Naomi Saphra nsaphra

I hereby claim:

To claim this, I am signing this object:

	"""
	Because pytorch does not expose the internal activations of a module,
	we must instead rerun the same exact function inside that module.
	This is written specifically for a 1 layer LSTM with all default settings.
	"""

	import torch
	import torch.nn as nn
	from torch.autograd import Variable

	import sys

	types = set()
	token_count = 0

	for i, line in enumerate(sys.stdin):
	if i % 1000 == 0:
	print('.')
	line = line.strip().split()
	types.update(line)

	# -- coding: utf-8 --
	import os
	from random import shuffle
	import argparse

	parser = argparse.ArgumentParser(description='shuffle a corpus such that the tags and the original tokenized text still align')
	parser.add_argument('--unshuffled_dir', type=str)
	parser.add_argument('--shuffled_dir', type=str)
	parser.add_argument('--tag_suffix', type=str, default='.tag')
	args = parser.parse_args()

	def zipf(size, exponent):
	x = np.arange(size, dtype='float')
	pmf = (x ** exponent).reciprocal()
	pmf /= pmf.sum()
	return stats.rv_discrete(values=range(size), pmf)

	class NoughtsAndCrosses:
	NOUGHT = "O"
	CROSS = "X"
	EMPTY = " "

	STALEMATE = "Nobody"

	def __init__(self):
	self.board = [[self.EMPTY] * 3, [self.EMPTY] * 3, [self.EMPTY] * 3]

	#!/bin/bash

	if [ "$TERM" != "screen" ]
	then
	if type tmux >/dev/null 2>&1
	then
	tmux att \|\| tmux \
	new -s tensorflow -n shell \; \
	neww -n notebook "source activate tensorflow; cd Documents/dynamic_curriculum; jupyter notebook" \; \
	neww -n dir "cd Documents/dynamic_curriculum"

	type SyntaxNode
	label::AbstractString
	parent::SyntaxNode
	children::Array{SyntaxNode}

	# TODO No error handling when going up a level with undefined parent.
	SyntaxNode() = (
	x = new();
	x.label = "";
	x.children = [];

	#!/usr/bin/python
	from collections import defaultdict
	import json
	import os
	import argparse
	import gzip
	import sys
	import codecs
	from time import asctime

	module SparsePy
	# TODO this is only for CSC sparse matrix PyObjects and julia matrices.
	# Add other types when julia releases them?

	require("PyCall")
	using PyCall

	export jlmat2pymat, pymat2jlmat

	@pyimport scipy.sparse as pysparse