muety

import numpy as np
from scipy.ndimage import convolve
from sklearn.neural_network import MLPClassifier
from sklearn.datasets import fetch_mldata
from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV
from sklearn.externals import joblib
import os.path

PATH = 'mlp_model.pkl'

muety

/* Simple neural net with one hidden layer consisting of one neuron */
/* Inspired by https://medium.com/technology-invention-and-more/how-to-build-a-simple-neural-network-in-9-lines-of-python-code-cc8f23647ca1 */

package main

import (
	"fmt"
	"math/rand"
	"math"
)

muety

# Inspired by https://medium.com/@tuzzer/cart-pole-balancing-with-q-learning-b54c6068d947

import gym
import numpy as np
import math
from collections import deque

class QCartPoleSolver():
    def __init__(self, buckets=(1, 1, 6, 12,), n_episodes=1000, n_win_ticks=195, min_alpha=0.1, min_epsilon=0.1, gamma=1.0, ada_divisor=25, max_env_steps=None, quiet=False, monitor=False):
        self.buckets = buckets # down-scaling feature space to discrete range

muety

import time
import multiprocessing
import numpy as np
from sklearn.model_selection import ParameterGrid

import qcartpole

N_RUNS = 10

grid_params = {

muety

# Inspired by https://keon.io/deep-q-learning/

import random
import gym
import math
import numpy as np
from collections import deque
from keras.models import Sequential
from keras.layers import Dense
from keras.optimizers import Adam

muety

import requests
import os

url = 'http://www.zar.kit.edu/rss/feed.rss'
keywords = ['steuerrecht']
cache_file = 'cache.txt'
hook_url = 'http://middleman.ferdinand-muetsch.de/api/messages'
hook_sender_id = 'Watcher'
hook_recipient_id = ''

muety

# gunicorn --bind 0.0.0.0:8000 --workers 4 wsgi:app
# gunicorn --bind 0.0.0.0:8000 --workers 1 --threads 12 wsgi:app

import time
from flask import Flask

app = Flask(__name__)

# Requests from one client are not blocked by long-lasting requests from another client, as long as there are workers available
@app.route('/sleep')

muety

'''
    A script to scrape restaurant reviews from tripadvisor.com or tripadvisor.de using Selenium.

    Author:     Ferdinand Mütsch <[email protected]>
    License:    MIT
    Updated:    January, 09 2018

    Installation:
        - Install `selenium` and `pandas` using pip
        - Install PhantomJS or get Chrome- or Firefox webdriver binaries and add them to your PATH (see http://selenium-python.readthedocs.io/installation.html#drivers)

muety

# Naive implementation of the Apriori algorithm in Python

# Example 2 from https://en.wikipedia.org/wiki/Apriori_algorithm
data = [
    {1,2,3,4},
    {1,2,4},
    {1,2},
    {2,3,4},
    {2,3},
    {3,4},

muety

/*
 1. Go to Deezer.com and open the playlist you want to export, e.g. https://www.deezer.com/en/profile/850026602/loved
 2. Scroll down to the very bottom
 3. Open your browser's dev tools (F12 on Linux an Windows) and go to the Console tab
 4. Paste the following one-line command and hit enter
*/

JSON.stringify(Array.prototype.slice.call(document.getElementsByClassName('datagrid-row song')).map(r => Object.assign({}, {title: r.getElementsByClassName('title')[0].childNodes[0].textContent, artist: Array.prototype.slice.call(r.querySelectorAll('[itemprop="byArtist"]')).map(a => a.textContent).reduce((acc, a) => a + ', ' + acc, '').slice(0, -2)})), null, 2)