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

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

import qcartpole

N_RUNS = 10

grid_params = {

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

/* 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

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

// https://play.golang.org/p/Y8eaSkYNgG

package main

import (
	"fmt"
)

const (
	logDebug = "DEBUG"

muety

package main

import (
	"fmt"
	"time"
)

func main() {
	N := int64(751395478255)
	n := N

muety

package main

import (
	"io"
	"log"
	"net"
	"net/http"
	"net/rpc"
	"net/rpc/jsonrpc"
)

muety

'use strict';

const fetch = require('node-fetch')
    , base64stream = require('base64-stream');

const URL = 'https://www.google.de/images/branding/googlelogo/2x/googlelogo_color_272x92dp.png';

fetch(URL)
    .then((res) => {
        return new Promise((resolve, reject) => {