| from __future__ import annotations | |
| from typing import AnyStr, Callable, Container, Tuple, Union | |
| class Choice: | |
| def __init__(self, name: AnyStr, beats: Choice = None): | |
| self._name = name | |
| self._beats = beats | |
| @property |
| """ | |
| PIL Line Writer | |
| Simple class for writing multiline text centered on an image with even line | |
| spacing. | |
| To use create a line writer object by passing a PIL image object and a padding | |
| value. Then to write text call the LineWriter's text method with the text you | |
| would like centered on the image over multiple lines. It also takes an optional | |
| line height argument which will scale the line heights. Additionally you can |
| """ | |
| Messing around with NLP and extracting topics. | |
| It works acceptably. Results are mediocre, many legal texts get categorized as religious. Generally identifies | |
| sports but they often get categorized as hockey. Likely too small of a training set. | |
| Referenced and based heavily on this article | |
| https://towardsdatascience.com/nlp-extracting-the-main-topics-from-your-dataset-using-lda-in-minutes-21486f5aa925 | |
| """ |
| """ | |
| Further messing around with NLP and extracting topics. | |
| Built a class for analyzing text to determine topics. It allows for part of speech analysis and has a pipeline for | |
| cleaning the data set. It then uses the Gensim LDA model to determine which words are the topics. | |
| Using the Gensim part of speech analysis to filter out everything but nouns, in conjunction with my previous | |
| lemmatization and stemming I was able to get results that seem much better even with still using only the SKLearn news | |
| group data set. | |
| """ |
| import csv | |
| import pandas as pd | |
| from functools import reduce | |
| #input below | |
| ranked_fighters = {'Leon Edwards': {'Rank': 7}, 'Rafael Dos Anjos': {'Rank': 16}, 'Walt Harris': {'Rank': 2}, 'Aleksei Oleinik': {'Rank': 9}, 'Juan Adams': {'Rank': 1}, 'Greg Hardy': {'Rank': 4}, 'Dan Hooker': {'Rank': 3}, 'James Vick': {'Rank': 15}, 'Alexander Hernandez': {'Rank': 6}, 'Francisco Trinaldo': {'Rank': 20}, 'Ben Rothwell': {'Rank': 10}, 'Andrei Arlovski': {'Rank': 14}, 'Alex Caceres': {'Rank': 19}, 'Steven Peterson': {'Rank': 11}, 'Irene Aldana': {'Rank': 8}, 'Raquel Pennington': {'Rank': 24}, 'Klidson Abreu': {'Rank': 5}, 'Sam Alvey': {'Rank': 21}, 'Jennifer Maia': {'Rank': 13}, 'Roxanne Modafferi': {'Rank': 22}, 'Ray Borg': {'Rank': 17}, 'Gabriel Silva': {'Rank': 23}, 'Dom Pilarte': {'Rank': 12}, 'Felipe Colares': {'Rank': 26}, 'Jinsoo Son': {'Rank': 18}, 'Mario Bautista': {'Rank': 25}} | |
| all_combinations = [row for row in csv.reader(open("C:/Users/micha/Desktop/Python/Diversification_data.csv", "r"))] |
| import jwt | |
| def create_token(payload, private_key): | |
| return jwt.encode(payload, private_key, algorithm="RS256") | |
| def decode_token(token, public_key, default=None): | |
| try: | |
| return jwt.decode(token, public_key, algorithms="RS256") |
Create a function that takes two arguments: a list and a number. In the list (the first argument), if an element occurs more than N times (the second argument), remove the extra occurrences and return the result.
delete_occurrences([1, 1, 1, 1], 2) ➞ [1, 1]
delete_occurrences([13, True, 13, None], 1) ➞ [13, True, None]
Suppose you are directionally challenged, and get lost easily. As a result, sometimes you walk in circles or make U-turns. You might take a sub-optimal route. Create a function that returns the difference in length between your path and the optimal path. Both paths reach the same destination.
You start at (0,0) and reach your destination by the end of the input list.
Your route: ["N", "S", "E", "W", "E", "E", "E", "N"] // 8
Optimal route: ["E", "E", "E", "N"] (or ["N", "E", "E", "E"], etc.) // 4
You are given an input array of bigrams, and an list of words.
Write a function that returns True if every single bigram from this list can be found at least once in a list of words.
can_find(["at", "be", "th", "au"], ["beautiful", "the", "hat"]) ➞ True
For this task, you will write two validators.
A few examples to illustrate these respective functions: