namoshizun

from __future__ import division
from matplotlib import pylab as plt
import numpy as np

MAX = np.inf
DEBUG = True
# DEBUG = False

class Arm:
	def __init__(self, name, mean, stddev, cost):

namoshizun

import sys
import os
import io
from prettytable import PrettyTable

def countLines(path, extension) :
	pt = PrettyTable()
	pt.field_names = ['filename', '#lines']
	total_line = 0
	total_files = 0

namoshizun

// Helper
function print(obj) {
	console.log(obj)
}

// Mimic Async-Await
function coRunner(feedback) {
	let iterator = this;

	let result = iterator.next(feedback);

namoshizun

def checktype(*types):
	def typerror(expect, got):
		raise TypeError('expecting {}, got{}'.format(expect, type(got)))

	def checker(fn):
		def receiver(*args, **kwargs):
			for _type, arg in zip(types, args):
				type(arg) is _type or typerror(_type, arg)
			return fn(*args, **kwargs)
		return receiver

namoshizun

import time
from functools import wraps

def timeit(comment=None):
    """
        auto establish and close mongo connection
    """
    def decorator(func):
        @wraps(func)
        def wrapper(*args, **kwargs):

namoshizun

"""
Implement a very simple gaussian process for regression task.
Credit : http://katbailey.github.io/post/gaussian-processes-for-dummies/
"""

import numpy as np
import matplotlib.pyplot as pl


def prepare_data(n, fn):

namoshizun

from __future__ import division
from random import random
import numpy as np
import pandas as pd

'''
    Use regret-matching algorithm to play Scissors-Rock-Paper.
'''

class RPS:

namoshizun

# Install GPFlow
!git clone https://github.com/namoshizun/GPflow.git
!mv ./GPflow/gpflow ./
!rm -rf GPflow
!pip install multipledispatch
!pip install pytest


# Install Other libraries
!pip install edward

namoshizun

import functools
import inspect
from tqdm import tqdm


def print_progress(total=None, enabled=True):
    def decorator(func):
        @functools.wraps(func)
        def wrapper(*args, **kwargs):
            if not enabled or total is None:

namoshizun

from vid_utils import Video, concatenate_videos

videos = [
  Video(speed=1.0, path="C:/temp/my_video_1.mp4"),
  Video(speed=2.0, path="C:/temp/my_video_2.mp4"),
  Video(speed=0.5, path="C:/temp/my_video_3.mp4"),
]

concatenate_videos(videos=videos, output_file=f"C:/temp/output_video.mp4")