
acdlite / app.js
Last active January 20, 2023 08:23
Quick and dirty code splitting with React Router v4
// getComponent is a function that returns a promise for a component
// It will not be called until the first mount
function asyncComponent(getComponent) {
return class AsyncComponent extends React.Component {
static Component = null;
state = { Component: AsyncComponent.Component };
componentWillMount() {
if (!this.state.Component) {
getComponent().then(Component => {
@awjuliani
awjuliani / Deep-Recurrent-Q-Network.ipynb
Last active July 18, 2023 19:18
An implementation of a Deep Recurrent Q-Network in Tensorflow.
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const {
NativeRouter,
Nav,
StackMatch,
StackScene,
StackTitle,
StackContent
} = 'react-router/native'
const App = () => (
const stuff = [
{ path: '/one', label: 'One' },
{ path: '/two', label: 'Two' },
{ path: '/three', label: 'Three' },
{ path: '/four', label: 'Four' }
]
class Item extends Component {
render() {
const { pathname='', isRoot, parentLocation } = this.props