karpathy

"""
The most atomic way to train and run inference for a GPT in pure, dependency-free Python.
This file is the complete algorithm.
Everything else is just efficiency.

@karpathy
"""

import os       # os.path.exists
import math     # math.log, math.exp

AnthonyFJGarner

AnthonyFJGarner

AnthonyFJGarner

AnthonyFJGarner

imtaehyun

# https://www.quantopian.com/posts/technical-analysis-indicators-without-talib-code

import numpy  
import pandas as pd  
import math as m

#Moving Average  
def MA(df, n):  
    MA = pd.Series(pd.rolling_mean(df['Close'], n), name = 'MA_' + str(n))  
    df = df.join(MA)  

gpocentek

import re
import sys

import requests

import gitlab


URL = 'https://gitlab.com'
SIGN_IN_URL = 'https://gitlab.com/users/sign_in'

abhi1010

edit mode |  history

10 hours
36000 seconds
60000 intervals of 0.600000 (3/5) seconds each

miguelgrinberg

import asyncio
loop = asyncio.get_event_loop()

async def hello():
    await asyncio.sleep(3)
    print('Hello!')
    
if __name__ == '__main__':
    loop.run_until_complete(hello())
    

abhi1010

#!/usr/bin/env python
# -*- coding: utf-8 -*-
from sqlsoup import SQLSoup as sql
from sys import argv

DIGRAPH = """digraph structs {
    graph [
       rankdir= "LR"
       bgcolor=white
    ]