Build a Battleship game engine that is separate from any presentation layer.
The required client is a small text client used to test and play the engine.
After the main part is accepted, the group may optionally add a browser demo on top of the same engine. The browser demo is out of scope until the group explicitly brings it into scope.
| пушистый | |
| и | |
| пудель | |
| котенок | |
| громкий | |
| мяукал | |
| лаял | |
| большой | |
| бегал | |
| мурлыкал |
| # just run this in the end of 01_pride_and_predjudice.ipynb from https://github.com/cytora/pycon-nlp-in-10-lines | |
| processed_sentences = [sent.lemma_.split() for sent in processed_text.sents] | |
| interchangeable_words_model = Word2Vec( | |
| sentences=processed_sentences, | |
| workers=multiprocessing.cpu_count() - 1, # use your cores | |
| window=2, sg=1) | |
| attributes_of_model = Word2Vec( | |
| sentences=processed_sentences, |
| #!/usr/bin/env python | |
| # -*- coding: utf-8 -*- | |
| # | |
| # Copyright (C) 2011 Radim Rehurek <radimrehurek@seznam.cz> | |
| # Licensed under the GNU LGPL v2.1 - http://www.gnu.org/licenses/lgpl.html | |
| # | |
| # Parts of the LDA inference code come from Dr. Hoffman's `onlineldavb.py` script, | |
| # (C) 2010 Matthew D. Hoffman, GNU GPL 3.0 | |
| from time import time | |
| import logging | |
| import numpy as np | |
| import pandas as pd | |
| from sklearn.datasets import fetch_20newsgroups | |
| from sklearn.feature_extraction.text import CountVectorizer | |
| from sklearn.decomposition import LatentDirichletAllocation | |
| from gensim.matutils import Sparse2Corpus | |
| from gensim.models.ldamodel import LdaModel |