| set nocompatible " be iMproved, required | |
| let g:python3_host_prog = '/usr/local/opt/python@3.8/bin/python3.8' | |
| if empty(glob('~/.vim/autoload/plug.vim')) | |
| silent !curl -fLo ~/.vim/autoload/plug.vim --create-dirs | |
| \ https://raw.githubusercontent.com/junegunn/vim-plug/master/plug.vim | |
| autocmd VimEnter * PlugInstall --sync | source $MYVIMRC | |
| endif | |
| call plug#begin('~/.vim/plugged') |
Below you will find the companing files for the Clustering via hypergraph modularity submitted to the PLOS ONE journal by Bogumił Kamiński, Valérie Poulin, Paweł Prałat, Przemysław Szufel and François Théberge.
In the paper, a hypergraph modularity function is proposed that generalizes its well established and widely used graph counterpart measure of how clustered a network is.
The provided code files contain implementation of heuristic algorithm
| class AttentionLSTM(LSTM): | |
| """LSTM with attention mechanism | |
| This is an LSTM incorporating an attention mechanism into its hidden states. | |
| Currently, the context vector calculated from the attended vector is fed | |
| into the model's internal states, closely following the model by Xu et al. | |
| (2016, Sec. 3.1.2), using a soft attention model following | |
| Bahdanau et al. (2014). | |
| The layer expects two inputs instead of the usual one: |
In this article, I will share some of my experience on installing NVIDIA driver and CUDA on Linux OS. Here I mainly use Ubuntu as example. Comments for CentOS/Fedora are also provided as much as I can.
| """ Trains an agent with (stochastic) Policy Gradients on Pong. Uses OpenAI Gym. """ | |
| import numpy as np | |
| import cPickle as pickle | |
| import gym | |
| # hyperparameters | |
| H = 200 # number of hidden layer neurons | |
| batch_size = 10 # every how many episodes to do a param update? | |
| learning_rate = 1e-4 | |
| gamma = 0.99 # discount factor for reward |
| """ | |
| Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy) | |
| BSD License | |
| """ | |
| import numpy as np | |
| # data I/O | |
| data = open('input.txt', 'r').read() # should be simple plain text file | |
| chars = list(set(data)) | |
| data_size, vocab_size = len(data), len(chars) |
| """ | |
| From this paper: http://acl.ldc.upenn.edu/acl2004/emnlp/pdf/Mihalcea.pdf | |
| I used python with nltk, and pygraph to do an implmentation of of textrank. | |
| for questions: http://twitter.com/voidfiles | |
| """ | |
| import nltk | |
| import itertools |
