vgoklani

'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.
It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data
In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/
- created cats/ and dogs/ subfolders inside train/ and validation/
- put the cat pictures index 0-999 in data/train/cats

vgoklani

import asyncio
import aiohttp

# ================================================
# for first run only
class first_async_scraper:
    def __init__(self):
        pass

    async def _fetch(self, symbol, url, session, headers):

vgoklani

import pandas as pd
import numpy as np
import re

# ================================================
class option_parser:
    def __init__(self, symbol, response):
        self.symbol = symbol
        self.response = response
    # ------------------------------------------------

vgoklani

import os
import sys
import pandas as pd
import pandas_datareader.data as web
import numpy as np
import time
import asyncio
from fake_useragent import UserAgent
'''set path variables'''
project_dir = "YOUR/PROJECT/DIR"

vgoklani

import time.sleep
import zmq
context = zmq.Context()
socket = context.socket(zmq.PUB)
socket.bind('tcp://127.0.0.1:2000')

# Allow clients to connect before sending data
sleep(10)
socket.send_pyobj({1:[1,2,3]})

vgoklani

# -*- coding: utf-8 -*-

import asyncio
import uvloop
from aiohttp.web import Application, MsgType, WebSocketResponse


def add_socket(app, socket, user_id):
    if user_id in app['connections']:
        pass

vgoklani

import aiohttp
import asyncio

async def get_body(url):
    response = await aiohttp.request('GET', url)
    raw_html = await response.read()
    return raw_html

async def main():
    # run them sequentially (but the loop can do other stuff in the meanwhile)

vgoklani

# -*- coding: utf-8 -*-
from __future__ import print_function
import os, sys, traceback, random

random.seed(42)

input_directory = "data/cornell_movie_dialogs_corpus"
output_directory = "data/cornell_movie_dialogs_corpus/processed"

if os.path.exists(output_directory) is False:

vgoklani

from time import sleep
from tornado.httpserver import HTTPServer
from tornado.ioloop import IOLoop
from tornado.web import Application, asynchronous, RequestHandler
from multiprocessing.pool import ThreadPool

_workers = ThreadPool(10)

def run_background(func, callback, args=(), kwds={}):
    def _callback(result):

vgoklani

import seaborn as sns
from scipy.optimize import curve_fit

# Function for linear fit
def func(x, a, b):
    return a + b * x

# Seaborn conveniently provides the data for
# Anscombe's quartet.
df = sns.load_dataset("anscombe")