杨海宏 RamonYeung

## English_stopwords
# Here is a list of stop words in English and it is taken from nltk.corpus.stopwords
# requirements: NLTK==3.2.4

stop_words = ['a', 'about', 'above', 'after', 'again', 'against', 'ain', 'all', 'am', 'an', 'and', 'any', 'are', 'aren', "aren't", 'as', 'at',
              'be', 'because', 'been', 'before', 'being', 'below', 'between', 'both', 'but', 'by',
              'can', 'couldn', "couldn't",
              'd', 'did', 'didn', "didn't", 'do', 'does', 'doesn', "doesn't", 'doing', 'don', "don't", 'down', 'during',
              'each',
              'few', 'for', 'from', 'further',
              'had', 'hadn', "hadn't", 'has', 'hasn', "hasn't", 'have', 'haven', "haven't", 'having', 'he', 'her', 'here', 'hers', 'herself', 'him', 'himself', 'his', 'how',

## rank_metrics.py
"""Information Retrieval metrics

Useful Resources:
http://www.cs.utexas.edu/~mooney/ir-course/slides/Evaluation.ppt
http://www.nii.ac.jp/TechReports/05-014E.pdf
http://www.stanford.edu/class/cs276/handouts/EvaluationNew-handout-6-per.pdf
http://hal.archives-ouvertes.fr/docs/00/72/67/60/PDF/07-busa-fekete.pdf
Learning to Rank for Information Retrieval (Tie-Yan Liu)
"""
import numpy as np

## WebQuestions_Eval
#!/usr/bin/python

import sys
import json

if len(sys.argv) != 3:
  sys.exit("Usage: %s <gold_file> <result_file>" % sys.argv[0])

"""load the answers to a list"""
def loadAnswers(filename):

## ijcai_17_pp_title_with_keyword
import requests
import re
from urllib.request import urlopen
import multiprocessing as mp
import os

def process(pair):
    idx, title = pair
    idx = '0' * (4 - len(idx)) + idx
    for word in keywords:

## pg-pong.py
""" 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

## chinese_char.py
# Recall that, in regex, we use [\u4e00-\u9fa5] to match chinese characters.

start = int("4e00", 16)
end = int("9fa5", 16)

for i in range(start, end + 1):
    uni = r"\u" + hex(i)[2:]
    result = uni.encode("utf-8").decode("unicode_escape")
    print(result)

## tree.md

      
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                RamonYeung
                / tree.md
            
            
              Created
              December 29, 2016 08:15
                — forked from upsuper/tree.md
            
              
                一行 Python 实现树
              
          
    一行 Python 实现树

使用 Python 内置的 defaultdict，我们可以很容易的定义一个树形数据结构：
def tree(): return defaultdict(tree)
就是这样！

  
## min-char-rnn.py
"""
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)

## tf_lstm.py
"""Short and sweet LSTM implementation in Tensorflow.

Motivation:
When Tensorflow was released, adding RNNs was a bit of a hack - it required
building separate graphs for every number of timesteps and was a bit obscure
to use. Since then TF devs added things like `dynamic_rnn`, `scan` and `map_fn`.
Currently the APIs are decent, but all the tutorials that I am aware of are not
making the best use of the new APIs.

Advantages of this implementation:
	# Here is a list of stop words in English and it is taken from nltk.corpus.stopwords
	# requirements: NLTK==3.2.4

	stop_words = ['a', 'about', 'above', 'after', 'again', 'against', 'ain', 'all', 'am', 'an', 'and', 'any', 'are', 'aren', "aren't", 'as', 'at',
	'be', 'because', 'been', 'before', 'being', 'below', 'between', 'both', 'but', 'by',
	'can', 'couldn', "couldn't",
	'd', 'did', 'didn', "didn't", 'do', 'does', 'doesn', "doesn't", 'doing', 'don', "don't", 'down', 'during',
	'each',
	'few', 'for', 'from', 'further',
	'had', 'hadn', "hadn't", 'has', 'hasn', "hasn't", 'have', 'haven', "haven't", 'having', 'he', 'her', 'here', 'hers', 'herself', 'him', 'himself', 'his', 'how',
	"""Information Retrieval metrics

	Useful Resources:
	http://www.cs.utexas.edu/~mooney/ir-course/slides/Evaluation.ppt
	http://www.nii.ac.jp/TechReports/05-014E.pdf
	http://www.stanford.edu/class/cs276/handouts/EvaluationNew-handout-6-per.pdf
	http://hal.archives-ouvertes.fr/docs/00/72/67/60/PDF/07-busa-fekete.pdf
	Learning to Rank for Information Retrieval (Tie-Yan Liu)
	"""
	import numpy as np
	#!/usr/bin/python

	import sys
	import json

	if len(sys.argv) != 3:
	sys.exit("Usage: %s <gold_file> <result_file>" % sys.argv[0])

	"""load the answers to a list"""
	def loadAnswers(filename):
	import requests
	import re
	from urllib.request import urlopen
	import multiprocessing as mp
	import os

	def process(pair):
	idx, title = pair
	idx = '0' * (4 - len(idx)) + idx
	for word in keywords:
	""" 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
	# Recall that, in regex, we use [\u4e00-\u9fa5] to match chinese characters.

	start = int("4e00", 16)
	end = int("9fa5", 16)

	for i in range(start, end + 1):
	uni = r"\u" + hex(i)[2:]
	result = uni.encode("utf-8").decode("unicode_escape")
	print(result)
	"""
	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)
	"""Short and sweet LSTM implementation in Tensorflow.

	Motivation:
	When Tensorflow was released, adding RNNs was a bit of a hack - it required
	building separate graphs for every number of timesteps and was a bit obscure
	to use. Since then TF devs added things like `dynamic_rnn`, `scan` and `map_fn`.
	Currently the APIs are decent, but all the tutorials that I am aware of are not
	making the best use of the new APIs.

	Advantages of this implementation: