def confuser(self, submission=None, comments=None, id=None, size=10):
# TODO: How long to confuse. Example 3 weeks or 1 hour
if id:
if submission:
sub = self.get_submission(id=id)
sub.edit(get_text(size))
quit(f'[i] Submission with id {id} confused')
elif comments:
com = self.get_comment(id=id)
com.edit(get_text(size))
quit(f'[i] Comment with id: {id} confused')
if submission:
print('[i] This may take some time.\nLoading data...')
data = self.user_activity(submission=True)
print('[i] Confusing submission text but NOT title...')
for s in data:
self.reddit.submission(s).edit(get_text(size))
elif comments:
print('[i] This may take some time.\nLoading data...')
data = self.user_activity(comments=True)
print('[i] Confusing comments...')
for c in data:
self.reddit.comment(c).edit(get_text(size))
print('[i] Confused {0} items.'.format(len(data)))
async def on_reaction_add(self, reaction, user): #TODO: video_id_to_url #TODO: when searched random, get a new random result instead of next??? try: if '👎' in str(reaction): associated_search_result = self.video_messages[reaction.message.id] # TODO: if random video was searched, random random one instead of next await reaction.message.channel.send(get_text(reaction.message.guild.id, "next_video")) next_video = associated_search_result.next_item() if not next_video: associated_search_result = associated_search_result.get_next_page(self.search) next_video = associated_search_result.first_item() await self.send_video(reaction.message.channel, next_video, associated_search_result) except KeyError as e: pass
def magic_eight_ball(guild):
return choice(get_text(guild.id, "magic_eight_ball"))
def get_random_quote(guild):
return choice(get_text(guild.id, "quotes"))
from gensim.models import Phrases
from helpers import get_file_names, get_text
from itertools import groupby
from pathlib import Path
from typing import Dict, List, Optional, Tuple
import logging
import matplotlib.pyplot as plt
import nltk
import os
import re
import rus_preprocessing_udpipe
nltk.download("stopwords")
stopwordsiso = get_text("stopwords-ru.txt").split("\n")
from nltk.corpus import stopwords
logging.basicConfig(format='%(asctime)s : %(levelname)s : %(message)s',
level=logging.INFO)
class ScholarlyPreprocessor(object):
"""Prepares a list of raw Russian text from scholarly parpers into a list of normalized
tokens.
"""
russian_stopwords = stopwords.words("russian") + stopwords.words("english") + \
stopwordsiso + ["что-то", "который", "это", "также", "диалог", "что-ловек", "чем-ловек", "как-то",
"поскольку", "никак", "текст", "явление", "являться", "автор", "вообще-то", "получать",
"сравнивать", "корпус", "исследование", "словарь", "конструкция", "таблица", "предложение",
"эксперимент", "причина", "отношение", "данные", "объект", "анализ", "рисяча", "во-вторых",
"во-первых", "в-третьих", "заключение", "выражение", "высказывание", "материал",
import helpers
import numpy as np
import random
import sys
# How many characters we look back.
SEQUENCE_LENGTH = 80
# On how many characters we split a sequence.
SEQUENCE_STEP = 1
# The file that contains the text.
CORPUS = "corpus.txt"
# How many epochs to train for.
EPOCHS = 10
# Get the text from corpus.
text = helpers.get_text(CORPUS)
# Get unique characters.
chars = helpers.get_unique_characters(text)
# Get length of unique chars.
chars_length = len(chars)
# Create sequences that are the input values and the next characters that are the labels.
values, labels = helpers.create_sequences(text, SEQUENCE_LENGTH, SEQUENCE_STEP)
char_to_index, index_to_char = helpers.create_dictionaries(chars)
# Convert to one hot arrays.
x, y = helpers.convert_to_one_hot(values, SEQUENCE_LENGTH, chars_length,
char_to_index, labels)
# Create model.