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'''
The table below gives the age (in year) and mileage (in KMs) of four used cars:

        WARTBURG       MOSKVICH       LADA        TRABI
Age:        5             7            15          28
Mileage:  30530         90000        159899       270564

1. Determine the weights w0 and w1 for a simple linear regression to predict mileage from age.
2. Use the model from (1) to predict the mileage for a 15-year old car.
'''

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'''
Professor Boolean is an evil villain who is up to no good.
It is your job to help bring her down. The is a gang working under her.
The chain of command of the gang is structured in such a way that each leader has 7 people working directly under him/her.
Your task is to find the total number of people in Prof. Boolean's workforce given the number of level of hierarchy.
'''
levels = int(input('Number of level(s): '))
peoples = 1
for i in range(levels):
    peoples += (7 ** (i + 1))

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# Usage: python convert.py glove_model.txt

import sys
import time

def file_len(fname):
    line = col = 0
    with open(fname, encoding = 'UTF-8') as f:
        for line, l in enumerate(f):
            if col == 0:

harshildarji

# Tutorial links:
# https://youtu.be/yMk_XtIEzH8?list=PLQVvvaa0QuDezJFIOU5wDdfy4e9vdnx-7
# https://youtu.be/Gq1Azv_B4-4?list=PLQVvvaa0QuDezJFIOU5wDdfy4e9vdnx-7
# https://youtu.be/CBTbifYx6a8?list=PLQVvvaa0QuDezJFIOU5wDdfy4e9vdnx-7

import gym
import numpy as np
import matplotlib.pyplot as plt

env = gym.make('MountainCar-v0')

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import random, time

def generate_random_number():
  number = list(map(str, str(time.time()).replace('.', '')))
  return int(''.join(random.sample(number, len(number))))

print(generate_random_number())

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import random, json, requests, string, sys

def game():
    chain = 0
    usedWords = []
    answer_Word = None
    chainGood = True
    min_length = 3
    while chainGood:
        # if chain != 0 and chain % 5 == 0: min_length += 1

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# Video link: https://youtu.be/3P3TcKaegbA?list=PLxvWWIq3F0k0GE1EYvricLniNSdv9gvZS

import os
import time
from datetime import datetime
from github import Github
from curtsies.fmtfuncs import yellow

ACCESS_TOKEN = open('token', 'r').read()
g = Github(ACCESS_TOKEN)

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def solution(secret, guess):
    while secret != guess:
        correct_position_guessed = [0] * len(secret)
        correct_color_guessed = [color for color  in secret if color in guess]

        for i in range(len(secret)):
            if secret[i] == guess[i]:
                correct_position_guessed[i] = 1

        print('Correct colors guessed: {}'.format(' '.join(color for color in correct_color_guessed)))

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# This Python script can be used to generate true Reber grammar sequences.
# To generate false Reber grammar sequences, just change tuple values in 'traversal' at line 8.

import numpy as np

chars = 'BTSXPVE'

traversal = [[('T', 'P'), (1, 4)], [('S', 'X'), (1, 2)], [('S', 'X'), (3, 4)], [('E'), (-1,)], [('V', 'T'), (5, 4)], [('V', 'P'), (3, 2)]]

def generate_seq(min_length):

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import os
import pickle
import warnings
from functools import reduce
from operator import add

import numpy as np
import pandas as pd
import torch
from tqdm import tqdm