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Repository: minsangkim142/R-net
Branch: master
Commit: a5721684dc0a
Files: 16
Total size: 333.1 KB
Directory structure:
gitextract_orh_ofxx/
├── GRU.py
├── LICENSE
├── Pipfile
├── README.md
├── data_load.py
├── demo.html
├── demo.py
├── evaluate.py
├── glove.840B.300d.char.txt
├── layers.py
├── model.py
├── params.py
├── process.py
├── requirements.txt
├── setup.sh
└── zoneout.py
================================================
FILE CONTENTS
================================================
================================================
FILE: GRU.py
================================================
# -*- coding: utf-8 -*-
#/usr/bin/python2
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import collections
import hashlib
import numbers
import tensorflow as tf
from tensorflow.python.ops import clip_ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import init_ops
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import nn_ops
from tensorflow.python.ops import variable_scope as vs
from tensorflow.python.util import nest
from tensorflow.contrib.rnn import RNNCell
from layers import gated_attention
from params import Params
_BIAS_VARIABLE_NAME = "bias"
_WEIGHTS_VARIABLE_NAME = "kernel"
class SRUCell(RNNCell):
"""Simple Recurrent Unit (SRU).
This implementation is based on:
Tao Lei and Yu Zhang,
"Training RNNs as Fast as CNNs,"
https://arxiv.org/abs/1709.02755
"""
def __init__(self, num_units, activation=None, is_training = True, reuse=None):
self._num_units = num_units
self._activation = activation or tf.tanh
self._is_training = is_training
@property
def output_size(self):
return self._num_units
@property
def state_size(self):
return self._num_units
def __call__(self, inputs, state, scope=None):
"""Run one step of SRU."""
with tf.variable_scope(scope or type(self).__name__): # "SRUCell"
with tf.variable_scope("x_hat"):
x = linear([inputs], self._num_units, False)
with tf.variable_scope("gates"):
concat = tf.sigmoid(linear([inputs], 2 * self._num_units, True))
f, r = tf.split(concat, 2, axis = 1)
with tf.variable_scope("candidates"):
c = self._activation(f * state + (1 - f) * x)
# variational dropout as suggested in the paper (disabled)
# if self._is_training and Params.dropout is not None:
# c = tf.nn.dropout(c, keep_prob = 1 - Params.dropout)
# highway connection
# Our implementation is slightly different to the paper
# https://arxiv.org/abs/1709.02755 in a way that highway network
# uses x_hat instead of the cell inputs. Check equation (7) from the original
# paper for SRU.
h = r * c + (1 - r) * x
return h, c
class GRUCell(RNNCell):
"""Gated Recurrent Unit cell (cf. http://arxiv.org/abs/1406.1078)."""
def __init__(self,
num_units,
activation=None,
reuse=None,
kernel_initializer=None,
bias_initializer=None,
is_training = True):
super(GRUCell, self).__init__(_reuse=reuse)
self._num_units = num_units
self._activation = activation or math_ops.tanh
self._kernel_initializer = kernel_initializer
self._bias_initializer = bias_initializer
self._is_training = is_training
@property
def state_size(self):
return self._num_units
@property
def output_size(self):
return self._num_units
def __call__(self, inputs, state, scope = None):
"""Gated recurrent unit (GRU) with nunits cells."""
if inputs.shape.as_list()[-1] != self._num_units:
with vs.variable_scope("projection"):
res = linear(inputs, self._num_units, False, )
else:
res = inputs
with vs.variable_scope("gates"): # Reset gate and update gate.
# We start with bias of 1.0 to not reset and not update.
bias_ones = self._bias_initializer
if self._bias_initializer is None:
dtype = [a.dtype for a in [inputs, state]][0]
bias_ones = init_ops.constant_initializer(1.0, dtype=dtype)
value = math_ops.sigmoid(
linear([inputs, state], 2 * self._num_units, True, bias_ones,
self._kernel_initializer))
r, u = array_ops.split(value=value, num_or_size_splits=2, axis=1)
with vs.variable_scope("candidate"):
c = self._activation(
linear([inputs, r * state], self._num_units, True,
self._bias_initializer, self._kernel_initializer))
# recurrent dropout as proposed in https://arxiv.org/pdf/1603.05118.pdf (currently disabled)
#if self._is_training and Params.dropout is not None:
#c = tf.nn.dropout(c, 1 - Params.dropout)
new_h = u * state + (1 - u) * c
return new_h + res, new_h
class gated_attention_Wrapper(RNNCell):
def __init__(self,
num_units,
memory,
params,
self_matching = False,
memory_len = None,
reuse=None,
kernel_initializer=None,
bias_initializer=None,
is_training = True,
use_SRU = False):
super(gated_attention_Wrapper, self).__init__(_reuse=reuse)
cell = SRUCell if use_SRU else GRUCell
self._cell = cell(num_units, is_training = is_training)
self._num_units = num_units
self._activation = math_ops.tanh
self._kernel_initializer = kernel_initializer
self._bias_initializer = bias_initializer
self._attention = memory
self._params = params
self._self_matching = self_matching
self._memory_len = memory_len
self._is_training = is_training
@property
def state_size(self):
return self._num_units
@property
def output_size(self):
return self._num_units
def __call__(self, inputs, state, scope = None):
"""Gated recurrent unit (GRU) with nunits cells."""
with vs.variable_scope("attention_pool"):
inputs = gated_attention(self._attention,
inputs,
state,
self._num_units,
params = self._params,
self_matching = self._self_matching,
memory_len = self._memory_len)
output, new_state = self._cell(inputs, state, scope)
return output, new_state
def linear(args,
output_size,
bias,
bias_initializer=None,
kernel_initializer=None):
"""Linear map: sum_i(args[i] * W[i]), where W[i] is a variable.
Args:
args: a 2D Tensor or a list of 2D, batch x n, Tensors.
output_size: int, second dimension of W[i].
bias: boolean, whether to add a bias term or not.
bias_initializer: starting value to initialize the bias
(default is all zeros).
kernel_initializer: starting value to initialize the weight.
Returns:
A 2D Tensor with shape [batch x output_size] equal to
sum_i(args[i] * W[i]), where W[i]s are newly created matrices.
Raises:
ValueError: if some of the arguments has unspecified or wrong shape.
"""
if args is None or (nest.is_sequence(args) and not args):
raise ValueError("`args` must be specified")
if not nest.is_sequence(args):
args = [args]
# Calculate the total size of arguments on dimension 1.
total_arg_size = 0
shapes = [a.get_shape() for a in args]
for shape in shapes:
if shape.ndims != 2:
raise ValueError("linear is expecting 2D arguments: %s" % shapes)
if shape[1].value is None:
raise ValueError("linear expects shape[1] to be provided for shape %s, "
"but saw %s" % (shape, shape[1]))
else:
total_arg_size += shape[1].value
dtype = [a.dtype for a in args][0]
# Now the computation.
scope = vs.get_variable_scope()
with vs.variable_scope(scope) as outer_scope:
weights = vs.get_variable(
_WEIGHTS_VARIABLE_NAME, [total_arg_size, output_size],
dtype=dtype,
initializer=kernel_initializer)
if len(args) == 1:
res = math_ops.matmul(args[0], weights)
else:
res = math_ops.matmul(array_ops.concat(args, 1), weights)
if not bias:
return res
with vs.variable_scope(outer_scope) as inner_scope:
inner_scope.set_partitioner(None)
if bias_initializer is None:
bias_initializer = init_ops.constant_initializer(0.0, dtype=dtype)
biases = vs.get_variable(
_BIAS_VARIABLE_NAME, [output_size],
dtype=dtype,
initializer=bias_initializer)
return nn_ops.bias_add(res, biases)
================================================
FILE: LICENSE
================================================
MIT License
Copyright (c) 2017 Min Sang Kim
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
================================================
FILE: Pipfile
================================================
[[source]]
url = "https://pypi.python.org/simple"
verify_ssl = true
name = "pypi"
[packages]
nltk = "*"
numpy = "*"
tqdm = "*"
spacy = "*"
[dev-packages]
[requires]
python_version = "2.7"
================================================
FILE: README.md
================================================
# R-NET: MACHINE READING COMPREHENSION WITH SELF MATCHING NETWORKS
Tensorflow implementation of https://www.microsoft.com/en-us/research/wp-content/uploads/2017/05/r-net.pdf

The dataset used for this task is Stanford Question Answering Dataset (https://rajpurkar.github.io/SQuAD-explorer/). Pretrained GloVe embeddings are used for both words (https://nlp.stanford.edu/projects/glove/) and characters (https://github.com/minimaxir/char-embeddings/blob/master/glove.840B.300d-char.txt).
**As of 26 Feb 2018, thanks to [@theSage21](https://github.com/thesage21) we have a working demo of R-net!**
## Requirements
* Python2.7
* NumPy
* tqdm
* spacy
* TensorFlow==1.2
# Downloads and Setup
Once you clone this repo, run the following lines from bash **just once** to process the dataset (SQuAD).
```shell
$ pipenv install
$ bash setup.sh
$ pipenv shell
$ python process.py --reduce_glove True --process True
```
# Training / Testing / Debugging / Interactive Demo
You can change the hyperparameters from params.py file to fit the model in your GPU. To train the model, run the following line.
```shell
$ python model.py
```
To test or debug your model after training, change mode="train" to debug or test from params.py file and run the model.
**To use demo, put batch size = 1**
# Tensorboard
Run tensorboard for visualisation.
```shell
$ tensorboard --logdir=r-net:train/
```

# Log
**26/02/18**
As of 26th Feb 2018, thanks to [@theSage21](https://github.com/thesage21) we have an html demo that can easily be launched to user's local host and try out R-net on custom paragraphs and questions.
**18/10/17**
After some hyperparameter searching, our model quickly reaches EM/F1 score of 50/60 in 4 hours with the hyperparameters suggested in params.py file. However, it quickly overfits after that. **Current best model reaches EM/F1 of 55/67 on dev set**.
**05/09/17**
After rewriting the architectures, the model converges with full dataset and it takes about 20 hours to reach F1/EM=67/60 on training set and 40/30 on dev set. with batch size of 54. Reproducing the results obtained by R-Net in the original paper is a new work in progress.
**02/09/17**
One of the challenges I faced while training was to fit a minibatch of size 32 or larger into my GTX 1080. Since SQuAD dataset displayed high variance in data, higher batch size was essential in training (otherwise the model doesn't converge). Reducing GPU memory usage significantly to fit batch size of 32 and higher is a work in progress. If you have any suggestions on reducing the GPU memory usage, please put forward a pr.
**27/08/17**
As a sanity check I trained the network with 3000 independent randomly sampled question-answering pairs. With my GTX 1080, it took about 4 hours and a half for the model to get the gist of what's going on with the data. With full dataset (90,000+ pairs) we are expecting longer time for convergence. Some sort of normalization method might help speed up convergence (though the authors of the original paper didn't mention anything about the normalization).
================================================
FILE: data_load.py
================================================
# -*- coding: utf-8 -*-
#/usr/bin/python2
from functools import wraps
import threading
from tensorflow.python.platform import tf_logging as logging
from params import Params
import numpy as np
import tensorflow as tf
from process import *
from sklearn.model_selection import train_test_split
# Adapted from the `sugartensor` code.
# https://github.com/buriburisuri/sugartensor/blob/master/sugartensor/sg_queue.py
def producer_func(func):
r"""Decorates a function `func` as producer_func.
Args:
func: A function to decorate.
"""
@wraps(func)
def wrapper(inputs, dtypes, capacity, num_threads):
r"""
Args:
inputs: A inputs queue list to enqueue
dtypes: Data types of each tensor
capacity: Queue capacity. Default is 32.
num_threads: Number of threads. Default is 1.
"""
# enqueue function
def enqueue_func(sess, op):
# read data from source queue
data = func(sess.run(inputs))
# create feeder dict
feed_dict = {}
for ph, col in zip(placeholders, data):
feed_dict[ph] = col
# run session
sess.run(op, feed_dict=feed_dict)
# create place holder list
placeholders = []
for dtype in dtypes:
placeholders.append(tf.placeholder(dtype=dtype))
# create FIFO queue
queue = tf.FIFOQueue(capacity, dtypes=dtypes)
# enqueue operation
enqueue_op = queue.enqueue(placeholders)
# create queue runner
runner = _FuncQueueRunner(enqueue_func, queue, [enqueue_op] * num_threads)
# register to global collection
tf.train.add_queue_runner(runner)
# return de-queue operation
return queue.dequeue()
return wrapper
class _FuncQueueRunner(tf.train.QueueRunner):
def __init__(self, func, queue=None, enqueue_ops=None, close_op=None,
cancel_op=None, queue_closed_exception_types=None,
queue_runner_def=None):
# save ad-hoc function
self.func = func
# call super()
super(_FuncQueueRunner, self).__init__(queue, enqueue_ops, close_op, cancel_op,
queue_closed_exception_types, queue_runner_def)
# pylint: disable=broad-except
def _run(self, sess, enqueue_op, coord=None):
if coord:
coord.register_thread(threading.current_thread())
decremented = False
try:
while True:
if coord and coord.should_stop():
break
try:
self.func(sess, enqueue_op) # call enqueue function
except self._queue_closed_exception_types: # pylint: disable=catching-non-exception
# This exception indicates that a queue was closed.
with self._lock:
self._runs_per_session[sess] -= 1
decremented = True
if self._runs_per_session[sess] == 0:
try:
sess.run(self._close_op)
except Exception as e:
# Intentionally ignore errors from close_op.
logging.vlog(1, "Ignored exception: %s", str(e))
return
except Exception as e:
# This catches all other exceptions.
if coord:
coord.request_stop(e)
else:
logging.error("Exception in QueueRunner: %s", str(e))
with self._lock:
self._exceptions_raised.append(e)
raise
finally:
# Make sure we account for all terminations: normal or errors.
if not decremented:
with self._lock:
self._runs_per_session[sess] -= 1
def load_data(dir_):
# Target indices
indices = load_target(dir_ + Params.target_dir)
# Load question data
print("Loading question data...")
q_word_ids, _ = load_word(dir_ + Params.q_word_dir)
q_char_ids, q_char_len, q_word_len = load_char(dir_ + Params.q_chars_dir)
# Load passage data
print("Loading passage data...")
p_word_ids, _ = load_word(dir_ + Params.p_word_dir)
p_char_ids, p_char_len, p_word_len = load_char(dir_ + Params.p_chars_dir)
# Get max length to pad
p_max_word = Params.max_p_len#np.max(p_word_len)
p_max_char = Params.max_char_len#,max_value(p_char_len))
q_max_word = Params.max_q_len#,np.max(q_word_len)
q_max_char = Params.max_char_len#,max_value(q_char_len))
# pad_data
print("Preparing data...")
p_word_ids = pad_data(p_word_ids,p_max_word)
q_word_ids = pad_data(q_word_ids,q_max_word)
p_char_ids = pad_char_data(p_char_ids,p_max_char,p_max_word)
q_char_ids = pad_char_data(q_char_ids,q_max_char,q_max_word)
# to numpy
indices = np.reshape(np.asarray(indices,np.int32),(-1,2))
p_word_len = np.reshape(np.asarray(p_word_len,np.int32),(-1,1))
q_word_len = np.reshape(np.asarray(q_word_len,np.int32),(-1,1))
# p_char_len = pad_data(p_char_len,p_max_word)
# q_char_len = pad_data(q_char_len,q_max_word)
p_char_len = pad_char_len(p_char_len, p_max_word, p_max_char)
q_char_len = pad_char_len(q_char_len, q_max_word, q_max_char)
for i in range(p_word_len.shape[0]):
if p_word_len[i,0] > p_max_word:
p_word_len[i,0] = p_max_word
for i in range(q_word_len.shape[0]):
if q_word_len[i,0] > q_max_word:
q_word_len[i,0] = q_max_word
# shapes of each data
shapes=[(p_max_word,),(q_max_word,),
(p_max_word,p_max_char,),(q_max_word,q_max_char,),
(1,),(1,),
(p_max_word,),(q_max_word,),
(2,)]
return ([p_word_ids, q_word_ids,
p_char_ids, q_char_ids,
p_word_len, q_word_len,
p_char_len, q_char_len,
indices], shapes)
def get_dev():
devset, shapes = load_data(Params.dev_dir)
indices = devset[-1]
# devset = [np.reshape(input_, shapes[i]) for i,input_ in enumerate(devset)]
dev_ind = np.arange(indices.shape[0],dtype = np.int32)
np.random.shuffle(dev_ind)
return devset, dev_ind
def get_batch(is_training = True):
"""Loads training data and put them in queues"""
with tf.device('/cpu:0'):
# Load dataset
input_list, shapes = load_data(Params.train_dir if is_training else Params.dev_dir)
indices = input_list[-1]
train_ind = np.arange(indices.shape[0],dtype = np.int32)
np.random.shuffle(train_ind)
size = Params.data_size
if Params.data_size > indices.shape[0] or Params.data_size == -1:
size = indices.shape[0]
ind_list = tf.convert_to_tensor(train_ind[:size])
# Create Queues
ind_list = tf.train.slice_input_producer([ind_list], shuffle=True)
@producer_func
def get_data(ind):
'''From `_inputs`, which has been fetched from slice queues,
then enqueue them again.
'''
return [np.reshape(input_[ind], shapes[i]) for i,input_ in enumerate(input_list)]
data = get_data(inputs=ind_list,
dtypes=[np.int32]*9,
capacity=Params.batch_size*32,
num_threads=6)
# create batch queues
batch = tf.train.batch(data,
shapes=shapes,
num_threads=2,
batch_size=Params.batch_size,
capacity=Params.batch_size*32,
dynamic_pad=True)
return batch, size // Params.batch_size
================================================
FILE: demo.html
================================================
<!DOCTYPE html>
<html>
<head>
<meta charset="UTF-8">
<script>
function hit(passage, question){
var xhr = new XMLHttpRequest();
xhr.open("POST", '/answer', true);
xhr.setRequestHeader("Content-type", "application/json");
xhr.onreadystatechange = function () {
console.log(xhr.status);
if (xhr.readyState === 4 && xhr.status === 200) {
var json = JSON.parse(xhr.responseText);
console.log(json.answer);
var ans = document.getElementById("answer");
ans.innerHTML = json.answer;
}
};
var params = JSON.stringify({"question": question, "passage": passage});
xhr.send(params);
console.log("sent");
}
</script>
<style>
body{
display: grid;
grid-template-columns: 7fr 3fr;
grid-template-rows: 9fr 1fr;
}
</style>
</head>
<body>
<textarea id='passage' placeholder='Your passage here'></textarea>
<textarea id='question' placeholder='Your Question here'></textarea>
<p id='answer'>Your answer appears here</p>
<button id='GetAnswer'>Get Answer</button>
<script>
var button = document.getElementById("GetAnswer");
button.onclick = function (){
var para = document.getElementById("passage");
var ques = document.getElementById("question");
console.log("hitting");
hit(para.value, ques.value);
};
</script>
</body>
</html>
================================================
FILE: demo.py
================================================
#!/usr/bin/env python
# coding=utf-8
import tensorflow as tf
import bottle
from bottle import route, run
import threading
from params import Params
from process import *
from time import sleep
app = bottle.Bottle()
query = []
response = ""
@app.get("/")
def home():
with open('demo.html', 'r') as fl:
html = fl.read()
return html
@app.post('/answer')
def answer():
passage = bottle.request.json['passage']
question = bottle.request.json['question']
# if not passage or not question:
# exit()
global query, response
query = (passage, question)
while not response:
sleep(0.1)
print("received response: {}".format(response))
Final_response = {"answer": response}
response = []
return Final_response
class Demo(object):
def __init__(self, model):
run_event = threading.Event()
run_event.set()
threading.Thread(target=self.demo_backend, args = [model, run_event]).start()
app.run(port=8080, host='0.0.0.0')
try:
while 1:
sleep(.1)
except KeyboardInterrupt:
print "Closing server..."
run_event.clear()
def demo_backend(self, model, run_event):
global query, response
dict_ = pickle.load(open(Params.data_dir + "dictionary.pkl","r"))
with model.graph.as_default():
sv = tf.train.Supervisor()
with sv.managed_session() as sess:
sv.saver.restore(sess, tf.train.latest_checkpoint(Params.logdir))
while run_event.is_set():
sleep(0.1)
if query:
data, shapes = dict_.realtime_process(query)
fd = {m:d for i,(m,d) in enumerate(zip(model.data, data))}
ids = sess.run([model.output_index], feed_dict = fd)
ids = ids[0][0]
if ids[0] == ids[1]:
ids[1] += 1
passage_t = tokenize_corenlp(query[0])
response = " ".join(passage_t[ids[0]:ids[1]])
query = []
================================================
FILE: evaluate.py
================================================
""" Official evaluation script for v1.1 of the SQuAD dataset. """
from __future__ import print_function
from collections import Counter
import string
import re
import argparse
import json
import sys
def f1_and_EM(index, ground_truth, passage, dict_):
if index[0] == index[1]:
pred_ind = [passage[index[0]].tolist()]
else:
pred_ind = passage[index[0]:index[1]].tolist()
if pred_ind is None:
pred_ind = [0]
if ground_truth[0] == ground_truth[1]:
answer_ind = [passage[ground_truth[0]].tolist()]
else:
answer_ind = passage[ground_truth[0]:ground_truth[1]].tolist()
answer = dict_.ind2word(pred_ind)
answer_ = dict_.ind2word(answer_ind)
f1 = f1_score(answer,answer_)
EM = exact_match_score(answer,answer_)
return f1, EM
def normalize_answer(s):
"""Lower text and remove punctuation, articles and extra whitespace."""
def remove_articles(text):
return re.sub(r'\b(a|an|the)\b', ' ', text)
def white_space_fix(text):
return ' '.join(text.split())
def remove_punc(text):
exclude = set(string.punctuation)
return ''.join(ch for ch in text if ch not in exclude)
def lower(text):
return text.lower()
return white_space_fix(remove_articles(remove_punc(lower(s))))
def f1_score(prediction, ground_truth):
prediction_tokens = normalize_answer(prediction).split()
ground_truth_tokens = normalize_answer(ground_truth).split()
common = Counter(prediction_tokens) & Counter(ground_truth_tokens)
num_same = sum(common.values())
if num_same == 0:
return 0
precision = 1.0 * num_same / len(prediction_tokens)
recall = 1.0 * num_same / len(ground_truth_tokens)
f1 = (2 * precision * recall) / (precision + recall)
return f1
def exact_match_score(prediction, ground_truth):
return (normalize_answer(prediction) == normalize_answer(ground_truth))
def metric_max_over_ground_truths(metric_fn, prediction, ground_truths):
scores_for_ground_truths = []
for ground_truth in ground_truths:
score = metric_fn(prediction, ground_truth)
scores_for_ground_truths.append(score)
return max(scores_for_ground_truths)
def evaluate(dataset, predictions):
f1 = exact_match = total = 0
for article in dataset:
for paragraph in article['paragraphs']:
for qa in paragraph['qas']:
total += 1
if qa['id'] not in predictions:
message = 'Unanswered question ' + qa['id'] + \
' will receive score 0.'
print(message, file=sys.stderr)
continue
ground_truths = list(map(lambda x: x['text'], qa['answers']))
prediction = predictions[qa['id']]
exact_match += metric_max_over_ground_truths(
exact_match_score, prediction, ground_truths)
f1 += metric_max_over_ground_truths(
f1_score, prediction, ground_truths)
exact_match = 100.0 * exact_match / total
f1 = 100.0 * f1 / total
return {'exact_match': exact_match, 'f1': f1}
if __name__ == '__main__':
expected_version = '1.1'
parser = argparse.ArgumentParser(
description='Evaluation for SQuAD ' + expected_version)
parser.add_argument('dataset_file', help='Dataset file')
parser.add_argument('prediction_file', help='Prediction File')
args = parser.parse_args()
with open(args.dataset_file) as dataset_file:
dataset_json = json.load(dataset_file)
if (dataset_json['version'] != expected_version):
print('Evaluation expects v-' + expected_version +
', but got dataset with v-' + dataset_json['version'],
file=sys.stderr)
dataset = dataset_json['data']
with open(args.prediction_file) as prediction_file:
predictions = json.load(prediction_file)
print(json.dumps(evaluate(dataset, predictions)))
================================================
FILE: glove.840B.300d.char.txt
================================================
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8 0.146808 -0.30308 0.273265 -0.02031 -0.271215 0.09482 0.063491 0.020512 0.005035 -1.568682 0.488405 0.017505 -0.213946 0.147993 0.213918 0.033633 0.121454 -1.098999 0.397455 0.097241 0.013171 0.162577 0.24712 0.108724 -0.000496 0.026046 0.174948 -0.107492 -0.141526 0.116075 0.14129 -0.1738 0.042377 -0.03145 -0.276776 0.116717 -0.002711 0.120164 0.112422 0.429137 -0.175104 -0.283044 -0.093598 0.159076 -0.074339 -0.034537 0.274945 -0.054183 -0.068728 0.10075 0.102496 0.160232 -0.016956 0.012821 0.106725 -0.004428 0.046089 0.061078 0.111119 0.155764 -0.071093 -0.111358 -0.06255 -0.586703 -0.374247 0.175236 0.099002 -0.139136 -0.107723 -0.339361 -0.06074 -0.159876 -0.299807 0.025667 -0.009049 0.025321 -0.008623 0.250737 -0.076825 -0.236443 -0.0988 -0.094741 0.207414 0.006464 -0.021555 0.373528 0.728908 -0.03422 -0.391805 -0.157151 0.188389 -0.193443 0.060032 0.128635 -0.080174 0.302332 -0.127296 -0.014318 -0.090419 0.054843 -0.000317 -0.098191 0.254008 0.190688 0.002885 -0.398395 -0.034586 -0.194817 -0.069394 -0.034757 0.029957 0.205941 -0.122468 0.096331 -0.110624 0.15022 -0.038625 -0.149979 -0.000747 -0.245371 -0.195562 0.067989 -0.074886 0.074195 -0.008408 -0.050567 -0.144242 0.159488 0.030347 0.047966 -0.017311 -0.037702 0.060556 0.041519 -0.281765 -0.072148 0.035568 0.151008 -0.146416 0.08249 0.909636 0.114206 -0.193661 0.012075 -0.020138 0.210638 0.095473 -0.12031 -0.02442 0.129125 -0.015068 -0.139038 -0.028111 -0.001274 0.091497 0.189319 0.24095 -0.075754 0.107592 0.201965 -0.05249 -0.036553 0.031541 0.11473 0.19115 0.317798 -0.230684 0.131763 -0.347053 -0.027312 0.071816 -0.014938 0.098176 0.200302 0.179127 -0.190105 -0.042489 -0.183493 -0.256112 -0.038869 0.019664 0.079723 0.231244 0.19005 0.280512 -0.060825 0.21613 -0.019089 -0.08628 0.109365 0.279792 0.149861 0.199535 -0.27503 -0.050676 -0.294272 0.05158 0.16705 0.234004 -0.249588 0.002371 0.157577 0.095791 -0.136094 -0.314569 -0.128338 -0.105967 0.069299 0.111969 0.044811 0.252093 0.052108 0.022801 0.246065 -0.059562 -0.217079 0.170675 -0.125626 0.410542 0.051763 0.17477 -0.16219 0.195668 -0.257048 -0.067044 -0.06701 0.029664 -0.263606 -0.030954 -0.362271 0.161398 -0.005332 -0.204916 -0.109149 0.02146 0.17096 0.027822 0.099825 0.217361 -0.268119 -0.100975 -0.091358 -0.089759 -0.188118 -0.201713 0.190483 0.125353 0.132828 0.40057 -0.024146 -0.134218 0.135552 0.092465 0.147697 -0.037727 -0.515998 -0.122589 0.11465 -0.055923 -0.119134 -0.226073 -0.244831 -0.008401 -0.156915 -0.14783 0.157448 0.049453 -0.096904 -0.626962 0.255129 -0.223466 0.019086 0.138979 0.343541 -0.092135 -0.006647 -0.034394 -0.220144 -0.183951 -0.29772 -0.329087 -0.177843 -0.056226 -0.058799 0.287281 0.136283 -0.21185 0.146488 -0.224818 0.281525 0.494466 -0.073013 -0.142564 0.119618 -0.101574 0.05801 0.070393 0.260456 -0.073825 -0.199545
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@ 0.231718 -0.187341 0.055388 0.135627 -0.200797 0.103881 0.068305 -0.013673 -0.071161 -1.525675 0.514453 -0.083295 -0.026072 0.2017 0.286957 0.144698 0.139279 -1.246508 0.369992 0.168245 -0.056786 0.27905 0.133407 0.069165 -0.28275 0.02161 0.198745 0.126516 -0.14346 0.036995 0.181059 -0.10561 0.172969 -0.050662 -0.412089 0.221125 0.1328 0.141561 0.154801 0.108753 -0.089084 -0.15018 -0.170847 -0.029947 -0.009499 0.040587 1.1e-05 0.034035 -0.093093 0.125529 0.21733 0.108817 0.08561 -0.166941 -0.052004 -0.248032 -0.141951 0.119479 0.06472 0.134843 -0.324602 -0.025714 0.177118 -0.545369 -0.199393 0.255506 -0.049099 -0.063348 0.056101 -0.193384 -0.060662 -0.156193 -0.147001 0.051408 -0.121257 -0.048222 -0.073638 0.089493 -0.120747 -0.217879 -0.075621 0.004502 0.108792 0.149504 0.051137 0.365213 0.710378 -0.294537 -0.34936 -0.230405 0.068334 -0.092504 0.111424 0.148128 0.005581 0.509536 -0.207264 -0.159092 0.123384 0.060105 -0.017303 -0.246821 0.270282 -0.003993 0.073912 -0.387772 -0.151516 -0.300233 -0.354017 -0.044204 -0.104903 0.107476 0.04301 0.007053 -0.09725 0.090219 -0.094989 -0.246147 0.012111 0.028565 -0.124642 -0.129676 0.116965 -0.038266 0.007686 -0.016971 -0.016907 -0.153212 -0.012903 0.083357 -0.094571 -0.097994 0.083006 0.063615 -0.225614 0.157227 0.056388 -0.062843 -0.192378 -0.024414 0.785108 -0.063089 -0.426027 -0.020774 -0.057763 0.022537 0.107153 0.185384 -0.216449 0.265764 0.041654 0.048645 -0.220593 -0.036798 0.221463 0.01356 0.218219 -0.350047 0.166566 0.141568 0.011932 -0.008373 -0.016458 0.277921 0.322952 0.236825 -0.107719 0.030438 -0.099932 0.016769 -0.026422 0.205689 0.093672 -0.021485 0.254278 -0.154683 0.086976 -0.141866 -0.305259 -0.184958 -0.128612 -0.041846 0.1658 -0.00545 0.336981 0.047024 0.315361 -0.171459 -0.097024 0.052982 0.337737 -0.083289 0.063423 -0.218001 0.098785 -0.174557 0.176068 0.157178 0.2712 -0.345367 -0.137526 0.182103 0.187073 -0.068791 -0.361484 0.021124 0.088385 0.041369 0.145365 -0.215188 0.227628 0.099602 0.126865 0.327061 -0.346776 -0.281389 0.193962 -0.033438 0.474338 -0.068703 0.128296 0.077507 0.224384 -0.104894 -0.127683 -0.111634 -0.19736 -0.194071 -0.134884 -0.466062 0.211152 -0.077113 -0.313587 -0.229271 -0.072955 0.290879 -0.055668 0.118314 0.228422 -0.037301 0.03408 -0.01692 -0.13788 -0.099685 -0.074883 0.237281 0.111989 0.169935 0.276911 0.113443 0.102446 -0.024826 -0.117122 0.069899 -0.261578 -0.576211 -0.034565 0.11453 -0.043064 -0.159356 -0.071322 -0.16681 0.075242 -0.393163 -0.171109 0.384152 0.074455 0.06628 -0.344892 0.225823 -0.208975 -0.086666 0.03363 0.225144 0.066331 0.032128 0.04712 -0.082863 -0.126281 -0.270457 -0.394453 -0.20715 -0.091101 -0.001414 0.222055 0.157381 0.009647 0.135849 -0.116818 0.337893 0.475337 -0.165885 -0.145943 0.107366 0.04059 -0.120343 0.127896 0.110137 -0.202924 -0.236713
D 0.233231 -0.361018 0.085259 -0.005739 -0.093721 0.073762 0.052024 0.100996 -0.042153 -1.457786 0.399359 -0.057417 -0.103147 0.220071 0.262747 0.009438 0.175158 -1.136951 0.279911 0.085576 -0.033924 0.054026 0.081685 0.020628 -0.100423 -0.042999 0.144117 -0.017404 -0.185999 -0.09966 0.094796 -0.185623 0.03672 0.033399 -0.242432 0.08387 0.028751 -0.011255 0.070922 0.192477 -0.054573 -0.036996 -0.084271 0.018571 -0.02508 -0.098183 0.177295 -0.176031 -0.046555 0.036618 0.090621 -0.038101 0.038863 -0.007282 0.029882 -0.042051 0.001319 0.010671 -0.03181 0.038818 -0.026531 0.015439 0.03502 -0.382182 -0.17257 0.142567 -0.017753 -0.130848 0.03885 -0.218661 -0.051473 0.001144 -0.180237 0.002022 -0.109445 -0.049674 -0.085188 0.030752 -0.031449 -0.148171 -0.042282 -0.12224 0.136915 0.030475 0.029154 0.263574 0.365298 -0.191564 -0.311648 -0.122984 0.103158 -0.013708 0.083947 0.092872 -0.131816 0.295846 -0.061112 0.004858 -0.058819 0.108025 0.022577 -0.146495 0.132078 0.054715 -0.034413 -0.355546 -0.049717 -0.143356 -0.122631 -0.122443 -0.105901 0.332455 -0.112507 0.034883 -0.055238 0.030035 -0.054523 -0.053002 0.039257 -0.043404 -0.049774 -0.0318 0.011019 0.150477 0.107027 -0.052262 -0.058161 0.035282 0.034442 0.125035 -0.137157 -0.012199 0.059778 0.080191 -0.154241 -0.024553 0.041463 0.010169 -0.049852 0.05905 0.8762 -0.017209 -0.26189 0.008329 0.012071 0.088007 0.12674 -0.002706 0.032233 0.144775 -0.010537 -0.005595 -0.03665 -8.7e-05 0.129849 0.128287 0.145399 -0.109966 0.038636 0.083439 -0.099683 0.067049 -0.031199 0.22197 0.326707 0.188161 -0.134012 0.165901 -0.235459 -0.027377 -0.015758 0.050788 0.13231 0.104249 0.143631 -0.088267 -0.010645 -0.072706 -0.222307 -0.01688 -0.042079 -0.038793 0.069653 0.162672 0.225836 -0.036933 0.187582 -0.080461 -0.050367 0.028209 0.233611 0.011628 0.099674 -0.112566 -0.002199 -0.199837 0.136818 0.150615 0.196138 -0.265349 -0.083497 0.1915 0.051332 -0.111958 -0.275516 -0.115528 -0.013289 0.096843 0.143707 0.008247 0.137343 0.163156 -0.017783 0.299054 -0.131135 -0.234297 0.140781 -0.165733 0.433674 -0.059053 0.048615 -0.06761 0.214025 -0.206746 -0.085919 -0.103393 -0.041155 -0.173279 -0.106384 -0.26529 0.116721 -0.015988 -0.184264 -0.089729 0.100496 0.128301 -0.001237 0.094954 0.118906 -0.147472 -0.104578 -0.043514 -0.1079 -0.178425 -0.090507 0.201325 0.127272 0.094597 0.302513 0.051696 -0.015299 -0.042742 0.042011 0.049649 -0.126313 -0.542211 -0.033091 0.157472 -0.007918 -0.077447 -0.126547 -0.281435 0.004461 -0.184565 -0.052437 0.28288 -0.012527 -0.076391 -0.408656 0.114349 -0.075792 0.019754 0.130525 0.258531 0.075807 -0.022165 -0.009192 -0.05222 0.01218 -0.306333 -0.14243 -0.177868 -0.056899 -9e-06 0.130737 0.128765 -0.142669 0.159691 -0.21467 0.037711 0.358907 -0.112172 -0.05966 0.045054 0.031216 0.034613 0.10302 0.162479 -0.074023 -0.183901
H 0.236003 -0.317698 0.074001 0.015729 -0.083927 0.084157 0.030204 0.11217 0.000246 -1.420011 0.40971 -0.063344 -0.098137 0.199354 0.275467 -0.021515 0.190882 -1.19823 0.28441 0.082153 -0.008205 0.102327 0.113373 -0.007171 -0.090963 -0.039857 0.167211 -0.020787 -0.182865 -0.083382 0.115387 -0.153223 0.02602 0.026228 -0.220436 0.089213 0.012173 0.017538 0.091616 0.193957 -0.100534 -0.057425 -0.117367 0.03026 0.00119 -0.079767 0.165336 -0.223393 -0.014006 0.022873 0.086917 -0.037139 0.049517 0.007882 0.011932 -0.059412 0.00803 0.003868 -0.033007 0.056309 -0.043526 0.017209 0.023378 -0.399667 -0.175844 0.126303 -0.008186 -0.122006 0.039663 -0.199215 -0.051976 0.001359 -0.195503 0.030446 -0.126664 -0.053981 -0.057302 0.057953 -0.053647 -0.198133 -0.076943 -0.112588 0.158645 0.043034 0.040981 0.241914 0.373265 -0.181788 -0.332945 -0.086553 0.105403 -0.008547 0.058148 0.079514 -0.127783 0.295875 -0.069132 -0.031883 -0.081599 0.131998 0.058947 -0.135583 0.120682 0.026425 -0.041908 -0.340731 -0.038779 -0.141543 -0.126276 -0.181466 -0.106558 0.324312 -0.119782 0.029673 -0.039614 0.020471 -0.052158 -0.053933 0.047899 -0.061428 -0.024726 -0.011786 0.041682 0.129383 0.117689 -0.034343 -0.051689 0.025243 0.04026 0.128992 -0.160199 -0.008295 0.067637 0.049946 -0.175002 -0.029695 0.013114 0.023163 -0.07309 0.087523 0.878328 -0.024446 -0.277926 0.018151 0.014658 0.115452 0.115891 0.016534 0.038907 0.126454 -0.038675 0.003965 -0.029448 -0.018766 0.136667 0.140159 0.16515 -0.144826 0.023278 0.069563 -0.063627 0.036247 -0.003781 0.204212 0.342379 0.199968 -0.095795 0.167294 -0.249455 0.008409 -0.021487 0.037211 0.149601 0.061265 0.10894 -0.09934 0.004909 -0.078406 -0.197795 -0.009177 -0.06263 -0.053181 0.0715 0.160167 0.224815 -0.041628 0.195832 -0.113664 -0.025463 0.011292 0.243797 0.01573 0.101124 -0.123469 -0.002402 -0.20254 0.12368 0.102658 0.175747 -0.270753 -0.074216 0.209876 0.037351 -0.129711 -0.249826 -0.132763 0.01065 0.073777 0.140391 -0.016478 0.140358 0.193482 -0.013222 0.313336 -0.130257 -0.249682 0.173406 -0.15785 0.455777 -0.060137 0.054523 -0.054354 0.211977 -0.202434 -0.074756 -0.071725 -0.05489 -0.162281 -0.122316 -0.262926 0.124025 -0.010593 -0.184377 -0.116045 0.0989 0.135055 0.015239 0.056625 0.111892 -0.136995 -0.125983 -0.045552 -0.076384 -0.176474 -0.083688 0.201508 0.091497 0.082105 0.346559 0.034518 -0.006688 -0.050609 0.050857 0.049997 -0.134386 -0.533993 0.000176 0.200941 0.001452 -0.118709 -0.139779 -0.281948 0.049939 -0.157965 -0.068706 0.327325 -0.047151 -0.04711 -0.446901 0.151343 -0.081901 0.004003 0.134217 0.266509 0.060877 -0.026057 0.000474 -0.052223 0.027718 -0.306949 -0.142078 -0.152895 -0.101383 -0.007438 0.104968 0.125378 -0.137706 0.153597 -0.221824 0.059299 0.357862 -0.115697 -0.064403 0.033895 0.061724 0.032688 0.125717 0.169908 -0.084187 -0.152226
L 0.260941 -0.333723 0.091799 0.042597 -0.053406 0.052608 0.039931 0.116546 -0.01477 -1.485284 0.383667 -0.035051 -0.126213 0.203779 0.266412 -0.006692 0.157759 -1.124547 0.28388 0.044996 -0.022086 0.077403 0.115117 0.025738 -0.068651 -0.036834 0.145336 -0.010231 -0.170842 -0.121693 0.109036 -0.154647 0.030928 0.016791 -0.26028 0.096247 0.066238 -0.022654 0.071405 0.224765 -0.069411 -0.015058 -0.0667 0.046682 -0.005214 -0.08123 0.204746 -0.208664 -0.008068 0.03145 0.092297 -0.05002 0.019879 -0.00263 0.009221 -0.052104 0.004312 0.007055 -0.019173 0.06562 -0.021714 0.012719 0.03544 -0.381409 -0.169152 0.132395 -0.026765 -0.129646 0.049172 -0.203081 -0.052376 0.020279 -0.186417 0.02519 -0.146777 -0.033513 -0.063823 0.034698 -0.031111 -0.207819 -0.08036 -0.131165 0.17495 0.050305 0.021622 0.263268 0.383267 -0.225629 -0.328809 -0.10222 0.118955 0.026615 0.070517 0.075622 -0.15053 0.260522 -0.07057 0.016689 -0.050383 0.116762 0.014992 -0.155778 0.126658 0.044951 -0.026482 -0.350826 -0.033896 -0.124984 -0.13588 -0.144813 -0.09361 0.326194 -0.090167 0.013151 -0.064171 0.003713 -0.053531 -0.065834 0.038793 -0.054972 -0.051527 -0.046332 0.025626 0.155109 0.142497 -0.056992 -0.033672 0.089285 0.032014 0.142842 -0.155753 -0.035085 0.041663 0.035728 -0.162623 -0.0447 -0.001922 0.0042 -0.051352 0.088456 0.882899 -0.021921 -0.284878 0.021195 0.005226 0.092229 0.129884 0.008611 0.057479 0.113489 0.003626 0.029296 -0.046559 -0.037394 0.137014 0.133478 0.142337 -0.148946 0.050391 0.096183 -0.102689 0.018125 -0.045898 0.190266 0.320995 0.211517 -0.104918 0.128388 -0.228887 -0.000618 -0.001666 0.054696 0.140773 0.082092 0.173776 -0.103976 0.001294 -0.077392 -0.204162 -0.019662 -0.026225 -0.025112 0.069879 0.178087 0.242792 -0.055212 0.178041 -0.126948 -0.04563 0.00917 0.22642 0.029208 0.127789 -0.122322 0.000208 -0.234844 0.120458 0.112738 0.157653 -0.261485 -0.057315 0.238581 0.041468 -0.110735 -0.289796 -0.132773 -0.011593 0.052125 0.118127 -0.004249 0.133064 0.194597 -0.028935 0.32691 -0.106609 -0.227251 0.164236 -0.240145 0.429484 -0.0647 0.071994 -0.047329 0.201848 -0.209926 -0.090068 -0.086519 -0.042351 -0.160894 -0.118968 -0.265825 0.154144 0.008883 -0.193491 -0.120385 0.133888 0.164046 0.0157 0.078391 0.084942 -0.139341 -0.098771 -0.086034 -0.09697 -0.203796 -0.11296 0.217186 0.111408 0.081587 0.308671 0.073612 -0.021622 -0.040975 0.056652 0.052529 -0.124809 -0.528529 -0.011101 0.178812 0.018473 -0.069458 -0.116571 -0.280764 0.049394 -0.144723 -0.049546 0.305598 -0.016973 -0.039278 -0.433399 0.081716 -0.126868 0.044391 0.13365 0.250183 0.0949 -0.041672 -0.017865 -0.078694 0.03803 -0.302762 -0.142024 -0.164384 -0.060053 0.020995 0.122024 0.135142 -0.149184 0.169768 -0.20332 0.051095 0.384626 -0.123287 -0.078604 0.048947 0.03783 0.039879 0.109786 0.145225 -0.083744 -0.169899
P 0.234239 -0.365499 0.091781 0.032724 -0.126538 0.088548 0.051903 0.113189 -0.028123 -1.438988 0.369648 -0.10358 -0.076908 0.178844 0.270188 0.01662 0.1703 -1.135172 0.276314 0.063418 0.004714 0.056535 0.104675 0.024469 -0.101939 -0.005214 0.13947 -0.024494 -0.161666 -0.056423 0.115093 -0.169505 0.035501 0.018595 -0.256143 0.106814 0.007713 -0.030453 0.068727 0.20065 -0.04573 -0.021553 -0.10486 -0.004236 -0.041327 -0.067876 0.163895 -0.170359 -0.056238 0.033678 0.069085 -0.035366 0.060638 -0.003717 0.008381 -0.051188 0.005211 0.013854 -0.041597 0.101004 -0.033918 0.023028 0.010246 -0.39065 -0.171787 0.144985 -0.039237 -0.119497 0.003344 -0.235814 -0.065435 -0.004662 -0.184545 0.031625 -0.110342 -0.066597 -0.078283 0.059846 -0.056153 -0.125929 -0.046615 -0.085864 0.123568 0.048251 7.6e-05 0.285957 0.332527 -0.088221 -0.313298 -0.131425 0.114109 -0.031501 0.077102 0.10162 -0.144299 0.312206 -0.069307 -0.008109 -0.059651 0.128074 0.033027 -0.136559 0.141462 0.072094 -0.004647 -0.360974 -0.045171 -0.130259 -0.138753 -0.108577 -0.11934 0.316103 -0.138184 0.02346 -0.078278 0.020122 -0.0279 -0.061917 -0.00452 -0.026139 -0.053687 -0.046755 0.017701 0.181706 0.046668 -0.067629 -0.077343 0.037397 0.05599 0.094614 -0.154521 0.00534 0.052141 0.076639 -0.144128 -0.037096 0.032712 -0.00655 -0.060556 0.088301 0.899398 -0.039054 -0.270864 -0.000302 0.007195 0.100653 0.117123 -0.002896 0.015846 0.161155 -0.003714 -0.015074 -0.016994 0.011111 0.126842 0.162388 0.143633 -0.096056 0.030059 0.069546 -0.088957 0.053977 -0.021017 0.232645 0.31158 0.16038 -0.129434 0.143176 -0.195996 -0.059566 0.006231 0.057718 0.173973 0.107525 0.100686 -0.059598 -0.039771 -0.074975 -0.251772 -0.011569 -0.022026 0.00117 0.080467 0.144952 0.237027 -0.016205 0.176271 -0.14943 -0.047821 0.01275 0.24945 -0.000717 0.089234 -0.107306 0.021653 -0.184304 0.126169 0.154007 0.205919 -0.24569 -0.088134 0.204628 0.035544 -0.117578 -0.258163 -0.098235 -0.026141 0.079142 0.101061 0.017215 0.139331 0.189893 -0.010872 0.300273 -0.131589 -0.228727 0.157294 -0.098918 0.440165 -0.066652 0.043519 -0.047288 0.231829 -0.208812 -0.091778 -0.102231 -0.03703 -0.176036 -0.122571 -0.24579 0.121948 0.009939 -0.169971 -0.083344 0.080448 0.123271 -0.002407 0.076583 0.116776 -0.140927 -0.099469 -0.051603 -0.139748 -0.190118 -0.10652 0.169932 0.146723 0.07014 0.284686 0.031234 -0.035405 -0.055722 -0.001473 0.069786 -0.093298 -0.493771 -0.028468 0.181428 -0.001256 -0.09776 -0.099814 -0.274993 -0.038639 -0.162856 -0.021635 0.294434 -0.025586 -0.074456 -0.378097 0.116892 -0.021987 0.016745 0.128222 0.279611 0.076194 -0.00242 -0.017038 -0.046929 0.005788 -0.269321 -0.162717 -0.130304 -0.068853 0.003919 0.136054 0.130636 -0.125907 0.17179 -0.210995 0.062931 0.384422 -0.126084 -0.03914 0.02626 0.039592 0.068564 0.08816 0.159098 -0.070985 -0.144452
T 0.249451 -0.331724 0.110173 0.042587 -0.116633 0.037745 0.038197 0.12535 -0.007541 -1.486589 0.374833 -0.050329 -0.090376 0.212932 0.287183 -0.013625 0.157938 -1.134128 0.304882 0.078741 -0.035047 0.070221 0.124624 0.015037 -0.07216 -0.031095 0.128229 -0.0069 -0.18509 -0.082146 0.106451 -0.154 0.041893 0.039755 -0.238385 0.111305 0.045849 -0.029039 0.057957 0.230149 -0.081335 -0.031317 -0.065835 0.04307 -0.027774 -0.077692 0.206983 -0.203213 -0.032336 0.021636 0.089956 -0.059586 0.030507 0.005679 -0.01296 -0.062448 0.026962 -0.00063 -0.011609 0.066755 -0.028948 0.02009 0.032116 -0.371216 -0.152785 0.151867 -0.014543 -0.119454 0.016439 -0.204147 -0.045737 -0.003721 -0.170106 0.012551 -0.129242 -0.041748 -0.068408 0.037824 -0.03762 -0.158055 -0.035375 -0.130549 0.161736 0.0729 0.027092 0.282992 0.326534 -0.179018 -0.344269 -0.116101 0.100234 0.008732 0.054094 0.081199 -0.131795 0.304353 -0.063736 -0.001313 -0.023334 0.13685 0.019836 -0.147841 0.140077 0.049883 -0.043285 -0.423214 -0.031837 -0.130521 -0.148686 -0.13181 -0.115654 0.309759 -0.092824 0.042108 -0.057097 0.056687 -0.054479 -0.050447 0.031972 -0.046193 -0.058469 -0.050759 0.011051 0.138735 0.10904 -0.029813 -0.039951 0.057466 0.020726 0.11 -0.148883 -0.054964 0.024503 0.051919 -0.166205 -0.036463 -0.00708 0.023558 -0.056174 0.042625 0.850979 -0.028259 -0.273045 -0.005351 0.025526 0.092768 0.131845 0.002533 0.071124 0.130093 -0.027201 0.015622 -0.02444 -0.014144 0.120651 0.128664 0.120811 -0.126593 0.024323 0.097215 -0.112562 0.033209 -0.03292 0.207988 0.311452 0.195606 -0.12165 0.11445 -0.225278 -0.027725 -0.004423 0.045189 0.159551 0.094349 0.140047 -0.059077 -0.005451 -0.080115 -0.24386 -0.003726 -0.047931 -0.015033 0.067262 0.183788 0.217103 -0.024446 0.169413 -0.140439 -0.042138 0.009918 0.233403 -0.011132 0.089038 -0.114743 -0.014386 -0.225255 0.135003 0.145765 0.185593 -0.25081 -0.061566 0.240527 0.031313 -0.119458 -0.258205 -0.094573 0.027729 0.052614 0.13172 0.043756 0.140443 0.172448 -0.034663 0.316914 -0.121171 -0.222741 0.163876 -0.201652 0.438833 -0.0523 0.065248 -0.063522 0.216253 -0.200882 -0.105728 -0.106379 -0.042082 -0.140715 -0.147513 -0.238778 0.158357 0.016598 -0.188055 -0.094446 0.095672 0.125826 -0.001827 0.082741 0.097821 -0.111544 -0.099637 -0.091023 -0.109754 -0.210228 -0.116986 0.195871 0.116855 0.083784 0.321354 0.052594 -0.048963 -0.033113 0.038988 0.067957 -0.137676 -0.495877 -0.024721 0.185384 0.011624 -0.066231 -0.106344 -0.304393 0.020279 -0.169419 -0.025384 0.305158 -0.023565 -0.035075 -0.397936 0.115644 -0.116151 0.022269 0.111716 0.238113 0.082927 -0.028141 -0.018886 -0.049402 0.053593 -0.30427 -0.135312 -0.154509 -0.119363 0.026097 0.107773 0.119635 -0.131269 0.172854 -0.205672 0.056421 0.381449 -0.123127 -0.0631 0.00557 0.045922 0.031779 0.099736 0.162094 -0.094503 -0.164826
X 0.281911 -0.389257 0.038969 0.021278 -0.073964 0.073647 0.045256 0.156634 0.008329 -1.630058 0.436731 -0.1151 -0.088962 0.180384 0.28348 0.026325 0.160961 -1.079843 0.273557 0.068748 -0.119158 0.041474 0.115769 0.018154 -0.090317 -0.006715 0.134375 -0.010015 -0.164488 -0.028427 0.065858 -0.152138 0.005831 0.027188 -0.287266 0.12051 0.053941 -0.165131 0.065233 0.236058 -0.052601 -0.064104 -0.138676 0.047001 -0.088463 -0.026851 0.19019 -0.227987 -0.075729 0.057401 0.039261 0.021961 0.064451 0.028862 0.032391 -0.023524 0.002838 -0.069317 -0.074758 0.131703 -0.012583 -0.027468 0.052705 -0.378015 -0.150878 0.13069 -0.05918 -0.104333 -0.027127 -0.289416 -0.080608 0.019922 -0.213412 0.039613 -0.139946 -0.020253 -0.038816 0.011008 -0.08501 -0.012099 -0.090787 -0.171628 0.110239 0.082597 0.047105 0.301602 0.335152 -0.121571 -0.346084 -0.101932 0.065973 0.089074 0.119256 0.054355 -0.130808 0.315894 -0.100464 -0.017545 -0.041945 0.130437 0.051477 -0.190407 0.186442 0.075292 0.035292 -0.446384 -0.010244 -0.085437 -0.123511 -0.067103 -0.140386 0.279914 -0.070099 0.053226 -0.080364 0.04253 -0.023096 -0.040342 0.049493 -0.066018 -0.09559 -0.003241 0.045961 0.141235 0.027582 -0.038706 -0.085935 0.018758 0.084252 0.060775 -0.149156 0.028017 0.019757 0.059573 -0.179567 -0.030788 0.035074 0.046775 -0.077653 0.05405 0.877636 -0.05778 -0.192634 -0.005805 0.079706 0.046797 0.15164 -0.068281 0.089381 0.125338 -0.061633 0.080532 0.072883 0.021343 0.143912 0.128246 0.166407 -0.105218 0.062618 0.109256 -0.176691 0.07348 -0.114681 0.166489 0.337964 0.201386 -0.065835 0.093999 -0.183424 -0.107956 -0.036447 0.076841 0.155825 0.122795 0.121496 -0.09521 -0.057608 -0.171182 -0.276461 -0.062081 -0.076342 0.004573 0.092625 0.134705 0.293991 -0.026143 0.153521 -0.086733 -0.073133 0.041414 0.27549 -0.003071 0.077641 -0.136152 0.014105 -0.208969 0.109504 0.109695 0.174019 -0.240183 -0.026606 0.17651 0.042973 -0.143286 -0.301933 -0.085819 0.017422 0.057418 0.103281 0.024489 0.115311 0.11736 -0.000625 0.259603 -0.119107 -0.246593 0.179972 -0.192834 0.481684 -0.007499 0.055058 -0.122793 0.186343 -0.180858 -0.088381 -0.153628 0.001758 -0.166646 -0.049925 -0.230075 0.137309 -0.082311 -0.176958 -0.06967 0.141215 0.138191 0.000893 0.163164 0.082227 -0.060583 -0.165449 -0.048702 -0.131752 -0.220189 -0.058983 0.158901 0.191095 0.035891 0.293004 0.118646 -0.071253 -0.019298 0.025147 0.017535 -0.169647 -0.599819 -0.057716 0.2012 -0.005014 -0.055389 -0.109758 -0.318606 0.009574 -0.265232 0.00269 0.266944 -0.110258 -0.088158 -0.352304 0.169155 0.063669 -0.004958 0.094206 0.286998 0.065377 -0.06554 -0.013812 -0.006359 -0.005869 -0.348573 -0.128129 -0.110337 -0.020444 -0.028583 0.165147 0.173298 -0.181338 0.195885 -0.172315 0.049426 0.299731 -0.055216 -0.076925 0.033688 -0.106355 -0.018177 0.112034 0.111608 -0.07955 -0.195224
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d 0.240929 -0.300166 0.124712 0.010674 -0.126232 0.103616 0.046273 0.081495 -0.090788 -1.224344 0.410215 -0.0759 -0.074241 0.188964 0.243243 0.031937 0.198679 -1.1557 0.323211 0.051766 0.018273 0.034254 0.103306 0.018582 -0.096406 -0.039557 0.132363 -0.019175 -0.124491 -0.008702 0.130547 -0.191698 0.070132 -0.01589 -0.225597 0.067724 -0.019325 -0.054882 0.050468 0.129784 -0.130038 -0.095199 -0.156592 0.000772 -0.042307 -0.049296 0.175428 -0.191338 -0.070173 0.026273 0.039821 -0.007971 0.044791 -0.005272 0.031022 -0.048337 0.00521 -0.024241 -0.002628 0.101409 -0.019267 0.049808 -0.005185 -0.380408 -0.122814 0.170453 -0.007071 -0.124203 -0.021085 -0.221498 -0.026631 -0.016913 -0.176591 0.007211 -0.092482 -0.129294 -0.096588 0.102398 -0.029091 -0.13619 -0.049557 -0.062601 0.083831 0.083701 -0.011636 0.27154 0.502417 -0.020235 -0.311634 -0.162315 0.088654 -0.104333 0.053901 0.156363 -0.073573 0.378005 -0.027251 -0.050574 -0.048309 0.057218 -0.018887 -0.062004 0.096275 0.040765 -0.043569 -0.756595 -0.040583 -0.123663 -0.124761 -0.126782 -0.133931 0.255777 -0.152709 0.025569 -0.053518 0.061798 -0.02416 -0.005568 0.043504 -0.05161 -0.040841 -0.027338 0.047193 0.172315 0.021278 -0.031979 -0.071921 0.006254 0.096176 0.064824 -0.133993 0.002078 0.017212 0.109223 -0.091274 -0.053098 0.012832 -0.027271 -0.096001 0.048531 0.651267 -0.044101 -0.268765 -0.055323 0.02573 0.077252 0.162799 -0.054228 -0.036301 0.152643 0.01706 -0.067553 0.010075 -0.02575 0.072331 0.135249 0.158899 -0.138006 0.038321 0.041682 -0.044033 0.054878 0.030662 0.259032 0.353654 0.13252 -0.144438 0.150735 -0.193554 -0.068107 0.00824 0.068168 0.185403 0.148727 0.103247 -0.065654 -0.042059 -0.098786 -0.289843 -0.035039 -0.030169 -0.006175 0.080275 0.117883 0.200738 -0.016144 0.170812 -0.115377 -0.016385 -0.002947 0.219561 0.026657 0.119197 -0.110944 0.032102 -0.10593 0.184801 0.135829 0.282735 -0.275218 -0.084549 0.164615 0.041136 -0.079846 -0.222954 -0.044559 -0.016741 0.096236 0.101109 0.036116 0.07985 0.189558 -0.024702 0.206793 -0.154084 -0.251682 0.134053 -0.027177 0.495565 -0.036858 0.04064 -0.100009 0.200331 -0.169345 -0.093393 -0.136305 -0.117083 -0.178409 -0.143201 -0.275509 0.124718 -0.025154 -0.107975 -0.054892 0.019691 0.086991 0.026169 0.102828 0.140331 -0.144722 -0.042425 0.005295 -0.117204 -0.072258 -0.06024 0.151601 0.079907 0.076801 0.31141 -0.033069 -0.061057 -0.056508 0.033183 0.084991 -0.058743 -0.503926 -0.02607 0.131954 -0.037737 -0.113779 -0.143577 -0.238283 -0.035367 -0.176007 -0.01538 0.3233 -0.006825 -0.02508 -0.442157 0.167177 -0.110549 -0.069145 0.147492 0.209141 0.035127 0.033903 0.007971 -0.015988 0.077919 -0.286862 -0.224234 -0.162319 -0.132019 -0.073147 0.094295 0.123028 -0.108962 0.128653 -0.208916 0.021793 0.337565 -0.112842 -0.020143 0.001554 0.049015 0.015484 0.056634 0.163956 -0.070336 -0.130445
h 0.23488 -0.266222 0.12076 0.01771 -0.119262 0.11475 0.054584 0.083935 -0.06733 -1.238466 0.430156 -0.093005 -0.054822 0.192662 0.239925 0.020059 0.212435 -1.225096 0.318582 0.056866 0.023823 0.049792 0.104503 -0.004988 -0.095305 -0.031715 0.140689 -0.030159 -0.141064 -0.007564 0.141668 -0.174045 0.05979 -0.003288 -0.222017 0.089995 -0.040169 -0.033604 0.051886 0.127351 -0.14898 -0.098638 -0.179746 -0.002633 -0.026241 -0.049626 0.165228 -0.201042 -0.071185 0.015302 0.075438 0.001588 0.051972 0.001553 0.053695 -0.065069 0.003928 -0.015027 -0.003154 0.092208 -0.032866 0.006771 -0.010751 -0.384564 -0.141628 0.143789 -0.017971 -0.09824 -0.008258 -0.207855 -0.038624 -0.034482 -0.184575 -0.007791 -0.120585 -0.129479 -0.093898 0.121686 -0.048225 -0.15545 -0.058623 -0.067724 0.120983 0.10524 -0.005932 0.250018 0.501151 -0.052628 -0.328783 -0.146615 0.088168 -0.106407 0.048339 0.15042 -0.052346 0.390771 -0.031273 -0.08627 -0.057149 0.091892 0.018174 -0.059055 0.113293 0.033671 -0.030269 -0.696924 -0.048326 -0.121956 -0.128041 -0.158701 -0.116767 0.268846 -0.171305 0.042125 -0.071932 0.069515 -0.039629 -0.016718 0.066277 -0.055768 -0.053515 -0.030546 0.043507 0.143634 0.024545 -0.026808 -0.087384 -0.024311 0.111955 0.095667 -0.141501 -0.030379 0.038992 0.113356 -0.104308 -0.062848 0.016053 -0.004919 -0.113736 0.054231 0.658854 -0.02332 -0.274062 -0.055753 0.018017 0.098171 0.157532 -0.014611 -0.041645 0.144544 -0.020686 -0.088292 0.006061 -0.029033 0.077249 0.152032 0.165425 -0.161029 0.019935 0.036611 -0.036609 0.042886 0.019063 0.266999 0.367623 0.130744 -0.145828 0.16059 -0.173119 -0.051573 -0.010013 0.054207 0.190721 0.123963 0.068389 -0.07518 -0.06362 -0.098304 -0.28622 -0.060032 -0.04362 -0.027176 0.078944 0.130907 0.20102 -0.005905 0.176767 -0.133538 -0.011846 0.001366 0.224115 0.019472 0.100466 -0.125343 0.035553 -0.098128 0.203297 0.114384 0.283395 -0.278238 -0.111159 0.172482 0.035202 -0.106577 -0.21692 -0.064167 -0.001033 0.087856 0.102086 0.033846 0.092502 0.205268 -0.015524 0.212442 -0.165935 -0.247388 0.132026 -0.008095 0.526656 -0.051189 0.040951 -0.091465 0.23222 -0.170181 -0.112651 -0.131055 -0.126327 -0.170629 -0.155165 -0.26567 0.11737 -0.032748 -0.152981 -0.066167 0.004641 0.099977 0.032809 0.101627 0.150956 -0.127232 -0.07528 0.02428 -0.120462 -0.08189 -0.046655 0.151281 0.060883 0.073606 0.320026 -0.032778 -0.045712 -0.059456 0.050152 0.078935 -0.052468 -0.497921 -0.010084 0.153922 -0.042477 -0.11912 -0.133572 -0.245328 -0.038241 -0.176312 -0.02932 0.325181 -0.018118 -0.01323 -0.44346 0.172925 -0.116995 -0.064423 0.139002 0.250116 0.031351 0.02945 0.024803 -0.003981 0.079935 -0.290176 -0.212449 -0.185128 -0.167938 -0.087862 0.088659 0.126019 -0.092133 0.137013 -0.19784 0.02615 0.34668 -0.130357 -0.032456 0.010317 0.070827 0.010841 0.057512 0.202422 -0.084067 -0.117659
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3 0.19339 -0.296952 0.29471 -0.01346 -0.282129 0.089555 0.077576 -0.008201 -0.001982 -1.577759 0.476057 0.02335 -0.208421 0.19858 0.175122 0.03228 0.142516 -1.081841 0.390047 0.124188 0.024792 0.178125 0.273662 0.111746 0.000798 0.065537 0.200262 -0.14473 -0.125424 0.149561 0.119425 -0.165595 0.014834 0.016717 -0.277218 0.129186 0.003031 0.108959 0.090633 0.430309 -0.153907 -0.323736 -0.119655 0.199357 -0.035556 -0.053994 0.273793 -0.0341 -0.088791 0.123162 0.104361 0.182562 -0.034742 0.027308 0.135409 0.028911 0.015352 0.066188 0.130476 0.19153 -0.072264 -0.139774 -0.067829 -0.593908 -0.415837 0.157479 0.091226 -0.099505 -0.091556 -0.38543 -0.005449 -0.207829 -0.322139 0.030239 -0.027529 0.030155 0.013659 0.272418 -0.088462 -0.246387 -0.104179 -0.074656 0.191657 -0.003967 -0.004764 0.400511 0.742759 -0.027121 -0.417532 -0.156949 0.203705 -0.205329 0.058323 0.14605 -0.041316 0.303783 -0.129179 -0.057448 -0.086778 0.058544 -0.027486 -0.086616 0.222251 0.191131 -0.00694 -0.412673 -0.053997 -0.227723 -0.081865 -0.015892 -0.000117 0.216768 -0.109201 0.129553 -0.089096 0.170267 -0.061067 -0.148203 -0.002168 -0.259798 -0.210948 0.076528 -0.097526 0.085556 -0.013805 -0.037778 -0.141201 0.140905 0.077096 0.075754 -0.031072 -0.04395 0.090001 0.069193 -0.295767 -0.039865 0.05922 0.136359 -0.145242 0.100246 0.898961 0.134162 -0.19066 0.000702 -0.015119 0.216902 0.078539 -0.101352 -0.035214 0.119807 -0.019866 -0.158934 -0.025831 0.0047 0.111039 0.193848 0.246181 -0.077028 0.093392 0.256264 -0.069598 -0.036216 0.051051 0.110282 0.173697 0.33929 -0.247962 0.124365 -0.383576 -0.029389 0.100309 -0.004036 0.108513 0.176329 0.202433 -0.193755 -0.085848 -0.177221 -0.247817 -0.020255 -0.007618 0.126548 0.235685 0.19374 0.28676 -0.009021 0.215594 -0.048016 -0.12373 0.081193 0.299 0.124693 0.195616 -0.317436 -0.041591 -0.304283 0.035056 0.17817 0.295353 -0.257997 -0.010481 0.17254 0.104263 -0.153336 -0.314763 -0.133368 -0.126638 0.081472 0.1411 0.035739 0.248335 0.018571 -0.008986 0.247085 -0.031705 -0.218119 0.192956 -0.131478 0.425937 0.041578 0.199601 -0.184724 0.171348 -0.275028 -0.057628 -0.062136 0.069897 -0.272845 -0.027795 -0.389658 0.173084 -0.02774 -0.229085 -0.110608 -0.004666 0.124248 0.005834 0.06518 0.208106 -0.260277 -0.082176 -0.114701 -0.155816 -0.236076 -0.171559 0.206749 0.147452 0.164459 0.412568 0.024195 -0.116583 0.080039 0.088008 0.178405 -0.004671 -0.526481 -0.127682 0.130543 -0.064796 -0.122611 -0.236827 -0.241924 -0.026773 -0.179439 -0.151976 0.184971 0.053067 -0.095993 -0.63688 0.273989 -0.251245 0.019507 0.150966 0.26263 -0.115989 0.000533 -0.061401 -0.274629 -0.192913 -0.254816 -0.347692 -0.194377 -0.084129 -0.04329 0.298427 0.129304 -0.192935 0.127847 -0.246594 0.30976 0.565956 -0.085858 -0.192359 0.154748 -0.116695 0.064869 0.077109 0.281424 -0.050216 -0.174304
7 0.142413 -0.302212 0.267442 -0.013667 -0.273514 0.083381 0.065153 0.027455 0.015034 -1.568367 0.492162 0.032196 -0.222691 0.145452 0.201623 0.032904 0.128356 -1.094331 0.391024 0.092456 0.014081 0.177091 0.255968 0.108263 0.008085 0.028508 0.187703 -0.109872 -0.134073 0.129791 0.127503 -0.173566 0.037662 -0.021701 -0.273826 0.11146 -0.005853 0.136817 0.11351 0.42633 -0.19085 -0.292027 -0.077212 0.168431 -0.080905 -0.029427 0.2811 -0.043912 -0.069542 0.100321 0.099762 0.154424 -0.018391 0.020493 0.109027 0.002504 0.057996 0.05204 0.116099 0.161014 -0.085668 -0.113491 -0.073176 -0.597485 -0.363055 0.174215 0.096525 -0.131631 -0.106176 -0.347505 -0.051849 -0.170691 -0.304956 0.03139 -0.015827 0.020237 -0.000787 0.248204 -0.066405 -0.240088 -0.0996 -0.089944 0.211192 0.002794 -0.01237 0.391895 0.733139 -0.029968 -0.392656 -0.166796 0.197517 -0.194648 0.05664 0.125938 -0.064874 0.308592 -0.128843 -0.021368 -0.10256 0.055369 0.000538 -0.086197 0.258018 0.199914 0.008998 -0.395742 -0.026307 -0.182624 -0.080884 -0.033241 0.046782 0.211484 -0.128073 0.100652 -0.112431 0.158186 -0.027024 -0.143871 -0.000592 -0.265616 -0.196112 0.067824 -0.082347 0.07767 -0.000666 -0.060112 -0.133451 0.164258 0.027773 0.060933 -0.01535 -0.039443 0.058044 0.034051 -0.28169 -0.074732 0.037276 0.141788 -0.145546 0.086622 0.910318 0.126153 -0.190688 0.018126 -0.023676 0.204858 0.080229 -0.10873 -0.010664 0.137886 -0.006534 -0.141292 -0.030661 -0.007865 0.079409 0.178822 0.234311 -0.078048 0.102878 0.195505 -0.053078 -0.031634 0.034887 0.113323 0.187591 0.325933 -0.226825 0.124985 -0.356546 -0.011422 0.073964 -0.017085 0.102607 0.200469 0.186956 -0.187396 -0.038379 -0.190042 -0.25704 -0.045759 0.022065 0.085969 0.236534 0.192543 0.285838 -0.055127 0.209489 -0.030218 -0.095351 0.113957 0.277545 0.13381 0.199809 -0.273559 -0.055377 -0.302972 0.04522 0.174027 0.232356 -0.257818 0.003993 0.151565 0.096989 -0.144198 -0.312709 -0.114231 -0.117542 0.065945 0.114575 0.039402 0.256359 0.057175 0.027506 0.246946 -0.053671 -0.208113 0.169135 -0.13451 0.4171 0.047867 0.188479 -0.168785 0.200903 -0.251069 -0.050024 -0.073047 0.03831 -0.269011 -0.036112 -0.363207 0.159013 0.012578 -0.223189 -0.107686 0.038609 0.168433 0.017262 0.101765 0.210593 -0.257922 -0.105714 -0.102336 -0.094212 -0.187507 -0.20023 0.197827 0.127742 0.13335 0.399925 -0.009182 -0.140233 0.141498 0.112504 0.156458 -0.034688 -0.508576 -0.12736 0.115961 -0.060456 -0.120456 -0.22837 -0.255582 -0.000251 -0.160237 -0.128985 0.157364 0.05284 -0.100012 -0.622902 0.241251 -0.227019 0.020609 0.156121 0.344893 -0.090942 -0.01738 -0.045352 -0.228005 -0.193736 -0.302942 -0.335093 -0.169464 -0.047896 -0.067431 0.291148 0.139497 -0.207608 0.143405 -0.230831 0.294159 0.502817 -0.070025 -0.15544 0.12541 -0.097405 0.064647 0.071721 0.266267 -0.065624 -0.207912
; 0.197448 -0.175376 0.144566 -0.033808 -0.007511 0.09263 0.153749 -0.104454 -0.143083 -1.271402 0.221909 -0.261187 -0.053803 0.220281 0.284182 -0.071137 0.088381 -0.819626 0.196636 0.194357 -0.028044 0.262888 0.048419 -0.015204 -0.12469 -0.24068 0.21528 0.082536 0.002978 0.028278 0.181247 -0.329791 0.147184 0.059753 -0.129319 0.033294 0.022758 0.208125 0.009964 0.259925 0.004148 -0.127883 0.018049 0.081869 -0.087121 0.011911 0.090143 -0.248078 0.026915 0.110202 -0.011121 0.031723 0.16337 -0.189808 0.092683 -0.085247 0.056674 -0.069748 0.159567 0.009094 -0.231134 -0.151565 0.125065 -0.46795 -0.247772 0.312439 0.051639 -0.125147 -0.050637 -0.180992 0.00113 0.020225 -0.031472 0.096412 -0.027894 0.026067 -0.212246 0.149714 -0.067866 -0.391507 -0.049889 0.026216 -0.053426 0.332579 -0.024703 0.268928 0.614521 -0.403505 -0.334321 -0.065897 -0.122166 -0.021383 0.119193 0.328346 -0.038042 0.602225 -0.131442 -0.078737 0.055457 0.070812 0.031591 -0.100629 0.267311 -0.057829 -0.119325 -0.40616 0.164307 -0.2548 -0.495854 -0.01302 0.030646 0.034686 -0.048117 0.017624 -0.008684 0.217518 -0.054556 -0.283783 -0.117448 -0.103283 -0.066178 -0.143051 0.154413 0.034399 -0.079744 0.01325 -0.117823 -0.087527 0.205205 0.002398 -0.158913 0.054074 -0.090845 0.055586 -0.026268 0.018802 0.101575 -0.149594 -0.19167 -0.125689 0.36683 0.050351 -0.095529 -0.188853 0.049769 0.021145 0.017112 0.08936 -0.291445 0.274748 0.018626 0.197983 -0.169996 0.068755 0.063241 0.145382 -0.031637 -0.1831 0.214432 -0.146863 0.159723 0.001951 0.046441 0.273691 0.328646 0.184661 -0.163247 -0.047649 0.009886 0.147673 -0.062866 0.109673 0.098868 0.11479 0.260022 -0.008271 0.067253 0.014107 -0.408766 -0.022224 -0.198975 -0.120069 0.137932 0.045174 0.076182 0.052166 0.164616 -0.094378 0.038385 0.070307 0.140167 -0.063496 0.191392 -0.043243 0.325113 -0.205919 0.303616 -0.140256 0.255621 -0.141359 -0.121867 0.112508 0.086761 -0.029818 -0.330414 0.048543 -0.02205 0.013369 0.163352 0.019082 0.003312 0.132982 0.13495 0.189046 -0.242467 -0.185835 0.210668 -0.000222 0.406037 0.00506 0.152368 0.152554 0.23271 -0.126157 -0.152798 -0.031988 -0.174859 -0.260185 -0.083056 -0.282355 -0.061344 -0.293684 -0.132535 -0.189482 -0.062136 0.159032 0.17366 -0.005997 -0.061119 -0.12193 0.072036 0.006304 0.002121 -0.069648 -0.031026 0.058325 0.138373 0.088028 0.213206 0.065345 -0.145935 -0.035657 -0.093081 -0.10963 -0.049789 -0.328172 -0.014266 0.067911 -0.175275 -0.131837 -0.229907 -0.082002 0.035262 -0.297844 0.015711 0.262069 0.101553 -0.032666 -0.088477 0.026284 -0.357029 -0.132256 -0.120106 0.131168 0.047045 0.06299 0.046017 -0.049095 0.042547 -0.247553 -0.223204 -0.308402 -0.149092 -0.034293 0.344639 -0.038086 -0.053399 0.273584 -0.148952 0.151517 0.463527 -0.111569 -0.271471 -0.022984 0.21594 -0.201953 0.095886 0.05045 -0.071583 -0.156301
? 0.230672 -0.166661 0.112118 0.011054 0.058076 -0.018576 0.176421 -0.108385 -0.034405 -1.225804 0.183236 -0.137361 -0.095049 0.444373 0.17321 -0.118597 0.116629 -0.805746 0.302629 -0.119114 0.182115 -0.000195 0.059844 -0.096483 -0.179655 -0.151935 0.141397 0.124376 -0.185735 0.108749 0.210302 -0.30497 0.122521 -0.030837 0.050188 0.258287 -0.065831 0.032257 0.151108 0.235164 -0.097539 -0.111812 -0.209686 -0.044457 0.032824 -0.01552 -0.015701 -0.156009 -0.176009 0.15451 -0.028336 -0.032147 -0.128743 -0.128588 -0.00845 -0.277135 0.088282 -0.027556 0.116693 0.074741 -0.131477 -0.049067 0.114403 -0.329863 -0.251574 0.157862 -0.012238 -0.187129 -0.080347 -0.329863 0.109719 -0.217937 0.113884 0.149607 -0.081078 0.162556 0.062662 0.097653 -0.088667 -0.098402 -0.143097 -0.257838 0.020352 0.04661 -0.096294 0.395261 0.505735 -0.302912 -0.240841 -0.161293 -0.095643 -0.113845 -0.13614 0.35811 -0.143627 0.447674 -0.25833 -0.193274 -0.039017 -0.016547 -0.234662 0.003428 0.286242 -0.117746 0.132487 -0.513667 -0.234568 -0.247646 -0.171833 0.008451 -0.009953 0.025254 0.091582 0.214314 0.055666 0.33537 -0.023028 -0.04832 -0.103819 -0.157918 -0.026726 -0.003608 -0.074214 0.137048 -0.133468 0.073371 -0.171597 -0.16568 0.352082 -0.080491 -0.012102 -0.003683 0.112426 0.15508 0.123906 -0.105702 0.028911 -0.059235 -0.225607 0.091653 0.456413 0.081193 -0.42958 -0.04187 0.133148 0.060338 0.113434 0.3267 -0.221853 0.202915 0.045566 0.181181 -0.153358 -0.065763 0.190606 0.192546 0.042386 -0.045901 0.053288 0.044925 0.085752 -0.018136 -0.060858 0.21053 0.367252 0.234678 -0.227372 -0.060106 0.119168 0.258068 -0.052268 0.139627 0.113581 0.255293 0.21956 0.158658 -0.042725 0.021432 -0.119037 -0.053417 -0.143995 0.130922 -0.023791 -0.068511 0.267633 0.05556 0.161433 -0.133674 0.031269 -0.041552 0.407896 0.248048 0.053882 -0.33886 0.147696 -0.085751 0.249824 0.209214 0.451307 -0.144943 0.095086 0.22993 -0.051081 -0.105121 -0.126518 -0.001901 0.203948 0.101434 0.299111 -0.003031 -0.024097 0.193752 0.098303 0.179972 -0.111642 -0.414578 -0.015841 -0.045943 0.41948 -0.002009 0.150503 -0.060506 0.16761 -0.034264 -0.206842 -0.151145 -0.170617 -0.249839 -0.076538 -0.423799 -0.068865 -0.219908 -0.063155 -0.165621 -0.114589 -0.181098 0.09873 0.138724 -0.074105 -0.221169 -0.172291 -0.160436 -0.163665 -0.040192 -0.002751 0.052411 0.356177 0.246724 0.185142 0.131998 0.130618 -0.216643 0.002013 0.049402 -0.304508 -0.538949 -0.112984 0.147569 0.004682 -0.0477 -0.191315 -0.225443 0.18025 -0.323849 0.075398 0.351981 -0.071384 0.04984 -0.296333 0.342425 -0.27221 0.149964 0.171767 0.246847 -0.229258 0.201897 -0.027642 -0.111673 0.059606 -0.130811 -0.282279 -0.105315 -0.119829 0.080198 0.224336 0.257609 -0.047429 0.1675 -0.236645 0.007892 0.464239 -0.289791 -0.189236 0.020953 0.220398 -0.101394 0.04381 0.162467 0.151086 -0.038481
C 0.238849 -0.360172 0.099211 0.006293 -0.090363 0.069534 0.028123 0.12704 -0.018202 -1.412029 0.381697 -0.099666 -0.101932 0.163519 0.268972 -0.006478 0.150871 -1.144588 0.279901 0.066227 -0.007437 0.060244 0.074777 0.018801 -0.071208 -0.015612 0.120906 -0.026291 -0.174593 -0.069907 0.103273 -0.181436 0.041843 0.033711 -0.243154 0.096044 0.024814 -0.021504 0.073083 0.20144 -0.053298 -0.028205 -0.067757 0.007326 -0.024332 -0.081698 0.201165 -0.203523 -0.052323 0.040662 0.083852 -0.043231 0.06321 -0.036093 0.017573 -0.069417 0.001551 0.007173 -0.042546 0.073004 -0.010497 0.019046 0.017155 -0.375677 -0.174652 0.174551 -0.007754 -0.113145 -0.005609 -0.22862 -0.047042 -0.010867 -0.183571 0.03626 -0.121074 -0.048853 -0.076598 0.044575 -0.015781 -0.183016 -0.037259 -0.104482 0.1144 0.025867 0.040949 0.280767 0.302756 -0.106337 -0.338885 -0.114805 0.137506 0.012532 0.080036 0.070575 -0.106305 0.298448 -0.061468 0.000489 -0.023935 0.111629 0.045618 -0.151285 0.115669 0.068804 -0.034785 -0.348239 -0.025045 -0.113112 -0.121841 -0.116407 -0.095591 0.331656 -0.127872 0.016549 -0.043831 0.061613 -0.0651 -0.040418 0.013788 -0.043609 -0.056202 -0.070801 0.033659 0.174662 0.080927 -0.048778 -0.034761 0.032394 0.080836 0.112026 -0.154082 0.01251 0.066015 0.08777 -0.112163 -0.019593 0.01847 0.010216 -0.070893 0.054793 0.924384 -0.037412 -0.252065 0.031156 0.010493 0.103873 0.115355 -0.019604 0.020309 0.159864 -0.015311 -0.011563 -0.028369 0.013712 0.116843 0.140595 0.156602 -0.111119 0.025913 0.073504 -0.101565 0.059498 -0.023623 0.220842 0.328162 0.188814 -0.148141 0.146431 -0.233836 -0.03362 0.024855 0.061243 0.165871 0.112006 0.11338 -0.076917 -0.030779 -0.057617 -0.246789 0.019857 -0.034395 -0.040874 0.075212 0.151863 0.237198 -0.034193 0.179958 -0.111108 -0.008173 0.019504 0.26074 -0.011924 0.073905 -0.102881 0.013914 -0.196628 0.157924 0.143398 0.18885 -0.253109 -0.074664 0.208154 0.003555 -0.097651 -0.238201 -0.131178 -0.018244 0.10774 0.121603 0.009692 0.132325 0.199838 -0.008921 0.31373 -0.137767 -0.193899 0.132919 -0.119642 0.430445 -0.039661 0.024874 -0.0836 0.204211 -0.205904 -0.065816 -0.106257 -0.077215 -0.168574 -0.093538 -0.246735 0.126211 0.01146 -0.176944 -0.080488 0.096047 0.125967 -0.017768 0.065368 0.130747 -0.151483 -0.089586 -0.045944 -0.105984 -0.207005 -0.090474 0.188149 0.13488 0.094092 0.276443 0.032256 -0.023125 -0.050376 0.012959 0.071799 -0.125205 -0.525359 -0.051813 0.167429 0.016998 -0.077773 -0.115134 -0.277943 -0.030296 -0.168833 -0.0337 0.274372 -0.053345 -0.065016 -0.39812 0.126407 -0.050562 0.013757 0.127195 0.27782 0.053982 -0.019575 -0.009565 -0.056523 0.007796 -0.297943 -0.181962 -0.136897 -0.041777 -0.000155 0.113916 0.135553 -0.117825 0.15875 -0.181811 0.041847 0.370853 -0.086974 -0.056817 -0.015443 0.018304 0.030352 0.105044 0.16927 -0.100494 -0.135144
G 0.232291 -0.332989 0.074088 0.016724 -0.072948 0.076775 0.053914 0.110761 -0.029952 -1.415062 0.408656 -0.047822 -0.09182 0.217596 0.265093 -0.035345 0.167726 -1.192917 0.274009 0.089329 0.023195 0.046107 0.111106 0.003731 -0.079103 -0.022707 0.165249 -0.012814 -0.174312 -0.095249 0.112508 -0.150698 0.056894 0.044856 -0.239519 0.090519 0.033695 -0.005008 0.077147 0.178496 -0.068577 -0.03993 -0.098083 0.019356 -0.023613 -0.083644 0.186916 -0.212526 -0.020572 0.007825 0.091415 -0.03496 0.044504 0.030629 0.030603 -0.07328 -0.000247 0.023762 -0.042766 0.071354 -0.022783 0.016466 0.020225 -0.407975 -0.166804 0.116491 -0.029199 -0.12597 0.012115 -0.203974 -0.066224 0.004928 -0.19847 0.016545 -0.143815 -0.039756 -0.069944 0.036554 -0.044416 -0.160131 -0.055523 -0.12122 0.171388 0.061577 0.037488 0.24908 0.364709 -0.174236 -0.326153 -0.113705 0.119113 -0.00251 0.052867 0.097873 -0.119304 0.284174 -0.061503 -0.016796 -0.066068 0.119112 0.038539 -0.140242 0.138171 0.060136 -0.039524 -0.339743 -0.035966 -0.113525 -0.114616 -0.142321 -0.081543 0.297439 -0.119466 0.024872 -0.080263 0.006201 -0.061176 -0.057444 0.032736 -0.057333 -0.026041 -0.043437 0.035844 0.136731 0.104439 -0.053634 -0.052795 0.048222 0.050611 0.12462 -0.162168 0.001585 0.07749 0.057904 -0.178128 -0.032383 0.011811 0.005733 -0.066283 0.093975 0.855056 -0.025963 -0.291937 0.003359 0.00432 0.115268 0.131457 0.005664 0.017116 0.154903 -0.014728 0.002427 -0.037084 -0.0314 0.097474 0.146537 0.149859 -0.123414 0.022093 0.098004 -0.069781 0.041307 -0.042553 0.204488 0.313626 0.165082 -0.11726 0.130559 -0.218232 -0.008107 -0.023893 0.04791 0.1418 0.099962 0.122995 -0.094819 0.00495 -0.078362 -0.209299 -0.011035 -0.02542 -0.027532 0.067987 0.185785 0.223406 -0.040375 0.195534 -0.10752 -0.046122 0.000948 0.248726 0.00118 0.090065 -0.11133 0.006638 -0.188783 0.134693 0.146292 0.153265 -0.257682 -0.08359 0.21755 0.046351 -0.103799 -0.260104 -0.111297 -0.009091 0.097747 0.113772 -0.018018 0.141884 0.183528 -0.013176 0.325628 -0.130468 -0.217554 0.154934 -0.1796 0.431118 -0.057059 0.057688 -0.057626 0.220427 -0.192709 -0.104046 -0.09737 -0.05097 -0.159303 -0.113676 -0.240812 0.126786 0.001865 -0.2038 -0.102277 0.123911 0.16524 0.003464 0.070377 0.117707 -0.136166 -0.11064 -0.042725 -0.13282 -0.191078 -0.123927 0.197139 0.13124 0.081948 0.296528 0.039576 0.004001 -0.063391 0.056844 0.054197 -0.091688 -0.531642 -0.005611 0.195576 -0.002392 -0.10957 -0.113373 -0.259852 -0.003936 -0.149636 -0.022795 0.31322 -0.023726 -0.077493 -0.440475 0.113551 -0.068319 0.00193 0.148859 0.24832 0.056396 -0.02887 -0.013065 -0.083929 0.055607 -0.297215 -0.169967 -0.151899 -0.078754 0.004824 0.10989 0.13496 -0.13613 0.173353 -0.212816 0.064039 0.348838 -0.1212 -0.074673 0.036119 0.042857 0.034622 0.115085 0.16568 -0.090848 -0.141551
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c 0.238415 -0.301016 0.128149 0.017833 -0.134339 0.108552 0.064181 0.112759 -0.08216 -1.267718 0.404538 -0.076438 -0.066725 0.181512 0.264913 0.037967 0.187166 -1.109026 0.335662 0.047088 0.041908 0.046388 0.098603 -0.004575 -0.091568 -0.032598 0.118702 -0.022845 -0.128582 -0.0106 0.148287 -0.1939 0.08869 -0.014563 -0.240424 0.083205 -0.029779 -0.04716 0.057957 0.146655 -0.115609 -0.086026 -0.154125 0.011455 -0.040351 -0.049767 0.190875 -0.180817 -0.087257 0.040991 0.060895 -0.007245 0.061437 -0.014717 0.032582 -0.077913 -0.003923 -0.016528 -0.013155 0.108426 -0.009976 0.070965 0.007377 -0.37636 -0.11964 0.167914 -0.016147 -0.115857 -0.031781 -0.218196 -0.031152 -0.044904 -0.178196 0.003758 -0.096501 -0.121317 -0.116858 0.102735 -0.021707 -0.131704 -0.062942 -0.067381 0.10246 0.082633 -0.02497 0.262784 0.484107 -0.013743 -0.326041 -0.14694 0.103823 -0.0851 0.052597 0.139776 -0.086042 0.366595 -0.045603 -0.063109 -0.040327 0.05047 -0.023429 -0.067506 0.105642 0.06056 -0.02148 -0.764226 -0.020972 -0.118397 -0.135533 -0.112966 -0.122621 0.261425 -0.158191 0.023095 -0.05726 0.08623 -0.02653 -0.031694 0.047844 -0.038654 -0.063694 -0.016405 0.045966 0.182433 -0.000286 -0.030203 -0.076594 0.014723 0.119914 0.068889 -0.126045 -0.01109 0.022582 0.110511 -0.082385 -0.043108 -0.003797 -0.049985 -0.094652 0.055084 0.680715 -0.038841 -0.2459 -0.049942 0.020952 0.086485 0.159077 -0.044076 -0.043145 0.174978 0.032444 -0.076672 0.002446 -0.027063 0.074319 0.151169 0.174477 -0.135717 0.052963 0.060294 -0.045598 0.060221 0.027499 0.238733 0.350295 0.139729 -0.161813 0.147843 -0.204267 -0.074761 0.009984 0.075796 0.191477 0.139917 0.111451 -0.058422 -0.058023 -0.087822 -0.292257 -0.036417 -0.024283 0.008333 0.087858 0.137099 0.22183 -0.033944 0.17426 -0.125943 -0.027133 -0.003151 0.215619 0.003673 0.125021 -0.121537 0.036751 -0.114839 0.19957 0.14886 0.299109 -0.263744 -0.081109 0.158611 0.048948 -0.072448 -0.258555 -0.054506 -0.014363 0.102569 0.085325 0.04684 0.082466 0.21672 -0.03193 0.230437 -0.166103 -0.255674 0.141412 -0.001618 0.500966 -0.024936 0.050757 -0.094698 0.184732 -0.158559 -0.094114 -0.14635 -0.12222 -0.191958 -0.134959 -0.280244 0.132367 -0.029392 -0.11608 -0.067583 0.029502 0.093664 0.027303 0.115847 0.148128 -0.139646 -0.034199 -0.012447 -0.120367 -0.104986 -0.053034 0.149916 0.083317 0.079861 0.315248 -0.042852 -0.038935 -0.049928 0.021338 0.088303 -0.084196 -0.516834 -0.055859 0.13599 -0.023385 -0.123369 -0.149502 -0.23672 -0.048341 -0.18839 -0.020066 0.316207 -0.014791 -0.031229 -0.406917 0.176413 -0.139584 -0.054221 0.154665 0.247109 0.034283 0.052839 0.009479 -0.01978 0.071906 -0.283435 -0.229701 -0.153431 -0.108586 -0.078139 0.083796 0.128999 -0.110072 0.13479 -0.184249 0.022299 0.343081 -0.114086 -0.043955 -0.007002 0.040686 0.024247 0.065019 0.165534 -0.066753 -0.115556
g 0.228195 -0.276816 0.115259 0.004166 -0.12058 0.113494 0.0592 0.087395 -0.085438 -1.223506 0.40033 -0.074004 -0.058844 0.184124 0.255605 0.023006 0.19669 -1.165595 0.314094 0.063409 0.049939 0.045597 0.091409 -0.001042 -0.0971 -0.024186 0.147933 -0.020827 -0.131112 -0.022747 0.136567 -0.175323 0.070774 -0.009352 -0.21398 0.074294 -0.029155 -0.043992 0.068423 0.134674 -0.136797 -0.092281 -0.184465 0.01648 -0.032676 -0.033839 0.17062 -0.182101 -0.079296 -0.003474 0.062605 -0.005743 0.043644 -0.00516 0.03789 -0.059575 0.016293 -0.017516 0.004037 0.098462 -0.019679 0.024022 -0.013306 -0.384336 -0.124672 0.135167 -0.015401 -0.104642 -0.024559 -0.220594 -0.032085 -0.038996 -0.177533 -0.010044 -0.101349 -0.121171 -0.090146 0.11375 -0.036256 -0.131192 -0.060842 -0.079292 0.114471 0.08972 -0.022611 0.266112 0.474668 -0.019942 -0.305102 -0.127178 0.08643 -0.112541 0.05322 0.149874 -0.075892 0.384803 -0.051425 -0.071512 -0.04641 0.063211 -0.002617 -0.067402 0.101651 0.043209 -0.021587 -0.737557 -0.024281 -0.114039 -0.114079 -0.123023 -0.122191 0.252634 -0.158882 0.015135 -0.049877 0.057091 -0.039429 -0.015664 0.052838 -0.064067 -0.049227 -0.013858 0.035859 0.156096 0.0138 -0.042463 -0.084921 0.005136 0.107653 0.082977 -0.151926 -0.00972 0.032352 0.102678 -0.091813 -0.066029 -0.000657 -0.022498 -0.090798 0.050824 0.634743 -0.034437 -0.26174 -0.050012 0.013878 0.098035 0.161619 -0.03325 -0.019991 0.171523 0.014195 -0.06909 0.003728 -0.041084 0.076604 0.145585 0.168872 -0.147578 0.050689 0.050316 -0.049343 0.055371 0.006618 0.262765 0.346093 0.12763 -0.159191 0.1457 -0.175138 -0.057456 -0.003055 0.074405 0.187751 0.143519 0.101395 -0.067461 -0.05495 -0.084775 -0.263107 -0.039391 -0.031259 -0.014926 0.072405 0.128385 0.209746 -0.011117 0.171721 -0.129684 -0.0157 -0.014511 0.220731 0.019329 0.116054 -0.127629 0.029777 -0.092318 0.198104 0.127243 0.273731 -0.275054 -0.084173 0.158144 0.04457 -0.075046 -0.232923 -0.052891 -0.016516 0.090798 0.086568 0.017496 0.089958 0.192783 -0.000754 0.214489 -0.175929 -0.251107 0.137006 -0.005082 0.498232 -0.03359 0.048975 -0.098721 0.213702 -0.163626 -0.119179 -0.145548 -0.12513 -0.180278 -0.129955 -0.278065 0.122087 -0.010481 -0.121085 -0.070056 0.010454 0.11494 0.021755 0.120335 0.139471 -0.139436 -0.059761 -0.002756 -0.118578 -0.085514 -0.075986 0.139596 0.058792 0.061143 0.331703 -0.032291 -0.042326 -0.065489 0.039632 0.096256 -0.064409 -0.496407 -0.037008 0.130659 -0.031957 -0.113746 -0.136627 -0.220575 -0.038598 -0.170771 -0.016789 0.318734 -0.013707 -0.023917 -0.423561 0.151876 -0.128408 -0.070137 0.143139 0.216088 0.048965 0.041771 0.016977 -0.019713 0.085056 -0.280477 -0.216475 -0.152658 -0.134113 -0.079779 0.087794 0.104183 -0.100949 0.125643 -0.184272 0.016771 0.333283 -0.112485 -0.019566 -0.01135 0.050954 0.038979 0.055232 0.187491 -0.099044 -0.128151
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. 0.156439 -0.289205 0.197438 0.028739 -0.18376 0.126963 0.073421 0.126997 0.026968 -1.485198 0.383139 -0.082614 -0.167821 0.149249 0.315251 0.051882 0.204744 -1.136922 0.332092 0.04678 0.000807 0.033358 0.132047 0.049669 -0.102286 0.012972 0.091629 -0.009285 -0.144621 0.009132 0.16269 -0.261059 0.107129 0.014783 -0.242847 0.089442 -0.024477 0.138645 0.100455 0.296948 -0.121629 -0.123353 -0.061149 0.047197 -0.082953 -0.064039 0.179204 -0.170682 -0.111646 0.049757 0.085 0.025207 0.014616 0.044347 0.073 -0.165607 -0.032537 0.031942 0.026915 0.074431 -0.006647 -0.126158 -0.088006 -0.448549 -0.265016 0.201097 -0.01226 -0.161956 -0.057112 -0.2056 -0.053301 -0.090923 -0.162521 -0.019806 0.03538 0.0093 -0.110463 0.12862 -0.072147 -0.166876 -0.083537 -0.037865 0.111316 0.048028 0.033626 0.26757 0.573334 -0.121911 -0.290549 -0.141143 0.096951 -0.040062 0.053041 0.142415 -0.160878 0.370641 -0.047189 -0.05785 0.01 0.060029 -0.039755 -0.104396 0.229014 0.103194 -0.020719 -0.550506 0.06427 -0.184138 -0.163429 -0.037381 -0.059961 0.211613 -0.109001 0.109064 -0.095264 0.1379 0.037388 -0.074459 0.11494 -0.171806 -0.174743 0.038221 -0.075505 0.107353 -0.070237 -0.062731 -0.100642 -0.002036 0.029748 0.034968 -0.134178 -0.011785 0.030175 0.12785 -0.157829 -0.051493 0.031171 0.043602 -0.14551 0.03737 0.804713 0.028504 -0.23073 -0.002284 0.07286 0.079825 0.135765 -0.025085 -0.14726 0.163325 0.023223 -0.041766 -0.062489 -0.029103 0.059348 0.18481 0.156426 -0.093312 0.173771 0.064192 0.012668 -0.036263 -0.01822 0.146786 0.295616 0.26202 -0.180151 0.206258 -0.228476 -0.063484 0.034714 0.074928 0.178423 0.107159 0.175005 -0.066522 -0.050121 -0.144342 -0.301797 -0.069271 -0.026343 0.029929 0.114064 0.177382 0.252149 0.039817 0.160045 -0.202922 -0.058487 0.09543 0.189783 0.042329 0.154371 -0.165476 0.046579 -0.179447 0.198602 0.138179 0.265598 -0.33453 0.054404 0.133229 0.051154 -0.02642 -0.246724 -0.045778 0.023902 0.041058 0.121904 0.050036 0.225054 0.152594 -0.003921 0.276786 -0.120723 -0.199616 0.166304 0.023551 0.385892 0.064713 0.086933 -0.156289 0.180113 -0.170802 -0.147823 -0.176529 -0.098525 -0.249798 -0.13334 -0.226612 0.13389 -0.000565 -0.155364 -0.125471 0.058404 0.110096 0.012535 0.12755 0.188067 -0.187247 -0.02854 -0.060475 -0.072948 -0.090595 -0.07503 0.137035 0.093227 0.113314 0.320394 -0.03224 -0.054269 0.099921 0.044384 0.067083 -0.086246 -0.514421 -0.042184 0.10426 -0.08264 -0.146656 -0.131826 -0.180384 0.036467 -0.251704 -0.071533 0.238408 0.009439 -0.100278 -0.453989 0.144559 -0.191331 -0.069267 0.030447 0.382874 0.056427 0.019211 0.033179 -0.105485 -0.021239 -0.328138 -0.228583 -0.303673 -0.083814 0.002002 0.142494 0.134648 -0.223058 0.085895 -0.192605 0.165538 0.404885 -0.197245 -0.050721 -0.065096 0.043141 0.0193 0.048917 0.175121 -0.056306 -0.164638
2 0.186963 -0.301911 0.28452 -0.025237 -0.338335 0.123156 0.070621 -0.000227 -0.053768 -1.576301 0.47359 -0.050857 -0.196269 0.195283 0.169219 0.021622 0.154229 -1.085735 0.416546 0.140897 0.012536 0.155764 0.300681 0.105326 -0.039097 0.099547 0.173878 -0.13148 -0.124023 0.143249 0.136141 -0.185353 0.023483 0.019907 -0.292216 0.156223 0.021596 0.15324 0.06414 0.443146 -0.159644 -0.286791 -0.127413 0.205805 -0.071175 -0.023028 0.270655 -0.06185 -0.07909 0.127532 0.112762 0.201012 -0.051051 0.035187 0.108964 0.033525 0.028089 0.068586 0.110163 0.174679 -0.054475 -0.142996 -0.04429 -0.581962 -0.415179 0.168136 0.089054 -0.137465 -0.08838 -0.343596 -0.016158 -0.208427 -0.35307 0.025552 0.006096 0.052297 -0.004518 0.230549 -0.10603 -0.238924 -0.061721 -0.035062 0.163175 0.006972 0.016067 0.404614 0.73686 -0.043261 -0.396684 -0.121903 0.20164 -0.175771 0.05934 0.147546 -0.061583 0.273814 -0.09258 -0.042388 -0.064666 0.083442 -0.052719 -0.079891 0.200861 0.161439 -0.039458 -0.387501 -0.051057 -0.263685 -0.081799 -0.000899 -0.018997 0.180655 -0.095148 0.121244 -0.097278 0.140073 -0.067704 -0.195557 0.01892 -0.232714 -0.208584 0.081506 -0.075696 0.088679 0.010837 -0.062293 -0.14534 0.148493 0.026296 0.061518 -0.007128 -0.063968 0.081059 0.06476 -0.330649 -0.045083 0.06271 0.168338 -0.145391 0.037621 0.896325 0.106413 -0.229841 0.028101 -0.05284 0.208724 0.095854 -0.108521 -0.061223 0.12804 -0.01476 -0.166488 -0.048391 0.025623 0.089454 0.196637 0.251963 -0.063028 0.065511 0.263165 -0.065719 -0.052878 0.074564 0.121078 0.191861 0.369816 -0.276974 0.137921 -0.378125 -0.016852 0.116091 0.003126 0.098068 0.188624 0.203082 -0.1646 -0.05112 -0.176813 -0.224621 0.004044 -0.015168 0.125262 0.223165 0.200554 0.258419 -0.030814 0.234825 -0.036709 -0.109578 0.035963 0.271723 0.14321 0.196521 -0.296585 -0.062592 -0.282689 0.031596 0.204714 0.303154 -0.250497 -0.024177 0.164989 0.112585 -0.105092 -0.296051 -0.149596 -0.134063 0.050544 0.130353 0.051471 0.260172 0.004349 -0.014878 0.263049 -0.049434 -0.245882 0.190419 -0.140489 0.397388 0.058797 0.17746 -0.156586 0.169402 -0.243165 -0.060565 -0.036819 0.058755 -0.281727 -0.010408 -0.360708 0.175143 0.01128 -0.238101 -0.085036 -0.0273 0.130227 -0.029349 0.056553 0.204488 -0.272785 -0.064566 -0.083595 -0.152505 -0.248976 -0.184706 0.190923 0.134011 0.158013 0.411221 0.033463 -0.120705 0.100107 0.048816 0.133384 -0.03729 -0.539198 -0.156227 0.097567 -0.077533 -0.149907 -0.239697 -0.238316 -0.026766 -0.144965 -0.174142 0.149505 0.080498 -0.116004 -0.619855 0.261709 -0.249597 -0.012799 0.128354 0.293107 -0.106786 -0.004195 -0.038091 -0.245842 -0.163304 -0.220994 -0.336351 -0.228113 -0.061963 -0.053395 0.278636 0.129226 -0.190749 0.130565 -0.228586 0.292387 0.557806 -0.092231 -0.173112 0.110297 -0.102192 0.064141 0.053707 0.260824 -0.043218 -0.158501
6 0.154496 -0.305115 0.267734 -0.011642 -0.276945 0.092295 0.072346 0.02688 0.021188 -1.576289 0.496774 0.025161 -0.213631 0.155481 0.206509 0.035069 0.122864 -1.085905 0.381281 0.090757 0.007382 0.169536 0.255127 0.09553 0.010662 0.029847 0.192409 -0.10798 -0.141365 0.142714 0.124322 -0.174987 0.034242 -0.024467 -0.278386 0.113119 -0.010683 0.13431 0.113325 0.429783 -0.182548 -0.288467 -0.083808 0.158122 -0.081506 -0.036985 0.280258 -0.049408 -0.084944 0.096887 0.094128 0.156061 -0.009111 0.029693 0.107895 0.007932 0.060394 0.055809 0.098238 0.158314 -0.092519 -0.101374 -0.074479 -0.594182 -0.361384 0.172027 0.092685 -0.125725 -0.104062 -0.347952 -0.068879 -0.166784 -0.293306 0.037052 -0.015628 0.023131 -0.014536 0.246286 -0.062667 -0.235575 -0.114481 -0.090239 0.203001 0.00782 -0.014935 0.395211 0.729765 -0.024568 -0.388022 -0.162318 0.199208 -0.182832 0.059576 0.137702 -0.071785 0.31136 -0.126924 -0.027862 -0.089636 0.053687 -0.002149 -0.088446 0.258324 0.200463 0.017538 -0.400877 -0.0213 -0.185121 -0.077085 -0.021477 0.034753 0.218221 -0.131168 0.086361 -0.108392 0.164708 -0.024293 -0.138657 0.005137 -0.259855 -0.191733 0.072148 -0.090436 0.082525 -0.009935 -0.04962 -0.130299 0.165249 0.031097 0.062957 -0.030965 -0.021694 0.067774 0.044117 -0.275719 -0.073961 0.042263 0.126953 -0.158754 0.095771 0.912894 0.12468 -0.182352 0.012016 -0.01785 0.191943 0.083141 -0.104301 -0.011636 0.135871 -0.003821 -0.143638 -0.029809 -0.011227 0.088215 0.177094 0.225802 -0.076579 0.105315 0.200546 -0.063987 -0.031639 0.029636 0.105309 0.195319 0.318902 -0.223928 0.122908 -0.351135 -0.016058 0.065356 -0.010195 0.107045 0.198815 0.175623 -0.17908 -0.048312 -0.190836 -0.251158 -0.053031 0.027147 0.079073 0.231838 0.195152 0.29157 -0.046789 0.203388 -0.029689 -0.095277 0.125745 0.280559 0.136462 0.205825 -0.2738 -0.054288 -0.30497 0.046762 0.157252 0.235664 -0.271968 0.008255 0.144655 0.090038 -0.151532 -0.322084 -0.11702 -0.12159 0.072421 0.109873 0.030735 0.257634 0.06575 0.02979 0.23138 -0.055904 -0.210817 0.175611 -0.137018 0.423012 0.039998 0.178009 -0.175958 0.216513 -0.260207 -0.055247 -0.078219 0.042736 -0.266055 -0.044984 -0.353748 0.166627 0.001282 -0.218766 -0.107894 0.044053 0.167148 0.020683 0.104259 0.198817 -0.258754 -0.109679 -0.09898 -0.105717 -0.197375 -0.198138 0.199799 0.129711 0.128554 0.401718 -0.00597 -0.131047 0.14669 0.110642 0.171081 -0.033283 -0.509083 -0.12795 0.119933 -0.063602 -0.119898 -0.223416 -0.245165 0.003011 -0.162917 -0.122327 0.154702 0.046678 -0.096574 -0.611961 0.236861 -0.220065 0.025805 0.148744 0.336582 -0.098251 -0.000696 -0.042833 -0.213512 -0.184921 -0.310121 -0.324585 -0.163728 -0.044049 -0.069371 0.288519 0.140761 -0.207973 0.138077 -0.223114 0.296411 0.499754 -0.072265 -0.149306 0.113989 -0.099607 0.07022 0.072529 0.26572 -0.075 -0.205631
: 0.331773 -0.240001 0.483144 -0.101342 -0.336078 0.229979 0.083288 -0.25523 -0.226371 -1.615766 0.423309 0.020729 -0.212404 0.361766 0.082449 -0.061567 0.181121 -1.062016 0.488464 0.341277 0.164727 0.20561 0.406234 0.232185 0.005361 0.285738 0.281419 -0.349641 -0.078236 0.174566 0.122687 -0.103638 -0.236557 0.242478 -0.372638 0.190457 -0.047142 0.031949 -0.001886 0.542723 -0.066043 -0.519465 -0.298581 0.430154 0.178543 -0.152837 0.230578 0.038797 -0.066249 0.318742 0.158043 0.481906 -0.217979 0.069308 0.356479 0.243729 -0.177501 0.070988 0.405658 0.352072 -0.025748 -0.324475 -0.024943 -0.580882 -0.701376 0.137823 0.136688 0.018713 -0.109225 -0.576749 0.282162 -0.531894 -0.556644 -0.001217 -0.015578 0.110246 0.139996 0.493922 -0.300606 -0.383483 -0.071802 0.09111 0.120286 -0.064077 0.038508 0.444432 0.897607 -0.096709 -0.592961 -0.123824 0.212627 -0.322742 0.04421 0.284349 0.083983 0.201447 -0.213916 -0.321628 -0.120248 0.12462 -0.153992 -0.062128 0.059891 0.235349 -0.123368 -0.472899 -0.256 -0.443168 -0.074533 0.096509 -0.172158 0.245194 0.048014 0.30106 -0.004294 0.256769 -0.256822 -0.239372 -0.02263 -0.289205 -0.367779 0.120257 -0.18294 0.068165 -0.034082 0.086302 -0.219906 0.102528 0.224493 0.119701 -0.067851 -0.112857 0.270295 0.185939 -0.413022 0.106737 0.193697 0.26601 -0.133329 0.216198 0.822232 0.279211 -0.176911 -0.077604 -0.081211 0.364401 0.045896 -0.084974 -0.165001 0.030686 -0.087314 -0.298398 -0.039789 0.109229 0.266139 0.304786 0.380214 -0.14042 -0.027335 0.60484 -0.196495 -0.02195 0.222975 0.04343 0.024282 0.454951 -0.380764 0.08487 -0.632841 -0.094939 0.241165 -0.018 0.083887 0.110332 0.284314 -0.296962 -0.291058 -0.103446 -0.218785 0.200923 -0.106514 0.3755 0.325498 0.168001 0.234331 0.099167 0.291134 -0.003246 -0.349006 -0.147931 0.378893 0.162615 0.198442 -0.652812 -0.105351 -0.386917 -0.077075 0.344321 0.602733 -0.177963 -0.077839 0.217882 0.151196 -0.161109 -0.251086 -0.165356 -0.140653 0.104642 0.238658 0.057774 0.301036 -0.251191 -0.209613 0.388671 0.169071 -0.252154 0.1912 -0.059848 0.413478 -0.012804 0.349888 -0.25044 -0.012237 -0.396642 -0.087707 0.056037 0.21175 -0.296605 0.058998 -0.55754 0.229676 -0.152525 -0.317309 -0.162589 -0.256327 -0.012048 -0.008599 -0.149543 0.145474 -0.311144 0.055707 -0.095181 -0.410556 -0.531331 -0.101727 0.176662 0.214917 0.327436 0.502585 0.178014 -0.054273 -0.222585 -0.032375 0.198854 0.174494 -0.606944 -0.213458 0.239419 -0.10909 -0.160737 -0.259521 -0.266509 -0.006944 -0.14499 -0.346129 0.285524 0.004835 -0.063303 -0.846876 0.49007 -0.409508 0.052224 0.100626 -0.114216 -0.309322 -0.001734 -0.165649 -0.6906 -0.353964 0.137506 -0.495487 -0.33644 -0.25776 0.038427 0.428948 0.054462 -0.067407 0.116349 -0.273518 0.493906 0.935483 -0.075337 -0.397707 0.423743 -0.195707 0.018693 0.079963 0.316928 0.023047 -0.023652
> 0.424627 -0.351106 0.15755 -0.145937 0.071899 0.064664 0.141268 -0.103891 -0.086197 -1.560992 0.424074 -0.308327 -0.019314 0.458726 0.540871 0.052464 0.306041 -1.109336 0.303829 0.168868 -0.167129 0.296808 0.139838 -0.064787 -0.253773 -0.07178 0.225874 -0.021347 -0.121985 0.098063 0.147005 -0.231969 0.311524 0.000892 -0.281239 0.166749 -0.045042 0.103934 0.089566 0.338049 0.005381 0.093288 -0.106053 0.049213 -0.267043 -0.148983 0.140722 -0.159109 0.141045 0.105159 -0.041781 0.052953 0.045446 -0.037391 0.182284 -0.149972 -0.035313 -0.057559 0.035834 0.094201 -0.245742 -0.074105 0.244914 -0.615784 -0.105455 0.414152 -0.018518 -0.001616 -0.05445 -0.293267 -0.122062 -0.160632 -0.122839 0.059931 -0.090249 0.11285 -0.396584 0.285737 0.0223 -0.574667 -0.06358 -0.065951 -0.035692 0.378779 -0.087765 0.440129 0.761172 -0.315861 -0.543176 0.054345 -0.08421 0.014662 0.220387 0.040114 -0.154826 0.500242 -0.18943 -0.132554 0.121105 0.186202 0.001041 -0.042446 0.24695 -0.056038 0.035986 -0.488014 0.322846 -0.407673 -0.481012 0.00695 -0.107643 -0.040538 -0.067753 0.115315 0.078843 0.361193 -0.079788 -0.076383 -0.158134 -0.160255 -0.366978 -0.374027 0.214391 -0.01883 -0.067605 -0.071912 -0.171624 -0.17319 0.18616 0.256616 -0.18885 0.100156 0.035037 0.161818 -0.044732 0.12071 0.053163 -0.066856 -0.181604 -0.146885 0.64313 -0.027261 -0.299298 -0.182551 -0.042595 0.110179 0.106228 0.124741 -0.206444 0.395527 0.220239 0.251673 -0.284154 0.159011 0.166834 0.050673 0.117693 -0.216235 0.18288 -0.056129 0.19311 0.02388 -0.011427 0.150045 0.275279 0.109643 -0.206494 0.001744 -0.175471 0.102736 0.035009 0.335371 -0.031999 0.126582 0.319199 0.037063 0.124225 0.064904 -0.377453 -0.110126 -0.149786 -0.168006 0.207537 -0.023667 0.099868 0.055499 0.247174 -0.088811 -0.123459 -0.095544 0.151833 -0.011258 0.094592 0.083933 0.397351 -0.193154 0.160215 -0.125479 0.329592 -0.201315 -0.12026 -0.127962 -0.006343 0.020201 -0.402591 0.119477 -0.001622 -0.018173 0.119496 0.045475 -0.034241 0.194751 -0.005468 0.153808 -0.278797 -0.441802 0.34153 0.018738 0.335381 -0.118805 0.136404 0.156432 0.227787 0.021321 -0.193502 -0.043723 -0.212819 -0.342805 0.001408 -0.584898 0.084136 -0.138775 -0.096677 -0.373779 -0.133311 0.191372 -0.013454 0.175523 0.054133 -0.059172 -0.039555 -0.027038 0.048005 -0.33513 -0.152187 0.282354 0.308081 0.019749 0.340422 -0.038942 -0.272948 0.016172 0.054307 -0.071622 -0.031202 -0.300538 0.067858 0.117871 -0.033964 -0.117878 -0.144292 -0.149309 0.041253 -0.364108 -0.041626 0.32514 0.128949 -0.174658 -0.145654 0.108501 -0.357253 -0.16875 0.177308 0.282767 0.108962 -0.168445 0.01201 0.073186 0.040485 -0.315797 -0.419514 -0.25996 -0.194081 -0.110202 0.507637 0.022307 -0.066958 0.33841 -0.030076 0.070199 0.643218 0.000222 -0.257784 -0.082464 0.097476 -0.170528 0.196163 0.095898 -0.191701 -0.173306
B 0.22128 -0.315272 0.072888 0.024696 -0.091412 0.099949 0.023413 0.120061 -0.01156 -1.370345 0.430653 -0.079659 -0.105251 0.217552 0.24275 -0.009083 0.177303 -1.233153 0.297851 0.068259 0.002549 0.089485 0.101444 0.010557 -0.080559 -0.034506 0.155278 -0.027792 -0.181227 -0.052205 0.092892 -0.150822 0.053227 0.049424 -0.219577 0.102759 0.015219 0.036776 0.085966 0.126896 -0.068455 -0.064765 -0.121299 0.023246 -0.033737 -0.072636 0.206007 -0.203551 -0.026464 0.006781 0.08291 -0.028909 0.044689 0.01985 0.034234 -0.071987 -0.008963 0.024635 -0.048286 0.079461 -0.008034 0.007306 0.008442 -0.404387 -0.178355 0.123677 -0.01231 -0.120426 0.011394 -0.210254 -0.057811 -0.025344 -0.16749 0.031322 -0.124543 -0.066982 -0.048129 0.044455 -0.05249 -0.204843 -0.050598 -0.108047 0.155252 0.042429 0.035341 0.27768 0.351999 -0.092412 -0.328626 -0.104548 0.122091 -0.018847 0.075456 0.118918 -0.095068 0.321136 -0.065411 0.002195 -0.057451 0.134177 0.066529 -0.116748 0.12937 0.07013 -0.014342 -0.295768 -0.039983 -0.110677 -0.095774 -0.136146 -0.074758 0.303101 -0.158533 0.030585 -0.075412 -0.004679 -0.044792 -0.04028 0.034107 -0.031317 -0.03986 -0.045487 0.043136 0.156662 0.095031 -0.042118 -0.057073 0.025558 0.054696 0.115208 -0.157123 -0.012718 0.084656 0.089529 -0.139637 -0.036655 0.034512 0.008137 -0.100331 0.059273 0.862766 -0.029403 -0.269144 0.013052 0.027932 0.105597 0.145041 0.010753 0.031225 0.181532 -0.029483 -0.009202 -0.026628 -0.020101 0.116886 0.14298 0.146354 -0.14951 0.007322 0.04884 -0.062651 0.011818 -0.022389 0.192377 0.34582 0.164976 -0.114038 0.168399 -0.199949 -0.025269 0.008716 0.044331 0.13377 0.081825 0.124699 -0.106483 -0.050252 -0.083448 -0.20619 -0.011087 -0.01343 -0.065918 0.085455 0.151207 0.222057 -0.01686 0.196095 -0.130553 -0.03364 0.026711 0.23288 -0.003628 0.069022 -0.104785 0.004518 -0.195174 0.142922 0.147647 0.203515 -0.261292 -0.100193 0.193108 0.040435 -0.105158 -0.225122 -0.139045 -0.013528 0.117588 0.09282 -0.011802 0.145716 0.197772 0.002951 0.308284 -0.127399 -0.229124 0.115654 -0.102955 0.435743 -0.055406 0.038432 -0.05083 0.258052 -0.197275 -0.084057 -0.098257 -0.068302 -0.192698 -0.10122 -0.220997 0.146286 0.00348 -0.189493 -0.08788 0.070398 0.132581 -0.007189 0.064265 0.134357 -0.132668 -0.087976 -0.023306 -0.097643 -0.180347 -0.099186 0.192861 0.138251 0.07725 0.285136 0.024177 0.004756 -0.062766 0.052074 0.062474 -0.076381 -0.531734 -0.029735 0.156944 0.000479 -0.106121 -0.128328 -0.278123 0.001928 -0.129306 -0.050797 0.313003 0.000317 -0.068442 -0.476727 0.15392 -0.037394 -0.040647 0.17178 0.263527 0.055274 -0.031797 -0.011815 -0.059181 0.05175 -0.293104 -0.197553 -0.166731 -0.078951 -0.019332 0.090115 0.130694 -0.1254 0.16876 -0.219462 0.041453 0.342957 -0.115443 -0.053558 0.030563 0.054615 0.019087 0.097245 0.151129 -0.086065 -0.162907
F 0.225226 -0.347607 0.087743 0.048817 -0.115192 0.075035 0.065877 0.144933 -0.034768 -1.429031 0.391477 -0.065475 -0.079852 0.220304 0.248838 -0.011895 0.163482 -1.174116 0.28837 0.115337 -0.003961 0.067367 0.10368 0.006617 -0.10903 -0.008018 0.121116 -0.014077 -0.168668 -0.083945 0.113513 -0.164763 0.044003 0.028356 -0.229452 0.085026 0.003128 -0.036376 0.077995 0.184178 -0.061923 -0.075714 -0.102529 0.019708 -0.024607 -0.061186 0.201528 -0.180682 -0.046827 0.045464 0.080448 -0.038627 0.031417 -0.006062 0.009082 -0.080272 -0.003559 -0.008427 -0.037766 0.078821 0.018289 0.026424 0.018278 -0.396681 -0.171429 0.133273 -0.036107 -0.108772 0.011054 -0.225239 -0.033611 -0.01082 -0.189775 0.022445 -0.100561 -0.025104 -0.062665 0.064609 -0.022009 -0.129887 -0.034137 -0.121088 0.118611 0.049519 0.005477 0.283796 0.308958 -0.072121 -0.318848 -0.137915 0.086688 -0.009681 0.079601 0.11703 -0.103605 0.32452 -0.088851 0.009009 -0.030663 0.119008 0.027683 -0.142709 0.115614 0.093322 -0.032745 -0.34277 -0.020327 -0.111974 -0.16007 -0.121493 -0.085137 0.306677 -0.136175 -0.003823 -0.078932 0.053739 -0.050442 -0.043878 0.033625 -0.039314 -0.076378 -0.039152 0.03475 0.140822 0.068253 -0.069114 -0.058038 0.000871 0.037426 0.093802 -0.137295 0.016547 0.067078 0.097216 -0.14122 -0.014598 0.011183 0.004231 -0.08881 0.054128 0.864867 -0.038383 -0.26049 0.017185 0.015961 0.085715 0.126957 -0.001887 -0.018981 0.183524 -0.006865 0.006566 -0.054095 -0.025365 0.128624 0.147624 0.149756 -0.105342 0.048469 0.070072 -0.081964 0.038314 -0.024217 0.221673 0.340988 0.155137 -0.122692 0.166079 -0.247072 -0.039577 0.014624 0.065863 0.155789 0.105208 0.125015 -0.063791 0.017269 -0.091901 -0.217867 0.010311 -0.020887 -0.030326 0.0712 0.166286 0.239151 -0.042381 0.172715 -0.109712 -0.043193 -0.004128 0.262966 0.023596 0.069611 -0.141742 0.029626 -0.147763 0.174894 0.127801 0.204613 -0.262291 -0.07892 0.204399 0.079159 -0.095407 -0.254927 -0.101683 -0.028001 0.090241 0.08444 0.010449 0.13794 0.202159 0.005996 0.296913 -0.148461 -0.214419 0.154764 -0.11763 0.430407 -0.050658 0.046104 -0.072384 0.230537 -0.193345 -0.078954 -0.095159 -0.056741 -0.190351 -0.081626 -0.244555 0.129054 0.006083 -0.182098 -0.086178 0.083803 0.110254 -0.007794 0.083359 0.149759 -0.154147 -0.086402 -0.025794 -0.109712 -0.168121 -0.102501 0.193233 0.144512 0.116417 0.31301 0.04089 -0.000319 -0.04406 0.036558 0.054042 -0.108915 -0.526608 -0.014769 0.163496 -0.007701 -0.109502 -0.13914 -0.281846 -0.049388 -0.18667 -0.020432 0.288476 -0.013001 -0.082439 -0.441694 0.133217 -0.015371 -0.012454 0.14091 0.260095 0.06657 -0.003406 -0.022538 -0.058349 0.040735 -0.272682 -0.165476 -0.158691 -0.082307 -0.001205 0.134933 0.122 -0.140828 0.170038 -0.208228 0.060734 0.36955 -0.090236 -0.050159 -0.006608 -0.001153 -0.021939 0.111127 0.15935 -0.056991 -0.152338
J 0.25249 -0.331328 0.104545 0.061086 -0.132756 0.098301 0.035274 0.107592 -0.060297 -1.350421 0.46426 -0.118368 -0.087068 0.219028 0.184002 -0.030676 0.196892 -1.307357 0.317906 0.139799 0.002265 0.070556 0.113201 0.012364 -0.120767 -0.014837 0.159436 -0.054505 -0.144415 -0.040978 0.127395 -0.173249 0.035174 -0.012422 -0.292012 0.081618 0.037235 0.048602 0.080454 0.126858 -0.098384 -0.081175 -0.173136 0.051806 -0.030151 -0.047744 0.171224 -0.196379 -0.011609 0.026234 0.088707 -0.003833 0.033384 0.04393 0.060449 -0.080108 0.022936 0.020582 -0.003132 0.046045 -0.062785 -0.012465 0.01569 -0.441457 -0.171724 0.078977 -0.02466 -0.083427 0.027707 -0.228184 -0.066533 -0.017025 -0.193243 0.022133 -0.122279 -0.039441 -0.018181 0.096786 -0.047766 -0.18521 -0.012855 -0.111449 0.134664 0.046358 0.019225 0.260042 0.446618 -0.125107 -0.335133 -0.064755 0.133878 -0.061699 0.062892 0.133292 -0.068182 0.339708 -0.010048 -0.034057 -0.082926 0.162171 0.038119 -0.125978 0.121276 0.050714 -0.037429 -0.313722 -0.055562 -0.132607 -0.108349 -0.098409 -0.097243 0.265977 -0.119801 0.059775 -0.083954 0.019806 -0.043946 -0.087629 0.0499 -0.065487 -0.052368 0.001273 -0.001594 0.112093 0.089769 -0.040405 -0.094126 0.013081 0.037313 0.105596 -0.159491 -0.023217 0.069021 0.054946 -0.192359 -0.027888 0.041841 0.013434 -0.094998 0.052819 0.835521 -0.012589 -0.334413 -0.003985 -0.035965 0.157066 0.122649 -0.003237 -0.007199 0.124908 -0.031803 -0.022649 -0.042525 -0.023494 0.116754 0.136715 0.16749 -0.143833 0.002604 0.095389 -0.039812 0.02949 0.028069 0.193053 0.318166 0.221186 -0.124403 0.199658 -0.229112 -0.035257 0.007667 0.04934 0.128237 0.074129 0.085474 -0.069098 -0.029588 -0.074888 -0.223071 -0.049248 0.003511 0.013986 0.085291 0.15353 0.239092 -0.017054 0.258133 -0.122288 -0.051304 -0.00322 0.245453 -0.020428 0.101876 -0.138797 0.003649 -0.17535 0.168073 0.161834 0.210309 -0.263025 -0.132204 0.20128 0.027094 -0.080555 -0.270401 -0.120358 -0.051805 0.065577 0.088097 0.019837 0.180331 0.148274 -0.01321 0.315415 -0.133642 -0.209709 0.146863 -0.102077 0.469988 -0.067174 0.052057 -0.033943 0.244899 -0.136347 -0.078793 -0.021281 -0.018836 -0.218134 -0.0769 -0.223416 0.105291 0.011826 -0.304761 -0.066828 0.036445 0.113431 -0.019442 0.122196 0.155977 -0.153869 -0.106134 0.026856 -0.117453 -0.098007 -0.075083 0.169834 0.110184 0.067023 0.314911 0.042592 -0.048206 -0.005425 0.01164 0.064746 -0.083452 -0.543812 -0.050521 0.145331 -0.011009 -0.143718 -0.129138 -0.272999 -0.006118 -0.136958 -0.121899 0.296022 0.003932 -0.044379 -0.481227 0.142035 -0.084344 -0.024749 0.164568 0.286498 0.044213 -0.046193 0.008195 -0.085517 0.037204 -0.267877 -0.17693 -0.21193 -0.075721 -0.008588 0.144423 0.110494 -0.111408 0.166242 -0.236611 0.051046 0.442807 -0.102995 -0.081616 0.047023 0.039931 -0.010494 0.063192 0.173896 -0.056241 -0.12742
N 0.275428 -0.351327 0.090063 0.034882 -0.071439 0.035204 0.033037 0.124956 -0.012416 -1.534887 0.33287 -0.000721 -0.127974 0.200616 0.298568 -0.007589 0.155986 -1.105475 0.27106 0.078557 -0.009318 0.098115 0.147099 0.030026 -0.051655 -0.064936 0.131192 -0.01868 -0.183982 -0.143972 0.125091 -0.148323 0.035327 0.017381 -0.237186 0.113017 0.072622 0.006356 0.077926 0.280901 -0.061204 0.012609 -0.025713 0.039165 0.004881 -0.092475 0.203942 -0.199484 -0.033081 0.016568 0.093445 -0.070863 0.023681 -0.018252 -0.027525 -0.055737 0.025176 -0.001137 -0.03246 0.058581 -0.038681 0.008397 0.04139 -0.369459 -0.193645 0.134695 -0.002716 -0.144353 0.049916 -0.196827 -0.059907 0.009708 -0.183595 0.030797 -0.117809 -0.022625 -0.066978 0.022753 -0.019903 -0.191783 -0.070118 -0.138059 0.17034 0.048523 0.02406 0.247042 0.391849 -0.299221 -0.323279 -0.091988 0.144591 0.068589 0.051839 0.055773 -0.163614 0.246056 -0.093416 -0.005775 -0.036705 0.124655 -0.012093 -0.154315 0.112899 0.033308 -0.069311 -0.401657 -0.049649 -0.143544 -0.117834 -0.12615 -0.074218 0.332364 -0.079121 0.030231 -0.087161 0.023701 -0.037116 -0.079422 0.044159 -0.067155 -0.062762 -0.034463 -0.000108 0.145806 0.160771 -0.052038 -0.022632 0.113495 -0.005755 0.127129 -0.148258 -0.076826 0.02626 0.029401 -0.182643 -0.019188 -0.016169 -0.005994 -0.010125 0.075338 0.900524 0.004264 -0.267294 0.020801 0.005675 0.106265 0.14427 0.010484 0.079353 0.092307 -0.007159 0.044654 -0.042709 -0.018194 0.15686 0.152075 0.117217 -0.143376 0.042403 0.127257 -0.102214 0.011736 -0.037238 0.194271 0.27149 0.216314 -0.109452 0.113183 -0.255157 0.024652 -0.022261 0.04085 0.137854 0.073872 0.181071 -0.072024 0.027415 -0.061469 -0.226286 -0.004944 -0.052004 -0.0247 0.090432 0.196251 0.24435 -0.067027 0.184771 -0.126047 -0.037897 0.020155 0.247524 0.033672 0.133766 -0.127583 -0.028118 -0.276452 0.104225 0.138496 0.135193 -0.254343 -0.051364 0.263819 0.047686 -0.1416 -0.282744 -0.134912 0.002651 0.045112 0.146697 -0.003163 0.139884 0.185633 -0.023167 0.369052 -0.119866 -0.219141 0.165001 -0.303527 0.417151 -0.073457 0.085092 -0.043837 0.200345 -0.223187 -0.106351 -0.093842 -0.033002 -0.143572 -0.146331 -0.253181 0.156216 0.018769 -0.179715 -0.100194 0.157417 0.15446 -0.00273 0.073976 0.07085 -0.132581 -0.109487 -0.106214 -0.08881 -0.237117 -0.112738 0.226426 0.124837 0.108067 0.34863 0.056907 -0.026683 -0.024229 0.037252 0.053499 -0.133794 -0.51184 -0.041058 0.175787 0.015793 -0.064995 -0.112741 -0.303854 0.079448 -0.125552 -0.059853 0.289281 -0.024032 -0.064548 -0.398994 0.074863 -0.183634 0.083144 0.118819 0.253994 0.095131 -0.045256 -0.015351 -0.085734 0.018488 -0.297551 -0.127834 -0.171826 -0.079129 0.033995 0.134429 0.117369 -0.166023 0.176868 -0.22371 0.076118 0.414823 -0.119279 -0.111262 0.009311 0.029467 0.038174 0.104655 0.149891 -0.104225 -0.168225
R 0.254634 -0.3428 0.081959 0.034517 -0.059913 0.062429 0.037083 0.125025 -0.013508 -1.52067 0.343568 -0.030835 -0.143181 0.205657 0.281292 -0.012978 0.15252 -1.108587 0.265495 0.062372 -0.009647 0.081345 0.137965 0.02644 -0.070028 -0.025882 0.129813 0.011997 -0.178915 -0.106931 0.121382 -0.154367 0.044542 0.016728 -0.25011 0.103168 0.064628 -0.015909 0.072567 0.249024 -0.052946 0.001768 -0.028625 0.034049 -0.015585 -0.08436 0.191346 -0.203326 -0.029646 0.039343 0.099683 -0.087088 0.028345 -0.0264 -0.014987 -0.04743 0.01398 0.003243 -0.022155 0.067506 -0.041455 0.019612 0.05538 -0.35519 -0.17074 0.13884 0.001907 -0.153726 0.049632 -0.205827 -0.03305 0.00464 -0.192188 0.012456 -0.115158 -0.049965 -0.06098 0.021456 -0.039374 -0.192944 -0.075123 -0.146841 0.182341 0.050712 0.034163 0.264624 0.385376 -0.265839 -0.330699 -0.104045 0.130145 0.037733 0.078302 0.06692 -0.162903 0.249208 -0.07483 0.011074 -0.042236 0.117947 -0.001756 -0.157731 0.130552 0.017067 -0.029961 -0.355989 -0.045319 -0.120593 -0.125802 -0.143429 -0.070171 0.313014 -0.071168 0.032962 -0.061043 0.02026 -0.042875 -0.072805 0.048027 -0.055759 -0.049411 -0.044071 0.004477 0.155958 0.129175 -0.038103 -0.052966 0.092917 0.01288 0.13931 -0.139518 -0.041959 0.026135 0.023061 -0.174018 -0.019242 -0.005054 0.009973 -0.029332 0.09181 0.900115 -0.006693 -0.261314 0.035606 0.022234 0.094615 0.123878 0.021195 0.087824 0.099156 -0.001593 0.027706 -0.044245 -0.012598 0.133646 0.139097 0.1278 -0.132889 0.032229 0.129806 -0.096714 0.027666 -0.033585 0.179702 0.299009 0.206628 -0.120371 0.118455 -0.237164 -0.004894 -0.009751 0.035784 0.131402 0.072201 0.182792 -0.097658 0.038555 -0.052383 -0.212403 -3.5e-05 -0.049705 -0.023969 0.076531 0.193454 0.237916 -0.04531 0.1774 -0.122459 -0.046385 0.022077 0.237082 0.018409 0.111838 -0.127909 -0.033263 -0.259223 0.11195 0.139108 0.133979 -0.259864 -0.071712 0.240348 0.048899 -0.131274 -0.276915 -0.130466 0.018539 0.046042 0.144386 -0.000291 0.141522 0.197936 -0.028922 0.352422 -0.103571 -0.215051 0.174214 -0.251379 0.40861 -0.061115 0.067637 -0.033178 0.223225 -0.206587 -0.093802 -0.097896 -0.052918 -0.13885 -0.115629 -0.259174 0.140995 0.017213 -0.19672 -0.102169 0.139923 0.146246 -0.009831 0.067579 0.07496 -0.139274 -0.112381 -0.079471 -0.101016 -0.227862 -0.119408 0.207451 0.120964 0.104091 0.31315 0.05646 -0.014918 -0.037506 0.036929 0.059241 -0.144317 -0.513847 -0.001753 0.171923 -0.003384 -0.042954 -0.139964 -0.299512 0.054355 -0.155849 -0.034055 0.301229 -0.012146 -0.05537 -0.399414 0.086214 -0.117027 0.054839 0.124401 0.240452 0.107929 -0.060245 -0.001279 -0.083856 0.010155 -0.285806 -0.128111 -0.170673 -0.06342 0.037563 0.13956 0.12609 -0.151451 0.166779 -0.235306 0.059993 0.38336 -0.113893 -0.085265 0.039402 0.04431 0.035932 0.119916 0.154999 -0.090355 -0.177763
V 0.244466 -0.369306 0.114347 0.008957 -0.101177 0.076024 0.071318 0.114911 -0.043184 -1.45924 0.452916 -0.084614 -0.111315 0.165506 0.2712 -0.012531 0.172909 -1.126077 0.289757 0.083463 -0.023942 0.064758 0.111302 0.019211 -0.068588 0.001605 0.129348 -0.025688 -0.164557 -0.075257 0.103038 -0.207303 0.048156 0.047649 -0.268553 0.130492 0.039357 -0.038269 0.056091 0.198371 -0.059132 -0.030908 -0.087035 -0.001116 -0.050448 -0.085933 0.203321 -0.174137 -0.042099 0.022586 0.100694 -0.056403 0.054594 0.00544 0.035254 -0.045499 0.041573 0.005394 -0.051022 0.089243 0.00417 0.014433 0.053639 -0.394682 -0.157167 0.148929 0.000111 -0.122899 0.017419 -0.229379 -0.093518 0.011362 -0.175369 0.00773 -0.14298 -0.075035 -0.062697 0.044082 -0.040201 -0.122489 -0.046139 -0.120711 0.150781 0.069723 0.051334 0.293229 0.318614 -0.135626 -0.330004 -0.138592 0.135805 0.002282 0.093764 0.081685 -0.139972 0.299095 -0.058185 -0.009052 -0.0489 0.147293 0.025876 -0.126391 0.166033 0.068664 -0.029443 -0.384234 -0.025122 -0.149959 -0.152911 -0.127118 -0.136705 0.294547 -0.116814 0.001791 -0.078794 0.034725 -0.045153 -0.046398 0.043913 -0.008656 -0.072623 -0.044241 0.040488 0.143254 0.064029 -0.060305 -0.055414 0.066189 0.082603 0.105569 -0.168545 0.0153 0.044319 0.073919 -0.146258 -0.015468 0.018025 0.012363 -0.084316 0.091806 0.886609 -0.033808 -0.284375 0.009942 -0.006151 0.111854 0.067973 -0.02619 0.037181 0.123846 -0.005674 -0.020239 -0.006673 -0.020891 0.088329 0.126767 0.167414 -0.107485 0.065465 0.057087 -0.11872 0.05052 0.002252 0.253716 0.341774 0.201523 -0.126534 0.201618 -0.219646 -0.055845 -0.034761 0.027529 0.163191 0.112714 0.16555 -0.102383 -0.044465 -0.087412 -0.208021 -0.011416 -0.017648 -0.004381 0.093575 0.161753 0.193158 -0.040225 0.17967 -0.125983 -0.062231 -0.002229 0.257958 -0.011025 0.103716 -0.099001 -0.008878 -0.194745 0.125779 0.1573 0.185067 -0.239707 -0.084973 0.220909 0.016487 -0.134296 -0.266893 -0.102081 0.028332 0.096779 0.093251 -0.010022 0.107148 0.139506 -0.014561 0.302344 -0.143762 -0.207007 0.174579 -0.141533 0.462752 -0.068253 0.04632 -0.069838 0.218018 -0.198837 -0.094471 -0.105931 -0.015079 -0.180747 -0.103799 -0.22178 0.144068 0.03141 -0.172709 -0.098331 0.11871 0.125283 -0.002951 0.078478 0.102165 -0.132879 -0.109408 -0.058452 -0.099739 -0.198777 -0.081383 0.177349 0.149212 0.07061 0.275927 0.042797 -0.011913 -0.003653 0.058414 0.009997 -0.156842 -0.505032 -0.016284 0.205597 0.008227 -0.088989 -0.08011 -0.27287 0.038935 -0.16872 -0.023934 0.273745 -0.017568 -0.066718 -0.444096 0.158201 -0.042009 -0.001994 0.125456 0.263435 0.086151 -0.024711 -0.004689 -0.053242 0.022144 -0.306672 -0.139866 -0.133197 -0.03137 -0.00022 0.138126 0.163083 -0.108893 0.182866 -0.189597 0.058281 0.373718 -0.125492 -0.083354 0.021706 0.02981 -0.016097 0.112971 0.14669 -0.041677 -0.156858
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gitextract_orh_ofxx/ ├── GRU.py ├── LICENSE ├── Pipfile ├── README.md ├── data_load.py ├── demo.html ├── demo.py ├── evaluate.py ├── glove.840B.300d.char.txt ├── layers.py ├── model.py ├── params.py ├── process.py ├── requirements.txt ├── setup.sh └── zoneout.py
SYMBOL INDEX (91 symbols across 9 files)
FILE: GRU.py
class SRUCell (line 27) | class SRUCell(RNNCell):
method __init__ (line 35) | def __init__(self, num_units, activation=None, is_training = True, reu...
method output_size (line 41) | def output_size(self):
method state_size (line 45) | def state_size(self):
method __call__ (line 48) | def __call__(self, inputs, state, scope=None):
class GRUCell (line 69) | class GRUCell(RNNCell):
method __init__ (line 72) | def __init__(self,
method state_size (line 87) | def state_size(self):
method output_size (line 91) | def output_size(self):
method __call__ (line 94) | def __call__(self, inputs, state, scope = None):
class gated_attention_Wrapper (line 121) | class gated_attention_Wrapper(RNNCell):
method __init__ (line 122) | def __init__(self,
method state_size (line 147) | def state_size(self):
method output_size (line 151) | def output_size(self):
method __call__ (line 154) | def __call__(self, inputs, state, scope = None):
function linear (line 168) | def linear(args,
FILE: data_load.py
function producer_func (line 17) | def producer_func(func):
class _FuncQueueRunner (line 65) | class _FuncQueueRunner(tf.train.QueueRunner):
method __init__ (line 67) | def __init__(self, func, queue=None, enqueue_ops=None, close_op=None,
method _run (line 77) | def _run(self, sess, enqueue_op, coord=None):
function load_data (line 115) | def load_data(dir_):
function get_dev (line 171) | def get_dev():
function get_batch (line 180) | def get_batch(is_training = True):
FILE: demo.py
function home (line 18) | def home():
function answer (line 24) | def answer():
class Demo (line 38) | class Demo(object):
method __init__ (line 39) | def __init__(self, model):
method demo_backend (line 51) | def demo_backend(self, model, run_event):
FILE: evaluate.py
function f1_and_EM (line 10) | def f1_and_EM(index, ground_truth, passage, dict_):
function normalize_answer (line 27) | def normalize_answer(s):
function f1_score (line 45) | def f1_score(prediction, ground_truth):
function exact_match_score (line 58) | def exact_match_score(prediction, ground_truth):
function metric_max_over_ground_truths (line 62) | def metric_max_over_ground_truths(metric_fn, prediction, ground_truths):
function evaluate (line 70) | def evaluate(dataset, predictions):
FILE: layers.py
function get_attn_params (line 23) | def get_attn_params(attn_size,initializer = tf.truncated_normal_initiali...
function encoding (line 46) | def encoding(word, char, word_embeddings, char_embeddings, scope = "embe...
function apply_dropout (line 52) | def apply_dropout(inputs, size = None, is_training = True):
function bidirectional_GRU (line 69) | def bidirectional_GRU(inputs, inputs_len, cell = None, cell_fn = tf.cont...
function pointer_net (line 105) | def pointer_net(passage, passage_len, question, question_len, cell, para...
function attention_rnn (line 133) | def attention_rnn(inputs, inputs_len, units, attn_cell, bidirection = Tr...
function question_pooling (line 148) | def question_pooling(memory, units, weights, memory_len = None, scope = ...
function gated_attention (line 157) | def gated_attention(memory, inputs, states, units, params, self_matching...
function mask_attn_score (line 171) | def mask_attn_score(score, memory_sequence_length, score_mask_value = -1...
function attention (line 177) | def attention(inputs, units, weights, scope = "attention", memory_len = ...
function cross_entropy (line 204) | def cross_entropy(output, target):
function total_params (line 210) | def total_params():
FILE: model.py
class Model (line 23) | class Model(object):
method __init__ (line 24) | def __init__(self,is_training = True, demo = False):
method encode_ids (line 83) | def encode_ids(self):
method attention_match_rnn (line 135) | def attention_match_rnn(self):
method bidirectional_readout (line 165) | def bidirectional_readout(self):
method pointer_network (line 174) | def pointer_network(self):
method outputs (line 180) | def outputs(self):
method loss_function (line 190) | def loss_function(self):
method summary (line 205) | def summary(self):
function debug (line 220) | def debug():
function test (line 224) | def test():
function main (line 242) | def main():
FILE: params.py
class Params (line 1) | class Params():
FILE: process.py
function str2bool (line 20) | def str2bool(v):
function tokenize_corenlp (line 36) | def tokenize_corenlp(text):
class data_loader (line 41) | class data_loader(object):
method __init__ (line 42) | def __init__(self,use_pretrained = None):
method ind2word (line 61) | def ind2word(self,ids):
method ind2char (line 67) | def ind2char(self,ids):
method process_glove (line 75) | def process_glove(self, wordvecs, dict_, count, emb_size):
method process_json (line 96) | def process_json(self,file_dir,out_dir, write_ = True):
method loop (line 105) | def loop(self, data, dir_ = Params.train_dir, write_ = True):
method process_word (line 133) | def process_word(self,line):
method process_char (line 142) | def process_char(self,line):
method add_to_dict (line 150) | def add_to_dict(self, line):
method realtime_process (line 179) | def realtime_process(self, data):
function load_glove (line 216) | def load_glove(dir_, name, vocab_size):
function reduce_glove (line 247) | def reduce_glove(dir_, dict_):
function find_answer_index (line 272) | def find_answer_index(context, answer):
function normalize_text (line 290) | def normalize_text(text):
function write_file (line 293) | def write_file(indices, dir_, separate = "\n"):
function pad_data (line 297) | def pad_data(data, max_word):
function pad_char_len (line 306) | def pad_char_len(data, max_word, max_char):
function pad_char_data (line 315) | def pad_char_data(data, max_char, max_words):
function get_char_line (line 328) | def get_char_line(line):
function load_target (line 338) | def load_target(dir):
function load_word (line 350) | def load_word(dir):
function load_char (line 364) | def load_char(dir):
function max_value (line 386) | def max_value(inputlist):
function main (line 394) | def main():
FILE: zoneout.py
class ZoneoutWrapper (line 12) | class ZoneoutWrapper(tf.nn.rnn_cell.RNNCell):
method __init__ (line 15) | def __init__(self, cell, state_zoneout_prob, is_training=True, seed=No...
method state_size (line 28) | def state_size(self):
method output_size (line 32) | def output_size(self):
method __call__ (line 35) | def __call__(self, inputs, state, scope=None):
Condensed preview — 16 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (344K chars).
[
{
"path": "GRU.py",
"chars": 8272,
"preview": "# -*- coding: utf-8 -*-\n#/usr/bin/python2\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom _"
},
{
"path": "LICENSE",
"chars": 1069,
"preview": "MIT License\n\nCopyright (c) 2017 Min Sang Kim\n\nPermission is hereby granted, free of charge, to any person obtaining a co"
},
{
"path": "Pipfile",
"chars": 198,
"preview": "[[source]]\n\nurl = \"https://pypi.python.org/simple\"\nverify_ssl = true\nname = \"pypi\"\n\n\n[packages]\n\nnltk = \"*\"\nnumpy = \"*\"\n"
},
{
"path": "README.md",
"chars": 3220,
"preview": "# R-NET: MACHINE READING COMPREHENSION WITH SELF MATCHING NETWORKS\n\nTensorflow implementation of https://www.microsoft.c"
},
{
"path": "data_load.py",
"chars": 7883,
"preview": "# -*- coding: utf-8 -*-\n#/usr/bin/python2\n\nfrom functools import wraps\nimport threading\n\nfrom tensorflow.python.platform"
},
{
"path": "demo.html",
"chars": 1796,
"preview": "<!DOCTYPE html>\n<html>\n <head>\n <meta charset=\"UTF-8\"> \n <script>\n function hit(passage, que"
},
{
"path": "demo.py",
"chars": 2167,
"preview": "#!/usr/bin/env python\n# coding=utf-8\n\nimport tensorflow as tf\nimport bottle\nfrom bottle import route, run\nimport threadi"
},
{
"path": "evaluate.py",
"chars": 4024,
"preview": "\"\"\" Official evaluation script for v1.1 of the SQuAD dataset. \"\"\"\nfrom __future__ import print_function\nfrom collections"
},
{
"path": "glove.840B.300d.char.txt",
"chars": 265233,
"preview": "$ 0.144288 -0.460809 0.205078 0.061188 -0.057054 -0.048921 -0.164738 0.132241 0.139505 -1.45673 0.396798 0.277961 -0.174"
},
{
"path": "layers.py",
"chars": 11834,
"preview": "# -*- coding: utf-8 -*-\n#/usr/bin/python2\n\nimport tensorflow as tf\nimport numpy as np\n\nfrom tensorflow.contrib.rnn impor"
},
{
"path": "model.py",
"chars": 16344,
"preview": "# -*- coding: utf-8 -*-\n#/usr/bin/python2\n\nfrom __future__ import print_function\n\nimport tensorflow as tf\nfrom tqdm impo"
},
{
"path": "params.py",
"chars": 2402,
"preview": "class Params():\n\n # data\n data_size = -1 # -1 to use all data\n num_epochs = 10\n train_prop = 0.9 # Not imple"
},
{
"path": "process.py",
"chars": 14725,
"preview": "# -*- coding: utf-8 -*-\n#/usr/bin/python2\n\nimport cPickle as pickle\nimport numpy as np\nimport json\nimport codecs\nimport "
},
{
"path": "requirements.txt",
"chars": 52,
"preview": "nltk==3.2.4\nnumpy==1.13.1\ntqdm==4.15.0\nspacy==2.0.0\n"
},
{
"path": "setup.sh",
"chars": 272,
"preview": "mkdir data\nwget -P data https://rajpurkar.github.io/SQuAD-explorer/dataset/train-v1.1.json\nwget -P data https://rajpurka"
},
{
"path": "zoneout.py",
"chars": 1587,
"preview": "# from ipywidgets import interact\r\nimport tensorflow as tf\r\n\r\nimport numpy as np\r\n\r\nfrom tensorflow.python.framework imp"
}
]
About this extraction
This page contains the full source code of the minsangkim142/R-net GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 16 files (333.1 KB), approximately 158.9k tokens, and a symbol index with 91 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.