gandroz

import pickle
import os.path
import requests
from googleapiclient.discovery import build
from google_auth_oauthlib.flow import InstalledAppFlow
from google.auth.transport.requests import Request
from email.mime.text import MIMEText
import base64

# If modifying these scopes, delete the file token.pickle.

gandroz

# Reset environment
state = env.reset()

# Render it
env.render()
time.sleep(0.5)

done = False
while not done:
    # Choose the action with the max expected reward i.e. max Q-value

gandroz

import numpy as np
import gym


# Define the environment
env = gym.make("Taxi-v2").env

# Initialize the q-table with zero values
q_table = np.zeros([env.observation_space.n, env.action_space.n])

gandroz

env.step(1)

gandroz

import gym

env = gym.make("Taxi-v3").env
env.render()

gandroz

import numpy as np
import pandas as pd
from collections import Counter


tags = []
wtags = []
for r,n in zip(df.tag, df.numclaps):
    tags.extend(r)
    wtags.extend(r * n)

gandroz

from bs4 import BeautifulSoup
import requests


def convert_to_num(s):
    if isinstance(s, int):
        return s
    elif 'K' in s:
        return int(1000 * float(s.replace('K','')))
    else:

gandroz

from python_graphql_client import GraphqlClient
import time


client = GraphqlClient(endpoint="https://medium.com/_/graphql")


def get_posts(data, posts):
    for post in data["data"]["topic"]["latestPosts"]["postPreviews"]:
        title = ""

gandroz

from python_graphql_client import GraphqlClient

client = GraphqlClient(endpoint="https://medium.com/_/graphql")

query = "query TopicHandler($topicSlug: ID!, $feedPagingOptions: PagingOptions, $sidebarPagingOptions: PagingOptions) {\n  topic(slug: $topicSlug) {\n    canonicalSlug\n    ...TopicScreen_topic\n    __typename\n  }\n}\n\nfragment PostListingItemFeed_postPreview on PostPreview {\n  post {\n    ...PostListingItemPreview_post\n    ...PostListingItemByline_post\n    ...PostListingItemImage_post\n    ...PostPresentationTracker_post\n    __typename\n  }\n  __typename\n}\n\nfragment PostListingItemPreview_post on Post {\n  id\n  mediumUrl\n  title\n  previewContent {\n    subtitle\n    isFullContent\n    __typename\n  }\n  isPublished\n  creator {\n    id\n    __typename\n  }\n  __typename\n}\n\nfragment PostListingItemByline_post on Post {\n  id\n  creator {\n    id\n    username\n    name\n    __typename\n  }\n  isLocked\n  readingTime\n  ...BookmarkButton_post\n  firstPublishedAt\n  updatedAt\n status

gandroz

def ridge_regression(data, predictors, alpha=None):
    # Fit the model
    if alpha is not None and predictors is not None:
        model = Ridge(alpha=alpha, normalize=True, max_iter=1e8)
    else:
        model = RidgeCV(alphas=np.linspace(1e-4, 1e-3, 1000), fit_intercept=True, normalize=True)
    
    model.fit(data[predictors],data['y'])
    y_pred = model.predict(data[predictors])