André Cruz AndreFCruz

System details : Ubuntu 14.04, python-2.7.

Download Humor-Sans ttf font from here.
Place it in /usr/share/fonts/truetype/. For OSx systems, you'd just need to double-click the font file to install.
Run the following commands, to refresh your system's font cache and to delete Maplotlib's font cache.

 sudo fc-cache -f -v
 cd ~/.cache/matplotlib; rm fontList.cache; cd #for OSx systems, this file resides in ~/.matplotlib/

People

Sans jQuery

// jQuery
$(document).ready(function() {
  // code
})

	''' Script for downloading all GLUE data.

	Note: for legal reasons, we are unable to host MRPC.
	You can either use the version hosted by the SentEval team, which is already tokenized,
	or you can download the original data from (https://download.microsoft.com/download/D/4/6/D46FF87A-F6B9-4252-AA8B-3604ED519838/MSRParaphraseCorpus.msi) and extract the data from it manually.
	For Windows users, you can run the .msi file. For Mac and Linux users, consider an external library such as 'cabextract' (see below for an example).
	You should then rename and place specific files in a folder (see below for an example).

	mkdir MRPC
	cabextract MSRParaphraseCorpus.msi -d MRPC

	'''
	TicTacToe Game

	Console based.
	MiniMax AI based on a scoring matrix of the game tree

	Python 2.7.9 - 32 bit

	@author Andre Cruz
	'''

	def dot_product(x, kernel):
	"""
	Wrapper for dot product operation, in order to be compatible with both
	Theano and Tensorflow
	Args:
	x (): input
	kernel (): weights
	Returns:
	"""
	if K.backend() == 'tensorflow':

	-- Hammerspoon configuration, heavily influenced by sdegutis default configuration

	-- init grid
	hs.grid.MARGINX = 0
	hs.grid.MARGINY = 0
	hs.grid.GRIDWIDTH = 8
	hs.grid.GRIDHEIGHT = 4

	-- disable animation
	hs.window.animationDuration = 0

	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:

	"""
	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)