stevenringo

| Title | Description

hagemann

function slugify(string) {
  const a = 'àáâäæãåāăąçćčđďèéêëēėęěğǵḧîïíīįìıİłḿñńǹňôöòóœøōõőṕŕřßśšşșťțûüùúūǘůűųẃẍÿýžźż·/_,:;'
  const b = 'aaaaaaaaaacccddeeeeeeeegghiiiiiiiilmnnnnoooooooooprrsssssttuuuuuuuuuwxyyzzz------'
  const p = new RegExp(a.split('').join('|'), 'g')

  return string.toString().toLowerCase()
    .replace(/\s+/g, '-') // Replace spaces with -
    .replace(p, c => b.charAt(a.indexOf(c))) // Replace special characters
    .replace(/&/g, '-and-') // Replace & with 'and'
    .replace(/[^\w\-]+/g, '') // Remove all non-word characters

codysherman

/* colors:
 * #ABB2BF default syntax (foreground)
 * #828997 medium syntax
 * #5C6370 darkest syntax
 * #56B6C2 cyan
 * #61AFEF blue
 * #C678DD purple
 * #98C379 green
 * #E06C75 light red
 * #BE5046 red

karpathy

"""
A bare bones examples of optimizing a black-box function (f) using
Natural Evolution Strategies (NES), where the parameter distribution is a 
gaussian of fixed standard deviation.
"""

import numpy as np
np.random.seed(0)

# the function we want to optimize

thvitt

#!/bin/sh

if [ "$PYENV_VERSION" -ne "" ]
then
    name=`pyenv version-name`
    python=`pyenv which python`
else
    name=`basename "$VIRTUAL_ENV"`
    python="$VIRTUALENV/bin/python"
fi

igodorogea

function NiceScale (lowerBound, upperBound, _maxTicks) {

  var maxTicks = _maxTicks || 10;
  var tickSpacing;
  var range;
  var niceLowerBound;
  var niceUpperBound;

  calculate();

ivannp

import pandas as pd
import numpy as np
import instrumentdb as idb
import math
import pickle

def good_entries(ohlcv, min_days = 3, days_out = 15, vola_len = 35, days_pos = 0.6, stop_loss = 1.5):
    if days_out <= min_days:
        raise RuntimeError('days_out must be greater than min_days.')

anilnairxyz

A zoomable candlestick chart.

Copyright © 2017-20, Sandhya Pillai - MIT License

karpathy

""" Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """
import numpy as np
import cPickle as pickle
import gym

# hyperparameters
H = 200 # number of hidden layer neurons
batch_size = 10 # every how many episodes to do a param update?
learning_rate = 1e-4
gamma = 0.99 # discount factor for reward

macournoyer

$ python xor.py
Training:
  Epoch   0   MSE: 1.765
  Epoch 100   MSE: 0.015
  Epoch 200   MSE: 0.005
  * Target MSE reached *
Evaluating:
  1 XOR 0 = 1 ( 0.904)   Error: 0.096
  0 XOR 1 = 1 ( 0.908)   Error: 0.092
  1 XOR 1 = 0 (-0.008)   Error: 0.008