Skip to content

C
CS-728-Assignments
Commit 98dae565 authored by Saswat's avatar Saswat
Browse files
Options

Initial commit

parents
Hide whitespace changes
Inline Side-by-side
Showing with 12049 additions and 0 deletions
assignment1/README.txt 0 → 100644
View file @ 98dae565
Team Members:
1. Singamsetty Sandeep (213050064)
2. Saswat Meher (22m0804)
Requirements:
Pytorch
Python3
Keras
Transformers
Datasets
numpy
scipy
sklearn
tensorflow
sklearn_crfsuite
Hardware Requirements:
>15 GB Ram
For BERT based model: GPU
Setup before running:
preprocesses data can be donwloaded from: https://drive.google.com/drive/folders/13bpmwRF5TV9ssU8eQgwXlvaLvp0nvqvW?usp=sharing
from folder additional. those are (train/test/dev)_data_preprocessed.json
keep them in the same folder as code.
Execution and Reproducing Results:
CRF:
Folder: /code/CRF_NER
File: crf_sklearn.py
To train using test data and predict for test data
Run: python crf_sklearn.py
Make sure the files used in this code, train_data_preprocessed.json etc. exist in the same folder.
You can also try running CRF.py which is an extention Prof. Soumen's code. But will take more that 6Hrs
for 1 epoch in cpu. Was failing to load in GPU because of the size.
LSTM:
Folder: /code/LSTM_NER
File: lstm_ner.py
To train call the train() function.
The test() function is also included to test for one example.
All the predefined parameters are included. (epochs etc.)
Make sure the files used in this code, train_data_preprocessed.json etc. exist in the same folder.
BERT:
Folder: /code/BERT_NER
File: bert_ner.py
To train call the train() function.
The test() function is also included to test for one example.
Note:
All the predefined parameters are included. (epochs etc.)
Make sure the files used in this code, train_data_preprocessed.json etc. exist in the same folder.
Also make sure you have GPU. Also, for seqeval of distilbert-base-uncased, Nvidia libraries are required. Depends on GPU configuration, so not mentioning them here. The model checkpoint used Hugging face API to access the base model, so make sure connection is accessible.
Erasing Concepts from Diffusion Models (ESD)
Our project aims to address concerns regarding large-scale diffusion models producing undesirable output such as sexually inappropriate content or copyrighted artistic styles. We analyze a fine-tuning method that can erase specific visual concepts from pre-trained diffusion model weights, given only the name of the style.
This approach offers several advantages over existing methods. Firstly, we can remove concepts from a diffusion model permanently, rather than modifying the output at inference time. This makes the method more secure and less prone to being circumvented even if a user has access to model weights. Secondly, we want to perform comparative analysis of the ESD method against existing approaches and demonstrate its effectiveness.
Overall, our project seeks to analyze the current novel method for erasing specific visual concepts from diffusion model weights, addressing concerns about the production of undesirable output.
Related Papers:
https://arxiv.org/pdf/2303.07345.pdf
Ideas from:
https://arxiv.org/abs/2207.12598
https://arxiv.org/abs/2004.06030
https://arxiv.org/pdf/2211.05105.pdf SLD
https://proceedings.mlr.press/v162/ravfogel22a.html
https://arxiv.org/abs/2111.08947
https://arxiv.org/abs/2007.15646
https://arxiv.org/abs/2209.02299
https://arxiv.org/abs/1912.03817
https://arxiv.org/abs/2104.08164 for LMs
https://arxiv.org/abs/1911.04933
https://arxiv.org/abs/2303.05699
Timeline:
Week 1:
Studying ESD paper and its references
Week 2:
Training diffusion model on custom constraints
Week 3:
Running various experiments, to analyse performance of our model.
Week 4:
Documentation of experiments and results, and exploring future works.
assignment1/additional/evaluation_of_ner.py 0 → 100644
View file @ 98dae565
import json
def f1(p, r):
if r == 0.:
return 0.
return 2 * p * r / float( p + r )
def loose_macro(true, pred):
num_entities = len(true)
p = 0.
r = 0.
for true_labels, predicted_labels in zip(true, pred):
if len(predicted_labels) > 0:
p += len(set(predicted_labels).intersection(set(true_labels))) / float(len(predicted_labels))
if len(true_labels):
r += len(set(predicted_labels).intersection(set(true_labels))) / float(len(true_labels))
precision = p / num_entities
recall = r / num_entities
return precision, recall, f1( precision, recall)
def loose_micro(true, pred):
num_predicted_labels = 0.
num_true_labels = 0.
num_correct_labels = 0.
for true_labels, predicted_labels in zip(true, pred):
num_predicted_labels += len(predicted_labels)
num_true_labels += len(true_labels)
num_correct_labels += len(set(predicted_labels).intersection(set(true_labels)))
if num_predicted_labels > 0:
precision = num_correct_labels / num_predicted_labels
else:
precision = 0.
recall = num_correct_labels / num_true_labels
return precision, recall, f1( precision, recall)
with open('test_data_predicted_LSTM_2.json') as f: #change the file name as required (in /output folder)
data = json.load(f)
predicted_list = []
for i in range(len(data)):
tags_list = data[i]['tags']
predicted_list+= tags_list
with open('test_data_preprocessed.json') as f: #can be found in /additional folder
data = json.load(f)
true_list = []
for i in range(len(data)):
tags_list = data[i]['tags']
true_list+= tags_list
print(len(predicted_list),len(true_list))
precision,recall,f1_micro = loose_micro(true_list,predicted_list)
print(precision,recall,f1_micro)
precision,recall,f1_macro = loose_macro(true_list,predicted_list)
print(precision,recall,f1_macro)
matched_count = 0
for i in range(len(true_list)):
if true_list[i] == predicted_list[i]:
matched_count+=1
print(matched_count)
assignment1/additional/preprocessing_ner_data.py 0 → 100644
View file @ 98dae565
import json
def tag_list_deducer(tag_sub_list):
res = max(tag_sub_list,key=len)
return res
with open('train.json') as f: #change files as required
data = json.load(f)
for i in range(len(data)):
tags_list = data[i]['tags']
temp_tags_list = []
for item in tags_list:
if isinstance(item,str):
temp_tags_list.append(item)
elif isinstance(item,list):
temp_tags_list.append(tag_list_deducer(item))
data[i]['tags'] = temp_tags_list
with open('train_data_preprocessed.json',"w") as final:
json.dump(data,final,indent=2)
assignment1/code/BERT_NER/bert_ner.py 0 → 100644
View file @ 98dae565
import os
import itertools
import pandas as pd
import numpy as np
from datasets import Dataset
from datasets import load_metric
from transformers import AutoTokenizer
from transformers import AutoModelForTokenClassification, TrainingArguments, Trainer
from transformers import DataCollatorForTokenClassification
import torch
import json
with open('train_data_preprocessed.json') as f:
data = json.load(f)
train_sentences, train_tags = [], []
for i in range(len(data)):
tags_list = data[i]['tags']
sent_list = data[i]['sent']
train_sentences.append(np.array(sent_list))
train_tags.append(np.array(tags_list))
with open('test_data_preprocessed.json') as f:
test_data = json.load(f)
test_sentences, test_tags = [], []
for i in range(len(test_data)):
tags_list = data[i]['tags']
sent_list = data[i]['sent']
test_sentences.append(np.array(sent_list))
test_tags.append(np.array(tags_list))
words, tags = set([]), set([])
for s in train_sentences:
for w in s:
words.add(w)
for ts in train_tags:
for t in ts:
tags.add(t)
label_list = list(tags)
task = "ner"
model_checkpoint = "distilbert-base-uncased"
batch_size = 16
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
def get_all_tokens_and_ner_tags(name):
return pd.concat([get_tokens_and_ner_tags(name)]).reset_index().drop('index', axis=1)
def get_tokens_and_ner_tags(name):
if name=='train':
return pd.DataFrame({'tokens':train_sentences , 'ner_tags':train_tags })
if name=='test':
return pd.DataFrame({'tokens':test_sentences , 'ner_tags':test_tags })
def get_dataset():
train_df = get_all_tokens_and_ner_tags('train')
test_df = get_all_tokens_and_ner_tags('test')
train_dataset = Dataset.from_pandas(train_df)
test_dataset = Dataset.from_pandas(test_df)
return (train_dataset, test_dataset)
train_dataset, test_dataset = get_dataset()
print(train_dataset)
def tokenize_and_align_labels(examples):
label_all_tokens = True
tokenized_inputs = tokenizer(list(examples["tokens"]), truncation=True, is_split_into_words=True)
labels = []
for i, label in enumerate(examples[f"{task}_tags"]):
word_ids = tokenized_inputs.word_ids(batch_index=i)
previous_word_idx = None
label_ids = []
for word_idx in word_ids:
if word_idx is None:
label_ids.append(-100)
elif label[word_idx] == '0':
label_ids.append(0)
previous_word_idx = word_idx
labels.append(label_ids)
tokenized_inputs["labels"] = labels
return tokenized_inputs
train_tokenized_datasets = train_dataset.map(tokenize_and_align_labels, batched=True)
test_tokenized_datasets = test_dataset.map(tokenize_and_align_labels, batched=True)
print(train_tokenized_datasets)
def train():
model = AutoModelForTokenClassification.from_pretrained(model_checkpoint, num_labels=len(label_list))
args = TrainingArguments(
f"test-{task}",
evaluation_strategy = "epoch",
learning_rate=1e-4,
per_device_train_batch_size=batch_size,
per_device_eval_batch_size=batch_size,
num_train_epochs=50,
weight_decay=1e-5,
)
data_collator = DataCollatorForTokenClassification(tokenizer)
metric = load_metric("seqeval")
def compute_metrics(p):
predictions, labels = p
predictions = np.argmax(predictions, axis=2)
true_predictions = [[label_list[p] for (p, l) in zip(prediction, label) if l != -100] for prediction, label in zip(predictions, labels)]
true_labels = [[label_list[l] for (p, l) in zip(prediction, label) if l != -100] for prediction, label in zip(predictions, labels)]
results = metric.compute(predictions=true_predictions, references=true_labels)
return {"precision": results["overall_precision"], "recall": results["overall_recall"], "f1": results["overall_f1"], "accuracy": results["overall_accuracy"]}
trainer = Trainer(
model,
args,
train_dataset=train_tokenized_datasets,
eval_dataset=test_tokenized_datasets,
data_collator=data_collator,
tokenizer=tokenizer,
compute_metrics=compute_metrics
)
trainer.train()
trainer.evaluate()
trainer.save_model('bert.model')
def test():
tokenizer = AutoTokenizer.from_pretrained('./bert.model/')
sentence = 'Delhi is capital of India.'
tokens = tokenizer(sentence)
torch.tensor(tokens['input_ids']).unsqueeze(0).size()
model = AutoModelForTokenClassification.from_pretrained('./bert.model/', num_labels=len(label_list))
predictions = model.forward(input_ids=torch.tensor(tokens['input_ids']).unsqueeze(0), attention_mask=torch.tensor(tokens['attention_mask']).unsqueeze(0))
predictions = torch.argmax(predictions.logits.squeeze(), axis=1)
predictions = [label_list[i] for i in preds]
words = tokenizer.batch_decode(tokens['input_ids'])
print(words)
print(predictions)
#train()
#test()
\ No newline at end of file
assignment1/code/CRF_NER/CRF.py 0 → 100644
View file @ 98dae565
#!/usr/bin/env python
# coding: utf-8
# In[20]:
import json
import os
import numpy as np
from scipy.special import logsumexp
import pandas as pd
from tqdm import tqdm
import matplotlib.pyplot as plt
import tensorflow.compat.v1 as tf
tf.disable_v2_behavior()
print(tf.__version__)
print('1: ', tf.config.list_physical_devices('GPU'))
print('2: ', tf.test.is_built_with_cuda)
print('3: ', tf.test.gpu_device_name())
print('4: ', tf.config.get_visible_devices())
# In[11]:
MAX_LEN = 45
BATCH_SIZE = 64
data_folder = "./"
train_file = "train_data_preprocessed.json"
dev_file = "dev_data_preprocessed.json"
test_file = "test_data_preprocessed.json"
train_file = test_file
#kner_path = os.path.expanduser()
# data = pd.read_csv("raw_data.csv", encoding="latin1")
# #data = data.drop(['POS'], axis =1)
# data = data.fillna(method="ffill")
# data.head(10)
train_data = []
test_data = []
with open(data_folder+ train_file) as f: #change files as required
train_data = json.load(f)
with open(data_folder+ test_file) as f: #change files as required
test_data = json.load(f)
# Print tag stats and prepare tag dictionary.
STATE_INIT = 0
tag_name_to_id = dict()
word_name_to_id = dict()
tag_name_to_id["init"] = STATE_INIT
num_sentences = len(train_data)
data = train_data
print("Performing tokenization:")
for i in tqdm(range(num_sentences)):
for j in range(len(train_data[i]["tags"])):
word = data[i]["sent"][j]
tag = data[i]["tags"][j]
if word not in word_name_to_id:
word_name_to_id[word] = len(word_name_to_id)
if tag not in tag_name_to_id:
tag_name_to_id[tag] = len(tag_name_to_id)
num_sentences += 1
NUM_STATES = len(tag_name_to_id)
NUM_FEATURES = len(word_name_to_id)
print("Number of states: ", NUM_STATES)
print("Number of words: ", NUM_FEATURES)
print("Number of sentence: ", num_sentences)
# tags_counts = data.groupby('Tag').size().reset_index(name='counts')
# for row_num, tag_count_row in tags_counts.iterrows():
# state = len(tag_name_to_id)
# tag = tag_count_row["Tag"]
# #count = tag_count_row["counts"]
# tag_name_to_id[tag] = state
# NUM_STATES = len(tag_name_to_id)
# tags_counts
# In[13]:
#data.groupby("Sentence #")[["Word"]].count().hist(bins=50)
# In[14]:
#print(data.groupby("Sentence #")[["Word"]].count().mean())
#print(data.groupby("Sentence #")[["Word"]].count().max())
# In[15]:
# Number of sentences.
# #sentence_words = data.groupby("Sentence #")[["Word"]].count()
# NUM_INST = sentence_words.count()["Word"]
# NUM_INST
# In[16]:
# Prepare feature dictionary.
# word_str_to_id = dict()
# for word in data.Word.unique():
# word_str_to_id[word] = len(word_str_to_id)
# NUM_FEATURES = len(word_str_to_id)
# NUM_FEATURES
# In[17]:
import numpy as np
class SentenceScanner(object):
def __init__(self, num_states, num_features, max_len, batch_size):
self._num_states = num_states
self._num_features = num_features
self._max_len = max_len
self._batch_size = batch_size
self._labeled_states = np.zeros((self._batch_size, self._max_len, self._num_states), dtype=np.int8)
self._labeled_emits = np.zeros((self._batch_size, self._max_len, self._num_features), dtype=np.int8)
# Also prepare a suffix mask to know where each sequence ended, a (B, T) tensor.
# This will let us ignore padded positions in the loss expression.
self._labeled_masks = np.zeros((self._batch_size, self._max_len), dtype=np.int8)
def __iter__(self):
return self
def get_batch(self):
# Collect instances into ndarrays declared above.
num_sentence = 0
for sentence in data:
num_token = 0
for i in range(len(sentence["sent"])):
xid = word_name_to_id[sentence["sent"][i]]
yid = tag_name_to_id[sentence["tags"][i]]
self._labeled_masks[num_sentence, num_token] = 1
self._labeled_emits[num_sentence, num_token, xid] = 1
self._labeled_states[num_sentence, num_token, yid] = 1
num_token += 1
if num_token >= self._max_len:
break
num_sentence += 1
if num_sentence >= self._batch_size:
yield (self._labeled_masks, self._labeled_emits, self._labeled_states)
self._labeled_masks.fill(0)
self._labeled_emits.fill(0)
self._labeled_states.fill(0)
num_sentence = 0
if num_sentence > 0:
yield (self._labeled_masks, self._labeled_emits, self._labeled_states)
# # TODO Add code to shuffle sentences randomly and sample into train, dev, test folds.
# num_sentences = 9996
# # with tqdm(total=NUM_INST) as pbar:
# # ss = SentenceScanner(NUM_STATES, NUM_FEATURES, MAX_LEN, BATCH_SIZE)
# # for (_masks, _emits, _states) in ss.get_batch():
# # num_sentences += _masks.shape[0]
# # pbar.update(BATCH_SIZE)
# print(num_sentences)
# In[22]:
class ChainCRF(object):
"""Implements linear chain CRF."""
def __init__(self, state_init, num_states, num_features, max_len, batch_size):
self._num_states = num_states
self._num_features = num_features
self._max_len = max_len
self._batch_size = batch_size
# Trainable transition weights. Rows = current state, columns = previous state.
self._edgew = tf.Variable(tf.random_uniform([self._num_states, self._num_states],
dtype=tf.float64, minval=-1., maxval=1.),
trainable=True, name="edgew") # (M, P)
# Trainable emission weights. For starters we will use only lexicalized features.
self._nodew = tf.Variable(tf.random_uniform([self._num_states, self._num_features],
dtype=tf.float64, minval=-1., maxval=1.),
trainable=True, name="nodew") # (M, F)
# Labeled instances.
# Features may not be 1-hot in general. 1-hot state rep may be wasteful.
self._masks = tf.placeholder(tf.float64, shape=(self._batch_size, self._max_len),
name="masks") # (B, T)
self._emits = tf.placeholder(tf.float64, shape=(self._batch_size, self._max_len,
self._num_features), name="emits") # (B, T, F)
self._states = tf.placeholder(tf.float64, shape=(self._batch_size, self._max_len,
self._num_states), name="states") # (B, T, M)
self._pad_states_np = np.zeros((self._batch_size, 1, self._num_states))
self._pad_states_np[:,:, state_init] = 1
pad_states = tf.constant(self._pad_states_np, dtype=tf.float64)
self._prev_states = tf.concat([pad_states, self._states[:,:-1,:] ],
axis=1, name="prev_states") # (B, T, P)
# P = M but we use a distinct symbol to highlight the distinction between previous and current states.
print(self._nodew)
print(self._edgew)
print(self._masks)
print(self._emits)
print(self._states)
print(self._prev_states)
# To look up w \cdot \varphi(x_t, m, p) for all instances in the batch, we need
# corresponding tensor wvarphi_t with shape (B, T, M, P).
# We want wvarphi_t[b, t, p, m] = ( sum_f nodew[m, f] emits[b, t, f] ) + edgew[p, m]
# for all possible combinations of m, p in [M] \times [P], not just the gold sequence.
# The first term results in shape (B, T, M) and the second term results in shape (M, P).
# These have to be expanded to shape (B, T, M, P).
var1 = tf.einsum("btf,mf->btm", self._emits, self._nodew, name="var1") # .... (B, T, M)
print(var1)
var2 = tf.expand_dims(var1, axis=3, name="var2") # .... (B, T, M, 1)
print(var2)
var3 = tf.tile(var2, [1, 1, 1, self._num_states], name="var3") # .... (B, T, M, P)
print(var3)
# edge_weights is (M, P)
var4 = tf.expand_dims(self._edgew, axis=0, name="var4") # (1, M, P)
print(var4)
var5 = tf.tile(var4, [self._max_len, 1, 1], name="var5") # (T, M, P)
print(var5)
var6 = tf.expand_dims(var5, axis=0, name="var6") # (1, T, M, P)
print(var6)
var7 = tf.tile(var6, [self._batch_size, 1, 1, 1], name="var7") # ... (B, T, M, P)
print(var7)
self._wvarphi_t = tf.add(var3, var7, name="wvarphi_t") # .... (B, T, M, P)
print(self._wvarphi_t)
# For given emissions and state labels, find score w \cdot \phi(x, y).
self._scores_t = tf.einsum("btmp,btp,btm->bt", self._wvarphi_t,
self._prev_states, self._states, name="scores_t") # (B,T)
print(self._scores_t)
self._scores = tf.reduce_sum(tf.multiply(self._scores_t, self._masks),
axis=1, name="scores") # ... (B)
print(self._scores)
# Alpha recurrence over time steps.
self._lalpha = tf.Variable(initial_value=np.zeros((self._batch_size, self._num_states)),
trainable=True, name="lalpha_0") # .... (B, M)
print(self._lalpha)
for t in range(self._max_len):
var8 = tf.tile(tf.expand_dims(self._lalpha, axis=1), [1, self._num_states, 1]) # (B, M, P)
next_lalpha = tf.reduce_logsumexp(var8 + self._wvarphi_t[:,t,:,:], # (B, M, P)
axis=2, name="lalpha_"+str(t+1))
mask_t = tf.tile(tf.expand_dims(self._masks[:,t], axis=1), [1, self._num_states])
self._lalpha = tf.multiply(mask_t, next_lalpha) + tf.multiply(1.-mask_t, self._lalpha)
print(self._lalpha)
# For given emissions, find log Z over all possible state label sequences.
self._logz = tf.reduce_logsumexp(self._lalpha, axis=1, name="logz") # ... (B)
print(self._logz)
# We have to maximize scores - logZ i.e. minimize logZ - score.
self._loss = tf.reduce_sum(self._logz - self._scores, name="loss") # ... (B)
print(self._loss)
adamopt = tf.train.AdamOptimizer(learning_rate=0.1)
self._train_op = adamopt.minimize(self._loss, var_list=[self._nodew, self._edgew])
def check_np_scores(self, sess, masks, emitss, statess):
"""
masks, emitss, statess are for a whole batch.
Calculates w \cdot \phi conventionally using numpy to check correctness.
"""
_nodew = sess.run(self._nodew)
_edgew = sess.run(self._edgew)
ans = np.zeros((self._batch_size))
for b in range(self._batch_size):
mask = masks[b,:]
emits = emitss[b,:,:]
states = statess[b,:,:]
prev_states = np.concatenate((self._pad_states_np[b,:,:], states[:-1,:]), axis=0)
potscore = 0
for t in range(self._max_len):
aemit = emits[t,:]
aprev_state = prev_states[t,:]
astate = statess[b,t,:]
nodepot = np.matmul(astate, np.matmul(_nodew, aemit))
edgepot = np.matmul(astate, np.matmul(_edgew, aprev_state))
potscore += (nodepot + edgepot)
ans[b] = potscore
return ans
def check_tf_scores(self, sess, masks, emitss, statess):
tf_scores = sess.run(self._scores, feed_dict = {
self._masks: masks, self._emits: emitss, self._states: statess })
return tf_scores
def check_np_logzs(self, sess, masks, emitss, statess):
"""
Calculates log Z conventionally using numpy to check correctness.
"""
np_wvarphi_t = sess.run(self._wvarphi_t, feed_dict={
self._masks: masks, self._emits: emitss, self._states: statess})
#print("np_wvarphi_t", np_wvarphi_t.shape) # (B, T, M, P)
logzs = np.zeros((self._batch_size))
for b in range(self._batch_size):
np_lalpha = np.zeros((self._num_states)) # (P) or (M)
for t in range(self._max_len):
np_lalpha_next = np.zeros((self._num_states)) # (M)
for m in range(self._num_states):
softsummand = np.zeros((self._num_states)) # (P)
for p in range(self._num_states):
softsummand[p] = np_wvarphi_t[b,t,m,p] + np_lalpha[p]
np_lalpha_next[m] = logsumexp(softsummand)
np_lalpha = np_lalpha_next
logzs[b] = logsumexp(np_lalpha)
return logzs
def check_tf_logzs(self, sess, masks, emitss, statess):
tf_logzs = sess.run(self._logz, feed_dict={
self._masks: masks, self._emits: emitss, self._states: statess})
return tf_logzs
def do_train(self, sess, num_epochs=10):
sess.run(tf.global_variables_initializer())
# TODO Add code to load any partially trained model for warm-start.
chart_batches, chart_losses = list(), list()
fig = plt.figure()
ax = fig.add_subplot(111)
#plt.ion()
fig.show()
fig.canvas.draw()
num_batches = 0
# TODO keep history of loss objectives
for _ in range(num_epochs):
with tqdm(total=num_sentences) as pbar:
ss = SentenceScanner(self._num_states, self._num_features, self._max_len, self._batch_size)
for (masks, emits, states) in ss.get_batch():
num_batches += 1
sess.run(self._train_op, feed_dict = {
self._masks: masks, self._emits: emits, self._states: states })
_logZ = sess.run(self._logz, feed_dict = {
self._masks: masks, self._emits: emits, self._states: states })
_scores = sess.run(self._scores, feed_dict = {
self._masks: masks, self._emits: emits, self._states: states })
_loss = np.sum(_logZ - _scores)
assert _loss >= 0
if (num_batches >= 1):
return
chart_batches.append(num_batches)
chart_losses.append(_loss)
ax.clear()
ax.plot(chart_batches, chart_losses)
fig.canvas.draw()
pbar.update(self._batch_size)
pbar.set_description("%10g" % _loss)
if np.min(_logZ - _scores) < 0:
print("tf_logzs - tf_scores", _logZ - _scores)
np_scores = self.check_np_scores(sess, masks, emits, states)
tf_scores = self.check_tf_scores(sess, masks, emits, states)
print("np_scores - tf_scores", np.linalg.norm(np_scores - tf_scores, ord=np.inf))
np_logzs = ccrf.check_np_logzs(sess, masks, emits, states)
tf_logzs = ccrf.check_tf_logzs(sess, masks, emits, states)
print("np_logzs - tf_logzs", np.linalg.norm(np_logzs - tf_logzs, ord=np.inf))
return
# TODO Add code to decide on ending training, saving model checkpoints.
def decode_viterbi(self, emits, masks=None):
"""
Perform viterbi decoding after training.
emits: Emmision matrix of the input #(B, T, F)
mask: Mask of the emmision matrix #(B, T)
"""
_wvarphi_t = sess.run(self._wvarphi_t, feed_dict = {
self._masks: masks, self._emits: emits}) #(B, T, M, P)
backpointers = []
alphas = _wvarphi_t[:,0,:,STATE_INIT] #(B, M)
for i in range(1, self._max_len):
alphas_t = tf.tile(tf.expand_dims(alphas, axis=2), [1,1,self._num_states]) #(B, M, P)
scores = _wvarphi_t[:,i,:,:] + alphas_t #(B, M, P)
prevmax_vals = tf.math.reduce_max(scores, axis=-1) #(B, M)
prevmax_args = tf.math.argmax(scores, axis=-1) #(B, M)
backpointers.append(prevmax_args)
is_valid = np.expand_dims(masks[:, i], (-1)) #(B,1)
alphas = is_valid * prevmax_vals + (1 - is_valid) * alphas
backpointers = tf.stack(backpointers)
last_max_states = tf.math.argmax(prevmax_vals, axis=1)
print("Backpointer structure: ",backpointers.shape)
decoded_pos_tags = [] #(B,T)
for i in tqdm(range(self._batch_size)):
sent_pos_tag = []
last_post_tag = last_max_states[i]
#print(last_post_tag)
sent_pos_tag.append(last_post_tag)
for j in range(self._max_len-1, 0, -1):
#print(last_post_tag)
#print(j,i,last_post_tag.item())
print("last post tag value: ",last_post_tag)
new_pos_tag = backpointers[j][i][last_post_tag]
sent_pos_tag.append(new_pos_tag)
last_post_tag = new_pos_tag
#print(backpointers[i][j])
#print(last_post_tag)
#print(len(sent_pos_tag))
decoded_pos_tags.append(sent_pos_tag)
return decoded_pos_tags
def get_fold_performance(self):
"""TODO Add code to calculate best labels sequences for current model, compare with gold
sequences, and return a measure of performance."""
print("Performing testing: ")
ss = SentenceScanner(self._num_states, self._num_features, self._max_len, self._batch_size)
with tqdm(total=num_sentences) as pbar:
for (masks, emits, states) in ss.get_batch():
decoded_tags = self.decode_viterbi(emits, masks)
pbar += pbar.update(self._batch_size)
#print(decoded_pos_tags.shape)
ccrf = ChainCRF(STATE_INIT, NUM_STATES, NUM_FEATURES, MAX_LEN, BATCH_SIZE)
save_variables = {
'crf/edge': ccrf._edgew,
'crf/node': ccrf._nodew
}
saver = tf.train.Saver(save_variables)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
ccrf.do_train(sess)
saver.save(sess, 'model/crf')
#ccrf.get_fold_performance()
assignment1/code/CRF_NER/crf_sklearn.py 0 → 100644
View file @ 98dae565
import json
#import torch
from itertools import chain
#import nltk
import sklearn
from sklearn.model_selection import cross_val_predict, cross_val_score
import sklearn_crfsuite
from sklearn_crfsuite import scorers,CRF
from sklearn_crfsuite.metrics import flat_classification_report
from sklearn_crfsuite import metrics
from sklearn.metrics import classification_report
from tqdm import tqdm
data_folder = "./"
train_file = "train_data_preprocessed.json"
dev_file = "dev_data_preprocessed.json"
test_file = "test_data_preprocessed.json"
#train_file = dev_file
train_data = []
test_data = []
with open(data_folder+ train_file) as f: #change files as required
train_data = json.load(f)
with open(data_folder+ test_file) as f: #change files as required
test_data = json.load(f)
# Print tag stats and prepare tag dictionary.
STATE_INIT = 0
tag_name_to_id = dict()
tag_name_to_id["init"] = STATE_INIT
# for i in range(len(data)):
# for j in range(len(data[i]["tags"])):
# tag = data[i]["tags"][j]
# if tag not in tag_name_to_id:
# tag_name_to_id[tag] = len(tag_name_to_id)
NUM_STATES = len(tag_name_to_id)
#print("Number of states", NUM_STATES)
def word2features(sent, i):
word = sent[i]
features = {
'bias': 1.0,
'word.lower()': word.lower(),
'word[-3:]': word[-3:],
'word[-2:]': word[-2:],
'word.isupper()': word.isupper(),
'word.istitle()': word.istitle(),
'word.isdigit()': word.isdigit(),
}
if i > 0:
word1 = sent[i-1]
features.update({
'-1:word.lower()': word1.lower(),
'-1:word.istitle()': word1.istitle(),
'-1:word.isupper()': word1.isupper(),
})
else:
features['BOS'] = True
if i < len(sent)-1:
word1 = sent[i+1]
features.update({
'+1:word.lower()': word1.lower(),
'+1:word.istitle()': word1.istitle(),
'+1:word.isupper()': word1.isupper(),
})
else:
features['EOS'] = True
return features
def sent2features(sent):
return [word2features(sent["sent"], i) for i in range(len(sent["sent"]))]
def sent2labels(sent):
return sent["tags"]
def gen_data():
X_train = []
y_train = []
X_test = []
y_test = []
for sentence in tqdm(train_data, mininterval=1):
X_train.append(sent2features(sentence))
y_train.append(sentence["tags"])
for sentence in tqdm(test_data):
X_test.append(sent2features(sentence))
y_test.append(sentence["tags"])
return X_train,y_train, X_test, y_test
X_train, y_train, X_test, y_test = gen_data()
print(len(X_train))
print(len(y_train))
print(len(X_test))
print(len(y_test))
#Creating the CRF model
crf = CRF(algorithm='lbfgs',
c1=0.25,
c2=0.3,
max_iterations=5,
all_possible_transitions=True,
verbose = True)
crf.fit(X_train,y_train)
y_pred_data = crf.predict(X_test)
y_true = []
y_pred = []
for i in range(len(y_test)):
if (len(y_pred_data[i]) != len(y_test[i])):
print(i,"pred: ",len(y_pred_data[i]))
print(i,"test: ",len(y_test[i]))
print()
y_true.extend(y_test[i])
y_pred.extend(y_pred_data[i])
print(len(y_true))
print(len(y_pred))
report = classification_report(y_true, y_pred)
print("Writing report into: report.txt")
with open('report.txt', 'w') as f:
print(report, file=f)
print(report)
for i in tqdm(range(len(test_data))):
test_data[i]["tags"] = y_pred_data[i]
print("Writing into output into: ", "pred_"+test_file)
with open("test_data_predicted_CRF.json", 'w') as fp:
json.dump(test_data, fp)
assignment1/code/LSTM_NER/lstm_ner.py 0 → 100644
View file @ 98dae565
import numpy as np
import json
with open('train_data_preprocessed.json') as f:
data = json.load(f)
with open('test_data_preprocessed.json') as f:
test_data = json.load(f)
train_sentences, train_tags = [], []
for i in range(len(data)):
tags_list = data[i]['tags']
sent_list = data[i]['sent']
train_sentences.append(np.array(sent_list))
train_tags.append(np.array(tags_list))
test_sentences, test_tags = [], []
for i in range(len(test_data)):
tags_list = data[i]['tags']
sent_list = data[i]['sent']
test_sentences.append(np.array(sent_list))
test_tags.append(np.array(tags_list))
words, tags = set([]), set([])
for s in train_sentences:
for w in s:
words.add(w)
for ts in train_tags:
for t in ts:
tags.add(t)
print(tags)
word2index = {w: i + 2 for i, w in enumerate(list(words))}
word2index['-PAD-'] = 0 # The special value used for padding
word2index['-OOV-'] = 1 # The special value used for OOVs
tag2index = {t: i + 1 for i, t in enumerate(list(tags))}
tag2index['-PAD-'] = 0 # The special value used to padding
train_sentences_X, test_sentences_X, train_tags_y, test_tags_y = [], [], [], []
for s in train_sentences:
s_int = []
for w in s:
try:
s_int.append(word2index[w])
except KeyError:
s_int.append(word2index['-OOV-'])
train_sentences_X.append(s_int)
for s in test_sentences:
s_int = []
for w in s:
try:
s_int.append(word2index[w])
except KeyError:
s_int.append(word2index['-OOV-'])
test_sentences_X.append(s_int)
for s in train_tags:
train_tags_y.append([tag2index[t] for t in s])
for s in test_tags:
test_tags_y.append([tag2index[t] for t in s])
print(train_sentences_X[0])
print(test_sentences_X[0])
print(train_tags_y[0])
print(test_tags_y[0])
MAX_LENGTH = len(max(train_sentences_X, key=len))
print(MAX_LENGTH)
from keras.preprocessing.sequence import pad_sequences
train_sentences_X = pad_sequences(train_sentences_X, maxlen=MAX_LENGTH, padding='post')
test_sentences_X = pad_sequences(test_sentences_X, maxlen=MAX_LENGTH, padding='post')
train_tags_y = pad_sequences(train_tags_y, maxlen=MAX_LENGTH, padding='post')
test_tags_y = pad_sequences(test_tags_y, maxlen=MAX_LENGTH, padding='post')
print(train_sentences_X[0])
print(test_sentences_X[0])
print(train_tags_y[0])
print(test_tags_y[0])
from keras.models import Sequential
from keras.layers import Dense, LSTM, InputLayer, Bidirectional, TimeDistributed, Embedding, Activation
from tensorflow.keras.optimizers import Adam
def to_categorical(sequences, categories):
cat_sequences = []
for s in sequences:
cats = []
for item in s:
cats.append(np.zeros(categories))
cats[-1][item] = 1.0
cat_sequences.append(cats)
return np.array(cat_sequences)
def train():
model = Sequential()
model.add(InputLayer(input_shape=(MAX_LENGTH, )))
model.add(Embedding(len(word2index), 128))
model.add(Bidirectional(LSTM(256, return_sequences=True)))
model.add(TimeDistributed(Dense(len(tag2index))))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy',
optimizer=Adam(0.001),
metrics=['accuracy'])
model.summary()
cat_train_tags_y = to_categorical(train_tags_y, len(tag2index))
print(cat_train_tags_y[0])
model.fit(train_sentences_X, to_categorical(train_tags_y, len(tag2index)), batch_size=128, epochs=200, validation_split=0.2)
scores = model.evaluate(test_sentences_X, to_categorical(test_tags_y, len(tag2index)))
print(f"{model.metrics_names[1]}: {scores[1] * 100}") # acc: 99.09751977804825
model.save('lstm_model')
def logits_to_tokens(sequences, index):
token_sequences = []
for categorical_sequence in sequences:
token_sequence = []
for categorical in categorical_sequence:
token_sequence.append(index[np.argmax(categorical)])
token_sequences.append(token_sequence)
return token_sequences
from tensorflow import keras
def test():
model = keras.models.load_model('lstm_model')
test_samples = [
"Running is very important for IIT Bombay students.".split()
]
print(test_samples)
test_samples_X = []
for s in test_samples:
s_int = []
for w in s:
try:
s_int.append(word2index[w.lower()])
except KeyError:
s_int.append(word2index['-OOV-'])
test_samples_X.append(s_int)
test_samples_X = pad_sequences(test_samples_X, maxlen=MAX_LENGTH, padding='post')
print(test_samples_X)
predictions = model.predict(test_samples_X)
print(predictions, predictions.shape)
print(logits_to_tokens(predictions, {i: t for t, i in tag2index.items()}))
#train()
#test()
assignment1/output/test_data_predicted_BERT.json 0 → 100644
View file @ 98dae565
This source diff could not be displayed because it is too large. You can view the blob instead.
assignment1/output/test_data_predicted_CRF.json 0 → 100644
View file @ 98dae565
[{"id": 0, "sent": ["A", "handful", "of", "professors", "in", "the", "UW", "Department", "of", "Chemistry", "are", "being", "recognized", "by", "the", "American", "Association", "for", "the", "Advancement", "of", "Science", "-LRB-", "AAAS", "-RRB-", "for", "their", "efforts", "and", "contributions", "to", "the", "scientific", "community", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 1, "sent": ["The", "AAAS", "is", "the", "largest", "scientific", "society", "in", "the", "world", "and", "publishes", "journals", "such", "as", "Science", ",", "and", "Science", "Translational", "Medicine", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 2, "sent": ["Last", "month", ",", "the", "organization", "elected", "539", "of", "its", "members", "as", "Fellows", "of", "AAAS", ",", "an", "honor", "that", "recognizes", "members", "``", "whose", "efforts", "on", "behalf", "of", "the", "advancement", "of", "science", "or", "its", "applications", "are", "scientifically", "or", "socially", "distinguished", ",", "''", "according", "to", "the", "association", "'s", "website", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 3, "sent": ["Nine", "of", "the", "individuals", "elected", "are", "UW", "faculty", "members", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 4, "sent": ["``", "Election", "to", "the", "AAAS", "is", "a", "very", "high", "honor", ",", "''", "said", "chemistry", "department", "chair", "Paul", "Hopkins", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 5, "sent": ["Hopkins", "said", "four", "fellow", "elections", "is", "curious", ",", "considering", "the", "size", "of", "his", "faculty", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 6, "sent": ["Of", "these", "nine", "fellows", ",", "four", "of", "them", "are", "part", "of", "the", "UW", "Department", "of", "Chemistry", ":", "Daniel", "T.", "Chiu", ",", "Daniel", "R.", "Gamelin", ",", "Karen", "I.", "Goldberg", ",", "and", "Bruce", "H.", "Robinson", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 7, "sent": ["``", "It", "'s", "an", "honor", "that", "the", "department", "was", "recognized", "in", "this", "way", ",", "''", "Gamelin", "said", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 8, "sent": ["Hopkins", "explained", "this", "year", "'s", "elected", "fellows", "were", "an", "especially", "varied", "group", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 9, "sent": ["Bruce", "Robinson", "is", "a", "theorist", "and", "spectroscopist", ",", "who", "has", "been", "contributing", "to", "the", "field", "for", "the", "past", "40", "years", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 10, "sent": ["Currently", ",", "Gamelin", "is", "researching", "the", "synthesis", "of", "nanomaterials", ";", "Chiu", "is", "working", "on", "manipulation", "of", "matter", "with", "lasers", ";", "and", "Goldberg", "is", "exploring", "inorganic", "catalysis", ",", "where", "she", "seeks", "to", "find", "more", "efficient", "ways", "of", "catalyzing", "chemical", "reactions", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 11, "sent": ["Newly", "elected", "fellows", "are", "invited", "to", "attend", "the", "annual", "Fellows", "Forum", ",", "held", "this", "year", "in", "Vancouver", ",", "B.C", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 12, "sent": ["The", "Fellows", "Forum", ",", "concerned", "in", "part", "with", "the", "induction", "of", "newly", "elected", "fellows", ",", "is", "just", "one", "event", "of", "the", "association", "'s", "annual", "meeting", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 13, "sent": ["It", "will", "take", "place", "Feb.", "16", "--", "20", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 14, "sent": ["Some", "of", "the", "UW", "'s", "Fellows", "believe", "these", "nominations", "are", "a", "testament", "to", "the", "faculty", "'s", "success", "despite", "financial", "troubles", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 15, "sent": ["``", "This", "speaks", "very", "highly", "of", "the", "quality", "of", "hiring", "we", "do", "in", "spite", "of", "the", "budget", "problems", ",", "''", "said", "professor", "Charles", "Campbell", ",", "who", "was", "elected", "as", "an", "Fellow", "in", "2010", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 16, "sent": ["Other", "newly", "elected", ",", "UW-based", "fellows", "include", "E.", "Virginia", "Armbrust", "of", "the", "School", "of", "Oceanography", ",", "Neil", "M.", "Nathanson", "of", "the", "Department", "of", "Pharmacology", ",", "Danny", "Shen", "and", "Jashvant", "Unadkat", "of", "the", "School", "of", "Pharmacy", ",", "and", "Michael", "Schick", "of", "the", "Department", "of", "Physics", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 17, "sent": ["A", "federal", "grand", "jury", "has", "indicted", "eight", "people", "suspected", "of", "operating", "a", "human-trafficking", "ring", "for", "interstate", "prostitution", "from", "a", "Korean", "nightclub", "in", "Federal", "Way", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 18, "sent": ["Prosecutors", "say", "the", "owner", ",", "manager", "and", "others", "at", "the", "Blue", "Moon", ",", "31140", "Pacific", "Highway", "South", ",", "were", "recruiting", "Korean", "women", ",", "many", "from", "overseas", ",", "to", "work", "as", "``", "hostesses", ".", "''"], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 19, "sent": ["The", "women", "were", "expected", "to", "entertain", "men", "and", "set", "up", "subsequent", "meetings", "for", "paid", "sex", "to", "repay", "their", "travel", "and", "living", "expenses", ",", "the", "U.S.", "Attorney", "'s", "Office", "announced", "Friday", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 20, "sent": ["The", "grand", "jury", "issued", "a", "12-count", "indictment", "Wednesday", "against", "eight", "men", "and", "women", "associated", "with", "the", "Blue", "Moon", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 21, "sent": ["Federal", "agents", "served", "search", "warrants", "on", "the", "Blue", "Moon", "and", "several", "homes", "Thursday", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 22, "sent": ["The", "club", "was", "closed", "Friday", "and", "might", "not", "reopen", ",", "federal", "officials", "said", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 23, "sent": ["The", "indictment", "is", "the", "result", "of", "a", "two-year", ",", "multi-agency", "investigation", "headed", "by", "the", "U.S.", "Immigration", "and", "Customs", "Enforcement", "'s", "homeland", "security", "investigations", "unit", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 24, "sent": ["``", "The", "organizers", "of", "this", "criminal", "scheme", "exploited", "vulnerable", "young", "women", "to", "satisfy", "their", "greed", ",", "''", "U.S.", "Attorney", "Jenny", "A.", "Durkan", "said", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 25, "sent": ["Agents", "arrested", "six", "of", "the", "accused", "Thursday", "morning", ",", "including", "the", "club", "'s", "owner", ",", "Chang", "Young", "Kim", ",", "a", "58-year-old", "Milton", "resident", ";", "and", "his", "wife", ",", "35-year-old", "Yeun", "Jeong", "Mun", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 26, "sent": ["Investigators", "suspect", "Mun", "worked", "as", "a", "former", "madam", "at", "the", "club", ",", "prosecutors", "said", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 27, "sent": ["Also", "arrested", "Thursday", "were", ":", "Miyoung", "Roberts", ",", "40", ",", "of", "Federal", "Way", ";", "accused", "of", "being", "the", "current", "madam", "at", "the", "club", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 28, "sent": ["Jung", "San", "So", ",", "55", ",", "of", "Seattle", ";", "suspected", "of", "being", "the", "current", "club", "manager", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 29, "sent": ["Raymond", "Jung", ",", "51", ",", "of", "Federal", "Way", ";", "accused", "of", "leasing", "apartments", "where", "the", "women", "were", "housed", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 30, "sent": ["Kwang", "Frank", "Lee", ",", "57", ",", "of", "Federal", "Way", ";", "accused", "of", "providing", "money", "to", "finance", "a", "bogus", "marriage", "tied", "to", "the", "scheme", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 31, "sent": ["During", "court", "appearances", "Thursday", ",", "the", "six", "defendants", "were", "ordered", "held", "without", "bond", "pending", "detention", "hearings", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 32, "sent": ["Two", "other", "defendants", "--", "Sung", "Hee", "Han", "and", "Hee", "Jae", "Cho", ",", "both", "40", "and", "of", "Federal", "Way", "--", "still", "are", "sought", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 33, "sent": ["Han", "is", "suspected", "of", "being", "an", "assistant", "madam", "at", "the", "Blue", "Moon", ",", "and", "Cho", "is", "a", "former", "manager", ",", "according", "to", "prosecutors", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 34, "sent": ["Investigators", "suspect", "Cho", "is", "in", "the", "Los", "Angeles", "area", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 35, "sent": ["Agents", "began", "investigating", "the", "club", "in", "November", "2009", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 36, "sent": ["Seattle", "police", "detectives", "had", "been", "investigating", "an", "unrelated", "embezzlement", "scheme", "when", "they", "received", "information", "on", "the", "sex-trafficking", "ring", "and", "passed", "it", "along", "to", "the", "federal", "agents", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 37, "sent": ["In", "addition", "to", "other", "charges", ",", "prosecutors", "suspect", "Kim", ",", "the", "club", "'s", "owner", ",", "tried", "to", "bribe", "an", "undercover", "officer", "in", "the", "case", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 38, "sent": ["Kim", "offered", "$", "15,000", "in", "exchange", "for", "a", "heads-up", "on", "law", "enforcement", "inspections", "and", "cooperation", "from", "immigration", "authorities", "to", "allow", "undocumented", "Korean", "women", "to", "come", "to", "the", "United", "States", ",", "prosecutors", "said", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 39, "sent": ["Kim", "has", "been", "charged", "with", "five", "crimes", "related", "to", "the", "bribery", "accusations", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 40, "sent": ["Seattle", "City", "Council", "members", "said", "Friday", "they", "are", "troubled", "that", "Mayor", "Mike", "McGinn", "would", "hire", "a", "consultant", "to", "advise", "the", "city", "on", "the", "development", "of", "a", "new", ",", "state-of-the-art", "sports", "facility", "that", "could", "draw", "an", "NBA", "team", "back", "to", "Seattle", "--", "without", "conferring", "with", "them", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 41, "sent": ["But", "if", "they", "-LSB-", "the", "Mayor", "'s", "Office", "-RSB-", "felt", "this", "was", "important", "enough", "to", "enter", "into", "a", "contract", ",", "I", "think", "it", "would", "have", "been", "appropriate", "to", "notify", "the", "council", "at", "that", "point", ",", "''", "said", "Councilmember", "Richard", "Conlin", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 42, "sent": ["McGinn", "agreed", "to", "a", "$", "19,500-per-month", "contract", "in", "July", "with", "a", "nationally", "prominent", "sports-facilities", "consultant", ",", "Carl", "Hirsh", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 43, "sent": ["McGinn", "is", "allowed", "to", "spend", "up", "to", "$", "250,000", "on", "contracts", "before", "he", "'s", "required", "to", "notify", "the", "council", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 44, "sent": ["The", "council", "knew", "nothing", "of", "Hirsh", "'s", "hiring", ",", "nor", "of", "the", "mayor", "'s", "discussions", "with", "a", "potential", "investment", "group", "that", "has", "acquired", "property", "in", "the", "Sodo", "District", ",", "until", "The", "Seattle", "Times", "in", "December", "was", "about", "to", "report", "that", "the", "city", "was", "examining", "an", "''", "opportunity", "''", "to", "bring", "an", "NBA", "team", "to", "Seattle", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 45, "sent": ["''", "It", "would", "have", "been", "nice", "to", "know", "about", "it", "sooner", ",", "even", "though", "I", "understand", "they", "do", "n't", "have", "a", "firm", "proposal", ",", "''", "said", "Councilmember", "Sally", "Clark", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 46, "sent": ["McGinn", "said", "Friday", "that", "his", "office", "was", "approached", "last", "year", "by", "''", "a", "private", "party", "interested", "in", "making", "a", "significant", "investment", "to", "construct", "a", "new", "arena", "in", "Seattle", ".", "''"], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 47, "sent": ["The", "Times", "also", "reported", "in", "December", "that", "the", "investment", "group", "is", "headed", "by", "San", "Francisco", "hedge-fund", "manager", "Christopher", "Hansen", ",", "who", "has", "family", "ties", "to", "Seattle", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 48, "sent": ["McGinn", "said", "he", "assembled", "an", "internal", "city", "team", "to", "evaluate", "the", "proposal", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 49, "sent": ["He", "said", "he", "and", "the", "team", "reached", "the", "conclusion", "that", "additional", "expertise", "was", "warranted", ",", "including", "hiring", "Hirsh", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 50, "sent": ["The", "city", "also", "hired", "a", "local", "bond", "attorney", ",", "Hugh", "Spitzer", ",", "in", "September", "to", "advise", "it", "on", "financial", "and", "legal", "issues", ",", "including", "Initiative", "91", ",", "the", "2006", "voter-approved", "initiative", "that", "requires", "the", "city", "to", "earn", "a", "return", "on", "investment", "for", "any", "sports", "venue", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 51, "sent": ["The", "Seattle", "Times", "requested", "copies", "of", "the", "consultants", "'", "contracts", "under", "the", "state", "Public", "Records", "Act", "in", "December", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 52, "sent": ["McGinn", "'s", "office", "initially", "said", "the", "documents", "would", "not", "be", "released", "until", "February", ",", "but", "changed", "course", "and", "made", "them", "available", "Friday", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 53, "sent": ["''", "My", "direction", "to", "city", "staff", "and", "our", "experts", "is", "to", "focus", "on", "two", "goals", ":", "1", "-RRB-", "Seriously", "explore", "and", "consider", "the", "opportunity", ",", "and", "2", "-RRB-", "Ensure", "that", "taxpayers", "and", "the", "city", "of", "Seattle", "are", "protected", ",", "particularly", "in", "light", "of", "the", "public", "'s", "direction", "through", "I-91", ",", "''", "McGinn", "said", "in", "an", "emailed", "statement", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 54, "sent": ["McGinn", "reiterated", "that", "the", "city", "has", "not", "received", "a", "concrete", "proposal", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 55, "sent": ["If", "it", "does", ",", "he", "said", ",", "''", "consulting", "with", "the", "City", "Council", "will", "be", "my", "first", "step", "in", "moving", "forward", ".", "''"], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 56, "sent": ["Clark", "said", "she", "appreciated", "that", "the", "contract", "with", "Hirsh", "included", "protecting", "the", "city", "'s", "financial", "interest", "and", "evaluating", "any", "proposal", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 57, "sent": ["''", "We", "know", "that", "the", "Sonics", "left", "an", "empty", "arena", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 58, "sent": ["Clark", "asked", "."], "tags": ["O", "O", "O"]}, {"id": 59, "sent": ["Hirsh", "worked", "for", "former", "Sonics", "owner", "Howard", "Schultz", ",", "when", "the", "team", "was", "evaluating", "how", "to", "make", "KeyArena", "financially", "profitable", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 60, "sent": ["Schultz", "sold", "the", "team", "in", "2006", "to", "Clay", "Bennett", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 61, "sent": ["Bennett", "said", "the", "city-owned", "arena", "lacked", "the", "amenities", "to", "support", "an", "NBA", "franchise", "and", "moved", "the", "team", "to", "Oklahoma", "City", "after", "failing", "to", "secure", "a", "new", "arena", "here", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 62, "sent": ["''", "I", "understand", "the", "challenges", "of", "KeyArena", "and", "the", "economics", "of", "the", "NBA", "and", "NHL", ",", "''", "Hirsh", "said", "Friday", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 63, "sent": ["Hirsh", ",", "managing", "partner", "of", "Stafford", "Sports", "in", "New", "Jersey", ",", "has", "advised", "the", "San", "Antonio", "Spurs", "through", "construction", "of", "their", "new", "arena", ",", "the", "AT&T", "Center", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 64, "sent": ["He", "worked", "with", "the", "city", "of", "Orlando", "to", "negotiate", "an", "agreement", "with", "the", "Orlando", "Magic", "for", "a", "new", "downtown", "arena", "and", "with", "senior", "management", "planning", "a", "new", "Madison", "Square", "Garden", "in", "New", "York", "City", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 65, "sent": ["Hirsh", "estimated", "it", "would", "cost", "$", "400", "million", "to", "build", "a", "new", "arena", ",", "although", "the", "NBA", "'s", "New", "Jersey", "Nets", "will", "spend", "$", "800", "million", "on", "one", "in", "Brooklyn", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 66, "sent": ["A", "large", "portion", "of", "that", "was", "the", "cost", "of", "land", ",", "Hirsh", "said", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 67, "sent": ["He", "said", "an", "arena", "could", "be", "built", "on", "as", "little", "as", "7", "to", "8", "acres", ",", "which", "is", "about", "the", "size", "of", "the", "parcel", "the", "Hansen", "investment", "group", "has", "shown", "an", "interest", "in", "acquiring", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 68, "sent": ["A", "limited", "liability", "corporation", "headed", "by", "Hansen", "recently", "purchased", "3", "acres", "on", "the", "east", "side", "of", "Occidental", "Avenue", "South", "between", "South", "Massachusetts", "and", "South", "Holgate", "streets", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 69, "sent": ["Hirsh", "pointed", "to", "San", "Antonio", "as", "an", "example", "of", "a", "small-market", "city", "making", "a", "new", "arena", "pencil", "out", "financially", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 70, "sent": ["The", "AT&T", "Center", "is", "home", "to", "three", "teams", "--", "the", "Spurs", ",", "the", "WNBA", "'s", "Silver", "Stars", "and", "the", "American", "Hockey", "League", "'s", "Rampage", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 71, "sent": ["The", "building", "was", "a", "partnership", "between", "the", "city", "and", "the", "Spurs", ",", "with", "San", "Antonio", "voters", "approving", "a", "visitor", "'s", "tax", "on", "hotels", ",", "motels", "and", "rental", "cars", "to", "finance", "three-fourths", "of", "the", "costs", "and", "the", "team", "contributing", "the", "rest", ",", "said", "Rick", "Pych", ",", "president", "of", "business", "operations", "for", "San", "Antonio", "Spurs", "Sports", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 72, "sent": ["Hirsh", "said", "many", "pieces", "remain", "to", "be", "put", "together", "to", "make", "a", "new", "arena", "work", "in", "Seattle", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 73, "sent": ["A", "deal", "also", "might", "help", "resurrect", "the", "political", "fortunes", "of", "McGinn", ",", "who", "in", "August", "lost", "the", "fight", "over", "the", "waterfront", "tunnel", ",", "which", "he", "stridently", "opposed", ",", "and", "suffered", "defeat", "of", "a", "proposed", "$", "60", "vehicle-license", "fee", ",", "which", "he", "favored", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 74, "sent": ["Christian", "Sinderman", ",", "a", "political", "consultant", ",", "said", "that", "while", "the", "number", "of", "people", "who", "want", "professional", "basketball", "returned", "to", "Seattle", "is", "high", ",", "the", "number", "who", "think", "it", "'s", "essential", "is", "low", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 75, "sent": ["Building", "a", "new", "arena", "and", "bringing", "a", "team", "back", "''", "is", "not", "a", "political", "game-changer", ",", "''", "Sinderman", "said", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 76, "sent": ["Homeless", "children", "in", "the", "U-District", "will", "get", "the", "opportunity", "to", "show", "their", "art", "pieces", "in", "an", "exhibit", "held", "at", "the", "UW", "School", "of", "Social", "Work", "this", "winter", "quarter", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 77, "sent": ["The", "pieces", "were", "compiled", "at", "the", "Sanctuary", "Art", "Center", ",", "a", "non-profit", ",", "University", "Lutheran", "Church-based", "organization", "designed", "to", "teach", "creativity", "to", "youth", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 78, "sent": ["``", "Some", "art", "pieces", "are", "very", "telling", ",", "''", "said", "Jamie", "Lee", ",", "director", "of", "development", "for", "the", "Sanctuary", "Art", "Center", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 79, "sent": ["The", "Sanctuary", "Art", "Center", "also", "has", "an", "education", "program", ",", "which", "is", "partnered", "with", "the", "Interagency", "Academy", ",", "an", "alternative", "school", "in", "Seattle", "that", "allows", "young", "adults", "to", "get", "credit", "toward", "their", "GEDs", "by", "participating", "in", "the", "program", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 80, "sent": ["The", "opening", "reception", "for", "the", "art", "exhibit", "was", "held", "at", "the", "UW", "School", "of", "Social", "Work", "on", "Jan.", "11", ",", "and", "the", "showing", "continues", "until", "March", "15", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 81, "sent": ["Madeline", "Galbraith", ",", "co-chair", "of", "the", "art", "committee", "at", "the", "School", "of", "Social", "Work", ",", "said", "homeless", "youth", "do", "n't", "usually", "get", "to", "show", "their", "artwork", "to", "the", "public", ",", "and", "most", "of", "the", "art", "pieces", "are", "very", "powerful", "and", "passionate", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 82, "sent": ["Some", "youth", "face", "issues", "with", "dysfunctional", "homes", ",", "the", "trauma", "of", "everyday", "life", ",", "and", "living", "on", "the", "streets", ",", "Galbraith", "said", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 83, "sent": ["Michael", "Winans", ",", "co-chair", "of", "the", "art", "committee", "at", "the", "UW", ",", "believes", "art", "has", "the", "ability", "to", "soak", "up", "bad", "memories", "and", "thoughts", "for", "these", "youth", "and", "work", "as", "an", "outlet", "for", "their", "life", "struggles", ",", "``", "Art", "can", "absorb", "time", "and", "mind", "and", "help", "-LSB-", "kids", "-RSB-", "get", "away", "from", "their", "everyday", "lives", ",", "''", "said", "Winans", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 84, "sent": ["For", "the", "art", "pieces", "on", "sale", ",", "90", "percent", "of", "the", "profits", "go", "to", "the", "young", "artists", "and", "the", "rest", "goes", "to", "the", "Sanctuary", "organization", ",", "whose", "mission", "is", "``", "to", "provide", "a", "safe", ",", "warm", ",", "and", "calm", "environment", "for", "homeless", "and", "street", "involved", "youth", "to", "experience", "creativity", "and", "success", "through", "the", "use", "of", "various", "artistic", "media", ".", "''"], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 85, "sent": ["``", "-LSB-", "The", "Art", "Center", "-RSB-", "is", "a", "place", "to", "go", "for", "children", "to", "create", "something", ",", "get", "off", "the", "streets", ",", "and", "clean", "up", ",", "''", "Galbraith", "said", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 86, "sent": ["Lee", "said", "the", "Sanctuary", "Art", "Center", "gives", "children", "the", "opportunity", "to", "connect", "with", "their", "community", "through", "their", "art", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 87, "sent": ["Art", "shows", "by", "this", "organization", "are", "held", "all", "over", "the", "Seattle", "area", "sporadically", "throughout", "the", "year", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 88, "sent": ["Sets", "of", "drums", "and", "other", "instruments", "are", "available", "at", "the", "art", "center", ",", "which", "will", "also", "hold", "a", "benefit", "concert", "Feb.", "14", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 89, "sent": ["``", "-LSB-", "Sanctuary", "-RSB-", "teaches", "creativity", "as", "an", "outlet", "for", "the", "stresses", "and", "the", "things", "youth", "are", "going", "through", ",", "''", "Lee", "said", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 90, "sent": ["Washington", "women", "'s", "head", "basketball", "coach", "Kevin", "McGuff", "anticipated", "his", "team", "'s", "depth", "would", "be", "one", "of", "its", "major", "strengths", "entering", "this", "season", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 91, "sent": ["A", "recent", "spate", "of", "injuries", ",", "though", ",", "has", "suddenly", "left", "the", "Huskies", "surprisingly", "thin", "in", "the", "frontcourt", "going", "into", "Saturday", "'s", "game", "against", "Washington", "State", "in", "Pullman", ",", "Wash.", ",", "where", "the", "UW", "will", "attempt", "to", "extend", "its", "32-game", "winning", "streak", "against", "the", "Cougars", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 92, "sent": ["The", "biggest", "cause", "for", "concern", "for", "McGuff", "is", "the", "bruised", "hamstring", "Regina", "Rogers", "suffered", "against", "Utah", "last", "Saturday", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 93, "sent": ["Rogers", ",", "the", "UW", "'s", "leading", "scorer", "-LRB-", "16.2", "points", "per", "game", "-RRB-", "and", "rebounder", "-LRB-", "7.8", "-RRB-", ",", "will", "be", "a", "game-time", "decision", "against", "Washington", "State", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 94, "sent": ["``", "I", "do", "n't", "want", "to", "think", "about", "that", "right", "now", ",", "''", "Rogers", "said", "of", "potentially", "missing", "the", "game", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 95, "sent": ["Alone", ",", "Rogers", "'", "potential", "absence", "would", "be", "a", "major", ",", "but", "bearable", "blow", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 96, "sent": ["But", ",", "when", "coupled", "with", "the", "ACL", "tear", "suffered", "by", "forward", "Marjie", "Heard", "earlier", "this", "week", "and", "the", "increasing", "likelihood", "that", "freshman", "forward", "Talia", "Walton", "will", "miss", "the", "rest", "of", "the", "season", "and", "pursue", "a", "medical", "redshirt", ",", "the", "Huskies", "might", "be", "left", "with", "just", "two", "players", "taller", "than", "6-foot", "that", "are", "ready", "to", "go", "against", "Washington", "State", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 97, "sent": ["If", "Rogers", "is", "unable", "to", "play", ",", "much", "of", "the", "onus", "of", "attempting", "to", "replace", "her", "minutes", "would", "fall", "on", "two", "seniors", ",", "Mackenzie", "Argens", "and", "Mollie", "Williams", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 98, "sent": ["Argens", "has", "been", "a", "consistent", "second", "banana", "in", "the", "past", "two", "weeks", ",", "but", "would", "probably", "be", "counted", "on", "to", "up", "her", "scoring", "against", "the", "Cougars", "with", "Rogers", "out", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 99, "sent": ["Williams", ",", "meanwhile", ",", "has", "been", "part", "of", "the", "UW", "'s", "revolving", "door", "at", "power", "forward", ",", "seeing", "major", "minutes", "some", "games", "and", "spending", "the", "majority", "of", "the", "game", "on", "the", "bench", "in", "others", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 100, "sent": ["With", "Heard", "done", "for", "the", "season", ",", "though", ",", "she", "figures", "to", "become", "a", "mainstay", "in", "the", "UW", "rotation", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 101, "sent": ["The", "Huskies", "could", "n't", "have", "picked", "a", "worse", "time", "to", "get", "bitten", "by", "the", "injury", "bug", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 102, "sent": ["After", "playing", "a", "deep", ",", "experienced", "Cougar", "team", "Saturday", ",", "the", "UW", "will", "begin", "preparing", "for", "its", "toughest", "road", "trip", "of", "the", "season", ",", "into", "the", "Bay", "Area", "to", "play", "No.", "4", "Stanford", "and", "Cal", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 103, "sent": ["But", "for", "now", ",", "the", "focus", "is", "on", "the", "Cougars", "and", "extending", "that", "famous", "winning", "streak", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 104, "sent": ["McGuff", ",", "though", ",", "cautioned", "putting", "too", "much", "emphasis", "on", "the", "streak", "in", "his", "first", "foray", "into", "the", "cross-state", "rivalry", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 105, "sent": ["Washington", "State", "'s", "an", "excellent", "team", ",", "and", "we", "'ll", "have", "our", "hands", "full", "with", "them", "based", "on", "what", "they", "do", "on", "the", "court", ".", "''"], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 106, "sent": ["The", "Cougars", ",", "led", "by", "former", "UW", "head", "coach", "June", "Daugherty", ",", "have", "six", "different", "players", "averaging", "between", "6.4", "and", "9.4", "points", "per", "game", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 107, "sent": ["Unlike", "the", "Huskies", ",", "who", "of", "late", "have", "mainly", "relied", "on", "Rogers", "and", "point", "guard", "Jazmine", "Davis", "to", "carry", "the", "scoring", "load", ",", "they", "have", "no", "real", "offensive", "pecking", "order", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 108, "sent": ["That", "fact", ",", "as", "McGuff", "discussed", ",", "can", "make", "them", "a", "challenge", "to", "prepare", "for", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 109, "sent": ["If", "Rogers", "is", "in", "the", "game", ",", "the", "Huskies", "will", "be", "much", "better", "equipped", "to", "match", "the", "Cougars", "in", "that", "aspect", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 110, "sent": ["If", "she", "is", "unable", "to", "play", ",", "the", "UW", "will", "likely", "have", "to", "try", "to", "beat", "Washington", "State", "at", "its", "own", ",", "well-balanced", "game", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 111, "sent": ["Outside", "Safeway", "in", "the", "U-District", ",", "students", "pass", "by", "the", "man", "sitting", "by", "the", "doors", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 112, "sent": ["''", "Real", "Change", "?", "''"], "tags": ["O", "O", "O", "O", "O"]}, {"id": 113, "sent": ["Known", "simply", "as", "``", "the", "Real", "Change", "guy", "''", "to", "most", ",", "Edward", "McClain", "has", "been", "sitting", "at", "his", "spot", "outside", "the", "grocery", "store", "for", "18", "years", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 114, "sent": ["Before", "he", "was", "``", "the", "Real", "Change", "guy", ",", "''", "McClain", "was", "a", "student", ",", "a", "caseworker", ",", "and", "a", "cook", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 115, "sent": ["He", "was", "born", "in", "Jackson", ",", "Miss.", ",", "but", "was", "raised", "in", "Chicago", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 116, "sent": ["He", "has", "a", "bachelor", "'s", "degree", "in", "political", "science", "and", "sociology", "from", "Northern", "Illinois", "University", "and", "other", "credit", "hours", "in", "microeconomics", "from", "Concordia", "University", "in", "Canada", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 117, "sent": ["He", "was", "a", "caseworker", "in", "Minnesota", "but", "left", "the", "job", "because", "he", "found", "himself", "perpetually", "sick", "from", "the", "environments", "in", "which", "he", "worked", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 118, "sent": ["After", "18", "years", ",", "McClain", "is", "still", "consistently", "one", "of", "the", "top", "sellers", "for", "Real", "Change", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 119, "sent": ["``", "Ed", "is", "one", "of", "our", "greatest", "success", "stories", ",", "''", "Harris", "said", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 120, "sent": ["Employees", "of", "Real", "Change", "work", "as", "their", "own", "bosses", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 121, "sent": ["For", "McClain", ",", "this", "means", "he", "does", "not", "have", "a", "set", "work", "schedule", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 122, "sent": ["``", "Makes", "no", "sense", "to", "come", "sit", "here", "and", "walk", "away", "with", "$", "10", "when", "I", "could", "walk", "away", "with", "$", "100", ",", "''", "McClain", "said", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 123, "sent": ["The", "best", "part", "of", "his", "job", "is", "that", "there", "is", "never", "a", "dull", "moment", ",", "McClain", "said", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 124, "sent": ["Sara", "Osborne", ",", "Safeway", "'s", "public", "and", "government", "affairs", "director", ",", "said", "the", "store", "has", "had", "one", "or", "two", "complaints", "over", "the", "years", "regarding", "customers", "disliking", "McClain", "'s", "solicitation", "outside", "the", "store", ",", "but", "she", "said", "it", "has", "been", "years", "since", "they", "'ve", "received", "a", "complaint", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 125, "sent": ["Osborne", "said", "the", "customers", "know", "McClain", "and", "the", "employees", "get", "along", "with", "him", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 126, "sent": ["She", "said", "there", "have", "been", "cases", "in", "which", "McClain", "will", "chase", "down", "shoplifters", "from", "Safeway", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 127, "sent": ["``", "-LSB-", "McClain", "-RSB-", "has", "been", "a", "fixture", "in", "the", "community", "for", "a", "long", "time", ",", "''", "Osborne", "said", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 128, "sent": ["McClain", "began", "his", "work", "outside", "Safeway", "in", "the", "beginning", "of", "his", "50s", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 129, "sent": ["Despite", "being", "past", "retirement", "age", "now", ",", "McClain", "plans", "to", "continue", "working", "as", "long", "as", "he", "is", "healthy", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 130, "sent": ["The", "Seattle", "Coalition", "on", "Homelessness", "counted", "2,442", "people", "sleeping", "on", "the", "streets", "of", "Seattle", "last", "year", "in", "its", "annual", "One", "Night", "Count", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 131, "sent": ["Having", "been", "one", "of", "them", ",", "McClain", "tries", "to", "give", "homeless", "people", "in", "the", "area", "advice", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 132, "sent": ["On", "the", "sidewalk", "next", "to", "McClain", "was", "a", "container", "of", "macaroni", "salad", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 133, "sent": ["McClain", "often", "does", "this", "as", "well", "--", "he", "likes", "to", "bake", "in", "his", "spare", "time", "and", "bring", "food", "for", "the", "employees", "at", "Safeway", "as", "well", "as", "Real", "Change", "to", "show", "his", "appreciation", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 134, "sent": ["McClain", "sat", "outside", "Safeway", "in", "heavy", "gear", "for", "the", "winter", "weather", ",", "selling", "the", "small", "bundle", "of", "papers", "in", "his", "hand", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 135, "sent": ["Students", "from", "the", "UW", "School", "of", "Law", "Innocence", "Project", "Northwest", "-LRB-", "IPNW", "-RRB-", "Clinic", "will", "testify", "in", "support", "of", "a", "bill", "that", "establishes", "a", "compensation", "statute", "for", "individuals", "wrongfully", "convicted", "in", "Washington", "State", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 136, "sent": ["House", "Bill", "2221", "aims", "to", "assist", "those", "who", "are", "wrongly", "convicted", "by", "providing", "legal", "redress", "to", "recover", "$", "50,000", "in", "damages", "per", "year", "of", "wrongful", "imprisonment", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 137, "sent": ["UW", "law", "students", "Amy", "Shebeck", ",", "Thomas", "Hudson", ",", "Caroline", "Bercier", ",", "and", "Michael", "Windle", "will", "testify", "during", "the", "House", "Committee", "on", "Judiciary", "'s", "public", "hearing", "today", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 138, "sent": ["The", "lobbying", "students", "are", "part", "of", "a", "partnership", "between", "IPNW", "and", "UW", "Law", "'s", "Legislative", "Advocacy", "Clinic", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 139, "sent": ["The", "IPNW", "grew", "from", "a", "volunteer", "effort", "in", "1997", "and", "aims", "at", "freeing", "inmates", "who", "have", "been", "wrongfully", "convicted", "of", "crimes", ",", "and", "the", "legislative", "advocacy", "division", "is", "new", "this", "year", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 140, "sent": ["Shebeck", ",", "a", "second-year", "law", "student", ",", "said", "other", "ideas", "the", "clinic", "is", "working", "on", "are", "allowing", "defense", "the", "right", "to", "propose", "DNA", "testing", "and", "creating", "legislation", "opposing", "testimony", "from", "jailhouse", "informants", "who", "receive", "compensation", "for", "tips", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 141, "sent": ["``", "We", "'re", "hoping", "to", "convince", "the", "Legislature", "...", "the", "moral", "reason", "why", ",", "as", "a", "matter", "of", "justice", ",", "this", "should", "be", "done", ",", "''", "said", "Hudson", ",", "also", "a", "second-year", "law", "student", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 142, "sent": ["``", "There", "'s", "a", "common", "widespread", "misconception", "that", "people", "who", "are", "exonerated", "can", "just", "go", "in", "and", "get", "a", "bunch", "of", "money", ",", "''", "said", "Lara", "Zarowsky", ",", "the", "IPNW", "policy", "staff-attorney", "who", "drafted", "the", "bill", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 143, "sent": ["The", "law", "students", "helped", "Zarowsky", "draft", "the", "bill", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 144, "sent": ["Testifying", "today", "alongside", "the", "students", "are", "Ted", "Bradford", ",", "Alan", "Northrop", ",", "and", "Larry", "Davis", ",", "exonerees", "that", "were", "freed", "with", "the", "help", "of", "IPNW", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 145, "sent": ["``", "Their", "stories", "are", "very", "compelling", ",", "''", "Shebeck", "said", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 146, "sent": ["Bradford", "was", "convicted", "of", "rape", "and", ",", "in", "2010", ",", "became", "the", "first", "person", "in", "Washington", "to", "be", "acquitted", "by", "the", "Court", "of", "Appeals", "based", "on", "DNA", "testing", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 147, "sent": ["Northrop", "--", "who", "is", "the", "focus", "of", "a", "CNN", "documentary", "--", "and", "Davis", "each", "served", "17", "years", "in", "prison", "after", "being", "wrongly", "convicted", "in", "a", "rape", "and", "burglary", "trial", "because", "of", "bad", "eyewitness", "identifications", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 148, "sent": ["Shebeck", "said", "eyewitness", "identification", "is", "the", "most", "common", "way", "people", "are", "wrongly", "convicted", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 149, "sent": ["The", "students", "have", "been", "working", "for", "the", "IPNW", "clinic", "since", "this", "summer", "and", "have", "had", "multiple", "work", "sessions", "with", "Rep.", "Tina", "Orwall", ",", "who", "sponsors", "the", "compensation", "statute", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 150, "sent": ["Hudson", "said", "the", "students", "are", "confident", "about", "the", "bill", "passing", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 151, "sent": ["``", "The", "effects", "go", "beyond", "the", "person", "who", "is", "wrongly", "convicted", ",", "''", "Hudson", "said", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 152, "sent": ["Shebeck", "said", "Northrop", "had", "three", "children", "when", "he", "went", "to", "prison", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 153, "sent": ["Shebeck", "and", "Hudson", "took", "an", "interest", "in", "the", "IPNW", "because", "of", "their", "respective", "interests", "in", "criminal", "law", "and", "government", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 154, "sent": ["For", "Zarowsky", ",", "no", "longer", "a", "student", ",", "a", "wrongful", "conviction", "case", "is", "what", "brought", "her", "to", "law", "school", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 155, "sent": ["Zarowsky", "said", "some", "states", "have", "more", "procedures", "for", "compensating", "the", "wrongly", "accused", "because", "they", "'ve", "taken", "the", "issue", "seriously", "after", "having", "exonerations", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 156, "sent": ["``", "Work", "needs", "to", "be", "done", "and", ",", "in", "Washington", "state", ",", "almost", "none", "of", "that", "work", "has", "been", "done", ",", "''", "Zarowsky", "said", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 157, "sent": ["Hudson", "said", "the", "use", "of", "science", "and", "the", "latest", "studies", "is", "a", "way", "to", "prevent", "wrongful", "accusations", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 158, "sent": ["``", "Tradition", "has", "a", "large", "weight", "on", "legal", "decisions", ",", "oftentimes", "trumping", "science", ",", "''", "Hudson", "said", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 159, "sent": ["Police", "procedures", "and", "how", "courts", "deal", "with", "certain", "evidence", "must", "change", ",", "Zarowsky", "said", ",", "and", "it", "'s", "not", "a", "quick", "process", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 160, "sent": ["Marek", "Wieczorek", ",", "UW", "associate", "professor", "of", "art", "history", ",", "remembers", "his", "first", "exhibition", "well", ":", "In", "1989", ",", "he", "curated", "``", "The", "Desire", "of", "the", "Museum", "''", "at", "the", "Whitney", "Museum", "in", "New", "York", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 161, "sent": ["It", "was", "the", "beginning", "of", "a", "passion", "to", "which", "Wieczorek", "would", "devote", "most", "of", "his", "life", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 162, "sent": ["``", "This", "was", "really", "exciting", "--", "-", "my", "first", "year", "in", "New", "York", ",", "fresh", "out", "of", "the", "Netherlands", ",", "''", "Wieczorek", "said", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 163, "sent": ["The", "exhibition", "``", "put", "the", "museum", "on", "the", "couch", ",", "''", "as", "he", "described", "it", ",", "``", "examining", "the", "museum", "as", "an", "institution", "under", "the", "Freudian", "lens", ".", "''"], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 164, "sent": ["He", "was", "a", "student", "in", "the", "Whitney", "Museum", "'s", "Independent", "Study", "Program", "at", "the", "time", ",", "studying", "under", "both", "critical", "studies", "and", "the", "curatorial", "program", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 165, "sent": ["Even", "then", ",", "there", "was", "one", "artist", "for", "whom", "he", "held", "an", "affinity", ":", "Carel", "Balth", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 166, "sent": ["Wieczorek", "first", "met", "Balth", "when", "he", "was", "still", "a", "graduate", "student", "in", "New", "York", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 167, "sent": ["His", "first", "publication", "was", "on", "Balth", "'s", "``", "Laser", "Paintings", ".", "''"], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 168, "sent": ["Since", "then", ",", "Wieczorek", "has", "visited", "Balth", "'s", "studio", "near", "Amsterdam", "many", "times", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 169, "sent": ["``", "That", "was", "a", "marvelous", "experience", ",", "being", "a", "graduate", "student", "and", "publishing", "something", ",", "''", "Wieczorek", "said", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 170, "sent": ["After", "years", "of", "wanting", "to", "curate", "such", "an", "exhibition", ",", "Wieczorek", "has", "collaborated", "with", "the", "Henry", "Art", "Gallery", "to", "feature", "26", "pieces", "of", "Balth", "'s", "``", "Videowatercolors", ".", "''"], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 171, "sent": ["Other", "pieces", "came", "from", "the", "Henry", "Art", "Gallery", "collections", "that", "he", "felt", "would", "coordinate", "with", "Balth", "'s", "works", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 172, "sent": ["A", "colloquium", "tomorrow", ",", "Jan.", "13", ",", "at", "6", "p.m.", ",", "will", "include", "presentations", "from", "four", "or", "five", "of", "Wieczorek", "'s", "former", "students", "before", "the", "exhibition", "tours", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 173, "sent": ["``", "The", "colloquium", "is", "the", "culmination", "of", "Marek", "'s", "classwork", "for", "this", "past", "quarter", ",", "''", "said", "Betsey", "Brock", ",", "the", "Henry", "Art", "Gallery", "'s", "associate", "director", "for", "communications", "and", "outreach", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 174, "sent": ["For", "Wieczorek", ",", "Balth", "'s", "work", "is", "powerful", "because", "of", "the", "interplay", "of", "different", "mediums", ",", "known", "as", "intermedia", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 175, "sent": ["Wieczorek", "said", "Balth", "addresses", "the", "question", ",", "``", "What", "does", "it", "mean", "to", "work", "with", "photography", ",", "paint", ",", "and", "new", "media", "?", "''"], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 176, "sent": ["``", "Balth", "'s", "work", ",", "in", "a", "sense", ",", "does", "a", "bit", "of", "all", ",", "''", "Wieczorek", "said", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 177, "sent": ["``", "That", "'s", "what", "I", "love", "about", "Balth", "'s", "art", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 178, "sent": ["Balth", "sometimes", "uses", "digital", "video", "to", "capture", "motifs", "and", "a", "videograb", "to", "capture", "moving", "light", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 179, "sent": ["In", "one", "of", "his", "``", "Videowatercolor", "''", "pieces", ",", "``", "Moving", "II", ",", "''", "Balth", "simply", "rotates", "an", "image", "of", "water", ",", "juxtaposing", "it", "with", "the", "original", "photo", "and", "making", "it", "unrecognizable", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 180, "sent": ["``", "Our", "eyes", "are", "so", "conditioned", "to", "see", "a", "certain", "way", ",", "''", "Wieczorek", "said", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 181, "sent": ["Wieczorek", "said", "Balth", "also", "plays", "with", "the", "``", "liminal", "conditions", "of", "perception", ".", "''"], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 182, "sent": ["In", "Balth", "'s", "work", "``", "Artwork", "without", "the", "Tulipman", ",", "''", "Balth", "took", "a", "Polaroid", "of", "a", "building", "and", "scratched", "the", "photo", "with", "his", "nails", "before", "removing", "the", "film", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 183, "sent": ["Wieczorek", "calls", "it", "``", "chemical", "light", ",", "''", "saying", "that", "Balth", "is", "``", "casting", "light", "into", "the", "dark", ".", "''"], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 184, "sent": ["``", "There", "was", "never", "any", "light", "in", "the", "photograph", ",", "but", "we", "think", "we", "see", "light", ",", "''", "Wieczorek", "said", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 185, "sent": ["In", "an", "interview", "with", "The", "Seattle", "Times", ",", "Balth", "said", "his", "intention", "in", "his", "art", "is", "``", "to", "come", "to", "the", "heart", "of", "seeing", ",", "the", "core", "of", "perception", ".", "''"], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 186, "sent": ["Speaking", "to", "the", "artist", "about", "his", "pieces", ",", "Wieczorek", "said", ",", "helped", "him", "understand", "Balth", "'s", "intentions", "behind", "the", "work", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 187, "sent": ["However", ",", "Wieczorek", "said", "the", "curator", "does", "n't", "need", "to", "know", "the", "artist", "personally", "to", "help", "create", "an", "exhibition", "of", "his", "or", "her", "work", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 188, "sent": ["In", "the", "end", ",", "Wieczorek", "said", "knowing", "the", "artist", "is", "not", "what", "makes", "the", "work", "powerful", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 189, "sent": ["Although", "the", "bill", "concerning", "it", "has", "been", "tabled", "by", "the", "ASUW", "Board", "of", "Directors", ",", "many", "students", "still", "feel", "a", "new", "commission", "representing", "South", "Asian", "student", "groups", "would", "be", "beneficial", "to", "the", "community", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 190, "sent": ["The", "proposed", "commission", "would", "reduce", "the", "number", "of", "constituent", "organizations", "under", "the", "Asian", "Student", "Commission", "-LRB-", "ASC", "-RRB-", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 191, "sent": ["Currently", "encompassing", "28", "RSOs", ",", "the", "ASC", "represents", "the", "widest", "community", "under", "the", "ASUW", "Joint", "Commission", "Committee", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 192, "sent": ["Divya", "Ramachandra", ",", "secretary", "of", "the", "Indian", "Student", "Association", "-LRB-", "ISA", "-RRB-", ",", "said", "that", "the", "broad", "community", "covered", "in", "ASC", "makes", "it", "difficult", "for", "some", "constituents", "to", "feel", "connected", "to", "others", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 193, "sent": ["``", "The", "South", "Asian", "student", "groups", "and", "cultures", "are", "very", "different", "than", "the", "East", "Asian", "student", "groups", "and", "cultures", ",", "and", "I", "know", "people", "want", "a", "separate", "commission", "to", "better", "fit", "the", "needs", "of", "these", "groups", ",", "''", "Ramachandra", "said", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 194, "sent": ["``", "It", "'s", "hard", "to", "get", "our", "constituents", "to", "go", "to", "other", "ASC", "events", "because", "they", "'re", "not", "interested", "or", "they", "do", "n't", "have", "time", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 195, "sent": ["Despite", "these", "views", ",", "Ramachandra", "said", "she", "personally", "has", "reservations", "about", "the", "proposed", "commission", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 196, "sent": ["``", "I", "guess", "I", "just", "see", "diversity", "not", "as", "segregating", "but", "more", "as", "bringing", "together", "other", "cultures", ",", "''", "Ramashandra", "said", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 197, "sent": ["The", "ASUW", "bill", "regarding", "a", "proposed", "South", "Asian", "commission", "created", "a", "task", "force", "to", "determine", "if", "a", "new", "commission", "was", "truly", "desired", "in", "the", "community", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 198, "sent": ["Jonathan", "Winn", ",", "ASUW", "director", "of", "diversity", "efforts", ",", "said", "he", "wrote", "the", "bill", "after", "hearing", "discussion", "about", "such", "a", "commission", "during", "recent", "years", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 199, "sent": ["``", "I", "wanted", "to", "explore", "the", "option", "because", "there", "has", "always", "been", "talk", "about", "it", ",", "but", "there", "has", "never", "been", "a", "task", "force", "to", "officially", "see", "if", "that", "was", "something", "that", "the", "ASUW", "would", "take", "on", ",", "''", "Winn", "said", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 200, "sent": ["Winn", "said", "he", "was", "not", "upset", "the", "bill", "was", "being", "tabled", "because", "it", "gives", "the", "community", "time", "to", "review", "the", "idea", "of", "forming", "a", "new", "commission", "before", "making", "a", "decision", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 201, "sent": ["ASC", "Director", "Melvin", "Taing", "said", "that", "because", "the", "commission", "is", "still", "in", "the", "exploratory", "stages", ",", "he", "has", "been", "trying", "to", "approach", "the", "subject", "objectively", "and", "has", "continued", "to", "ask", "his", "constituents", "'", "opinions", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 202, "sent": ["A", "forum", "will", "be", "held", "Jan.", "18", "for", "students", "to", "discuss", "the", "matter", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 203, "sent": ["Discussion", "between", "the", "ASC", "constituents", "has", "been", "a", "struggle", "with", "such", "a", "large", "community", ",", "Taing", "said", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 204, "sent": ["According", "to", "statistics", "released", "by", "the", "UW", "Office", "of", "the", "Registrar", "last", "quarter", ",", "Asian", "and", "Asian-American", "students", "currently", "make", "up", "22.2", "percent", "of", "the", "UW", "population", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 205, "sent": ["Representing", "nearly", "a", "quarter", "of", "the", "current", "student", "body", ",", "ASC", "has", "been", "overflowing", "with", "constituents", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 206, "sent": ["This", "would", "not", "be", "the", "first", "time", "the", "ASC", "community", "has", "been", "divided", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 207, "sent": ["In", "2000", ",", "student", "groups", "split", "from", "ASC", "in", "order", "to", "form", "the", "Pacific", "Islander", "Student", "Commission", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 208, "sent": ["Taing", "said", "that", "some", "students", "feel", "as", "though", "smaller", "commissions", "could", "allow", "for", "better", "communication", "between", "RSOs", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 209, "sent": ["``", "All", "the", "South", "Asian", "groups", "are", "pretty", "close", "with", "each", "other", "and", "communicate", "with", "each", "other", "fairly", "often", ",", "but", "that", "is", "not", "being", "done", "at", "all", "with", "ASC", ",", "''", "Taing", "said", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 210, "sent": ["``", "Some", "people", "have", "felt", "that", "South", "Asian", "people", "have", "been", "left", "out", "of", "the", "Asian", "Student", "Commission", "and", "that", "they", "do", "n't", "feel", "like", "they", "are", "being", "represented", "well", "in", "the", "ASC", ".", "''"], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 211, "sent": ["Taing", "said", "the", "new", "commission", "would", "also", "shrink", "his", "workload", "and", "increase", "the", "funding", "that", "reaches", "his", "constituents", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 212, "sent": ["``", "A", "lot", "of", "the", "work", "that", "I", "have", "to", "do", "every", "day", "is", "busy-work", "trying", "to", "get", "all", "of", "the", "28", "groups", "on", "the", "same", "page", ",", "''", "Taing", "said", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 213, "sent": ["Ramachandra", "said", "she", "believes", "these", "resources", "would", "help", "fund", "more", "constituents", "'", "events", ",", "such", "as", "Bhangra", "Bash", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 214, "sent": ["``", "Bhangra", "Bash", "used", "to", "be", "our", "biggest", "event", "because", "we", "would", "fly", "in", "people", "from", "all", "of", "the", "country", ",", "but", "as", "one", "RSO", ",", "it", "was", "way", "too", "hard", "for", "us", "to", "put", "it", "on", "as", "a", "group", ",", "''", "Ramachandra", "said", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 215, "sent": ["``", "Having", "the", "South", "Asian", "student", "commission", "would", "give", "more", "opportunities", "for", "some", "of", "the", "events", "that", "we", "have", "had", "in", "the", "past", "but", "are", "not", "able", "to", "do", "now", "because", "we", "do", "n't", "have", "enough", "people", "or", "enough", "time", "or", "money", ".", "''"], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 216, "sent": ["``", "Seattle", "is", "one", "of", "the", "capitals", "of", "global", "health", ",", "and", "yet", "there", "are", "n't", "enough", "young", "people", "aware", "of", "the", "opportunities", "to", "do", "something", "positive", "with", "their", "lives", "and", "to", "actually", "have", "a", "career", "working", "in", "global", "health", ",", "''", "said", "William", "Heisel", ",", "a", "representative", "at", "the", "Institute", "for", "Health", "Metrics", "and", "Evaluation", "-LRB-", "IHME", "-RRB-", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 217, "sent": ["UW", "staff", "from", "the", "Foster", "School", "of", "Business", "and", "IHME", "helped", "create", "the", "contest", ",", "which", "is", "sponsored", "by", "the", "Global", "Health", "Nexus", "--", "an", "initiative", "of", "the", "Washington", "Global", "Health", "Alliance", "that", "works", "to", "raise", "statewide", "awareness", "of", "global", "health", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 218, "sent": ["``", "Be", "the", "Change", "''", "committee", "members", "used", "the", "Foster", "School", "of", "Business", "'", "annual", "Global", "Social", "Entrepreneurship", "Competition", "-LRB-", "GSEC", "-RRB-", "as", "a", "model", "to", "design", "their", "competition", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 219, "sent": ["``", "The", "goal", "with", "Foster", "'s", "-LSB-", "contest", "-RSB-", "is", "to", "generate", "innovative", "ideas", "and", "kind", "of", "push", "the", "envelope", "from", "a", "business", "perspective", ",", "and", "we", "'re", "hoping", "to", "do", "the", "same", "thing", "from", "a", "global", "health", "perspective", ",", "''", "Heisel", "said", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 220, "sent": ["Finalists", "are", "chosen", "by", "a", "panel", "of", "judges", "made", "up", "of", "local", "business", "leaders", "and", "educators", "and", "will", "receive", "revisions", "to", "their", "solution", "in", "March", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 221, "sent": ["The", "winner", "will", "be", "announced", "next", "summer", ",", "but", "even", "students", "who", "do", "n't", "win", "may", "still", "have", "the", "opportunity", "to", "discuss", "their", "solution", "with", "someone", "from", "a", "relevant", "organization", "--", "such", "as", "the", "Bill", "&", "Melinda", "Gates", "Foundation", ";", "the", "international", ",", "nonprofit", "organization", "PATH", ";", "the", "Seattle", "Children", "'s", "Hospital", ",", "and", "IHME", ",", "a", "UW", "affiliate", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 222, "sent": ["``", "There", "'s", "the", "chance", "not", "just", "to", "win", ",", "but", "the", "idea", "that", "students", "are", "able", "to", "engage", "with", "people", "in", "global", "health", "who", "might", "be", "beneficial", "to", "their", "career", ",", "''", "Heisel", "said", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 223, "sent": ["The", "latter", "topic", "is", "what", "freshman", "Jamie", "Choe", "chose", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 224, "sent": ["Choe", "'s", "team", ",", "dubbed", "``", "Ali", "Baba", ",", "''", "hopes", "that", "this", "basic", "contraption", "could", "be", "used", "in", "countries", "such", "as", "India", ",", "which", "is", "currently", "plagued", "with", "a", "water", "crisis", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 225, "sent": ["``", "It", "sounds", "like", "a", "simple", "solution", ",", "but", "it", "can", "save", "lives", "in", "other", "places", ",", "''", "Choe", "said", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 226, "sent": ["UW", "faculty", "member", "Alyssa", "Taylor", "instructed", "the", "students", "from", "``", "Ali", "Baba", "''", "in", "a", "bioengineering", "class", "last", "quarter", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 227, "sent": ["Taylor", "said", "the", "contest", "is", "a", "way", "for", "students", "to", "easily", "engage", "with", "important", "global", "issues", ",", "even", "when", "lacking", "technical", "knowledge", "and", "experience", "that", "comes", "with", "age", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 228, "sent": ["The", "emotion", "in", "the", "room", "was", "palpable", "when", "state", "Adirondack", "Park", "Agency", "commissioners", "approved", "a", "permit", "for", "the", "Adirondack", "Club", "and", "Resort", "Friday", "morning", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 229, "sent": ["After", "Executive", "Director", "Terry", "Martino", "announced", "the", "final", "vote", "count", "-", "10", "to", "1", "-", "a", "round", "of", "applause", "erupted", "from", "the", "many", "Tupper", "Lakers", "and", "local", "government", "representatives", "packed", "into", "the", "APA", "'s", "board", "room", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 230, "sent": ["''", "I", "look", "at", "this", "as", "a", "historic", "moment", ",", "''", "Gerald", "Delaney", ",", "chairman", "of", "the", "Local", "Government", "Review", "Board", ",", "said", "during", "a", "public", "comment", "session", "after", "the", "vote", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 231, "sent": ["A", "day", "of", "twists", "and", "turns", "in", "the", "Michael", "Scaringe", "rape", "case", "ended", "with", "Franklin", "County", "Court", "Judge", "Robert", "Main", "declaring", "a", "mistrial", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 232, "sent": ["The", "move", "came", "late", "Friday", "afternoon", "after", "Scaringe", "dismissed", "Brian", "Barrett", "of", "Lake", "Placid", "as", "one", "of", "his", "defense", "attorneys", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 233, "sent": ["''", "This", "afternoon", "Judge", "Main", "granted", "a", "defense", "motion", "for", "a", "mistrial", ",", "''", "county", "Court", "Clerk", "Bruce", "Cox", "said", "in", "a", "message", "left", "with", "the", "Enterprise", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 234, "sent": ["Barrett", "had", "represented", "the", "former", "Saranac", "Lake", "Youth", "Center", "director", "since", "he", "was", "arrested", "in", "January", "2009", "on", "charges", "of", "raping", "a", "then-13-year-old-girl", "who", "frequented", "the", "center", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 235, "sent": ["Barrett", "brought", "on", "Mary", "Rain", "of", "Ogdensburg", "as", "his", "co-counsel", "in", "the", "case", "at", "some", "point", "last", "year", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 236, "sent": ["Barrett", "confirmed", "to", "the", "Enterprise", "earlier", "this", "afternoon", "that", "Scaringe", "had", "dismissed", "him", ",", "though", "the", "lawyer", "initially", "declined", "to", "go", "into", "detail", "as", "to", "why", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 237, "sent": ["''", "Mr.", "Scaringe", "opted", "to", "continue", "with", "Ms.", "Rain", ",", "and", "I", "wish", "him", "the", "best", "of", "luck", ".", "''"], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 238, "sent": ["Rain", "declined", "to", "speak", "to", "the", "press", "about", "the", "circumstances", "surrounding", "Barrett", "'s", "departure", "or", "the", "status", "of", "the", "case", "when", "she", "left", "the", "courthouse", "with", "Scaringe", "and", "his", "family", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 239, "sent": ["That", "was", "before", "the", "Enterprise", "was", "notified", "that", "Judge", "Main", "had", "declared", "a", "mistrial", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 240, "sent": ["The", "newspaper", "later", "left", "a", "message", "with", "Rain", "that", "had", "n't", "been", "returned", "as", "of", "press", "time", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 241, "sent": ["Scaringe", "'s", "decision", "to", "dismiss", "Barrett", "came", "one", "day", "after", "a", "jury", "was", "finally", "seated", "for", "the", "trial", "and", "the", "prosecution", "and", "the", "defense", "delivered", "opening", "statements", "in", "the", "case", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 242, "sent": ["Less", "than", "an", "hour", "after", "the", "trial", "was", "scheduled", "to", "resume", "Friday", "morning", ",", "Judge", "Main", "sent", "the", "jury", "home", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 243, "sent": ["Spencer", "Anderson", "was", "in", "veterinary", "school", "when", "he", "joined", "the", "U.S.", "Army", "in", "2000", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 244, "sent": ["The", "33-year-old", "Billings", "native", "enlisted", "as", "a", "military", "veterinarian", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 245, "sent": ["``", "I", "thought", "it", "was", "an", "opportunity", "for", "me", "to", "still", "be", "in", "the", "military", "and", "be", "a", "veterinarian", ",", "''", "said", "Spencer", ",", "as", "he", "prefers", "to", "be", "called", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 246, "sent": ["After", "his", "military", "graduation", ",", "Spencer", "spent", "three", "years", "tending", "the", "Army", "'s", "dogs", "and", "horses", "--", "nearly", "a", "year", "of", "that", "in", "Iraq", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 247, "sent": ["The", "``", "working", "''", "German", "shepherds", "and", "Belgian", "malinois", "he", "cared", "for", "were", "either", "attack", "dogs", "or", "bomb", "or", "narcotics", "sniffers", ",", "he", "said", ",", "standing", "in", "his", "year-old", "Baxter", "Creek", "Veterinary", "Clinic", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 248, "sent": ["``", "It", "was", "neat", "to", "see", "that", "work", "because", "you", "do", "n't", "get", "a", "lot", "of", "that", "in", "civilian", "work", ",", "''", "Spencer", "said", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 249, "sent": ["While", "serving", "he", "taught", "himself", "acupuncture", "."], "tags": ["O", "O", "O", "O", "O", "O", "O"]}, {"id": 250, "sent": ["When", "he", "left", "the", "Army", ",", "Spencer", "got", "a", "job", "in", "Bozeman", ",", "where", "he", "used", "acupuncture", "to", "save", "a", "dog", "that", "could", "n't", "walk", "anymore", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 251, "sent": ["``", "It", "does", "n't", "always", "work", "that", "way", ",", "''", "Spencer", "said", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 252, "sent": ["Jennifer", "Leight", "has", "been", "bringing", "her", "17-year-old", "Newfoundland", ",", "Barley", ",", "to", "Spencer", "since", "June", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 253, "sent": ["Leight", "was", "skeptical", "about", "acupuncture", "but", "was", "sold", "when", "Barley", "``", "bounced", "out", "of", "the", "room", "''", "after", "his", "first", "treatment", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 254, "sent": ["``", "When", "we", "go", "in", "he", "'s", "barely", "walking", ",", "''", "Leight", "said", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 255, "sent": ["A", "tree", "fell", "on", "a", "power", "line", "in", "Bridger", "Canyon", "on", "Sunday", "morning", ",", "knocking", "out", "power", "to", "850", "customers", ",", "including", "the", "Bridger", "Bowl", "ski", "area", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 256, "sent": ["Northwestern", "Energy", "spokeswoman", "Claudia", "Rapkoch", "said", "the", "outage", "started", "at", "10:50", "a.m", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 257, "sent": ["However", ",", "the", "outage", "shut", "down", "Bridger", "Bowl", "for", "the", "rest", "of", "the", "day", ",", "according", "to", "a", "posting", "on", "the", "ski", "area", "'s", "Facebook", "page", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 258, "sent": ["Officials", "at", "Bridger", "could", "not", "be", "reached", "for", "comment", "Sunday", "afternoon", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 259, "sent": ["Earlier", "this", "month", ",", "Sony", "released", "the", "first", "XQD", "format", "memory", "card", ",", "but", "the", "company", "is", "n't", "stopping", "there", "with", "pushing", "into", "new", "storage", "options", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 260, "sent": ["Sony", "has", "now", "announced", "that", "they", "'re", "creating", "a", "new", "line", "of", "high-speed", "SD", "cards", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 261, "sent": ["The", "flagship", "UHS-I", "series", "will", "be", "available", "in", "8GB", ",", "16GB", ",", "and", "32GB", "versions", ",", "and", "promises", "94MB/s", "read", "and", "45MB/s", "write", "speeds", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 262, "sent": ["Starting", "at", "$", "44.99", "a", "pop", ",", "Sony", "has", "also", "treated", "them", "to", "be", "water", "resistant", ",", "and", "will", "have", "them", "on", "shelves", "in", "March", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 263, "sent": ["Also", "at", "CES", ",", "the", "SD", "Association", "announced", "a", "new", "standard", "for", "WiFi", "communications", "across", "all", "the", "SD", "cards", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 264, "sent": ["With", "the", "huge", "popularity", "of", "EyeFi", "cards", ",", "this", "should", "make", "wireless", "communication", "directly", "from", "an", "SD", "card", "even", "easier", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 265, "sent": ["According", "to", "PhotographyBlog", ",", "SanDisk", "and", "Lexar", "have", "no", "immediate", "plans", "to", "produce", "XQD", "or", "WiFi", "SD", "cards", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 266, "sent": ["Speaking", "at", "CES", ",", "both", "companies", "have", "no", "plans", "to", "jump", "to", "the", "new", "formats", "in", "the", "near", "future", ",", "and", "Lexar", "pointed", "out", "that", "its", "new", "1000x", "card", "is", "faster", "than", "Sony", "'s", "XQD", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 267, "sent": ["Two", "years", "after", "a", "catastrophic", "earthquake", "occured", "near", "the", "capital", "of", "Port-au-Prince", ",", "most", "of", "the", "images", "we", "see", "coming", "out", "of", "Haiti", "show", "damage", "and", "despair", ",", "and", "most", "come", "from", "foreign", "photographers", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 268, "sent": ["This", "is", "something", "American", "photographer", "Maggie", "Steber", "-LRB-", "who", "has", "been", "shooting", "in", "Haiti", "for", "30", "years", "-RRB-", "is", "trying", "to", "change", "through", "organizations", "like", "the", "nonprofit", "FotoKonbit", ",", "where", "she", "is", "an", "adviser", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 269, "sent": ["While", "the", "7.0", "earthquake", "was", "characterized", "by", "the", "United", "Nations", "as", "``", "the", "largest", "urban", "disaster", "in", "modern", "history", ",", "''", "and", "rebuilding", "efforts", "have", "been", "slow", ",", "Steber", "wants", "the", "world", "to", "know", "that", "tragedy", "is", "n't", "the", "only", "narrative", "the", "country", "has", "to", "offer", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 270, "sent": ["Students", "in", "FotoKonbit", "workshops", "used", "Holga", "cameras", "to", "document", "their", "communities", ",", "and", "the", "resulting", "images", "are", "beautiful", "in", "their", "complexity", "--", "struggle", ",", "pride", ",", "love", ",", "simplicity", ",", "hope", ",", "despair", "--", "in", "a", "word", ":", "life", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 271, "sent": ["``", "When", "you", "see", "what", "Haitians", "think", "is", "beautiful", "to", "photograph", ",", "important", ",", "profound", ",", "''", "says", "Steber", ",", "``", "you", "learn", "more", "about", "them", "than", "anything", "an", "outsider", "can", "show", "you", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 272, "sent": ["To", "put", "cameras", "in", "the", "hands", "of", "Haitians", "give", "them", "the", "power", "to", "show", "us", "what", "they", "think", "is", "important", ".", "''"], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 273, "sent": ["The", "study", "is", "from", "the", "College", "of", "Medical", ",", "Veterinary", "&", "Life", "Sciences", ",", "University", "of", "Glasgow", ",", "Glasgow", ",", "UK", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 274, "sent": ["Questionnaires", "were", "distributed", "to", "all", "members", "of", "the", "Companion", "Animal", "Society", ",", "part", "of", "the", "New", "Zealand", "Veterinary", "Association", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 275, "sent": ["However", ",", "further", "study", "regarding", "the", "actual", "number", "of", "these", "animals", "kept", "as", "pets", "in", "New", "Zealand", "is", "required", "for", "validation", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 276, "sent": ["Many", "respondents", "felt", "their", "knowledge", "of", "issues", "relating", "to", "pain", "recognition", ",", "anaesthesia", "and", "analgesia", "in", "rabbits", "and", "guinea", "pigs", "was", "inadequate", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}, {"id": 277, "sent": ["The", "study", "is", "from", "the", "Unitec", "Institute", "of", "Technology", ",", "Auckland", ",", "New", "Zealand", "."], "tags": ["O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O", "O"]}]
\ No newline at end of file
assignment1/output/test_data_predicted_LSTM_2.json 0 → 100644
View file @ 98dae565
This source diff could not be displayed because it is too large. You can view the blob instead.
assignment2/README.md 0 → 100644
View file @ 98dae565
# CS728 Assignment 2
**Singamsetty Sandeep (213050064)**
**Saswat Meher (22m0804)**
This file contains instruction on how to run the code and structure of the code.
## Requirement
*Execute All the commands from inside submit folder.
Install required libraries by running the following command,
```bash
pip install -r requirements.txt // for pip
conda install --file requirements.txt // for conda
```
## Folder Structure
submit/
>code/
>>Q1.py // Contains both the model architecture, training and testing for Q1
>>DTW.py // Contains the Model for DTW non_crossing and crossing
>>Q3.py // Code for Training and testing of DTW models.
>>dataset.py // Dataset class for Glue dataset.
>output/
>> Q1_test.txt // Finetuned model predicted output for Test dataset of GLUE.
>> Q1_val.txt // Finetuned model predicted output for Validation dataset of GLUE.
>> Q3_non_crossing_test.txt // DTW non Crossing model predicted output for Test dataset of GLUE.
>> Q3_non_crossing_val.txt // DTW non Crossing model predicted output for Val dataset of GLUE.
>> Q3_crossing_test.txt // DTW Crossing model predicted output for Test dataset of GLUE.
>> Q3_crossing_val.txt // DTW Crossing model predicted output for Test dataset of GLUE.
>additional_files/
>>models/ // Contains save parameters for all the model (Q1,Q2,Q3).
>README.md
>Requirements.txt
>Report.pdf
## Method to reproduce the result and test the model
### Q1
#### Training
To fine tune the the BERT-Tiny model with BST task. Use the following command.
* Note this will overwrite the existing model params in additional folder. To only test, use the commands in the next segment.
```bash
python3 code/Q1.py -t
```
This will also generate results for validation and test dataset in "*outputs/*" directory
#### Inference
To test/infer the model use the following command:
```bash
python3 code/Q1.py -i
```
This will give the user a prompt to enter sentence 1 and sentence 2 respectively and It will show the Correlation score.
### Q3
#### Training
To train a,b of tanh in DTW with **non crossing** constraint, run the following command.
* Note this will overwrite the existing model params in additional folder. To only test, use the commands in the next segment.
```bash
python3 code/Q3.py -t
```
To train a,b of tanh in DTW with **crossing** constraint, add another arg "-c" to the above command.
```bash
python3 code/Q3.py -c -t
```
This will also generate results for validation and test dataset in "*outputs/*" directory with the names of the files as *{DTW_{non_crossing/crossing}_{val/test}.txt}*
#### Inference
To test/infer the model use the following command:
```bash
python3 Q3.py -i
```
Similar to training add "-c" for inference using crossing in above command.
This will also give the user a prompt to enter sentence 1 and sentence 2 respectively and It will show the Correlation score and a mapping between the tokens of smaller sentence with the larger sentence.
assignment2/code/DTW.py 0 → 100644
View file @ 98dae565
import torch
from transformers import BertModel
class DTW(torch.nn.Module):
"""
Model that uses DTW to check similarity between two sequence of sentence.
"""
def __init__(self,pre_trained_model_name, crossing = True):
super(DTW, self).__init__()
self.crossing = crossing
self.bert_model = BertModel.from_pretrained(pre_trained_model_name, return_dict=False)
for param in self.bert_model.parameters():
param.requires_grad = False
self.cos = torch.nn.CosineSimilarity(dim = -1)
self.a = torch.nn.Parameter(torch.rand(1, requires_grad = True, dtype = torch.float))
self.b = torch.nn.Parameter(torch.rand(1, requires_grad = True, dtype = torch.float))
self.tanh = torch.nn.Tanh()
def forward(self, input_ids_1 = None, attention_mask_1 = None,
token_type_ids_1 = None, input_ids_2 = None,
attention_mask_2 = None, token_type_ids_2 = None, test = False):
"""
Forward of this model that takes sentence as separate input and predict the similarity score using DTW.
"""
output1 = self.bert_model(input_ids = input_ids_1, attention_mask = attention_mask_1, token_type_ids = token_type_ids_1)[0]
output2 = self.bert_model(input_ids = input_ids_2, attention_mask = attention_mask_2, token_type_ids = token_type_ids_2)[0]
sim_scores = []
for i in range(len(output1)):
sim_score = self.get_DTW_score(output1[i][attention_mask_1[i] == 1][1:-1], output2[i][attention_mask_2[i] == 1][1:-1],
return_map = False, crossing = self.crossing)
sim_scores.append(sim_score)
return torch.cat(sim_scores)
def score(self,s1_i, s2_j, eps=1e-8):
"""
Given two list of word embeddings find the cosine similarity between every pair of tokens in both sentence.
"""
a_n, b_n = s1_i.norm(dim=1)[:, None], s2_j.norm(dim=1)[:, None]
a_norm = s1_i / torch.clamp(a_n, min=eps)
b_norm = s2_j / torch.clamp(b_n, min=eps)
sim_mt = torch.mm(a_norm, b_norm.transpose(0, 1))
return self.tanh(sim_mt * self.a + self.b)
def get_DTW_score(self, s1, s2, return_map = False, crossing = False):
"""
Method that helps in getting DTW similarity score given embeddings of tokens in sentences.
"""
if len(s1) < len(s2):
s1, s2 = s2, s1
I = len(s1)
J = len(s2)
sim_mat = self.score(s1, s2)
if crossing:
sim = torch.sum(torch.max(sim_mat, dim = 0)[0]) / J
if return_map:
k = torch.argmax(sim_mat,dim=0)
return sim.reshape(1), k
return sim.reshape(1)
else:
M = sim_mat > 0
P = torch.zeros((I,J))
K = torch.zeros((I,J), dtype = torch.int)
for i in range(I):
P[i][0] = sim_mat[i][0]
K[:,0] = -1
for j in range(1, J):
max_val = float('-inf')
ptr = None
P[0][j] = max(0, sim_mat[0][j])
for i in range(1,I):
if max_val < P[i-1][j-1]:
max_val = P[i-1][j-1]
ptr = i-1
P[i][j] = max_val + max(0, sim_mat[i][j])
K[i][j] = ptr
# print("sim_mat")
# print(sim_mat)
# print("P")
# print(P)
# print("M")
# print(M)
# print("K")
# print(K)
if return_map:
m = [None] * J
k = [None] * J
I_prime = int(torch.argmax(P,dim=0)[J-1])
m[-1] = I_prime
K[I_prime][0] = I_prime
if M[I_prime][J-1]:
k[-1] = m[-1]
else:
k[-1] = None
for j in range(J-2, -1, -1):
m[j] = K[m[j+1]][j+1]
if m[j] == -1 or m[j] == None:
break
if M[m[j]][j]:
k[j] = m[j].item()
else:
k[j] = None
return P[I-1][J-1].reshape(1), k
return P[I-1][J-1].reshape(1) / J
assignment2/code/Q1.py 0 → 100644
View file @ 98dae565
import torch
from transformers import BertModel, AutoTokenizer
from torch.utils.data import DataLoader
from transformers import AdamW
from tqdm import tqdm
from dataset import *
from datasets import load_dataset, load_metric
from functools import partial
import os
import argparse
class STSBERTModel(torch.nn.Module):
"""
Bert-tiny Model with a linear layer at the end to predict the value of similarity score
"""
def __init__(self, pre_trained_model_name):
"""
Init a bert model and a linear layer with a dropout layer in between
"""
super(STSBERTModel, self).__init__()
self.bert_model = BertModel.from_pretrained(pre_trained_model_name, return_dict=False)
self.bert_drop = torch.nn.Dropout(0.3)
self.fnn = torch.nn.Linear(128, 1)
def forward(self, input_ids, attention_mask, token_type_ids):
"""
Pass the input sent1 and sent2 together to bert model and use the pooled_output from it to predict similarity score
"""
_, pooled_output = self.bert_model(input_ids = input_ids, attention_mask = attention_mask, token_type_ids = token_type_ids)
output = self.fnn(self.bert_drop(pooled_output))
return output
def all_to_all_collate_fn(data, tokenizer):
"""
A helper function to create a batch of encoded sentence so that it can be passed to the bert model
"""
input_sents = [i[0] for i in data]
labels = [i[1] for i in data]
input_encoded = tokenizer.batch_encode_plus(input_sents, padding="max_length", max_length = 32, truncation=True, return_tensors='pt')
return input_encoded, labels
class STSBERTModelTrainer:
"""
A class that can help in finetuning the bert-tiny for the STS task.
"""
def __init__(self,pre_trained_model_name, device):
"""
Initialise the STSBERTModel along with tokenizer and optimiser
"""
self.task = "stsb"
self.device = device
self.model = STSBERTModel(pre_trained_model_name).to(device)
self.tokenizer = AutoTokenizer.from_pretrained(pre_trained_model_name)
self.optimizer = AdamW(self.model.parameters(), lr=1e-3)
self.metric = load_metric('glue', self.task)
return
def freeze_bert(self,_freeze=True):
"""
Method to freeze the bert params for some of the epochs.
"""
for param in self.model.bert_model.parameters():
param.requires_grad = not _freeze
def train(self, train_loader, val_loader, EPOCH, fz_bert_epoch):
"""
Method to train the model.
For first fx_ber_epoch freeze the bert params and only train the params in linear layer.
"""
self.freeze_bert()
for epoch in range(EPOCH):
print("Epoch ", epoch, ":")
if epoch == fz_bert_epoch:
self.freeze_bert(_freeze = False)
for g in self.optimizer.param_groups:
g['lr'] = 1e-5
self.model.train()
train_loss = 0
for inputs, labels in tqdm(train_loader):
input_ids = torch.tensor(inputs['input_ids']).to(self.device)
input_attention_mask = torch.tensor(inputs['attention_mask']).to(self.device)
input_token_type_ids = torch.tensor(inputs['token_type_ids']).to(self.device)
labels = torch.tensor(labels).to(self.device)
self.optimizer.zero_grad()
outputs = self.model(input_ids = input_ids, attention_mask = input_attention_mask, token_type_ids = input_token_type_ids)
loss = torch.nn.functional.mse_loss(outputs, labels.view(-1, 1))
loss.backward()
self.optimizer.step()
train_loss += loss.item()
train_loss /= len(train_loader)
print("Train Loss:", train_loss)
val_loss, (targets, preds) = self.eval(val_loader)
return
def eval(self, val_loader, save_file = None):
"""
Method to perform evaluation on the model.
"""
self.model.eval()
val_loss = 0
with torch.no_grad():
predictions = []
targets = []
for inputs, labels in val_loader:
input_ids = torch.tensor(inputs['input_ids']).to(self.device)
input_attention_mask = torch.tensor(inputs['attention_mask']).to(self.device)
input_token_type_ids = torch.tensor(inputs['token_type_ids']).to(self.device)
labels = torch.tensor(labels).to(self.device)
outputs = self.model(input_ids = input_ids, attention_mask = input_attention_mask, token_type_ids = input_token_type_ids)
loss = torch.nn.functional.mse_loss(outputs, labels.view(-1, 1))
val_loss += loss.item()
outputs = outputs.squeeze()
predictions.extend(outputs.tolist())
targets.extend(labels)
val_loss /= len(val_loader)
print("Val Loss:", val_loss)
print(predictions)
print(targets)
print("Sim Metric:", self.metric.compute(predictions=predictions, references=targets))
if save_file:
print("Saving outputs in ", save_file)
with open(save_file,'w') as ofile:
for item, _ in enumerate(predictions):
ofile.write(str(predictions[item])+"\n")
return val_loss, (targets, predictions)
def save_model(self, model_dir = None):
"""
Method to save the model
"""
if not os.path.exists(model_dir):
os.makedirs(model_dir)
file_name = "all_to_all_bert"
file_name = model_dir + file_name + ".pt"
print("saving model into ", file_name)
torch.save(self.model.state_dict(),
file_name)
return
def load_model(self, model_dir = None):
"""
method to load the model.
"""
file_name = "all_to_all_bert"
file_name = model_dir + file_name + ".pt"
print("Loading from ", file_name)
self.model.load_state_dict(torch.load(file_name))
return
def test_sentence(self):
"""
Method to perform inference on the sentences.
"""
print("Press Ctrl+C to end this prompt:")
while True:
sent1 = str(input("Enter Sentence 1: "))
sent2 = str(input("Enter Sentence 2: "))
print("Sent1: ",sent1)
print("Sent2: ",sent2)
encodded_sent = self.tokenizer.encode_plus((sent1,sent2), return_tensors='pt')
print(encodded_sent)
self.model.eval()
with torch.no_grad():
sim_score = self.model(input_ids = encodded_sent['input_ids'], attention_mask = encodded_sent['attention_mask'], token_type_ids = encodded_sent['token_type_ids'])
print("Sim Score:", sim_score)
def main():
parser = argparse.ArgumentParser(description="STS using BERT-Tiny",
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument("-t", "--train", action='store_true', help="Whether to train the model")
parser.add_argument("-i", "--infer", action='store_true', help="Whether to infer using command line")
parser.add_argument("-e", "--epoch", default=30, help="number of epoch to train")
parser.add_argument("-b", "--batchsz", default=16, help="Size of the batch to use while training")
args = vars(parser.parse_args())
BATCH_SIZE = int(args["batchsz"])
EPOCH = int(args["epoch"])
train = args["train"]
infer = args["infer"]
task = "stsb"
fz_bert_epoch = 10
seed = 42
pre_trained_model_name = "prajjwal1/bert-tiny"
torch.manual_seed(seed)
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
print("Using device: ",device)
trainer = STSBERTModelTrainer(pre_trained_model_name, device)
if train:
print("Finetuning the Model")
dataset = load_dataset("glue", task)
train_dataset = STSDataset(dataset["train"])
val_dataset = STSDataset(dataset["validation"])
test_dataset = STSDataset(dataset["test"])
train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True, collate_fn =partial(all_to_all_collate_fn, tokenizer = trainer.tokenizer))
val_loader = DataLoader(val_dataset, batch_size=BATCH_SIZE,collate_fn = partial(all_to_all_collate_fn, tokenizer = trainer.tokenizer))
test_loader = DataLoader(test_dataset, batch_size=BATCH_SIZE,collate_fn = partial(all_to_all_collate_fn, tokenizer = trainer.tokenizer))
trainer.load_model(model_dir="./additional/models/")
#trainer.train(train_loader, val_loader, EPOCH, fz_bert_epoch)
#print("Testing for Validation dataset")
#trainer.eval(val_loader, "./output/Q1_val.txt")
print("Testing for Test dataset")
trainer.eval(test_loader, "./output/Q1_test.txt")
trainer.save_model("./additional/models/")
if infer:
print("Infering from the model")
trainer.load_model(model_dir="./additional/models/")
trainer.test_sentence()
if __name__ == "__main__":
main()
\ No newline at end of file
assignment2/code/Q3.py 0 → 100644
View file @ 98dae565
import torch
from DTW import *
from transformers import AutoTokenizer
from torch.utils.data import DataLoader
from transformers import AdamW
from tqdm import tqdm
from dataset import *
from datasets import load_dataset, load_metric
from functools import partial
import time
import os
import json
import pickle
import argparse
def getBack(var_grad_fn):
print(var_grad_fn)
for n in var_grad_fn.next_functions:
if n[0]:
try:
tensor = getattr(n[0], 'variable')
print(n[0])
print('Tensor with grad found:', tensor)
print(' - gradient:', tensor.grad)
print()
except AttributeError as e:
getBack(n[0])
def sep_collate_fn(data, tokenizer):
input_sents = [i[0] for i in data]
sent1_batch = [sent1 for sent1, _ in input_sents]
sent2_batch = [sent2 for _, sent2 in input_sents]
labels = [i[1] for i in data]
sent1_encoded = tokenizer(sent1_batch, padding="max_length", max_length = 32, truncation=True, return_tensors='pt')
sent2_encoded = tokenizer(sent2_batch, padding="max_length", max_length = 32, truncation=True, return_tensors='pt')
return sent1_encoded, sent2_encoded, labels
class TDWModelTrainer:
"""
A class that can help in training the bert-tiny for the STS task using DTW sim score.
reduce the learing in half after each epoch.
"""
def __init__(self,pre_trained_model_name, device, crossing = False):
self.task = "stsb"
self.device = device
self.model = DTW(pre_trained_model_name, crossing = crossing).to(device)
self.tokenizer = AutoTokenizer.from_pretrained(pre_trained_model_name)
self.optimizer = AdamW(self.model.parameters(), lr=0.005)
self.metric = load_metric('glue', self.task)
return
def train(self, train_loader, val_loader, EPOCH):
"""
Method to train the model.
"""
loss_history = {"train_loss":[], "val_loss":[]}
for epoch in range(EPOCH):
print("Epoch :", epoch)
if epoch and epoch%5 == 0:
print("reducing learing rate by a factor of 0.7")
for g in self.optimizer.param_groups:
g['lr'] *= 0.7
self.model.train()
train_loss = 0
for sent1_encoded, sent2_encoded, labels in tqdm(train_loader):
self.model.train()
input_ids_1 = torch.tensor(sent1_encoded['input_ids']).to(self.device)
input_attention_mask_1 = torch.tensor(sent1_encoded['attention_mask']).to(self.device)
input_token_type_ids_1 = torch.tensor(sent1_encoded['token_type_ids']).to(self.device)
input_ids_2 = torch.tensor(sent2_encoded['input_ids']).to(self.device)
input_attention_mask_2 = torch.tensor(sent2_encoded['attention_mask']).to(self.device)
input_token_type_ids_2 = torch.tensor(sent2_encoded['token_type_ids']).to(self.device)
labels_t = (torch.tensor(labels).to(self.device)*2) / 5 - 1
self.optimizer.zero_grad()
outputs = self.model(input_ids_1 = input_ids_1,
attention_mask_1 = input_attention_mask_1,
token_type_ids_1 = input_token_type_ids_1,
input_ids_2 = input_ids_2,
attention_mask_2 = input_attention_mask_2,
token_type_ids_2 = input_token_type_ids_2
)
loss = torch.nn.functional.mse_loss(outputs.view(-1, 1), labels_t.view(-1, 1))
train_loss += loss.item()
loss.backward()
self.optimizer.step()
train_loss /= len(train_loader)
print("Train Loss:", train_loss)
loss_history["train_loss"].append(train_loss)
val_loss, (targets, preds) = self.eval(val_loader, save_file = "output/Q3_1_val.txt")
loss_history["val_loss"].append(val_loss)
return
def eval(self, val_loader, save_file=None):
"""
Method to perform evaluation on the model.
"""
self.model.eval()
val_loss = 0
max_lim = 1000
cnt = 0
with torch.no_grad():
predictions = []
targets = []
for sent1_encoded, sent2_encoded, labels in tqdm(val_loader):
input_ids_1 = torch.tensor(sent1_encoded['input_ids']).to(self.device)
input_attention_mask_1 = torch.tensor(sent1_encoded['attention_mask']).to(self.device)
input_token_type_ids_1 = torch.tensor(sent1_encoded['token_type_ids']).to(self.device)
input_ids_2 = torch.tensor(sent2_encoded['input_ids']).to(self.device)
input_attention_mask_2 = torch.tensor(sent2_encoded['attention_mask']).to(self.device)
input_token_type_ids_2 = torch.tensor(sent2_encoded['token_type_ids']).to(self.device)
labels_t = (torch.tensor(labels).to(self.device)*2) / 5 - 1
outputs = self.model(input_ids_1 = input_ids_1,
attention_mask_1 = input_attention_mask_1,
token_type_ids_1 = input_token_type_ids_1,
input_ids_2 = input_ids_2,
attention_mask_2 = input_attention_mask_2,
token_type_ids_2 = input_token_type_ids_2
)
loss = torch.nn.functional.mse_loss(outputs.view(-1, 1), labels_t.view(-1, 1))
val_loss += loss.item()
outputs = outputs.squeeze()
predictions.extend(outputs.tolist())
targets.extend(labels_t)
cnt += 1
if cnt == max_lim:
break
val_loss /= len(val_loader)
print("Val Loss:", val_loss)
print("Sim Metric:", self.metric.compute(predictions=predictions, references=targets))
if save_file:
print("Saving outputs in ", save_file)
with open(save_file,'w') as ofile:
for item, _ in enumerate(predictions):
ofile.write(str(predictions[item])+str(targets[item])+"\n")
return val_loss, (targets, predictions)
def save_model(self, model_dir = None):
if not os.path.exists(model_dir):
os.makedirs(model_dir)
file_name = "DTW"
if self.model.crossing:
file_name += "_crossing"
else:
file_name += "_non_crossing"
file_name = model_dir + file_name + ".pt"
torch.save({"a": self.model.a,"b": self.model.b},
file_name)
return
def load_model(self, model_dir = None):
file_name = "DTW"
if self.model.crossing:
file_name += "_crossing"
else:
file_name += "_non_crossing"
file_name = model_dir + file_name + ".pt"
print("Loading a,b from ", file_name)
try:
params = torch.load(file_name)
self.model.a = params["a"]
self.model.b = params["b"]
print("a,b: ",self.model.a, self.model.b)
except:
print("Failed to load model from ", file_name)
return
def test_sentence(self, crossing):
"""
Method to perform inference on the sentences.
"""
print("Press Ctrl+C to end this prompt:")
while True:
sent1 = str(input("Enter Sentence 1: "))
sent2 = str(input("Enter Sentence 2: "))
print(sent1)
print(sent2)
encoded_sent1 = self.tokenizer(sent1, return_tensors='pt')
encoded_sent2 = self.tokenizer(sent2, return_tensors='pt')
self.model.eval()
with torch.no_grad():
output1, _ = self.model.bert_model(input_ids = encoded_sent1['input_ids'], attention_mask = encoded_sent1['attention_mask'], token_type_ids = encoded_sent1['token_type_ids'])
output2, _ = self.model.bert_model(input_ids = encoded_sent2['input_ids'], attention_mask = encoded_sent2['attention_mask'], token_type_ids = encoded_sent2['token_type_ids'])
#print(output1)
s1_emb = torch.squeeze(output1)[1:-1]
s2_emb = torch.squeeze(output2)[1:-1]
sim_score, k = self.model.get_DTW_score(s1_emb, s2_emb, return_map = True, crossing = crossing)
tokenized_sent1 = self.tokenizer.convert_ids_to_tokens(encoded_sent1['input_ids'][0])[1:-1]
tokenized_sent2 = self.tokenizer.convert_ids_to_tokens(encoded_sent2['input_ids'][0])[1:-1]
if len(tokenized_sent1) < len(tokenized_sent2):
tokenized_sent1, tokenized_sent2 = tokenized_sent2, tokenized_sent1
print(tokenized_sent1)
print(tokenized_sent2)
print("Sim Score:", (sim_score[0] + 1)*5/2)
print("Allignment:", k)
for i,_k in enumerate(list(k)):
print(tokenized_sent2[i], "\t: ", tokenized_sent1[_k] if _k != None and _k != -1 else None)
def main():
parser = argparse.ArgumentParser(description="STS using BERT-Tiny",
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument("-c", "--crossing", action='store_true', help="Whether to allow crossing between allignment.")
parser.add_argument("-t", "--train", action='store_true', help="Whether to train the model")
parser.add_argument("-i", "--infer", action='store_true', help="Whether to infer using command line")
parser.add_argument("-e", "--epoch", default=1, help="number of epoch to train")
parser.add_argument("-b", "--batchsz", default=16, help="Size of the batch to use while training")
args = vars(parser.parse_args())
BATCH_SIZE = int(args["batchsz"])
EPOCH = int(args["epoch"])
train = args["train"]
infer = args["infer"]
crossing = args["crossing"]
task = "stsb"
pre_trained_model_name = "prajjwal1/bert-tiny"
seed = 42
if crossing:
c_name = "crossing"
else:
c_name = "non_crossing"
torch.autograd.set_detect_anomaly(True)
device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
print("Using device: ",device)
torch.manual_seed(seed)
trainer = TDWModelTrainer(pre_trained_model_name, device, crossing = crossing)
if train:
dataset = load_dataset("glue", task)
train_dataset = STSDataset(dataset["train"])
val_dataset = STSDataset(dataset["validation"])
test_dataset = STSDataset(dataset["test"])
train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True, collate_fn =partial(sep_collate_fn, tokenizer = trainer.tokenizer))
val_loader = DataLoader(val_dataset, batch_size=BATCH_SIZE,collate_fn = partial(sep_collate_fn, tokenizer = trainer.tokenizer))
test_loader = DataLoader(test_dataset, batch_size=BATCH_SIZE,collate_fn = partial(sep_collate_fn, tokenizer = trainer.tokenizer))
trainer.train(train_loader, val_loader, EPOCH)
print("Testing for Val dataset")
trainer.eval(val_loader, save_file = "./output/Q3_"+c_name+"_val.txt")
print("Testing for Test dataset")
trainer.eval(test_loader, save_file = "./output/Q3_"+c_name+"_test.txt")
trainer.save_model("./additional/models/")
if infer:
trainer.load_model("./additional/models/")
trainer.test_sentence(crossing)
if __name__ == "__main__":
main()
\ No newline at end of file
assignment2/code/dataset.py 0 → 100644
View file @ 98dae565
import torch
class STSDataset(torch.utils.data.Dataset):
"""
Dataset class to help in creating a batch of data.
"""
def __init__(self, data):
self.data = data
def __getitem__(self, index):
text1 = self.data[index]['sentence1']
text2 = self.data[index]['sentence2']
label = self.data[index]['label']
return (text1, text2), label
def __len__(self):
return len(self.data)
assignment2/output/Q1_test.txt 0 → 100644
View file @ 98dae565
2.204585552215576
4.268622875213623
4.22967004776001
4.21016263961792
2.538215160369873
3.13405704498291
3.9848427772521973
3.922865390777588
3.1657865047454834
1.9527584314346313
1.9527584314346313
4.216670513153076
0.5931682586669922
4.095262050628662
1.8867285251617432
2.911790609359741
4.193086624145508
3.3037734031677246
2.8352441787719727
0.9007745981216431
3.0374293327331543
0.8923555016517639
4.341912746429443
4.067165374755859
2.206681728363037
3.900738477706909
1.540726661682129
4.140501499176025
1.1207126379013062
3.73284912109375
4.072742938995361
4.148754119873047
0.6844601631164551
4.289888858795166
3.580808639526367
0.26276707649230957
1.2232558727264404
3.5002894401550293
3.9361703395843506
0.5311264395713806
4.0208659172058105
3.9276366233825684
3.7596797943115234
0.42354053258895874
1.924552083015442
0.7631773948669434
-0.087535560131073
0.6683633327484131
4.284327507019043
3.637314796447754
1.425978660583496
1.5605360269546509
1.3025596141815186
0.2870612144470215
2.352590322494507
0.5073016285896301
4.009466171264648
1.4854273796081543
2.70924711227417
1.9773725271224976
4.141218185424805
0.4764832854270935
3.96152925491333
1.0469579696655273
3.678271532058716
0.6494287252426147
0.4268672466278076
3.778048515319824
-0.17116320133209229
3.9591622352600098
3.1505532264709473
3.8390350341796875
0.5555895566940308
3.9692561626434326
3.555508613586426
0.768054723739624
1.1191422939300537
0.028841137886047363
3.7886195182800293
4.213759899139404
0.2576594352722168
1.5200581550598145
2.709832191467285
2.9437599182128906
0.7998208999633789
1.837660789489746
4.16210412979126
3.696789026260376
4.284181118011475
2.051717758178711
0.9245947599411011
3.5821876525878906
1.7689745426177979
4.113664627075195
2.2724952697753906
4.028924942016602
3.711561441421509
3.947017192840576
2.891315221786499
2.5137200355529785
0.14077013731002808
1.9987720251083374
3.993828773498535
3.0947868824005127
3.653830051422119
3.5270168781280518
0.3763049244880676
3.1776275634765625
1.1043623685836792
4.177091598510742
3.9846549034118652
4.116565704345703
3.8290300369262695
3.9530043601989746
-0.018134132027626038
0.6101118326187134
3.9937095642089844
3.1833338737487793
0.7474756240844727
0.8512163162231445
1.3658077716827393
0.6397415995597839
1.2338975667953491
0.6876877546310425
1.0366610288619995
4.193507194519043
0.9330484867095947
2.7680037021636963
4.23995304107666
4.2114081382751465
3.4562153816223145
3.027848720550537
3.669175863265991
4.082447528839111
3.7645461559295654
3.9694676399230957
4.254114627838135
3.0742292404174805
1.802527666091919
4.214553356170654
4.134115219116211
4.1032304763793945
3.78920841217041
4.147985935211182
3.320962905883789
1.9815113544464111
3.7546236515045166
3.867135763168335
2.333151340484619
0.24406445026397705
3.7006337642669678
3.2211012840270996
3.8186609745025635
3.7072010040283203
3.75209903717041
4.0097150802612305
-0.0642385482788086
1.2905118465423584
4.006117343902588
0.6389436721801758
4.242687702178955
3.6045265197753906
0.9939298629760742
3.3288164138793945
3.7287511825561523
4.19251823425293
3.8968024253845215
3.9706788063049316
4.036952018737793
3.714329719543457
3.965460777282715
0.7431516647338867
3.9924025535583496
3.2767210006713867
3.1150829792022705
4.209873676300049
4.097031593322754
3.7202892303466797
0.3853882849216461
0.41985833644866943
0.5338742733001709
3.8938095569610596
2.745762825012207
0.8323241472244263
2.207554817199707
0.10231096297502518
0.23112045228481293
0.4252784550189972
0.8959102630615234
0.5347373485565186
3.9549572467803955
4.046743392944336
1.1940748691558838
3.4346096515655518
4.128897666931152
4.170884132385254
3.8481411933898926
0.9762791395187378
0.3925221562385559
3.0338778495788574
1.5236852169036865
0.9896140098571777
1.6495832204818726
1.3636809587478638
0.07602114975452423
1.3649709224700928
3.885329484939575
-0.28563427925109863
2.148533344268799
1.878408432006836
1.2549071311950684
0.8264661431312561
0.9742549657821655
1.4554393291473389
0.9226682186126709
1.2373031377792358
2.2712595462799072
4.100213050842285
4.077226638793945
2.9036717414855957
0.23359325528144836
0.16574713587760925
0.6231483221054077
3.5449013710021973
0.6918799877166748
0.3496341109275818
3.1693148612976074
2.224733829498291
1.070153832435608
2.511651039123535
0.7174893021583557
0.002701282501220703
3.8005852699279785
3.168255090713501
1.8911857604980469
3.3101000785827637
0.2779991328716278
0.5223654508590698
1.7433624267578125
0.15342837572097778
1.3655941486358643
0.5246554613113403
0.43955379724502563
0.38582703471183777
3.0792906284332275
0.925871729850769
4.028903007507324
1.4212100505828857
0.39088356494903564
0.2973439693450928
3.58062744140625
4.1334123611450195
3.181452989578247
2.380309581756592
2.8941917419433594
3.994865655899048
1.2915363311767578
3.899714231491089
3.746788263320923
0.34633785486221313
4.305138111114502
4.045711040496826
4.007551193237305
4.306821346282959
3.5171570777893066
2.5292747020721436
1.074172854423523
3.8195133209228516
1.0643001794815063
4.113521099090576
4.11827278137207
0.6611203551292419
1.850414514541626
3.9417054653167725
3.927440643310547
4.103106498718262
0.4468117356300354
0.24791644513607025
3.391777515411377
3.157003164291382
1.107882022857666
3.2398900985717773
3.955022096633911
3.5342936515808105
3.7283248901367188
3.434338331222534
2.360366106033325
3.840742588043213
4.012157917022705
1.1230562925338745
4.032074928283691
1.0959166288375854
2.768723964691162
3.9201908111572266
0.5802860260009766
1.0417081117630005
2.5415146350860596
3.919908046722412
3.6490530967712402
4.124425888061523
4.225342273712158
3.613391160964966
2.1816043853759766
3.8469913005828857
2.429241895675659
3.9657270908355713
3.8833210468292236
3.809624671936035
4.189826488494873
3.8898797035217285
2.5470404624938965
0.5816526412963867
1.783491849899292
3.5859146118164062
2.926243305206299
3.968388080596924
3.6722218990325928
3.860572099685669
0.26460814476013184
0.14574038982391357
3.7133407592773438
3.833767890930176
3.8176040649414062
3.431006908416748
4.183780670166016
0.46367788314819336
1.9921832084655762
4.004555702209473
4.1969757080078125
3.7545878887176514
4.10854434967041
3.5774219036102295
4.091399669647217
3.8951728343963623
3.1212620735168457
1.2232385873794556
4.047663688659668
3.6582605838775635
0.748198926448822
0.7686685919761658
3.82212495803833
4.151884078979492
4.036060333251953
2.9151368141174316
2.8004207611083984
1.265897512435913
4.033194065093994
0.2758989632129669
3.016477346420288
0.5586032867431641
3.959538459777832
4.105883598327637
0.7293495535850525
3.8839786052703857
3.69623064994812
0.31879520416259766
2.7809643745422363
4.050870895385742
1.2252899408340454
3.667612075805664
4.174252033233643
4.02318000793457
3.0743699073791504
3.587979316711426
4.1343159675598145
0.3572651147842407
1.016516923904419
3.214452028274536
4.000436782836914
4.121170520782471
4.044026851654053
3.9191536903381348
0.5113012194633484
1.1874151229858398
1.6510722637176514
3.8631350994110107
3.527721405029297
0.4592244625091553
1.9202795028686523
2.564655065536499
4.058722496032715
4.023948669433594
3.3728830814361572
0.5920166969299316
3.760852336883545
2.2880964279174805
4.0405168533325195
2.5838403701782227
3.7409427165985107
2.368237018585205
3.900263786315918
3.222666025161743
0.2944023013114929
3.0495243072509766
3.4831786155700684
1.0820775032043457
1.908735990524292
4.178627014160156
3.9092910289764404
3.8637032508850098
2.208564043045044
1.4503107070922852
1.689321756362915
3.996506690979004
3.781949520111084
4.061701774597168
0.401395320892334
3.6661343574523926
3.5941362380981445
3.599762201309204
4.097150802612305
4.15334415435791
1.6573026180267334
2.9014170169830322
0.8692797422409058
2.5362343788146973
0.15532255172729492
4.2196364402771
3.5519485473632812
4.035533905029297
4.125896453857422
1.9986681938171387
4.240803241729736
1.4702787399291992
2.6248552799224854
2.323726177215576
0.6271153688430786
4.21581506729126
3.9748759269714355
3.9235422611236572
0.8849151730537415
4.145613670349121
0.3399469554424286
3.737823724746704
2.2218074798583984
2.875561475753784
2.8491339683532715
4.26470422744751
3.8834123611450195
3.9652466773986816
3.555908679962158
3.2866249084472656
0.35185444355010986
4.190305709838867
3.106873035430908
0.27418002486228943
1.762959599494934
3.7595033645629883
1.4130959510803223
1.726874828338623
3.8017687797546387
4.200326919555664
0.32426923513412476
0.3654612898826599
4.332944393157959
1.5305383205413818
1.519986629486084
4.130776882171631
1.4334790706634521
3.5955581665039062
1.5421955585479736
1.7659032344818115
0.09113450348377228
1.9878239631652832
3.926858901977539
3.84139347076416
2.723705291748047
4.285953044891357
4.051733016967773
1.033191204071045
3.2930078506469727
4.141758918762207
2.3860883712768555
4.18937349319458
3.7664377689361572
3.230217456817627
0.8536214828491211
3.2297844886779785
4.168120384216309
2.4857499599456787
4.026987075805664
2.044095039367676
4.023269176483154
0.7073718309402466
4.079108238220215
1.2395055294036865
3.4831724166870117
4.169806480407715
3.160738468170166
4.255504608154297
4.122656345367432
3.6967475414276123
3.8922970294952393
4.013141632080078
3.2834672927856445
3.4406697750091553
0.7628328800201416
4.017867088317871
0.6101071834564209
4.1300787925720215
1.8675618171691895
3.2414069175720215
1.2594667673110962
3.5774590969085693
3.5685653686523438
3.8544774055480957
2.850003719329834
2.397460460662842
3.777930498123169
0.915459156036377
1.6263571977615356
3.738898277282715
2.432138442993164
1.7255628108978271
3.609333038330078
2.7020840644836426
3.5284581184387207
3.008988857269287
3.227698564529419
4.177487373352051
2.084710121154785
4.1766815185546875
4.1646528244018555
3.2559709548950195
2.9155004024505615
3.6023523807525635
3.1250970363616943
2.054173707962036
3.1113533973693848
2.1213297843933105
1.6953158378601074
2.111070156097412
3.275928497314453
1.5232239961624146
1.8151177167892456
1.0360900163650513
3.1658132076263428
2.2055864334106445
2.823270082473755
3.1879079341888428
4.362632751464844
2.47200345993042
3.493286371231079
4.1694536209106445
4.15599250793457
4.237115383148193
0.8622631430625916
3.583256483078003
2.4431066513061523
3.7539379596710205
3.001281976699829
3.1994783878326416
4.168881416320801
3.9846720695495605
3.3928916454315186
3.9230105876922607
3.5027284622192383
3.884385824203491
3.983201026916504
2.2713229656219482
3.6158738136291504
1.8363829851150513
2.622026205062866
1.9263498783111572
2.440225124359131
3.9732985496520996
3.895411252975464
3.2906312942504883
1.8136234283447266
3.017449378967285
3.811182975769043
3.199749708175659
0.7407671809196472
3.902496814727783
3.848863124847412
4.190145969390869
4.093367576599121
3.8207859992980957
3.4497616291046143
2.6844398975372314
3.0412309169769287
3.800513744354248
3.965331554412842
1.621251106262207
3.2947208881378174
3.8976364135742188
1.4272193908691406
4.083410263061523
3.498744487762451
3.4914135932922363
1.9746659994125366
4.138007640838623
3.8427937030792236
1.323255181312561
2.223194122314453
4.16475772857666
3.476097345352173
0.803048312664032
3.9978530406951904
3.9927539825439453
3.766254425048828
1.7185688018798828
3.6836304664611816
1.1531041860580444
3.7585229873657227
3.934262752532959
3.9123809337615967
3.184687614440918
4.031811714172363
4.186816215515137
4.022461891174316
3.579178810119629
2.554927349090576
2.187636137008667
3.5029892921447754
3.1653761863708496
2.9796440601348877
1.2217094898223877
3.833484172821045
4.236350059509277
1.8621318340301514
3.8708128929138184
1.0117056369781494
4.048001289367676
3.6677823066711426
3.978513479232788
3.4271438121795654
1.1591503620147705
2.3962156772613525
2.876826763153076
2.4961516857147217
2.4232919216156006
2.1129040718078613
3.3494887351989746
2.4354300498962402
2.097792387008667
2.6683874130249023
2.7958626747131348
2.5023202896118164
3.4260549545288086
2.677506446838379
3.6290862560272217
2.8173184394836426
3.5099518299102783
2.7415778636932373
3.6502609252929688
3.324610710144043
2.8620190620422363
3.5999338626861572
2.889312982559204
2.827139377593994
3.8063127994537354
1.8677728176116943
1.371519684791565
2.9509243965148926
2.7809643745422363
3.677133560180664
1.480912446975708
2.9777469635009766
3.0994725227355957
2.9732816219329834
3.9746596813201904
2.32851505279541
3.411362648010254
3.4747490882873535
2.7035317420959473
2.789412021636963
2.1565561294555664
3.3508858680725098
2.66587495803833
2.7158336639404297
3.6734232902526855
3.783700942993164
3.6803841590881348
3.522482395172119
1.1497713327407837
3.1048383712768555
2.7276053428649902
2.2022247314453125
1.6741820573806763
2.7928578853607178
2.1573548316955566
2.914348602294922
2.84724760055542
2.527454376220703
1.7534992694854736
2.664222478866577
1.8554644584655762
2.3204238414764404
3.4232661724090576
3.205474853515625
2.3368000984191895
3.5222089290618896
2.5091071128845215
3.08836030960083
1.8843144178390503
2.916583299636841
1.894789457321167
2.20941162109375
2.9188477993011475
2.2530064582824707
2.823923110961914
3.0761828422546387
3.4745290279388428
3.0413107872009277
1.9666202068328857
3.265302896499634
2.2668473720550537
3.1091067790985107
4.063827037811279
3.5941104888916016
1.7046635150909424
2.229962110519409
3.7194344997406006
2.2452445030212402
2.2316155433654785
3.2301454544067383
2.4038288593292236
1.426306962966919
3.1777398586273193
3.5023345947265625
2.7198781967163086
2.7455978393554688
2.115670680999756
3.187445640563965
2.7004921436309814
3.0510880947113037
3.574307918548584
2.6613411903381348
2.959829330444336
2.4593849182128906
2.5026321411132812
2.9815430641174316
1.493607521057129
2.985661506652832
2.0682926177978516
2.881197452545166
2.299107551574707
2.285719871520996
3.077348232269287
2.43035626411438
2.8734519481658936
2.9549152851104736
3.435194492340088
2.4344725608825684
2.7565855979919434
3.1797266006469727
3.1471357345581055
3.2732131481170654
1.5011301040649414
2.4258694648742676
2.3722496032714844
2.1983485221862793
1.9955461025238037
2.716002941131592
3.5030665397644043
2.2912843227386475
2.356198787689209
2.467372179031372
2.6326406002044678
3.6242756843566895
0.9424563646316528
0.871536910533905
3.555285930633545
3.0800647735595703
3.3155438899993896
3.6492931842803955
2.442690849304199
2.7669730186462402
2.3472304344177246
2.919656276702881
3.320697784423828
2.8947503566741943
2.9784786701202393
3.218433380126953
3.233319044113159
2.661097526550293
1.2656402587890625
1.7454938888549805
2.934735059738159
3.332981586456299
2.6818535327911377
2.9452362060546875
3.4483675956726074
2.1923229694366455
2.1509649753570557
3.267011880874634
3.2668094635009766
3.997032642364502
2.422390937805176
2.719909191131592
2.567054271697998
1.9131014347076416
2.6439504623413086
2.496041774749756
3.706394672393799
3.226790428161621
3.3922009468078613
1.188932180404663
3.520689010620117
2.714989423751831
2.668609142303467
2.531825065612793
1.4498224258422852
3.079465627670288
3.2290728092193604
2.9617395401000977
3.8564834594726562
3.3724982738494873
1.819014310836792
3.347769260406494
2.6830976009368896
1.6350457668304443
2.547574281692505
2.5953125953674316
1.8992726802825928
2.133289337158203
2.9514355659484863
3.8090085983276367
3.081885814666748
2.3103959560394287
2.001293897628784
2.8504951000213623
3.1167593002319336
3.013113498687744
2.353611469268799
2.999631881713867
2.2588179111480713
2.7193374633789062
3.6914968490600586
2.1189820766448975
2.6802420616149902
3.416151523590088
3.4040374755859375
4.064762592315674
1.6552467346191406
3.3244032859802246
1.282702088356018
2.4130873680114746
2.9638583660125732
2.655515432357788
2.8912527561187744
2.168994426727295
2.1574525833129883
3.7250595092773438
1.6120487451553345
3.4616482257843018
3.2872400283813477
3.152439832687378
3.202380418777466
2.9770054817199707
2.622859001159668
3.854980707168579
3.4933857917785645
3.567096710205078
2.993173837661743
1.9165010452270508
2.647346019744873
3.5381107330322266
2.911900043487549
2.952793598175049
3.538034439086914
3.7348718643188477
1.9692522287368774
2.7066566944122314
1.691231369972229
2.4714198112487793
1.7133309841156006
1.2178564071655273
3.423809051513672
2.354177951812744
2.522347927093506
2.860720634460449
3.2457046508789062
2.377641201019287
4.0019612312316895
3.404592514038086
3.1771528720855713
3.435624122619629
2.5741286277770996
3.970654249191284
3.330420970916748
3.5950937271118164
4.428585529327393
2.451598644256592
4.149481773376465
4.189699172973633
4.241303443908691
2.871703624725342
3.592170238494873
3.981966972351074
3.8131918907165527
1.4373146295547485
4.134859085083008
4.323213577270508
4.187461853027344
2.8073441982269287
4.108811855316162
3.843576669692993
3.451328992843628
3.945903778076172
4.1820454597473145
2.522538185119629
3.4653334617614746
4.0888471603393555
3.7630088329315186
3.526545286178589
3.2379817962646484
3.60249662399292
3.446348190307617
3.4409475326538086
2.4502434730529785
3.8117728233337402
3.907731533050537
2.22035551071167
4.151150226593018
1.5090172290802002
4.0459370613098145
3.3633666038513184
3.8018639087677
3.757166624069214
3.8305840492248535
3.960357666015625
3.9670863151550293
3.586404800415039
1.0976784229278564
3.105462074279785
3.455697774887085
2.954180955886841
3.9170002937316895
2.6813931465148926
4.014956474304199
3.8156790733337402
3.485410690307617
3.1805191040039062
3.034236431121826
2.6137022972106934
3.719113349914551
4.061175346374512
2.4824297428131104
4.005359649658203
3.9146554470062256
3.0955305099487305
4.071793079376221
3.0339767932891846
3.862881660461426
2.6243557929992676
1.5579867362976074
3.5127174854278564
2.7046732902526855
3.639111042022705
2.3092269897460938
3.5098862648010254
3.499527931213379
4.032874584197998
2.1120412349700928
3.956925630569458
2.4963860511779785
4.0475754737854
3.9079177379608154
3.183938503265381
3.4142158031463623
4.012131214141846
3.2454190254211426
2.703672409057617
4.157742977142334
3.8240950107574463
3.3651368618011475
3.6837387084960938
3.80264949798584
3.0410308837890625
3.9951300621032715
3.9955921173095703
1.6806995868682861
4.259568214416504
4.267404079437256
3.4045281410217285
4.281628608703613
3.9475202560424805
3.6206564903259277
3.7557735443115234
4.006105899810791
1.9492114782333374
3.723886728286743
3.7672901153564453
3.628312587738037
4.072991847991943
4.088351249694824
2.8752944469451904
3.8131027221679688
4.171813011169434
3.75803542137146
3.363818407058716
2.036176919937134
2.9261341094970703
1.7543259859085083
2.873149871826172
3.468167543411255
2.676015853881836
1.002845048904419
3.709745407104492
3.329866886138916
3.5964784622192383
3.9109957218170166
1.8018970489501953
2.616096019744873
4.151650428771973
4.008337020874023
3.950070858001709
3.9679346084594727
3.961559772491455
3.912301540374756
4.055907249450684
3.177755832672119
3.9238436222076416
3.6679584980010986
3.73557448387146
4.027554035186768
3.700544595718384
3.5081584453582764
3.6090805530548096
3.3979830741882324
3.457479476928711
3.8064799308776855
1.626845121383667
4.078516006469727
3.2120349407196045
1.6372051239013672
3.622225046157837
2.838207483291626
3.486720561981201
2.375715970993042
4.0678510665893555
3.844557285308838
4.266331195831299
3.0669116973876953
4.263765811920166
3.857475519180298
3.566977024078369
4.021006107330322
3.7813873291015625
3.471967935562134
3.470689535140991
3.561500072479248
4.064175605773926
3.8799502849578857
3.2865424156188965
3.139179229736328
4.035240173339844
3.1528682708740234
4.01053524017334
3.942819118499756
4.1454668045043945
3.6354541778564453
3.9123482704162598
3.9687681198120117
4.204660415649414
4.199891567230225
3.5724525451660156
3.1496992111206055
3.9730722904205322
4.181248664855957
3.4535179138183594
3.5958352088928223
3.8269901275634766
1.2695081233978271
4.1738409996032715
3.5854434967041016
2.8057610988616943
3.441467761993408
1.9302616119384766
4.0651984214782715
2.370007038116455
0.6658170223236084
3.446661949157715
3.677456855773926
3.739060163497925
4.191532611846924
4.221350193023682
3.1172356605529785
3.087161064147949
4.053658962249756
3.2687039375305176
1.248862862586975
3.7099108695983887
4.180571556091309
3.0265164375305176
3.149597644805908
3.415346384048462
3.67962646484375
4.22526741027832
4.169485092163086
3.845402717590332
4.0588059425354
3.2834510803222656
3.293313503265381
4.307226181030273
3.9106032848358154
3.015906810760498
3.2253551483154297
4.057621002197266
3.8170595169067383
4.0347700119018555
3.25889253616333
3.8563485145568848
3.7444112300872803
4.169583320617676
3.5279688835144043
3.8436193466186523
3.341015338897705
1.9077732563018799
3.1559507846832275
2.6531622409820557
3.4015700817108154
3.789834976196289
4.190382957458496
3.965860366821289
3.0946059226989746
3.3987975120544434
3.940211772918701
4.042532444000244
2.0676345825195312
4.036958694458008
3.722801446914673
3.355480432510376
4.096410274505615
3.9125254154205322
3.8677282333374023
3.1904289722442627
3.445564031600952
1.748732089996338
3.3735151290893555
3.931546688079834
0.915431559085846
2.9473764896392822
3.8649678230285645
2.724245071411133
4.183091640472412
2.098426342010498
4.152894020080566
2.2324488162994385
1.231476068496704
3.197343349456787
4.113973617553711
2.3318898677825928
3.5337438583374023
3.0215463638305664
3.96932315826416
3.2484130859375
4.3354620933532715
0.782128095626831
0.062104225158691406
4.141258239746094
3.8862690925598145
4.359185218811035
3.415072441101074
2.8692870140075684
0.5889459848403931
3.75510835647583
2.093158721923828
4.149388313293457
3.4308035373687744
2.318730354309082
4.032384395599365
3.9897308349609375
1.1262965202331543
3.420366048812866
1.8146071434020996
3.1737236976623535
1.373910665512085
2.785667657852173
4.194201469421387
2.782848358154297
4.2231621742248535
1.3655555248260498
0.11312741041183472
4.107769966125488
2.8156871795654297
4.312873363494873
1.4570631980895996
1.564455270767212
3.6395912170410156
3.8472673892974854
2.986995220184326
1.4085307121276855
4.114579200744629
4.0489959716796875
3.4747445583343506
3.1579227447509766
3.8802199363708496
2.754687786102295
3.805311918258667
3.2686362266540527
2.2632668018341064
1.7690706253051758
3.96099591255188
3.809488296508789
4.2885518074035645
3.6720752716064453
3.628683090209961
3.076676368713379
2.919989585876465
4.02949333190918
3.9601452350616455
3.6545872688293457
3.053093910217285
3.8973464965820312
4.00202751159668
3.167875289916992
3.832010269165039
2.8751707077026367
3.7089858055114746
4.15380859375
4.281511306762695
4.336683750152588
4.134160041809082
2.1712708473205566
3.9730288982391357
2.5678532123565674
3.201664447784424
2.1916701793670654
2.7632851600646973
4.179856777191162
2.220087766647339
3.8283748626708984
3.411123275756836
3.906123399734497
3.7286934852600098
4.1784467697143555
3.097623825073242
4.106573104858398
3.207024097442627
3.7710986137390137
4.045925617218018
4.125419616699219
3.0058045387268066
3.1901488304138184
2.632148027420044
2.9924964904785156
2.583967685699463
2.253739833831787
4.107086181640625
4.11116886138916
4.135201930999756
0.5923386812210083
4.384982585906982
4.094935417175293
2.347768783569336
0.43734967708587646
3.984905481338501
4.254568576812744
4.203524112701416
3.4972543716430664
3.879706382751465
0.7849383354187012
4.195018768310547
2.986928939819336
4.21757698059082
3.129385471343994
4.11915397644043
3.604757308959961
1.8907580375671387
4.101376533508301
3.0262532234191895
3.3447954654693604
3.99057674407959
3.6031687259674072
3.242372512817383
2.424835681915283
2.5140087604522705
2.815380096435547
3.705578565597534
2.302084445953369
1.8525465726852417
3.6495234966278076
2.5139055252075195
1.4138250350952148
4.198213577270508
2.1945714950561523
4.187883377075195
-0.23039421439170837
0.23944580554962158
3.4490978717803955
4.324798107147217
2.571725368499756
3.931057929992676
4.244985103607178
1.4810787439346313
0.7376670837402344
1.9278035163879395
4.189142227172852
0.9588311910629272
3.9130818843841553
3.7042813301086426
2.8560264110565186
1.467069387435913
3.734957695007324
0.8171525001525879
1.3340792655944824
4.24511194229126
3.5062711238861084
4.145512580871582
3.7170794010162354
3.160120964050293
3.121347665786743
1.444432020187378
2.309558391571045
2.39339017868042
2.5888991355895996
2.975862979888916
3.3495192527770996
3.8468008041381836
1.9183229207992554
2.858934164047241
2.0809826850891113
4.254873752593994
4.082760810852051
4.021284103393555
2.8384337425231934
0.21590539813041687
4.257903099060059
1.9561376571655273
3.8489270210266113
3.3902738094329834
4.178413391113281
4.419675350189209
4.127485752105713
2.7490339279174805
2.1446197032928467
4.090049743652344
4.22894811630249
3.5387892723083496
2.240804672241211
3.793124198913574
3.6351912021636963
3.6489949226379395
4.045238494873047
4.115557670593262
1.49485182762146
3.4660327434539795
3.3980586528778076
4.250638484954834
3.600978136062622
1.8268159627914429
1.4650115966796875
2.4239680767059326
4.1865234375
4.1186909675598145
4.399143218994141
2.9166030883789062
4.066991806030273
4.395979881286621
3.8865878582000732
4.102135181427002
3.9150657653808594
2.3582558631896973
4.009209632873535
4.221920490264893
3.8509721755981445
2.6778383255004883
1.268721342086792
3.796499729156494
1.3349778652191162
2.029513120651245
4.0084228515625
2.843268394470215
1.8896939754486084
2.65800142288208
0.7672704458236694
2.975411891937256
2.182300567626953
0.8831702470779419
2.427326202392578
1.9090335369110107
0.9588481783866882
2.422584056854248
1.4866784811019897
0.6987799406051636
2.008223533630371
assignment2/output/Q1_val.txt 0 → 100644
View file @ 98dae565
4.128605365753174
4.07204532623291
4.130853176116943
3.043844223022461
3.8662428855895996
4.038922309875488
4.238485336303711
1.565281867980957
4.2629828453063965
3.838446617126465
3.054323434829712
1.7974015474319458
4.3046135902404785
4.185055732727051
4.025395393371582
1.3909058570861816
3.9686970710754395
4.199419021606445
4.308898448944092
1.235102891921997
3.5327036380767822
1.9527584314346313
3.9196717739105225
2.0159335136413574
1.2023625373840332
2.808532238006592
3.9627292156219482
3.7768588066101074
1.4165112972259521
0.21523720026016235
4.295955181121826
3.7375316619873047
4.1331610679626465
4.156222343444824
4.151782512664795
4.304687976837158
4.18415641784668
4.114779949188232
3.594174385070801
0.9685472249984741
3.056241035461426
3.8002238273620605
3.142197370529175
3.6026511192321777
2.3932912349700928
0.5355045795440674
0.27649059891700745
0.435438871383667
3.859454870223999
3.824906349182129
1.361511468887329
4.092378616333008
4.311104774475098
4.019438743591309
0.6621031165122986
1.912885069847107
0.15976658463478088
0.6349365711212158
1.8168212175369263
0.13928329944610596
1.7598904371261597
3.9550957679748535
1.1080524921417236
4.161272048950195
3.7217822074890137
0.3941272497177124
4.216000080108643
3.4541757106781006
3.5876474380493164
3.8158950805664062
4.157523155212402
2.0154809951782227
1.6994338035583496
1.0618289709091187
0.2219201922416687
4.129819393157959
2.4949822425842285
4.135270118713379
3.0078494548797607
4.139575958251953
2.5356945991516113
0.19687017798423767
0.21047961711883545
3.7801320552825928
4.124146461486816
0.4552741050720215
2.2911312580108643
3.880016803741455
3.8544089794158936
3.4248292446136475
3.943699359893799
2.1204686164855957
2.863079786300659
0.4069552421569824
1.027538776397705
4.093045234680176
0.9658591747283936
0.7283805012702942
0.1804734468460083
2.6277785301208496
0.146847665309906
0.6833200454711914
2.5336499214172363
2.799163818359375
0.4660385847091675
3.879992961883545
4.05007266998291
3.306148052215576
1.7376865148544312
0.7164512276649475
0.9244997501373291
0.3237069249153137
-0.08657240867614746
0.7432111501693726
0.41194117069244385
0.491563618183136
1.8987681865692139
3.6520981788635254
0.09996506571769714
0.25136101245880127
2.849426746368408
0.5112001299858093
1.4269375801086426
0.6363547444343567
1.829801321029663
2.9501962661743164
0.31292831897735596
0.15238195657730103
3.6372432708740234
3.575803756713867
0.1107461154460907
4.25669002532959
4.289065837860107
4.159102916717529
4.179471015930176
4.179471015930176
4.20279598236084
2.3629183769226074
3.7565884590148926
3.7389864921569824
1.0461132526397705
4.2387871742248535
4.295339584350586
3.896366834640503
3.986107349395752
2.370506763458252
1.084865927696228
1.1591041088104248
2.0159335136413574
1.4942467212677002
3.5368313789367676
3.413020133972168
4.1248884201049805
2.1379029750823975
0.5631330013275146
3.3361425399780273
0.8057950735092163
0.6477445363998413
2.3057799339294434
3.90198016166687
1.602712869644165
1.8286430835723877
4.223110675811768
3.7008748054504395
2.0279347896575928
2.9661221504211426
4.075876712799072
3.1617510318756104
1.580289602279663
3.895066261291504
3.958881378173828
3.0791313648223877
3.6453022956848145
0.10179030150175095
4.1738739013671875
4.192866325378418
0.7736901640892029
3.875249147415161
0.4624146819114685
3.6267433166503906
0.7498867511749268
1.5519230365753174
1.5759706497192383
0.7744120955467224
3.557255268096924
4.050985336303711
0.31036412715911865
4.1214189529418945
1.6845147609710693
1.6412127017974854
3.7074878215789795
2.620375871658325
3.6501951217651367
3.938140392303467
3.095803737640381
0.7779731750488281
0.3764294683933258
4.159549713134766
3.845608711242676
-0.2654731869697571
0.25138288736343384
0.39272889494895935
-0.17116320133209229
2.1246981620788574
1.0253627300262451
0.8740956783294678
0.7242664694786072
2.489119052886963
3.5914769172668457
0.4275599718093872
0.96406489610672
1.753609538078308
3.3494150638580322
0.5887155532836914
1.8056094646453857
0.5267149806022644
2.5239133834838867
0.4193304777145386
3.1987838745117188
2.2794532775878906
3.8449947834014893
0.3933141231536865
3.486142635345459
1.3914313316345215
4.2076568603515625
3.5239357948303223
0.3654123544692993
4.075706481933594
0.7431887984275818
0.3168233633041382
2.114851951599121
0.6841527223587036
0.16787447035312653
2.893321990966797
3.4304447174072266
3.288971185684204
2.543332576751709
2.0897750854492188
3.964430093765259
0.9596079587936401
0.7572076916694641
1.6427397727966309
1.108808159828186
3.1305994987487793
0.2885225713253021
0.25320965051651
-0.09300079941749573
3.2642529010772705
3.8383688926696777
1.4478026628494263
4.1803693771362305
0.19493034482002258
1.719170331954956
3.625214099884033
3.4759016036987305
3.6161575317382812
0.49809372425079346
3.9402549266815186
3.6076483726501465
2.8054375648498535
0.4718054533004761
1.2497749328613281
4.131682395935059
3.698639392852783
4.351049900054932
3.9780850410461426
3.090045928955078
3.583200454711914
3.7602479457855225
3.9393177032470703
4.032270908355713
2.2107863426208496
4.137082576751709
3.71793794631958
2.8969788551330566
3.6963205337524414
0.5670051574707031
4.066455841064453
3.7185611724853516
1.146043062210083
3.986126661300659
4.364851951599121
4.108275413513184
4.2006120681762695
0.5984405279159546
1.6010217666625977
3.728745698928833
0.8101900815963745
0.43258893489837646
3.6603803634643555
3.8459229469299316
0.49576395750045776
2.173196792602539
1.960850477218628
3.980008363723755
3.28426194190979
2.957679271697998
4.3778462409973145
0.4804472029209137
3.8195862770080566
4.193129539489746
0.03845486044883728
3.8490772247314453
3.964714527130127
3.7730088233947754
3.5103068351745605
4.219410419464111
4.235856533050537
0.40837717056274414
3.5440754890441895
0.8533081412315369
3.390399217605591
1.4859142303466797
2.213014602661133
3.211857557296753
0.37755027413368225
3.7763075828552246
3.4615697860717773
4.045281887054443
0.5029775500297546
3.4837646484375
4.12247896194458
2.903229236602783
0.8814868927001953
4.03516960144043
3.0325517654418945
3.9532597064971924
4.060519218444824
2.6916003227233887
3.4622974395751953
3.8235058784484863
2.6331050395965576
4.1716132164001465
0.4915691614151001
0.5993742942810059
4.0379252433776855
0.6721494197845459
3.292025566101074
3.975022792816162
3.9721837043762207
0.574897050857544
3.945284843444824
2.9935169219970703
3.657992124557495
3.3866753578186035
3.473876476287842
4.034271717071533
3.6696696281433105
2.828803539276123
4.024742603302002
4.114270210266113
4.05759859085083
3.820528507232666
3.8610734939575195
3.9117720127105713
3.8981523513793945
3.7694785594940186
3.8175671100616455
4.024271011352539
1.3235564231872559
0.14688017964363098
4.224354267120361
1.0357056856155396
0.7756690979003906
3.64823842048645
4.148571968078613
1.1931335926055908
3.9508590698242188
4.113339424133301
4.099958896636963
1.3675222396850586
3.661151170730591
3.5387911796569824
3.2297112941741943
4.248562812805176
4.252409934997559
3.990807056427002
3.956860303878784
3.9571890830993652
0.9828841686248779
0.9271281957626343
3.0925891399383545
3.85196590423584
1.4347478151321411
1.5616984367370605
4.248447895050049
3.789752244949341
4.254263401031494
2.796363353729248
4.035475730895996
4.195134162902832
3.7562806606292725
1.2612321376800537
2.6876869201660156
3.672297477722168
3.8533637523651123
4.046114921569824
4.197640419006348
4.005185604095459
3.7833824157714844
3.4811160564422607
1.2458362579345703
4.2115044593811035
1.3379111289978027
2.398442029953003
3.755635976791382
0.45162636041641235
0.9490146636962891
0.7632933855056763
4.214524745941162
4.022310256958008
1.9095916748046875
1.0698779821395874
1.3783957958221436
2.957247495651245
4.1428751945495605
4.239187717437744
4.1284894943237305
1.6670652627944946
4.003108978271484
3.2732155323028564
2.6369967460632324
0.5623276233673096
3.776150703430176
3.7908589839935303
4.257184982299805
3.1048710346221924
4.135127544403076
0.47422999143600464
3.9949951171875
2.361079216003418
4.053990364074707
0.5612978935241699
3.2589850425720215
4.003558158874512
1.137478232383728
3.6964502334594727
0.3745868504047394
0.5267276763916016
4.200436592102051
4.049684047698975
3.4395625591278076
0.36269688606262207
2.201523542404175
3.910367250442505
4.1257195472717285
1.1370213031768799
3.2017879486083984
4.118992805480957
2.5780158042907715
4.182631492614746
3.0170974731445312
3.9081873893737793
0.22363638877868652
1.1024707555770874
4.161696434020996
1.9220890998840332
0.14497721195220947
4.085472106933594
2.0673937797546387
4.111008644104004
3.6716818809509277
1.6303681135177612
1.9850506782531738
1.121994972229004
1.6781566143035889
1.8854048252105713
2.0016183853149414
2.2819442749023438
3.6530933380126953
3.8546454906463623
3.7210705280303955
3.8609249591827393
1.5754121541976929
4.001258373260498
4.135326385498047
4.151779651641846
0.21947365999221802
2.1202707290649414
4.186821937561035
4.217504978179932
1.1196497678756714
2.0603537559509277
2.3431711196899414
4.310740947723389
1.0241539478302002
0.9178615212440491
0.2642279863357544
2.8268473148345947
1.471798300743103
3.8319931030273438
0.43293696641921997
3.752040147781372
3.2107372283935547
2.435849189758301
0.5341640710830688
3.6649889945983887
2.8147010803222656
3.9283318519592285
4.105506420135498
1.5896942615509033
2.3447115421295166
2.8272011280059814
0.5952669382095337
3.7750465869903564
2.993121862411499
3.2395763397216797
3.933673858642578
2.2683262825012207
4.2145161628723145
1.2170180082321167
0.6308550834655762
4.1916656494140625
4.087553024291992
1.6737706661224365
4.253859043121338
2.692586660385132
3.6762590408325195
4.077296257019043
4.248506546020508
3.424544334411621
2.2181572914123535
1.902536153793335
2.6878790855407715
3.946408271789551
4.293278217315674
3.364683151245117
2.0384862422943115
2.466592788696289
2.7829270362854004
3.0719034671783447
3.270575523376465
4.206439971923828
2.8873729705810547
3.9888367652893066
3.944305181503296
2.9437038898468018
4.225691795349121
1.9229084253311157
4.108671188354492
2.656212091445923
1.6519511938095093
3.7866857051849365
3.885047435760498
1.0764522552490234
1.593332052230835
4.027289390563965
4.247429847717285
3.463963031768799
1.1569567918777466
3.110976219177246
2.7749836444854736
0.09429989755153656
2.628829002380371
1.9751421213150024
1.1655964851379395
3.981933832168579
3.717141628265381
1.1236865520477295
4.074878215789795
4.231761932373047
2.7748794555664062
4.119896411895752
3.8760218620300293
2.4863979816436768
3.2471227645874023
0.8524589538574219
0.9077104330062866
3.153751850128174
1.2553470134735107
0.8269932866096497
3.7425918579101562
3.2937474250793457
2.279031753540039
4.175418853759766
3.577456474304199
4.072186470031738
3.9236397743225098
1.8355233669281006
2.0538923740386963
2.2307677268981934
3.6942081451416016
0.7464651465415955
4.145880699157715
4.168877601623535
3.1740105152130127
4.250412464141846
2.1446800231933594
3.2998781204223633
2.2941484451293945
0.687652051448822
1.8962382078170776
2.519894599914551
1.1461001634597778
2.8374440670013428
3.902200698852539
4.284591197967529
2.977168083190918
1.1942931413650513
2.063044548034668
1.3837668895721436
3.6653518676757812
3.3929638862609863
1.3326115608215332
3.6205501556396484
3.9753527641296387
1.9114158153533936
2.903660297393799
3.7750606536865234
3.915499210357666
3.427236557006836
4.197238445281982
2.4744021892547607
4.371415615081787
3.5340960025787354
2.6288368701934814
3.5679941177368164
3.6867685317993164
3.446568489074707
3.934969902038574
2.2715494632720947
0.6535170078277588
3.960512638092041
3.998805046081543
4.037996292114258
3.4371538162231445
2.7128734588623047
3.134533405303955
2.54613995552063
2.0027718544006348
2.032881259918213
3.041717529296875
2.416592597961426
2.3240838050842285
2.989516258239746
3.206979990005493
2.128547191619873
2.620609760284424
1.8310153484344482
1.6379393339157104
1.325887680053711
2.647780179977417
2.999182939529419
3.584522247314453
3.761228561401367
3.5881621837615967
3.637058734893799
3.2208504676818848
2.21439528465271
3.0528321266174316
2.3140294551849365
2.086876153945923
3.8339171409606934
2.6402716636657715
2.770799398422241
3.7794392108917236
1.5716168880462646
2.6930952072143555
1.2835122346878052
2.5006251335144043
2.8555941581726074
3.7780346870422363
3.7929625511169434
2.976402521133423
3.1438612937927246
1.6453431844711304
3.443544864654541
1.979736566543579
1.9960206747055054
1.9636867046356201
2.342195510864258
2.7434537410736084
1.897343635559082
2.249213933944702
2.4554243087768555
1.8374035358428955
2.156735897064209
4.037466526031494
2.9846925735473633
3.186582088470459
3.8053832054138184
3.5269880294799805
1.7226155996322632
3.5740175247192383
3.8742668628692627
3.001032829284668
3.334223985671997
2.817960262298584
3.5327696800231934
1.5736312866210938
2.474863052368164
2.9592597484588623
1.567408800125122
1.0843086242675781
2.637881278991699
2.3601040840148926
3.257140636444092
3.664048194885254
2.5916123390197754
1.659776210784912
2.436537742614746
1.101436734199524
2.5526158809661865
2.4508955478668213
1.6331703662872314
3.0662288665771484
2.029155969619751
2.807128667831421
2.8188605308532715
3.2345681190490723
2.7784650325775146
2.871860980987549
3.4886703491210938
2.4761433601379395
3.025939702987671
2.6158323287963867
2.3768577575683594
3.545764446258545
3.2590794563293457
1.942899227142334
2.0502707958221436
2.868534803390503
2.0793304443359375
1.963768720626831
1.0626227855682373
3.4057493209838867
1.504377007484436
1.7595350742340088
2.258126974105835
2.3826494216918945
2.946676254272461
1.106104850769043
3.11582088470459
3.0893049240112305
1.5048185586929321
0.8973265290260315
3.3575656414031982
1.0552316904067993
2.420370578765869
3.4783246517181396
2.7761833667755127
0.8170542120933533
3.5044565200805664
1.9646389484405518
2.904611825942993
2.403233289718628
1.2970130443572998
3.0664162635803223
1.8278591632843018
2.3422927856445312
1.7683533430099487
3.0625085830688477
2.9377827644348145
2.4615695476531982
3.81282901763916
2.674189567565918
0.8461177945137024
1.5408642292022705
3.5104174613952637
2.8524088859558105
0.9124444127082825
2.6362557411193848
3.1868534088134766
2.7023746967315674
3.524034023284912
1.319366455078125
2.042477607727051
1.5535814762115479
3.5448007583618164
3.158714771270752
3.1890716552734375
3.6644997596740723
2.9576256275177
0.9549766778945923
2.9470529556274414
2.1333200931549072
2.8219106197357178
2.427706718444824
3.040755033493042
2.0148332118988037
3.3181281089782715
2.3944954872131348
2.9786038398742676
2.4929094314575195
3.8454294204711914
2.747823476791382
3.2091424465179443
3.760202407836914
3.4159107208251953
2.6456799507141113
0.8226180672645569
0.9881483316421509
1.2324516773223877
2.3887135982513428
2.8701932430267334
1.936137318611145
1.9187867641448975
1.546090006828308
1.137861728668213
1.357466459274292
3.5796756744384766
3.9741153717041016
3.3258538246154785
2.207919120788574
3.2618918418884277
2.093137741088867
1.6974323987960815
3.7355756759643555
3.3273766040802
2.3200888633728027
2.986983060836792
3.18196964263916
3.704777956008911
1.9137375354766846
1.2238105535507202
1.7448670864105225
2.9489753246307373
1.101912260055542
2.2028744220733643
0.7831481695175171
3.0238513946533203
2.975909471511841
1.5075868368148804
2.9523258209228516
4.008974552154541
1.8342080116271973
3.0735676288604736
1.5759375095367432
1.8719923496246338
3.4084184169769287
3.8708951473236084
2.8261451721191406
2.688663959503174
1.3984088897705078
2.7895872592926025
0.4553207755088806
3.5017576217651367
4.104626655578613
3.551923990249634
2.0590288639068604
1.2522695064544678
1.329089641571045
3.1749746799468994
2.581449031829834
1.3051931858062744
1.8922090530395508
2.485720157623291
3.754897117614746
3.885347604751587
3.452237129211426
1.4952083826065063
2.8001465797424316
1.911534070968628
3.065035581588745
1.4173966646194458
2.324942111968994
2.079653263092041
3.8246965408325195
2.444581985473633
3.292257308959961
3.020639419555664
2.0907399654388428
2.4268693923950195
3.7644271850585938
3.1716346740722656
3.102384567260742
3.2602314949035645
2.6310338973999023
1.1047838926315308
2.945314407348633
1.094306230545044
2.02492094039917
2.8283119201660156
1.880929708480835
2.5864710807800293
1.857602596282959
1.99009370803833
2.578479766845703
0.8701935410499573
0.9213889837265015
2.333061695098877
1.495367169380188
4.127182960510254
3.5965394973754883
3.687154769897461
2.471806049346924
2.852627754211426
3.2311716079711914
2.9142584800720215
2.293902635574341
1.2840150594711304
3.1364095211029053
2.9380083084106445
1.4811292886734009
1.4967732429504395
2.1223840713500977
3.70095157623291
1.4421370029449463
2.208409547805786
2.68643856048584
3.204339027404785
2.450584650039673
4.091588020324707
1.728611707687378
2.249032735824585
1.6943869590759277
2.541792631149292
3.426320791244507
2.332594394683838
1.5697752237319946
2.3922412395477295
3.3036623001098633
2.0035324096679688
1.5205754041671753
1.963168740272522
2.6079108715057373
1.4994196891784668
3.5527541637420654
1.8586297035217285
1.6528359651565552
2.0941762924194336
2.9189443588256836
2.6599106788635254
2.4184176921844482
2.303478717803955
1.5050578117370605
2.4795620441436768
1.4536161422729492
2.0538060665130615
3.0636556148529053
3.5450494289398193
1.4288097620010376
2.3762640953063965
1.7958152294158936
2.5699028968811035
2.290372133255005
2.042057752609253
2.2984256744384766
1.7605359554290771
1.7543272972106934
1.9789658784866333
4.148490905761719
4.001218795776367
3.8091938495635986
4.176745891571045
3.66593337059021
3.367063045501709
3.6328911781311035
2.107133626937866
1.6183698177337646
3.3045670986175537
1.4243338108062744
2.8270680904388428
2.8034777641296387
3.83347225189209
1.7311357259750366
3.578392505645752
2.3286149501800537
0.6389338970184326
1.7764029502868652
1.7174018621444702
2.126405715942383
1.6904195547103882
3.472149133682251
2.3622217178344727
4.119575500488281
3.609624147415161
3.0045995712280273
2.5813419818878174
3.0768280029296875
2.4790477752685547
3.6819612979888916
1.5276813507080078
2.232321262359619
0.7864236831665039
1.91879141330719
1.5124378204345703
2.3666422367095947
0.9273707270622253
2.0895512104034424
0.43330514430999756
2.508984088897705
2.0647621154785156
2.5931344032287598
3.3021841049194336
3.233820915222168
2.625603675842285
1.2954442501068115
2.8370349407196045
1.3817288875579834
2.0330517292022705
3.5607221126556396
2.2673614025115967
2.905043125152588
1.2564367055892944
2.6999166011810303
2.275068759918213
1.495496153831482
1.6996937990188599
2.404067039489746
2.467407703399658
3.5826287269592285
4.026861190795898
4.05046272277832
3.771310567855835
3.1608123779296875
4.298110485076904
3.5666279792785645
3.8070106506347656
4.010993957519531
3.7281880378723145
3.5805959701538086
3.9890248775482178
4.1277756690979
3.52227520942688
3.139216661453247
1.3779778480529785
3.7421741485595703
3.367854595184326
4.100214004516602
2.631417989730835
3.8705339431762695
4.237954616546631
3.469759702682495
3.333106517791748
3.875891923904419
3.413151979446411
3.5918664932250977
3.7338733673095703
3.8848299980163574
2.91080904006958
4.112238883972168
4.063419342041016
3.9682624340057373
3.8223774433135986
4.198999404907227
3.9703586101531982
4.23607873916626
2.6374526023864746
4.203510284423828
4.198048114776611
3.5584235191345215
3.9359941482543945
3.9718801975250244
3.9306869506835938
3.6174349784851074
3.2353129386901855
3.6649372577667236
1.4315526485443115
3.9836089611053467
3.8352482318878174
3.6334757804870605
3.8186514377593994
3.5587143898010254
4.07613468170166
4.214626312255859
4.011110305786133
3.9505791664123535
4.234018325805664
3.928159713745117
4.004326820373535
4.100790977478027
3.75589919090271
4.219642162322998
2.7618143558502197
3.519362449645996
3.5283849239349365
4.0203962326049805
4.224905967712402
2.905818223953247
3.5513200759887695
3.634197950363159
2.674196243286133
4.246693134307861
4.018249034881592
3.8385813236236572
3.9593586921691895
3.0009851455688477
4.095432758331299
3.978079319000244
3.175691604614258
3.682276964187622
2.93117356300354
3.3575778007507324
4.140615463256836
4.028690338134766
3.0170202255249023
1.9025020599365234
4.228100776672363
4.158764362335205
3.443183422088623
4.286705493927002
3.482786178588867
4.018675804138184
3.828139066696167
2.9843008518218994
4.126349925994873
2.848202705383301
1.7089836597442627
3.454190969467163
4.113520622253418
4.0761237144470215
1.012460470199585
4.020168304443359
3.507145881652832
4.081101894378662
4.049169540405273
4.242516994476318
3.4160499572753906
3.5215110778808594
3.576537609100342
3.9729127883911133
2.849454402923584
3.3523998260498047
3.9702558517456055
4.155759811401367
4.00446891784668
3.7837588787078857
4.1202311515808105
1.917685866355896
3.490473747253418
4.266808986663818
2.52243709564209
3.5746898651123047
3.3589084148406982
3.292792797088623
1.5798680782318115
3.8668463230133057
4.029325485229492
2.010124683380127
3.948760509490967
4.0556960105896
4.290992736816406
1.9614250659942627
3.580138683319092
3.4557676315307617
4.163055419921875
3.1020634174346924
2.770510196685791
3.550949811935425
3.3714935779571533
3.76082444190979
4.176273345947266
3.728944778442383
3.971296548843384
3.6481709480285645
3.428189754486084
2.8266868591308594
4.285388469696045
3.936389923095703
3.4774930477142334
3.777339458465576
3.803417682647705
3.694094657897949
4.008467197418213
3.059021234512329
4.074068546295166
3.6556551456451416
2.935330629348755
3.4707164764404297
4.121129989624023
4.212979793548584
3.9723196029663086
3.391789674758911
3.8575563430786133
1.5038318634033203
2.8403143882751465
2.6666741371154785
3.7514963150024414
3.888519287109375
3.5971622467041016
4.1270904541015625
3.880129337310791
4.001152038574219
3.9392123222351074
3.6900649070739746
3.339278221130371
3.4536807537078857
3.298318386077881
4.139178276062012
2.9647889137268066
3.3209235668182373
4.2671380043029785
2.738116502761841
4.224963665008545
3.908437728881836
3.9537110328674316
3.9198496341705322
3.1056065559387207
3.4263193607330322
4.097497940063477
3.38512921333313
4.253515243530273
4.162293434143066
4.2129998207092285
3.7187798023223877
3.515817642211914
2.6999692916870117
4.149524688720703
3.3431804180145264
3.682875394821167
3.3165547847747803
3.8818235397338867
3.69868540763855
3.897557020187378
4.201788902282715
4.094959259033203
3.3442845344543457
4.215356349945068
3.869886875152588
2.8010454177856445
2.3218350410461426
3.0428500175476074
3.9211254119873047
3.084341526031494
4.108670711517334
3.5487494468688965
3.208531141281128
3.7385501861572266
3.9974775314331055
4.045187950134277
4.098283767700195
4.212094783782959
4.082884788513184
4.159885406494141
4.202811241149902
3.72373628616333
3.685418128967285
3.085890293121338
4.007955551147461
3.416332244873047
4.149397850036621
3.491990804672241
3.508913516998291
4.293453693389893
4.0196990966796875
3.7678422927856445
1.3079562187194824
4.297908306121826
4.103168964385986
1.8876738548278809
3.4380455017089844
3.911125659942627
4.293051242828369
3.129167079925537
4.186346054077148
4.293062210083008
4.0410637855529785
3.1887946128845215
3.612229824066162
4.002974987030029
2.821427822113037
4.216020107269287
1.9710233211517334
1.6448352336883545
4.412578105926514
3.7319092750549316
1.694608449935913
4.043994426727295
3.2110238075256348
3.727672576904297
3.2979397773742676
1.07291579246521
4.087141036987305
3.9674184322357178
1.5989105701446533
2.5698492527008057
4.086360454559326
1.1720887422561646
3.705601453781128
3.2149128913879395
3.6757781505584717
1.2054316997528076
4.085790634155273
2.4535133838653564
3.493654489517212
1.2247908115386963
3.1195528507232666
3.574720859527588
3.719093084335327
2.9723944664001465
3.421799898147583
4.291075229644775
4.317409515380859
3.5022335052490234
3.644197940826416
2.030219078063965
1.5129501819610596
1.4273161888122559
2.1595256328582764
4.033815860748291
1.4068864583969116
3.904353141784668
2.834591865539551
2.4539432525634766
2.560396671295166
2.0781304836273193
4.25098180770874
4.057081699371338
4.351855754852295
3.807889699935913
4.255815029144287
4.0860700607299805
3.8595128059387207
2.4462690353393555
3.560962677001953
3.916057586669922
3.7944869995117188
3.2444229125976562
3.2559690475463867
2.807534694671631
3.867781400680542
3.846839427947998
3.9504401683807373
2.0019235610961914
1.5281165838241577
2.7904000282287598
3.934446334838867
3.574601173400879
3.9255545139312744
3.498041868209839
3.596221685409546
4.110761642456055
4.182892799377441
3.5221893787384033
0.798959493637085
2.7450766563415527
0.21955633163452148
3.008406400680542
3.169755458831787
3.804680824279785
2.199629545211792
3.952033519744873
3.2729809284210205
3.9874820709228516
4.317545413970947
3.1227927207946777
4.391671657562256
2.1659047603607178
3.5159130096435547
3.9725584983825684
3.4216606616973877
1.3576034307479858
2.708390712738037
1.0508979558944702
2.6494531631469727
2.9609200954437256
2.3099513053894043
0.6255810260772705
3.7580058574676514
4.1301045417785645
0.396528959274292
4.1223955154418945
1.1243326663970947
2.7681469917297363
1.51249098777771
3.932739496231079
0.8588783740997314
4.156394004821777
0.9821398258209229
4.337515354156494
3.629237651824951
4.268691539764404
4.045218467712402
2.406858444213867
3.2281558513641357
2.813497543334961
4.258295059204102
3.5979456901550293
2.652709722518921
4.1780195236206055
1.6480586528778076
3.3110787868499756
1.242271900177002
2.8494954109191895
3.937169075012207
1.5164830684661865
3.1410369873046875
4.358879089355469
2.944976806640625
2.9285788536071777
3.3753581047058105
4.1206865310668945
0.6002863049507141
4.287837028503418
4.333181381225586
3.4064464569091797
4.201871395111084
-0.08715906739234924
4.213193893432617
4.059393882751465
1.442352533340454
3.9123778343200684
2.6174750328063965
3.2393550872802734
4.184499740600586
3.566145420074463
3.7885923385620117
1.3304595947265625
3.218222141265869
3.1432886123657227
4.218422889709473
2.689053535461426
1.001569151878357
0.9670275449752808
3.9466302394866943
4.453546524047852
1.877362847328186
3.4847803115844727
4.21335506439209
1.2307372093200684
4.239166736602783
3.7961347103118896
3.312967300415039
3.344207763671875
0.3484755754470825
2.8260998725891113
4.061518669128418
0.328896701335907
2.62941837310791
1.7486162185668945
3.507077693939209
3.7851336002349854
2.388061285018921
3.847048044204712
2.0000483989715576
1.6140352487564087
4.191895484924316
3.5778872966766357
2.948155403137207
1.2673797607421875
0.3396220803260803
2.7334930896759033
2.987271785736084
0.5296134948730469
2.4290354251861572
0.6932579874992371
1.5421583652496338
0.786675751209259
1.9695756435394287
1.2502864599227905
2.748692512512207
2.748401165008545
4.000152587890625
3.2500693798065186
3.6472434997558594
2.3040857315063477
3.868776321411133
1.3248398303985596
0.22368952631950378
0.9009165167808533
0.21804773807525635
1.2697117328643799
2.26531982421875
0.7745340466499329
3.6239407062530518
0.36317169666290283
0.6328496932983398
3.606290817260742
3.834059953689575
4.351144313812256
0.5558058023452759
2.241260051727295
2.0347044467926025
1.425091028213501
3.4531078338623047
1.704669713973999
1.3966127634048462
1.18245267868042
4.265236854553223
2.860884666442871
2.499016761779785
4.130448341369629
4.0296430587768555
2.6076464653015137
0.6916922330856323
0.5956147909164429
4.055237770080566
4.231677055358887
3.8749475479125977
3.7270569801330566
4.033099174499512
4.229279518127441
3.630368232727051
3.7868292331695557
3.3734867572784424
2.4868533611297607
4.297163486480713
2.966703176498413
3.428147077560425
3.7560503482818604
3.9828104972839355
3.191044807434082
3.767287015914917
1.1344338655471802
3.9970455169677734
1.5967190265655518
3.331023693084717
1.4574718475341797
1.959505558013916
assignment2/output/Q3_1_val.txt 0 → 100644
View file @ 98dae565
0.21952109038829803tensor(1.)
0.23782777786254883tensor(0.9000)
0.22611626982688904tensor(1.)
0.20487292110919952tensor(-0.0400)
0.20857982337474823tensor(0.1000)
0.19488589465618134tensor(0.0460)
0.21176186203956604tensor(1.)
0.20323744416236877tensor(-0.0668)
0.22220832109451294tensor(0.5000)
0.17408636212348938tensor(1.)
0.22654947638511658tensor(0.2800)
0.22811055183410645tensor(-0.3668)
0.2148173451423645tensor(1.)
0.2188432663679123tensor(1.)
0.2072826772928238tensor(0.9636)
0.20535282790660858tensor(-0.6800)
0.20276841521263123tensor(-0.0400)
0.21137219667434692tensor(1.)
0.2177630066871643tensor(0.6000)
0.21471473574638367tensor(-0.7456)
0.20317435264587402tensor(0.2000)
0.2298237532377243tensor(-0.3144)
0.210434228181839tensor(0.2800)
0.21239811182022095tensor(-0.1332)
0.2127140313386917tensor(-0.6000)
0.2312510758638382tensor(-0.2332)
0.20917381346225739tensor(0.7000)
0.18962012231349945tensor(0.2000)
0.1855674386024475tensor(-0.6000)
0.19451506435871124tensor(-0.7600)
0.17294295132160187tensor(0.0400)
0.2014499306678772tensor(1.)
0.209279865026474tensor(0.8400)
0.19915391504764557tensor(1.)
0.20909467339515686tensor(0.9200)
0.2131439745426178tensor(0.5200)
0.19119079411029816tensor(1.)
0.19854183495044708tensor(1.)
0.2013477087020874tensor(0.6800)
0.21653999388217926tensor(-0.4400)
0.15919999778270721tensor(0.4400)
0.21264298260211945tensor(0.1200)
0.18273192644119263tensor(-0.3600)
0.21713757514953613tensor(0.2000)
0.21038496494293213tensor(-0.4400)
0.12800642848014832tensor(-0.9000)
0.14271655678749084tensor(-0.9000)
0.13734029233455658tensor(-1.)
0.18281681835651398tensor(0.6000)
0.1977405846118927tensor(0.8000)
0.14425887167453766tensor(-0.8000)
0.2041633576154709tensor(0.5200)
0.18560700118541718tensor(0.9200)
0.1734110414981842tensor(1.)
0.1612943559885025tensor(-0.9000)
0.20057038962841034tensor(-0.5200)
0.17363129556179047tensor(-0.7600)
0.15812665224075317tensor(-0.6800)
0.1882983297109604tensor(0.5200)
0.10606071352958679tensor(-1.)
0.12680551409721375tensor(0.4000)
0.1440865397453308tensor(0.8000)
0.12657012045383453tensor(0.1200)
0.21740467846393585tensor(0.5200)
0.20710618793964386tensor(0.5200)
0.18154871463775635tensor(-1.)
0.1863163709640503tensor(0.6000)
0.20473028719425201tensor(0.7000)
0.1706537902355194tensor(0.1248)
0.21361090242862701tensor(0.7000)
0.1792641431093216tensor(0.2000)
0.1275566965341568tensor(-0.6000)
0.16319316625595093tensor(0.5000)
0.1803625226020813tensor(-1.)
0.0989532545208931tensor(-0.8400)
0.18354813754558563tensor(0.6000)
0.16619978845119476tensor(0.1200)
0.1803804636001587tensor(0.5000)
0.18132324516773224tensor(-0.5384)
0.1719921976327896tensor(0.1000)
0.1980201154947281tensor(0.1200)
0.11704200506210327tensor(-1.)
0.1526172161102295tensor(-1.)
0.1510888934135437tensor(0.3600)
0.1829804629087448tensor(1.)
0.1584928333759308tensor(-0.6800)
0.1488611102104187tensor(-0.1000)
0.13696132600307465tensor(0.1000)
0.1341213881969452tensor(0.8000)
0.16979573667049408tensor(0.0400)
0.15451394021511078tensor(0.5200)
0.16003933548927307tensor(0.1200)
0.13384783267974854tensor(-0.3600)
0.15077005326747894tensor(-1.)
0.16401468217372894tensor(-0.4668)
0.16393303871154785tensor(0.0400)
0.17549139261245728tensor(-0.7600)
0.14981071650981903tensor(-1.)
0.11813931167125702tensor(-1.)
0.12317473441362381tensor(0.2000)
0.12132212519645691tensor(-1.)
0.15419535338878632tensor(-0.9200)
0.17724628746509552tensor(0.2800)
0.16117659211158752tensor(-0.2000)
0.13400578498840332tensor(-1.)
0.15760385990142822tensor(0.2000)
0.1972872018814087tensor(0.2364)
0.1749957650899887tensor(0.1000)
0.16320721805095673tensor(-0.5200)
0.06988326460123062tensor(-0.8000)
0.1444753259420395tensor(-0.9000)
0.14351274073123932tensor(-1.)
0.04320165142416954tensor(-1.)
0.1422291249036789tensor(-0.8000)
0.14577536284923553tensor(-1.)
0.12661631405353546tensor(-0.7600)
0.14432793855667114tensor(0.6000)
0.15439939498901367tensor(0.3600)
0.11530300974845886tensor(-1.)
0.12186361104249954tensor(-1.)
0.1254180371761322tensor(-0.6000)
0.15393532812595367tensor(-1.)
0.1406518518924713tensor(-0.0400)
0.12210708856582642tensor(-0.8400)
0.15302293002605438tensor(-0.2800)
0.08761551976203918tensor(0.)
0.09963203966617584tensor(-0.9668)
0.06123895198106766tensor(-1.)
0.1301640123128891tensor(-0.6000)
0.12276408821344376tensor(0.8000)
0.06715775281190872tensor(-1.)
0.2153923660516739tensor(0.4400)
0.23494695127010345tensor(1.)
0.21139885485172272tensor(1.)
0.2140115648508072tensor(1.)
0.2140115648508072tensor(1.)
0.22464847564697266tensor(0.5200)
0.1974422186613083tensor(1.)
0.21319794654846191tensor(0.)
0.2255629152059555tensor(0.5296)
0.20792533457279205tensor(-0.3600)
0.23402009904384613tensor(1.)
0.21507686376571655tensor(1.)
0.1925256848335266tensor(-0.0500)
0.20486827194690704tensor(1.)
0.22330422699451447tensor(-0.5000)
0.19493475556373596tensor(-0.5000)
0.18902906775474548tensor(0.2000)
0.21239811182022095tensor(-0.1332)
0.22126026451587677tensor(-0.4400)
0.2037319391965866tensor(0.6000)
0.22771777212619781tensor(0.4400)
0.20628510415554047tensor(0.9428)
0.19800761342048645tensor(-0.8400)
0.20475606620311737tensor(-0.9200)
0.21628090739250183tensor(-0.4800)
0.21490231156349182tensor(-0.5200)
0.21490231156349182tensor(-0.5200)
0.2119624763727188tensor(0.7600)
0.21669089794158936tensor(-0.2800)
0.2167542725801468tensor(-0.4400)
0.20590904355049133tensor(-0.8400)
0.17366120219230652tensor(0.7600)
0.17896521091461182tensor(0.3000)
0.1973838061094284tensor(-0.6888)
0.18931002914905548tensor(-0.1000)
0.17367437481880188tensor(0.1000)
0.1899791657924652tensor(-0.2000)
0.18172775208950043tensor(-0.6668)
0.17987360060214996tensor(0.5000)
0.20243671536445618tensor(-0.2800)
0.14789125323295593tensor(0.7332)
0.2000957429409027tensor(0.6856)
0.12851615250110626tensor(-1.)
0.1895725131034851tensor(0.3600)
0.19307856261730194tensor(0.6800)
0.17303505539894104tensor(-0.6800)
0.17950062453746796tensor(0.6800)
0.13775955140590668tensor(0.1000)
0.19438917934894562tensor(-0.1200)
0.15612705051898956tensor(-0.5200)
0.1904379427433014tensor(-0.6000)
0.19095955789089203tensor(-0.3868)
0.20404282212257385tensor(-0.9200)
0.14525079727172852tensor(0.2000)
0.18667253851890564tensor(0.5200)
0.16019761562347412tensor(-0.7000)
0.20511570572853088tensor(0.2000)
0.1653820127248764tensor(-0.8400)
0.16393809020519257tensor(0.3600)
0.15630830824375153tensor(0.5200)
0.20624804496765137tensor(-0.4400)
0.21458958089351654tensor(0.3600)
0.12776941061019897tensor(0.2800)
0.17695868015289307tensor(-0.0668)
0.170821875333786tensor(-0.6800)
0.15638796985149384tensor(-1.)
0.18088488280773163tensor(0.2000)
0.17886961996555328tensor(0.5000)
0.1300341635942459tensor(-1.)
0.1662473827600479tensor(-1.)
0.13839593529701233tensor(-1.)
0.1375056654214859tensor(-0.8000)
0.1986047476530075tensor(-0.8000)
0.16522769629955292tensor(-0.8400)
0.10768487304449081tensor(-0.7000)
0.13424314558506012tensor(0.)
0.21157892048358917tensor(-0.2800)
0.07559621334075928tensor(0.)
0.15435351431369781tensor(-0.8000)
0.1254611760377884tensor(-0.8400)
0.16923093795776367tensor(-0.5200)
0.18232788145542145tensor(0.1000)
0.14047151803970337tensor(-0.8400)
0.18700923025608063tensor(-0.7600)
0.14317211508750916tensor(-0.6800)
0.14336687326431274tensor(0.2000)
0.1366833746433258tensor(-0.8400)
0.18293538689613342tensor(0.2800)
0.20795214176177979tensor(-0.5000)
0.18053756654262543tensor(1.)
0.1593831479549408tensor(-0.9200)
0.18588058650493622tensor(0.2000)
0.14985297620296478tensor(-0.4400)
0.12510165572166443tensor(0.5200)
0.15611417591571808tensor(0.1000)
0.11351925879716873tensor(-1.)
0.1352771371603012tensor(0.1200)
0.14462806284427643tensor(-1.)
0.11183277517557144tensor(-1.)
0.14178472757339478tensor(0.0400)
0.1547503024339676tensor(-0.9600)
0.11967171728610992tensor(-1.)
0.11038559675216675tensor(-0.2000)
0.16192662715911865tensor(0.5000)
0.12391580641269684tensor(0.2400)
0.17649534344673157tensor(0.0768)
0.1597699522972107tensor(0.7716)
0.1679179072380066tensor(1.)
0.12027426809072495tensor(-1.)
0.12472488731145859tensor(-0.6800)
0.14868344366550446tensor(-0.4668)
0.134051114320755tensor(-1.)
0.10038960725069046tensor(-0.7600)
0.09572199732065201tensor(-1.)
0.09107469022274017tensor(-1.)
0.1316457837820053tensor(-1.)
0.10456763207912445tensor(0.3600)
0.1865563541650772tensor(-0.1200)
0.1358366310596466tensor(-1.)
0.19614967703819275tensor(0.4400)
0.11329727619886398tensor(-1.)
0.1729498654603958tensor(-0.4400)
0.14331288635730743tensor(-0.1200)
0.12418988347053528tensor(0.5200)
0.18807271122932434tensor(0.0400)
0.10656706243753433tensor(-0.8400)
0.2013329714536667tensor(0.6000)
0.18385165929794312tensor(-0.1000)
0.14565549790859222tensor(0.2800)
0.13471035659313202tensor(-1.)
0.14519040286540985tensor(-0.8400)
0.17722895741462708tensor(0.5200)
0.20739325881004333tensor(-0.6800)
0.21891023218631744tensor(0.6800)
0.19898292422294617tensor(0.4400)
0.1316789835691452tensor(-0.2000)
0.15652307868003845tensor(0.6800)
0.09026879072189331tensor(0.3600)
0.1540524661540985tensor(0.2000)
0.19549717009067535tensor(0.3600)
0.2251725196838379tensor(0.4400)
0.1755216270685196tensor(0.2000)
0.19446487724781036tensor(-0.0400)
0.17131613194942474tensor(-0.2000)
0.20776869356632233tensor(0.6000)
0.039152223616838455tensor(-1.)
0.17970803380012512tensor(0.6000)
0.18830449879169464tensor(0.6800)
0.15720151364803314tensor(-0.6800)
0.15758894383907318tensor(-0.1200)
0.1848653107881546tensor(0.5200)
0.18562225997447968tensor(0.2800)
0.20226624608039856tensor(0.7600)
0.14094308018684387tensor(-1.)
0.09520897269248962tensor(-0.4400)
0.15200679004192352tensor(0.0400)
0.1659354418516159tensor(-0.8400)
0.0856734961271286tensor(-1.)
0.1422804594039917tensor(0.2800)
0.18697458505630493tensor(0.8400)
0.10866742581129074tensor(-1.)
0.1433115303516388tensor(-0.7600)
0.1490950733423233tensor(-0.5200)
0.2243758738040924tensor(0.5200)
0.18240445852279663tensor(0.2800)
0.17145468294620514tensor(0.0400)
0.18332433700561523tensor(1.)
0.1365073323249817tensor(-1.)
0.1857757419347763tensor(0.2800)
0.1981777399778366tensor(0.6000)
0.18982474505901337tensor(-0.5200)
0.2125098705291748tensor(0.7600)
0.17283837497234344tensor(0.3600)
0.21480412781238556tensor(0.7600)
0.2108636498451233tensor(-0.6800)
0.18919900059700012tensor(0.8400)
0.20582839846611023tensor(0.6800)
0.09318532049655914tensor(-1.)
0.13205993175506592tensor(0.2800)
0.12721742689609528tensor(-1.)
0.16318939626216888tensor(-0.4400)
0.14603199064731598tensor(0.2800)
0.1759437918663025tensor(-0.2800)
0.16388745605945587tensor(-0.2800)
0.08655139058828354tensor(-1.)
0.17535506188869476tensor(0.5200)
0.16123969852924347tensor(0.5200)
0.20193597674369812tensor(0.6000)
0.11664671450853348tensor(-1.)
0.20437943935394287tensor(0.2800)
0.17186793684959412tensor(-0.3600)
0.12383332848548889tensor(0.4400)
0.167347714304924tensor(-0.6000)
0.16962990164756775tensor(0.4400)
0.12689244747161865tensor(-0.2000)
0.2004716992378235tensor(0.4400)
0.19749660789966583tensor(0.6000)
0.15616647899150848tensor(0.4400)
0.12055361270904541tensor(0.2800)
0.18859808146953583tensor(0.1200)
0.11611733585596085tensor(-0.0400)
0.17857807874679565tensor(0.4400)
0.10726254433393478tensor(-0.2800)
0.09590332955121994tensor(-0.9200)
0.17341817915439606tensor(0.3600)
0.15045017004013062tensor(-0.5200)
0.09832731634378433tensor(-0.4400)
0.15005944669246674tensor(0.0400)
0.19138266146183014tensor(-0.0400)
0.13605545461177826tensor(-1.)
0.18580646812915802tensor(0.6000)
0.13740459084510803tensor(0.2000)
0.23495334386825562tensor(0.6000)
0.1618029624223709tensor(0.6000)
0.1931174099445343tensor(-0.2000)
0.11645138263702393tensor(0.6000)
0.18135736882686615tensor(-0.1200)
0.18194840848445892tensor(-0.1200)
0.15510909259319305tensor(0.3600)
0.2184053659439087tensor(0.2800)
0.21103371679782867tensor(0.6000)
0.2033967673778534tensor(0.2800)
0.17762808501720428tensor(0.5200)
0.19069059193134308tensor(0.2000)
0.15333421528339386tensor(0.6000)
0.22145961225032806tensor(-0.1200)
0.2213836908340454tensor(0.3600)
0.22715269029140472tensor(0.5200)
0.11528938263654709tensor(-1.)
0.08748601377010345tensor(-1.)
0.22091121971607208tensor(0.9200)
0.11106610298156738tensor(0.2000)
0.13209114968776703tensor(-0.6800)
0.20159970223903656tensor(0.6800)
0.20199374854564667tensor(0.6800)
0.13471904397010803tensor(-1.)
0.1832374781370163tensor(0.6800)
0.19927720725536346tensor(0.8400)
0.15462526679039001tensor(1.)
0.12945470213890076tensor(-1.)
0.11942054331302643tensor(0.2800)
0.20199275016784668tensor(0.0400)
0.1908886432647705tensor(-0.1200)
0.23125487565994263tensor(0.8400)
0.12787982821464539tensor(1.)
0.1947147399187088tensor(0.8400)
0.16320012509822845tensor(0.8400)
0.22123058140277863tensor(0.9200)
0.1456812173128128tensor(-0.7600)
0.07407398521900177tensor(-0.9200)
0.19859476387500763tensor(-0.1200)
0.1360691487789154tensor(0.9200)
0.10157038271427155tensor(-0.5200)
0.17238971590995789tensor(-0.1200)
0.19093972444534302tensor(0.8400)
0.17866188287734985tensor(0.0400)
0.19757629930973053tensor(0.4400)
0.12266788631677628tensor(-0.6800)
0.22512292861938477tensor(-0.0400)
0.20251554250717163tensor(0.9200)
0.20013198256492615tensor(1.)
0.13609187304973602tensor(-0.8400)
0.15574251115322113tensor(0.2800)
0.16234798729419708tensor(-0.2800)
0.20681166648864746tensor(0.2800)
0.2104719877243042tensor(0.3600)
0.22338435053825378tensor(0.9200)
0.19375619292259216tensor(0.6800)
0.2052852064371109tensor(0.0400)
0.15243327617645264tensor(-0.1200)
0.11913357675075531tensor(-0.4400)
0.17727136611938477tensor(0.0400)
0.12745758891105652tensor(-0.5200)
0.18232029676437378tensor(-0.2800)
0.1626170426607132tensor(0.6000)
0.13294187188148499tensor(-0.8400)
0.08278171718120575tensor(-1.)
0.12398846447467804tensor(-0.8400)
0.21471112966537476tensor(0.2000)
0.2262236475944519tensor(0.6000)
0.15860918164253235tensor(-0.4400)
0.11407069116830826tensor(-0.8400)
0.1275598108768463tensor(-0.0400)
0.09927322715520859tensor(0.1200)
0.20882894098758698tensor(0.8400)
0.21688489615917206tensor(0.6800)
0.1647098809480667tensor(0.2800)
0.14783933758735657tensor(-0.5200)
0.19567745923995972tensor(-0.2800)
0.15859104692935944tensor(0.0400)
0.21403411030769348tensor(-0.3600)
0.10385552048683167tensor(-1.)
0.2079208940267563tensor(0.5200)
0.20125024020671844tensor(0.6000)
0.22828061878681183tensor(0.9200)
0.20388081669807434tensor(-0.0400)
0.2013065665960312tensor(1.)
0.14398251473903656tensor(-1.)
0.16815143823623657tensor(0.4400)
0.18155662715435028tensor(-0.0400)
0.2178577333688736tensor(0.6800)
0.14676328003406525tensor(-0.9200)
0.17797748744487762tensor(0.6800)
0.21926996111869812tensor(0.2800)
0.1411927342414856tensor(-0.7600)
0.15884166955947876tensor(-0.3600)
0.11957917362451553tensor(-1.)
0.17641808092594147tensor(-0.4400)
0.22348210215568542tensor(0.9200)
0.20924445986747742tensor(0.6000)
0.1582152098417282tensor(0.2000)
0.09179122000932693tensor(-0.9200)
0.12855854630470276tensor(-0.3600)
0.16475142538547516tensor(0.7600)
0.22978535294532776tensor(0.9200)
0.13445451855659485tensor(-0.8400)
0.10635241121053696tensor(-0.0400)
0.19978567957878113tensor(0.2000)
0.17759105563163757tensor(-0.2000)
0.20906542241573334tensor(0.8400)
0.15597577393054962tensor(-0.6000)
0.19239096343517303tensor(1.)
0.09750203043222427tensor(-0.4400)
0.14727512001991272tensor(-1.)
0.18051017820835114tensor(0.2000)
0.11252055317163467tensor(-0.7600)
0.08297224342823029tensor(-1.)
0.20134888589382172tensor(0.0400)
0.15197709202766418tensor(-0.5200)
0.1852095127105713tensor(1.)
0.157537043094635tensor(0.3600)
0.12354758381843567tensor(-0.6800)
0.11638432741165161tensor(-0.6800)
0.13662727177143097tensor(-0.6000)
0.17577864229679108tensor(-1.)
0.115805484354496tensor(-1.)
0.1514233946800232tensor(-0.2000)
0.16102229058742523tensor(-0.0400)
0.17351694405078888tensor(-0.1200)
0.1651153564453125tensor(0.5200)
0.14351250231266022tensor(-0.1200)
0.1311900019645691tensor(0.8400)
0.12280235439538956tensor(0.1200)
0.10939732939004898tensor(-0.2000)
0.2157556116580963tensor(0.6800)
0.20947593450546265tensor(0.8000)
0.11183619499206543tensor(-1.)
0.1797509640455246tensor(-0.2800)
0.22263923287391663tensor(0.6800)
0.1939178705215454tensor(0.6800)
0.10361939668655396tensor(-0.4400)
0.10807380080223083tensor(0.4400)
0.12068158388137817tensor(-0.7600)
0.1741792857646942tensor(1.)
0.1618080735206604tensor(-0.6800)
0.1190752163529396tensor(-1.)
0.11395204812288284tensor(-1.)
0.17862311005592346tensor(-0.2800)
0.12561814486980438tensor(-0.6800)
0.20182505249977112tensor(0.1200)
0.0751802995800972tensor(-1.)
0.1919555962085724tensor(-0.6800)
0.1356445699930191tensor(-0.2800)
0.1681707799434662tensor(-0.4400)
0.1738000065088272tensor(-0.2800)
0.1797473430633545tensor(-0.0400)
0.1637478619813919tensor(-0.3600)
0.19011719524860382tensor(0.5200)
0.2067737579345703tensor(0.9200)
0.10261224955320358tensor(-0.9200)
0.1367662400007248tensor(-0.6000)
0.11655715852975845tensor(-0.8400)
0.11579441279172897tensor(-0.8400)
0.20960988104343414tensor(0.6000)
0.1868198812007904tensor(0.3600)
0.17722055315971375tensor(0.2000)
0.21503762900829315tensor(-0.2800)
0.13673166930675507tensor(-0.1200)
0.22107824683189392tensor(0.4400)
0.1512995809316635tensor(-0.8400)
0.09790536761283875tensor(-0.9200)
0.2341938465833664tensor(0.5200)
0.2303488552570343tensor(0.6800)
0.1091434434056282tensor(-0.5200)
0.21080972254276276tensor(0.3600)
0.17831459641456604tensor(-0.0400)
0.23258942365646362tensor(0.2800)
0.22255149483680725tensor(-0.1200)
0.23520246148109436tensor(0.7600)
0.178996741771698tensor(0.0400)
0.12253978848457336tensor(-0.9200)
0.16016776859760284tensor(0.2800)
0.1513330638408661tensor(-0.1200)
0.17617714405059814tensor(1.)
0.16498157382011414tensor(1.)
0.12545861303806305tensor(-0.1200)
0.12865401804447174tensor(-0.8400)
0.12001704424619675tensor(-0.6000)
0.10244826227426529tensor(-0.1200)
0.12188634276390076tensor(-0.2000)
0.18493735790252686tensor(-0.5200)
0.15144576132297516tensor(0.6000)
0.22380149364471436tensor(-0.6000)
0.23608185350894928tensor(0.6800)
0.1693858951330185tensor(0.3600)
0.1806410551071167tensor(0.6000)
0.23818789422512054tensor(0.9200)
0.13107191026210785tensor(-0.8400)
0.21519477665424347tensor(0.7600)
0.09579803049564362tensor(-0.8400)
0.12460693717002869tensor(-1.)
0.19143891334533691tensor(0.2000)
0.22535160183906555tensor(0.8400)
0.1424633115530014tensor(-0.8400)
0.16589730978012085tensor(-0.4400)
0.19005964696407318tensor(0.2000)
0.20769357681274414tensor(-0.3600)
0.20768888294696808tensor(-0.0400)
0.11918934434652328tensor(-1.)
0.18896065652370453tensor(-0.3600)
0.1550711840391159tensor(-0.3600)
0.11359503865242004tensor(-0.9200)
0.18507231771945953tensor(-0.1200)
0.20225369930267334tensor(-0.2000)
0.13334433734416962tensor(-0.8400)
0.22490032017230988tensor(0.9200)
0.18165023624897003tensor(-0.2000)
0.08319410681724548tensor(-0.9200)
0.18868525326251984tensor(0.5200)
0.15806885063648224tensor(-0.1200)
0.1726353019475937tensor(-0.2000)
0.23531073331832886tensor(0.6000)
0.1808777004480362tensor(0.8400)
0.1411917805671692tensor(-0.4400)
0.10940553992986679tensor(-0.6800)
0.1769331693649292tensor(-0.7600)
0.10769849270582199tensor(-0.5200)
0.14710453152656555tensor(-0.3600)
0.11197832226753235tensor(-1.)
0.14165420830249786tensor(-0.7600)
0.1524682492017746tensor(0.2000)
0.22128701210021973tensor(-0.0400)
0.0970553457736969tensor(-0.7600)
0.1937704086303711tensor(0.8400)
0.14527584612369537tensor(-0.7600)
0.21699953079223633tensor(0.9200)
0.21442368626594543tensor(1.)
0.14488953351974487tensor(-0.9200)
0.1578524261713028tensor(-0.7600)
0.16005855798721313tensor(-0.6800)
0.11036648601293564tensor(0.4400)
0.045620452612638474tensor(-1.)
0.22140063345432281tensor(0.7600)
0.22342608869075775tensor(0.8400)
0.10446999967098236tensor(0.1200)
0.20271332561969757tensor(0.6000)
0.12388812005519867tensor(-0.6000)
0.21809136867523193tensor(1.)
0.1308685690164566tensor(-0.8400)
0.07442247867584229tensor(-0.6800)
0.13659796118736267tensor(-0.8400)
0.10851002484560013tensor(-1.)
0.10051233321428299tensor(-0.3600)
0.12606015801429749tensor(-0.3600)
0.2163524180650711tensor(0.4400)
0.1632906198501587tensor(0.2000)
0.16882477700710297tensor(0.1200)
0.1434861272573471tensor(-0.9200)
0.12150558829307556tensor(-0.6000)
0.1636759489774704tensor(-0.4400)
0.22352425754070282tensor(0.1200)
0.132660374045372tensor(0.2000)
0.10443982481956482tensor(-0.3600)
0.21587954461574554tensor(0.1200)
0.22517187893390656tensor(0.6000)
0.1714743971824646tensor(-0.6800)
0.17290149629116058tensor(-0.2000)
0.2372768223285675tensor(0.7600)
0.1682676523923874tensor(0.8400)
0.21441873908042908tensor(-0.0400)
0.21754103899002075tensor(0.7600)
0.12853461503982544tensor(-0.6800)
0.16926608979701996tensor(0.5200)
0.10601773858070374tensor(-0.4400)
0.1565149575471878tensor(0.1200)
0.14192146062850952tensor(0.2000)
0.23315051198005676tensor(-0.3600)
0.15998832881450653tensor(-0.6000)
0.1433938443660736tensor(0.0400)
0.1371707171201706tensor(-0.3600)
0.1512061506509781tensor(-0.9200)
0.20368361473083496tensor(0.2000)
0.22452247142791748tensor(0.2000)
0.17979709804058075tensor(0.2800)
0.11860068887472153tensor(0.2000)
0.06996510922908783tensor(-0.7600)
0.06677684932947159tensor(-0.6000)
0.06894636899232864tensor(-0.7600)
0.11733776330947876tensor(-0.6800)
0.08184415102005005tensor(-0.0400)
0.06686651706695557tensor(-0.6000)
0.06625086069107056tensor(-0.9200)
0.06002577766776085tensor(-0.7600)
0.10252383351325989tensor(0.4400)
0.059982120990753174tensor(-0.9200)
0.06066873297095299tensor(-0.9200)
0.05836592987179756tensor(-1.)
0.09061562269926071tensor(-1.)
0.042103856801986694tensor(-0.5200)
0.03993712365627289tensor(-1.)
0.058126289397478104tensor(-0.8400)
0.10311893373727798tensor(-0.8400)
0.10765204578638077tensor(0.2000)
0.09587886929512024tensor(0.7600)
0.0942838042974472tensor(0.2800)
0.09193766862154007tensor(0.3600)
0.09583122283220291tensor(0.3600)
0.10269835591316223tensor(0.4400)
0.10960717499256134tensor(-0.6800)
0.06526666134595871tensor(-0.6800)
0.05150081217288971tensor(-0.6000)
0.07131826877593994tensor(-0.3600)
0.06836497038602829tensor(-0.6000)
0.0889066681265831tensor(0.2800)
0.08366414159536362tensor(-0.2000)
0.02886185795068741tensor(-1.)
0.0781145766377449tensor(-0.7600)
0.037548284977674484tensor(-1.)
0.10341083258390427tensor(-0.4400)
0.11853298544883728tensor(-0.2000)
0.07571075111627579tensor(0.0400)
0.08094697445631027tensor(0.6800)
0.08510108292102814tensor(0.1200)
0.10280289500951767tensor(-1.)
0.11124327778816223tensor(-1.)
0.07694973796606064tensor(-0.2000)
0.05509539693593979tensor(-1.)
0.06896848231554031tensor(0.0400)
0.0667336955666542tensor(0.0400)
0.06385104358196259tensor(-1.)
0.06220762059092522tensor(0.0400)
0.06228742003440857tensor(-1.)
0.06991153210401535tensor(-0.3600)
0.03435347229242325tensor(-1.)
0.06068241596221924tensor(-0.7600)
0.04000449925661087tensor(-0.2800)
0.13515906035900116tensor(0.5200)
0.0687272697687149tensor(-0.3600)
0.10531941801309586tensor(0.2800)
0.07781021296977997tensor(0.0400)
0.06638915091753006tensor(0.2000)
0.0789942592382431tensor(-0.4400)
0.11414124816656113tensor(-0.3600)
0.10123205929994583tensor(-0.3600)
0.06554862856864929tensor(-0.6000)
0.09732712060213089tensor(-0.2800)
0.07254157215356827tensor(-0.2800)
0.08391833305358887tensor(-0.4400)
0.050708699971437454tensor(0.0400)
0.06146402657032013tensor(-0.2000)
0.06252795457839966tensor(-0.6000)
0.058784935623407364tensor(-0.4400)
0.061997510492801666tensor(-0.7600)
0.06873490661382675tensor(-0.4400)
0.12228299677371979tensor(-0.8400)
0.08709906786680222tensor(-0.2800)
0.11633183807134628tensor(0.6000)
0.060173794627189636tensor(-0.7600)
0.060295701026916504tensor(0.1200)
0.07968377321958542tensor(0.0400)
0.06444267928600311tensor(0.0400)
0.07102542370557785tensor(-0.7600)
0.114140085875988tensor(0.7600)
0.09305185824632645tensor(-0.1200)
0.08784745633602142tensor(-0.6000)
0.06774889677762985tensor(-1.)
0.04729451984167099tensor(-0.3600)
0.07227997481822968tensor(-0.2800)
0.09943866729736328tensor(0.5200)
0.08704831451177597tensor(0.2000)
0.07880393415689468tensor(-0.4400)
0.09158746153116226tensor(0.2000)
0.06772437691688538tensor(-0.8400)
0.07078664004802704tensor(0.4400)
0.055091388523578644tensor(0.3600)
0.06220021843910217tensor(0.1200)
0.12415582686662674tensor(0.6800)
0.11242426931858063tensor(0.6800)
0.05204285681247711tensor(-0.2800)
0.06860662996768951tensor(-0.6000)
0.07693121582269669tensor(-0.2000)
0.07411042600870132tensor(-0.6000)
0.07515888661146164tensor(-0.3600)
0.06729345768690109tensor(0.0400)
0.07676170021295547tensor(0.2800)
0.04842609912157059tensor(-0.8400)
0.025904301553964615tensor(-1.)
0.0895763635635376tensor(-1.)
0.06176883354783058tensor(-0.1200)
0.10238521546125412tensor(-0.4000)
0.10928881168365479tensor(-0.7600)
0.11947387456893921tensor(0.6000)
0.056273531168699265tensor(-0.2800)
0.0694270059466362tensor(-0.7600)
0.06031299754977226tensor(-0.3600)
0.0693625882267952tensor(-0.0400)
0.05002191290259361tensor(-0.6000)
0.10493462532758713tensor(-0.2000)
0.06909937411546707tensor(-0.3600)
0.10338281095027924tensor(-0.9200)
0.05198877304792404tensor(-0.9200)
0.07390774041414261tensor(-0.2000)
0.052589841187000275tensor(-1.)
0.06972728669643402tensor(0.2800)
0.07487358152866364tensor(-0.6000)
0.055521201342344284tensor(-0.2000)
0.07859823107719421tensor(-0.0400)
0.05287164822220802tensor(-1.)
0.03999995440244675tensor(-0.3600)
0.08749157935380936tensor(0.5200)
0.1148362010717392tensor(-0.5200)
0.1097012534737587tensor(-0.2000)
0.0798087865114212tensor(-0.6000)
0.10497570037841797tensor(-0.2000)
0.06502870470285416tensor(-0.7600)
0.04713524505496025tensor(-1.)
0.07184869050979614tensor(-1.)
0.07348126918077469tensor(0.3600)
0.07884491235017776tensor(0.0400)
0.04633929207921028tensor(-0.8400)
0.05538959428668022tensor(0.1200)
0.09666421264410019tensor(-0.2000)
0.0546647273004055tensor(-0.3600)
0.06871264427900314tensor(-0.4400)
0.035957373678684235tensor(-0.5200)
0.04265861585736275tensor(-0.1200)
0.046579811722040176tensor(-0.7600)
0.09314415603876114tensor(0.0400)
0.08005831390619278tensor(0.2000)
0.09821899980306625tensor(-0.1200)
0.08929144591093063tensor(-0.2800)
0.1131405159831047tensor(-0.7600)
0.040930040180683136tensor(-1.)
0.06799940019845963tensor(-0.3600)
0.060019660741090775tensor(-0.6800)
0.04054172709584236tensor(-0.9200)
0.057525552809238434tensor(-0.6000)
0.07972699403762817tensor(0.1200)
0.04932282865047455tensor(-0.7600)
0.09820957481861115tensor(0.1200)
0.05326586216688156tensor(-0.3600)
0.09182409197092056tensor(-0.4400)
0.08163481205701828tensor(-0.4400)
0.09714944660663605tensor(0.2000)
0.11192543059587479tensor(0.2800)
0.07395883649587631tensor(0.1200)
0.102455735206604tensor(0.3600)
0.0940299853682518tensor(-0.2000)
0.07512232661247253tensor(0.1200)
0.040880460292100906tensor(-1.)
0.05169679597020149tensor(-0.9200)
0.039053045213222504tensor(-0.6800)
0.06810827553272247tensor(-1.)
0.04321931302547455tensor(0.6800)
0.07210388034582138tensor(-0.9200)
0.0667327269911766tensor(0.2800)
0.02612116187810898tensor(-1.)
0.054275792092084885tensor(-0.7600)
0.052055757492780685tensor(-1.)
0.18893760442733765tensor(0.9200)
0.10465499013662338tensor(0.5200)
0.1263120472431183tensor(-0.2800)
0.08626026660203934tensor(-0.8400)
0.07763858139514923tensor(0.0400)
0.0930590108036995tensor(-0.3600)
0.0854049101471901tensor(-0.4400)
0.1266796886920929tensor(-0.2000)
0.0787000060081482tensor(-0.2800)
0.06963452696800232tensor(-0.5200)
0.07694792747497559tensor(-0.6800)
0.06689947843551636tensor(-0.1200)
0.1098942905664444tensor(0.2800)
0.06960266083478928tensor(-0.4400)
0.024806426838040352tensor(-0.4400)
0.059299614280462265tensor(-1.)
0.05080275610089302tensor(-0.3600)
0.06684742867946625tensor(-0.6800)
0.09075115621089935tensor(0.1200)
0.05016402155160904tensor(-0.2800)
0.087657131254673tensor(-0.7320)
0.10311480611562729tensor(0.0400)
0.050186317414045334tensor(-0.8400)
0.10118679702281952tensor(-0.0400)
0.105742909014225tensor(0.1200)
0.06864495575428009tensor(-0.2000)
0.08212641626596451tensor(0.2000)
0.08215388655662537tensor(0.0400)
0.05493345856666565tensor(-0.2800)
0.13965703547000885tensor(0.5200)
0.04774272441864014tensor(0.2000)
0.0679595097899437tensor(-0.4400)
0.09421931952238083tensor(-0.3600)
0.061095379292964935tensor(-1.)
0.06720376014709473tensor(-0.7600)
0.0413409024477005tensor(-1.)
0.07987009733915329tensor(0.3600)
0.10968008637428284tensor(0.4400)
0.09381632506847382tensor(-0.1200)
0.08442386239767075tensor(0.2000)
0.06470995396375656tensor(-0.2800)
0.08564872294664383tensor(-0.0400)
0.060080256313085556tensor(0.0400)
0.0743197575211525tensor(-0.4400)
0.043422192335128784tensor(-0.3600)
0.07293049991130829tensor(-0.3600)
0.053508684039115906tensor(-0.4400)
0.1377849280834198tensor(0.1200)
0.12796175479888916tensor(0.5200)
0.11923372745513916tensor(0.3600)
0.06998847424983978tensor(-1.)
0.0985039696097374tensor(-0.2000)
0.05805971100926399tensor(-1.)
0.08363897353410721tensor(0.5200)
0.04255245253443718tensor(-0.9200)
0.06938347220420837tensor(-0.7600)
0.09247715771198273tensor(-0.2800)
0.08193735033273697tensor(-0.2800)
0.0648047924041748tensor(-0.5200)
0.06512439996004105tensor(-1.)
0.09104029089212418tensor(-0.6000)
0.06442565470933914tensor(-0.8400)
0.06772330403327942tensor(-0.6800)
0.09068232774734497tensor(0.2000)
0.09576278924942017tensor(0.5200)
0.11076266318559647tensor(0.2800)
0.10133455693721771tensor(0.0400)
0.1100044846534729tensor(0.2000)
0.07638396322727203tensor(0.0400)
0.0664571076631546tensor(-0.2800)
0.06774033606052399tensor(0.0400)
0.0471884161233902tensor(-0.6800)
0.06978265941143036tensor(-0.3600)
0.05041122063994408tensor(-0.7600)
0.06600528210401535tensor(0.0400)
0.04360955208539963tensor(-0.5200)
0.07044138759374619tensor(-1.)
0.06676562130451202tensor(-0.0400)
0.0676833763718605tensor(-0.8400)
0.046294692903757095tensor(-0.7600)
0.06076645106077194tensor(-0.6000)
0.06590636074542999tensor(0.0400)
0.14694027602672577tensor(0.6000)
0.07471433281898499tensor(0.4400)
0.09933678060770035tensor(0.0400)
0.08197019249200821tensor(-0.3600)
0.10719726234674454tensor(0.6000)
0.13904182612895966tensor(0.2000)
0.05992332845926285tensor(-0.7600)
0.04423932358622551tensor(-1.)
0.05406465753912926tensor(-0.8400)
0.06763502210378647tensor(-0.8400)
0.10083623975515366tensor(-0.2000)
0.04661809653043747tensor(-0.7600)
0.03044629469513893tensor(-1.)
0.08558646589517593tensor(0.2800)
0.1116381511092186tensor(0.1200)
0.07112627476453781tensor(0.0400)
0.0681232288479805tensor(0.1200)
0.0494864247739315tensor(-0.9200)
0.1608443409204483tensor(0.2800)
0.0872381180524826tensor(-0.7600)
0.11643831431865692tensor(0.4400)
0.06294696033000946tensor(-1.)
0.09097111225128174tensor(-1.)
0.07186342775821686tensor(-1.)
0.07192344963550568tensor(-1.)
0.05368124321103096tensor(-0.1200)
0.08338689804077148tensor(-1.)
0.06914117932319641tensor(-0.8400)
0.05286329239606857tensor(-1.)
0.0678638145327568tensor(0.0400)
0.09470223635435104tensor(-0.6800)
0.06944020837545395tensor(-0.8400)
0.11074554920196533tensor(0.2000)
0.08947308361530304tensor(-0.1200)
0.08694350719451904tensor(-0.7600)
0.057572104036808014tensor(0.3600)
0.09664343297481537tensor(-0.1200)
0.03533110395073891tensor(-0.2800)
0.06326062977313995tensor(-0.6800)
0.043962202966213226tensor(-1.)
0.052044257521629333tensor(-0.3600)
0.05824817717075348tensor(0.1200)
0.06353865563869476tensor(-0.3320)
0.08843818306922913tensor(-0.2800)
0.10251210629940033tensor(0.1200)
0.04374433681368828tensor(-1.)
0.06960179656744003tensor(-0.7600)
0.04846859350800514tensor(-0.2800)
0.09125281870365143tensor(-0.8400)
0.02776302583515644tensor(-0.9200)
0.05578514188528061tensor(-1.)
0.055710259824991226tensor(-0.4400)
0.0446867011487484tensor(-1.)
0.07649753242731094tensor(-0.7600)
0.0725017562508583tensor(-0.6800)
0.09512197971343994tensor(-0.9200)
0.06360553950071335tensor(-0.1200)
0.0858076959848404tensor(-0.2800)
0.059990882873535156tensor(-0.9200)
0.1793230026960373tensor(0.6000)
0.12273914366960526tensor(0.6800)
0.11833781749010086tensor(0.7600)
0.09270551055669785tensor(0.2000)
0.0877777487039566tensor(0.0400)
0.10608988255262375tensor(-0.7600)
0.11943556368350983tensor(0.2800)
0.07804727554321289tensor(0.0400)
0.06379834562540054tensor(-0.5200)
0.08363361656665802tensor(0.2000)
0.044273633509874344tensor(-1.)
0.09778593480587006tensor(0.0400)
0.08075778931379318tensor(-0.5200)
0.09018567949533463tensor(0.1200)
0.04117107391357422tensor(-0.9200)
0.09086763113737106tensor(-0.2800)
0.05036270618438721tensor(-1.)
0.03497250750660896tensor(-1.)
0.07593423873186111tensor(0.2000)
0.06348763406276703tensor(-1.)
0.08138018101453781tensor(-0.0400)
0.021082788705825806tensor(-0.8400)
0.12244702130556107tensor(0.0400)
0.06323377043008804tensor(-1.)
0.1353948414325714tensor(0.4400)
0.11679327487945557tensor(-0.2800)
0.08360980451107025tensor(-0.3600)
0.08744142204523087tensor(-0.2000)
0.08412223309278488tensor(-0.2000)
0.0670066773891449tensor(-1.)
0.11312022060155869tensor(0.0400)
0.05129019543528557tensor(-0.8400)
0.08004879951477051tensor(-1.)
0.04183156415820122tensor(-0.9200)
0.060414962470531464tensor(-0.2800)
0.04807211086153984tensor(-0.3600)
0.05797523260116577tensor(-0.6000)
0.05401058867573738tensor(-0.7600)
0.0495246984064579tensor(-0.6000)
0.055092208087444305tensor(0.1200)
0.10671265423297882tensor(0.0400)
0.11502908915281296tensor(0.3600)
0.07264292240142822tensor(-0.3600)
0.10378343611955643tensor(0.6000)
0.07152608782052994tensor(-0.2800)
0.06015247851610184tensor(-0.9200)
0.0629473477602005tensor(-1.)
0.08918797224760056tensor(-0.4400)
0.05601724237203598tensor(-1.)
0.06840457022190094tensor(-0.5200)
0.06456799060106277tensor(0.4400)
0.08867625892162323tensor(0.2000)
0.09267663955688477tensor(-0.5200)
0.04077684506773949tensor(-0.2800)
0.08825524151325226tensor(-0.6800)
0.05735380947589874tensor(-0.5200)
0.07401784509420395tensor(-1.)
0.054056696593761444tensor(0.2800)
0.04266321286559105tensor(-0.9200)
0.15657617151737213tensor(-0.6800)
0.15109382569789886tensor(0.4400)
0.1890057474374771tensor(1.)
0.10820557177066803tensor(-0.4400)
0.18578271567821503tensor(0.0400)
0.11017512530088425tensor(-0.2000)
0.22362756729125977tensor(0.5000)
0.09653349220752716tensor(0.6000)
0.15083901584148407tensor(0.5000)
0.14551357924938202tensor(0.2800)
0.18451565504074097tensor(0.6000)
0.16223224997520447tensor(-0.0400)
0.08025643974542618tensor(0.7600)
0.2161441594362259tensor(0.6800)
0.11155086755752563tensor(0.2800)
0.14659753441810608tensor(0.2000)
0.11268409341573715tensor(-0.6800)
0.11299097537994385tensor(0.2800)
0.15749360620975494tensor(0.5200)
0.14664573967456818tensor(0.2000)
0.1774851381778717tensor(-0.0400)
0.1308962106704712tensor(1.)
0.19349651038646698tensor(0.5200)
0.1107378676533699tensor(0.2800)
0.14942310750484467tensor(0.5200)
0.1346442848443985tensor(0.5200)
0.08724971115589142tensor(0.5000)
0.15339265763759613tensor(0.2800)
0.14741270244121552tensor(0.3600)
0.17419348657131195tensor(0.8400)
0.06649467349052429tensor(0.3332)
0.1416660100221634tensor(0.4000)
0.09487863630056381tensor(-0.3000)
0.1044016182422638tensor(0.2000)
0.09938265383243561tensor(0.7600)
0.12405756860971451tensor(0.5200)
0.16103951632976532tensor(0.2000)
0.20939156413078308tensor(0.8000)
0.15363965928554535tensor(-0.1200)
0.19852818548679352tensor(0.6000)
0.1838451325893402tensor(0.8400)
0.086475670337677tensor(0.4000)
0.2056141495704651tensor(0.2000)
0.1890709549188614tensor(0.6000)
0.10467807203531265tensor(0.7332)
0.19242490828037262tensor(0.3000)
0.14345195889472961tensor(0.3000)
0.11138688772916794tensor(-0.3000)
0.09883628040552139tensor(-0.6800)
0.11967958509922028tensor(0.7600)
0.20112472772598267tensor(0.2000)
0.10264305770397186tensor(0.2000)
0.1360296905040741tensor(0.3000)
0.153983011841774tensor(0.7600)
0.19370009005069733tensor(1.)
0.13406595587730408tensor(1.)
0.1820715218782425tensor(0.2800)
0.11317534744739532tensor(-0.1200)
0.17688260972499847tensor(0.3000)
0.08018606156110764tensor(0.5000)
0.10509025305509567tensor(0.2800)
0.10396265238523483tensor(0.7600)
0.12781377136707306tensor(0.0400)
0.21836653351783752tensor(0.4000)
0.06224902346730232tensor(0.5000)
0.10156750679016113tensor(0.4000)
0.17532825469970703tensor(0.7000)
0.10599994659423828tensor(-0.0400)
0.2053963840007782tensor(0.3000)
0.11079281568527222tensor(0.5200)
0.08032440394163132tensor(0.3600)
0.14339572191238403tensor(0.4000)
0.10316693037748337tensor(-0.0400)
0.08395222574472427tensor(0.5200)
0.19721107184886932tensor(0.5200)
0.1645456701517105tensor(0.6000)
0.14485467970371246tensor(0.3600)
0.1910422444343567tensor(0.3000)
0.12681792676448822tensor(0.8400)
0.19953301548957825tensor(0.4856)
0.21738339960575104tensor(0.3332)
0.22097046673297882tensor(0.3600)
0.09543994069099426tensor(0.1200)
0.1662885993719101tensor(0.2000)
0.16900548338890076tensor(0.2000)
0.09730332344770432tensor(0.5000)
0.1216694712638855tensor(0.7600)
0.12623728811740875tensor(-0.6000)
0.15742705762386322tensor(0.1200)
0.1513732522726059tensor(0.3000)
0.07737997174263tensor(-0.3000)
0.12131720036268234tensor(-0.3000)
0.10318685322999954tensor(1.)
0.23377388715744019tensor(0.4400)
0.193138986825943tensor(0.2000)
0.1424471139907837tensor(0.1200)
0.11600998044013977tensor(0.4400)
0.1494482159614563tensor(0.2000)
0.09752153605222702tensor(-0.6000)
0.11977273970842361tensor(1.)
0.0755121111869812tensor(0.4000)
0.16602164506912231tensor(0.8000)
0.08897612988948822tensor(0.9200)
0.12666456401348114tensor(0.4000)
0.07313497364521027tensor(0.4768)
0.1247655600309372tensor(0.6000)
0.12537236511707306tensor(0.4000)
0.15713953971862793tensor(0.9000)
0.12801477313041687tensor(0.2800)
0.15784595906734467tensor(0.0400)
0.09747076034545898tensor(0.2000)
0.15055043995380402tensor(0.3000)
0.16015571355819702tensor(-0.1200)
0.17614807188510895tensor(0.4000)
0.21875032782554626tensor(0.9000)
0.15658637881278992tensor(0.2800)
0.13288664817810059tensor(0.3000)
0.1912735402584076tensor(0.5000)
0.1437816470861435tensor(0.2000)
0.05408409610390663tensor(0.0400)
0.15570668876171112tensor(0.3000)
0.22565680742263794tensor(0.3600)
0.08290725946426392tensor(-0.4668)
0.14099358022212982tensor(1.)
0.16457973420619965tensor(0.6000)
0.05822291970252991tensor(0.4400)
0.10797110944986343tensor(-0.6800)
0.14171220362186432tensor(0.4000)
0.16532541811466217tensor(0.2000)
0.1032351553440094tensor(-0.3332)
0.22634689509868622tensor(0.5000)
0.14377464354038239tensor(-0.1000)
0.14435285329818726tensor(0.6800)
0.05724632367491722tensor(0.4400)
0.15176990628242493tensor(0.3600)
0.07804319262504578tensor(0.2000)
0.22064118087291718tensor(0.3000)
0.20326218008995056tensor(0.4400)
0.08385902643203735tensor(0.2000)
0.09899023175239563tensor(-0.3000)
0.06069619581103325tensor(0.3000)
0.1688353717327118tensor(0.7600)
0.15891560912132263tensor(0.1200)
0.1575726717710495tensor(0.1000)
0.20663386583328247tensor(0.2000)
0.07285406440496445tensor(0.4400)
0.09862026572227478tensor(0.2000)
0.18765997886657715tensor(-0.2000)
0.0989244282245636tensor(0.2000)
0.15241041779518127tensor(0.7600)
0.13361875712871552tensor(-0.0400)
0.049809280782938004tensor(0.7600)
0.1752464473247528tensor(0.7600)
0.08381223678588867tensor(0.0400)
0.13384230434894562tensor(0.2000)
0.08544216305017471tensor(0.8000)
0.19339069724082947tensor(0.2000)
0.14345568418502808tensor(-0.3000)
0.13473612070083618tensor(-0.2000)
0.10077577829360962tensor(-0.0400)
0.11041254550218582tensor(0.9200)
0.14151988923549652tensor(0.6000)
0.15229271352291107tensor(0.6000)
0.16229631006717682tensor(0.7600)
0.1366676241159439tensor(0.6800)
0.06109293922781944tensor(0.4400)
0.15251389145851135tensor(0.)
0.11711303889751434tensor(-0.4000)
0.15497668087482452tensor(1.)
0.14478479325771332tensor(0.5000)
0.12450239807367325tensor(-0.3000)
0.15787293016910553tensor(0.7600)
0.19996334612369537tensor(0.2000)
0.14208637177944183tensor(0.5200)
0.10430789738893509tensor(0.3000)
0.1380041539669037tensor(0.1000)
0.07597842067480087tensor(0.3600)
0.09282626211643219tensor(0.2800)
0.11957515776157379tensor(-0.2000)
0.1325601190328598tensor(0.2000)
0.19287727773189545tensor(0.3000)
0.06373829394578934tensor(1.)
0.08102250099182129tensor(0.3332)
0.08325854688882828tensor(0.4400)
0.1730927973985672tensor(0.5200)
0.16426338255405426tensor(0.2000)
0.12436502426862717tensor(0.7000)
0.11336473375558853tensor(0.4000)
0.16846415400505066tensor(0.2800)
0.16852280497550964tensor(-0.0400)
0.15378515422344208tensor(0.6000)
0.12810969352722168tensor(0.8668)
0.15636086463928223tensor(0.3332)
0.19025345146656036tensor(0.2000)
0.20419247448444366tensor(0.3000)
0.1943768709897995tensor(0.6000)
0.12115341424942017tensor(0.4000)
0.10460444539785385tensor(-0.2000)
0.2143198847770691tensor(0.4400)
0.1544017195701599tensor(0.3600)
0.12378369271755219tensor(0.4000)
0.07778675854206085tensor(-0.0400)
0.13199037313461304tensor(0.4000)
0.08631624281406403tensor(0.1200)
0.10466421395540237tensor(0.9200)
0.10585516691207886tensor(0.2000)
0.131882905960083tensor(0.3600)
0.07703190296888351tensor(0.3000)
0.11392991989850998tensor(0.9000)
0.14755365252494812tensor(0.4400)
0.08432557433843613tensor(0.6800)
0.12755604088306427tensor(-0.2000)
0.1157873347401619tensor(0.5200)
0.14258073270320892tensor(-0.3600)
0.14870283007621765tensor(0.3000)
0.1697574257850647tensor(1.)
0.1836908608675003tensor(0.3668)
0.0883650854229927tensor(0.1000)
0.11782419681549072tensor(0.6444)
0.14568206667900085tensor(0.4400)
0.13894906640052795tensor(0.4400)
0.15033972263336182tensor(0.7332)
0.16444166004657745tensor(0.2800)
0.11512230336666107tensor(-0.1000)
0.13051758706569672tensor(0.8000)
0.10153035819530487tensor(0.1000)
0.10893924534320831tensor(0.6000)
0.21641847491264343tensor(0.3600)
0.13814422488212585tensor(-0.1200)
0.14533795416355133tensor(0.3000)
0.12098712474107742tensor(0.4668)
0.13985329866409302tensor(0.9000)
0.15166310966014862tensor(0.4000)
0.08744220435619354tensor(0.2000)
0.11820767819881439tensor(0.3000)
0.09811855852603912tensor(0.4400)
0.20947793126106262tensor(0.5200)
0.10916900634765625tensor(-0.6000)
0.15209904313087463tensor(0.3332)
0.08199092745780945tensor(0.7000)
0.18676885962486267tensor(-0.3000)
0.17459356784820557tensor(0.1000)
0.17737925052642822tensor(0.3000)
0.12449584156274796tensor(0.)
0.17388708889484406tensor(0.2364)
0.07412558794021606tensor(0.7600)
0.16710856556892395tensor(0.9200)
0.18633432686328888tensor(1.)
0.1436946988105774tensor(0.2000)
0.12311311811208725tensor(0.2800)
0.22276312112808228tensor(0.7600)
0.1517471969127655tensor(-0.2800)
0.21950820088386536tensor(0.6000)
0.16205386817455292tensor(-0.6000)
0.15489503741264343tensor(-0.7600)
0.2166478931903839tensor(0.8400)
0.14486069977283478tensor(-0.2800)
0.08900722116231918tensor(-0.5200)
0.17153024673461914tensor(0.8400)
0.13165244460105896tensor(-0.4400)
0.17769549787044525tensor(0.2800)
0.09428092837333679tensor(0.1200)
0.10391625016927719tensor(-0.6800)
0.18381324410438538tensor(0.5200)
0.21973678469657898tensor(0.5200)
0.1112796887755394tensor(0.6800)
0.18250557780265808tensor(-0.3600)
0.20135216414928436tensor(0.5200)
0.11555842310190201tensor(-0.4400)
0.21877463161945343tensor(0.4400)
0.16276273131370544tensor(-0.2800)
0.1333620697259903tensor(0.5200)
0.1077733263373375tensor(-0.9200)
0.13967247307300568tensor(-0.3600)
0.08006605505943298tensor(-0.8400)
0.0741242840886116tensor(-0.0667)
0.08563303202390671tensor(-0.8400)
0.11555031687021255tensor(0.4400)
0.12015656381845474tensor(0.3600)
0.2285747081041336tensor(0.0400)
0.19923947751522064tensor(-0.1200)
0.1341344118118286tensor(0.0400)
0.21990036964416504tensor(0.9200)
0.1633370965719223tensor(0.9200)
0.19237682223320007tensor(0.9200)
0.18173480033874512tensor(0.0400)
0.11641912162303925tensor(-0.3600)
0.05557055398821831tensor(-0.1200)
0.095156230032444tensor(-0.6000)
0.11426493525505066tensor(-0.5200)
0.1583193987607956tensor(0.3600)
0.17095281183719635tensor(-0.7600)
0.16034354269504547tensor(0.2800)
0.12270230799913406tensor(0.2800)
0.11638446897268295tensor(-0.2800)
0.20286811888217926tensor(-0.5200)
0.1418055146932602tensor(-0.3600)
0.20391175150871277tensor(0.9200)
0.17074044048786163tensor(0.4400)
0.18920071423053741tensor(0.5200)
0.10190127044916153tensor(0.2000)
0.1450309157371521tensor(0.4400)
0.18959221243858337tensor(0.7600)
0.17498749494552612tensor(-0.2000)
0.14569894969463348tensor(-0.0400)
0.0889558345079422tensor(0.2800)
0.1678875833749771tensor(0.3600)
0.14129741489887238tensor(0.1200)
0.12274348735809326tensor(0.2800)
0.1300591081380844tensor(-0.2800)
0.14726880192756653tensor(0.1200)
0.1436905413866043tensor(0.3600)
0.21260082721710205tensor(0.8400)
0.2336854785680771tensor(0.5200)
0.08735974878072739tensor(-0.3600)
0.07879571616649628tensor(-0.6800)
0.10740429162979126tensor(-0.6000)
0.1274515986442566tensor(0.0400)
0.18568502366542816tensor(0.6800)
0.20646396279335022tensor(0.7600)
0.14601661264896393tensor(-0.1200)
0.21923623979091644tensor(0.0400)
0.17510734498500824tensor(0.1200)
0.21999911963939667tensor(0.6000)
0.1409997195005417tensor(0.2800)
0.016632884740829468tensor(-1.)
0.12351001799106598tensor(-0.2800)
0.04984692856669426tensor(-1.)
0.2147400677204132tensor(0.0400)
0.17414090037345886tensor(0.1000)
0.12355341762304306tensor(0.2800)
0.19583721458911896tensor(-0.2800)
0.137812539935112tensor(0.6800)
0.2313961386680603tensor(0.6800)
0.14011843502521515tensor(0.0400)
0.1950841099023819tensor(0.6800)
0.13254280388355255tensor(-0.2000)
0.23083597421646118tensor(1.)
0.12517835199832916tensor(-0.6800)
0.1689414083957672tensor(0.5200)
0.20080220699310303tensor(-0.3600)
0.135813370347023tensor(0.3600)
0.1338849514722824tensor(-0.7600)
0.19881272315979004tensor(-0.4400)
0.11475562304258347tensor(0.5200)
0.07478567957878113tensor(-0.5200)
0.17905288934707642tensor(0.2000)
0.17614097893238068tensor(-0.1200)
0.10090915113687515tensor(-0.6000)
0.1625327467918396tensor(-0.1200)
0.09729336202144623tensor(-0.2800)
0.07025746256113052tensor(-1.)
0.19574730098247528tensor(0.6800)
0.11373120546340942tensor(-0.8400)
0.22839391231536865tensor(0.0400)
0.08030565083026886tensor(-0.7600)
0.15079976618289948tensor(0.5200)
0.07450550049543381tensor(-1.)
0.20532074570655823tensor(1.)
0.10243158787488937tensor(-0.6000)
0.16154275834560394tensor(0.2800)
0.1795278936624527tensor(0.1200)
0.2312384843826294tensor(0.8400)
0.140786275267601tensor(0.3600)
0.09174133837223053tensor(-0.5200)
0.1444629579782486tensor(0.6000)
0.2319001704454422tensor(-0.2800)
0.12746240198612213tensor(0.6000)
0.21679434180259705tensor(-0.0400)
0.2211339771747589tensor(-0.2800)
0.20638172328472137tensor(0.5200)
0.07490963488817215tensor(-0.4400)
0.18688809871673584tensor(0.0400)
0.06705497205257416tensor(-0.4400)
0.14384311437606812tensor(-0.3600)
0.18554188311100006tensor(0.9200)
0.06351140141487122tensor(-0.6800)
0.1320352703332901tensor(0.3600)
0.1526670604944229tensor(0.2800)
0.16251379251480103tensor(-0.1200)
0.09304572641849518tensor(-0.5200)
0.1486445665359497tensor(0.2000)
0.20497310161590576tensor(0.0400)
0.062324680387973785tensor(-0.8400)
0.15489621460437775tensor(0.8400)
0.15112543106079102tensor(0.6000)
0.08310254663228989tensor(0.1200)
0.20763856172561646tensor(0.5200)
0.049428991973400116tensor(-1.)
0.11956185102462769tensor(0.3600)
0.16460809111595154tensor(0.8400)
0.0865495577454567tensor(-0.6800)
0.1230904683470726tensor(0.5200)
0.13707344233989716tensor(-0.1200)
0.14319519698619843tensor(0.2800)
0.19177186489105225tensor(1.)
0.09899164736270905tensor(-0.3600)
0.13245351612567902tensor(-0.1200)
0.16978345811367035tensor(-0.6800)
0.08471869677305222tensor(0.4400)
0.20225238800048828tensor(0.)
0.17614831030368805tensor(0.2000)
0.13594719767570496tensor(-0.1200)
0.04175996035337448tensor(-1.)
0.10021861642599106tensor(-0.9200)
0.1390247792005539tensor(0.2800)
0.19965331256389618tensor(1.)
0.1405506581068039tensor(-0.1200)
0.12870007753372192tensor(0.7600)
0.21323177218437195tensor(0.5200)
0.11423824727535248tensor(-0.5200)
0.18192993104457855tensor(0.8400)
0.08951079845428467tensor(0.5200)
0.20494012534618378tensor(-0.4400)
0.21643801033496857tensor(0.5200)
0.07868974655866623tensor(-1.)
0.14644894003868103tensor(-0.3600)
0.16568389534950256tensor(1.)
0.0556023083627224tensor(-0.8400)
0.16450826823711395tensor(-0.0400)
0.10914310812950134tensor(0.2800)
0.17779237031936646tensor(0.5200)
0.19513386487960815tensor(0.0400)
0.21644727885723114tensor(-0.6000)
0.15602996945381165tensor(0.6000)
0.12433335930109024tensor(-0.2000)
0.08738037198781967tensor(-0.1200)
0.21014969050884247tensor(0.8400)
0.14182449877262115tensor(0.7600)
0.2234821319580078tensor(-0.2800)
0.09754414856433868tensor(-1.)
0.03975173458456993tensor(-1.)
0.11738212406635284tensor(-0.2000)
0.14112338423728943tensor(0.3600)
0.04956607148051262tensor(-0.9200)
0.14288471639156342tensor(-0.0400)
0.12547267973423004tensor(-1.)
0.13475678861141205tensor(-0.8400)
0.14451809227466583tensor(-0.2800)
0.1475871056318283tensor(-0.6800)
0.09716036170721054tensor(-0.8400)
0.1283160299062729tensor(-0.1200)
0.12961049377918243tensor(-0.2800)
0.23258794844150543tensor(0.8400)
0.12285372614860535tensor(-0.1200)
0.06777477264404297tensor(-0.6000)
0.09527100622653961tensor(-0.6000)
0.13936251401901245tensor(0.6800)
0.06017792224884033tensor(-0.8400)
0.07299642264842987tensor(-0.9200)
0.04060047119855881tensor(-1.)
0.033767759799957275tensor(-1.)
0.08858264237642288tensor(-1.)
0.09627792984247208tensor(-0.7600)
0.06775067001581192tensor(-0.7600)
0.17392368614673615tensor(0.6000)
0.05780477821826935tensor(-1.)
0.12707191705703735tensor(-0.6800)
0.1420026421546936tensor(0.2800)
0.1948411911725998tensor(0.9200)
0.15434472262859344tensor(0.6800)
0.0902843177318573tensor(-0.1200)
0.08240611851215363tensor(-0.6000)
0.1924615502357483tensor(0.4400)
0.07384775578975677tensor(-0.3600)
0.144351527094841tensor(0.2000)
0.21657899022102356tensor(-0.0400)
0.07839164137840271tensor(-0.8400)
0.11633919179439545tensor(-1.)
0.24297372996807098tensor(1.)
0.06886332482099533tensor(-0.5200)
0.15490524470806122tensor(-0.4400)
0.22832036018371582tensor(0.6000)
0.20743070542812347tensor(0.8400)
0.10694000869989395tensor(0.3600)
0.08265257626771927tensor(-0.8400)
0.0327269546687603tensor(-1.)
0.2268715500831604tensor(0.9200)
0.20337727665901184tensor(1.)
0.15413744747638702tensor(0.6000)
0.13964933156967163tensor(0.6000)
0.21173201501369476tensor(0.2000)
0.1933497190475464tensor(1.)
0.14066237211227417tensor(-0.2000)
0.13604919612407684tensor(0.2000)
0.09899909794330597tensor(0.2000)
0.11352479457855225tensor(-0.6000)
0.22438283264636993tensor(1.)
0.2048407793045044tensor(-0.2000)
0.23727907240390778tensor(-0.2000)
0.14679262042045593tensor(-0.2000)
0.20197315514087677tensor(1.)
0.16213366389274597tensor(0.2000)
0.19847168028354645tensor(0.2000)
0.12821368873119354tensor(-1.)
0.12000229209661484tensor(-0.2000)
0.06947322934865952tensor(-1.)
0.07184262573719025tensor(-0.2000)
0.03750338405370712tensor(-1.)
0.08032049983739853tensor(-1.)
assignment2/output/Q3_crossing_test.txt 0 → 100644
View file @ 98dae565
0.23758435249328613tensor(-1.4000)
0.22897474467754364tensor(-1.4000)
0.2282581329345703tensor(-1.4000)
0.24711424112319946tensor(-1.4000)
0.23912574350833893tensor(-1.4000)
0.20739613473415375tensor(-1.4000)
0.2494242936372757tensor(-1.4000)
0.2607801556587219tensor(-1.4000)
0.2074417620897293tensor(-1.4000)
0.2526857554912567tensor(-1.4000)
0.2526857554912567tensor(-1.4000)
0.2245226502418518tensor(-1.4000)
0.20670154690742493tensor(-1.4000)
0.2325410395860672tensor(-1.4000)
0.2380913645029068tensor(-1.4000)
0.2483920305967331tensor(-1.4000)
0.21475599706172943tensor(-1.4000)
0.2441638857126236tensor(-1.4000)
0.2257218360900879tensor(-1.4000)
0.20446324348449707tensor(-1.4000)
0.17693744599819183tensor(-1.4000)
0.1621471792459488tensor(-1.4000)
0.2024478316307068tensor(-1.4000)
0.2221490442752838tensor(-1.4000)
0.1971755027770996tensor(-1.4000)
0.24487748742103577tensor(-1.4000)
0.23715612292289734tensor(-1.4000)
0.2105780839920044tensor(-1.4000)
0.2210802286863327tensor(-1.4000)
0.2307385802268982tensor(-1.4000)
0.2082047015428543tensor(-1.4000)
0.22498825192451477tensor(-1.4000)
0.1533462107181549tensor(-1.4000)
0.22785760462284088tensor(-1.4000)
0.2177654653787613tensor(-1.4000)
0.15255412459373474tensor(-1.4000)
0.1784331202507019tensor(-1.4000)
0.20546318590641022tensor(-1.4000)
0.2082659900188446tensor(-1.4000)
0.18751844763755798tensor(-1.4000)
0.21206463873386383tensor(-1.4000)
0.1956719607114792tensor(-1.4000)
0.21501944959163666tensor(-1.4000)
0.17354239523410797tensor(-1.4000)
0.20533522963523865tensor(-1.4000)
0.14194084703922272tensor(-1.4000)
0.1578320413827896tensor(-1.4000)
0.18727406859397888tensor(-1.4000)
0.18733839690685272tensor(-1.4000)
0.18241102993488312tensor(-1.4000)
0.21337473392486572tensor(-1.4000)
0.18488715589046478tensor(-1.4000)
0.17985285818576813tensor(-1.4000)
0.15905165672302246tensor(-1.4000)
0.2090502232313156tensor(-1.4000)
0.15239235758781433tensor(-1.4000)
0.19510942697525024tensor(-1.4000)
0.20517371594905853tensor(-1.4000)
0.20578475296497345tensor(-1.4000)
0.19435971975326538tensor(-1.4000)
0.22552010416984558tensor(-1.4000)
0.1216101422905922tensor(-1.4000)
0.16098229587078094tensor(-1.4000)
0.14868749678134918tensor(-1.4000)
0.213547945022583tensor(-1.4000)
0.19435472786426544tensor(-1.4000)
0.1919529289007187tensor(-1.4000)
0.19876468181610107tensor(-1.4000)
0.12786506116390228tensor(-1.4000)
0.21353454887866974tensor(-1.4000)
0.1587609052658081tensor(-1.4000)
0.19987821578979492tensor(-1.4000)
0.12144555151462555tensor(-1.4000)
0.20534174144268036tensor(-1.4000)
0.19150425493717194tensor(-1.4000)
0.15835073590278625tensor(-1.4000)
0.15948519110679626tensor(-1.4000)
0.08401155471801758tensor(-1.4000)
0.09276217222213745tensor(-1.4000)
0.1952604502439499tensor(-1.4000)
0.1678314507007599tensor(-1.4000)
0.1771640032529831tensor(-1.4000)
0.1765371412038803tensor(-1.4000)
0.15991546213626862tensor(-1.4000)
0.13275156915187836tensor(-1.4000)
0.12621641159057617tensor(-1.4000)
0.12656351923942566tensor(-1.4000)
0.19291381537914276tensor(-1.4000)
0.18778491020202637tensor(-1.4000)
0.1577240377664566tensor(-1.4000)
0.1388625055551529tensor(-1.4000)
0.22458496689796448tensor(-1.4000)
0.1503429263830185tensor(-1.4000)
0.17706085741519928tensor(-1.4000)
0.18563218414783478tensor(-1.4000)
0.08222465962171555tensor(-1.4000)
0.1907193809747696tensor(-1.4000)
0.15306667983531952tensor(-1.4000)
0.13175880908966064tensor(-1.4000)
0.18388128280639648tensor(-1.4000)
0.1223224401473999tensor(-1.4000)
0.13843874633312225tensor(-1.4000)
0.19140410423278809tensor(-1.4000)
0.21068300306797028tensor(-1.4000)
0.18620635569095612tensor(-1.4000)
0.18872439861297607tensor(-1.4000)
0.14040230214595795tensor(-1.4000)
0.15510213375091553tensor(-1.4000)
0.0968041867017746tensor(-1.4000)
0.15540441870689392tensor(-1.4000)
0.15684540569782257tensor(-1.4000)
0.12150269746780396tensor(-1.4000)
0.16002260148525238tensor(-1.4000)
0.1908603310585022tensor(-1.4000)
0.11318524926900864tensor(-1.4000)
0.10862801969051361tensor(-1.4000)
0.16675183176994324tensor(-1.4000)
0.18632368743419647tensor(-1.4000)
0.16448412835597992tensor(-1.4000)
0.1776243895292282tensor(-1.4000)
0.11328461766242981tensor(-1.4000)
0.14006786048412323tensor(-1.4000)
0.10219099372625351tensor(-1.4000)
0.1313018500804901tensor(-1.4000)
0.0981559008359909tensor(-1.4000)
0.24975095689296722tensor(-1.4000)
0.22736956179141998tensor(-1.4000)
0.2216845601797104tensor(-1.4000)
0.2360972762107849tensor(-1.4000)
0.19267600774765015tensor(-1.4000)
0.24770593643188477tensor(-1.4000)
0.24041128158569336tensor(-1.4000)
0.2543860673904419tensor(-1.4000)
0.23966363072395325tensor(-1.4000)
0.2435259073972702tensor(-1.4000)
0.2048247754573822tensor(-1.4000)
0.24078969657421112tensor(-1.4000)
0.24168705940246582tensor(-1.4000)
0.25037500262260437tensor(-1.4000)
0.23886772990226746tensor(-1.4000)
0.23961548507213593tensor(-1.4000)
0.24275831878185272tensor(-1.4000)
0.24316413700580597tensor(-1.4000)
0.24582089483737946tensor(-1.4000)
0.18329019844532013tensor(-1.4000)
0.2292335480451584tensor(-1.4000)
0.19664566218852997tensor(-1.4000)
0.2328321635723114tensor(-1.4000)
0.2396327257156372tensor(-1.4000)
0.21989771723747253tensor(-1.4000)
0.2445489764213562tensor(-1.4000)
0.2008064240217209tensor(-1.4000)
0.24868614971637726tensor(-1.4000)
0.254021555185318tensor(-1.4000)
0.23547814786434174tensor(-1.4000)
0.22964851558208466tensor(-1.4000)
0.17951755225658417tensor(-1.4000)
0.23442792892456055tensor(-1.4000)
0.2512173354625702tensor(-1.4000)
0.2229376584291458tensor(-1.4000)
0.21996933221817017tensor(-1.4000)
0.21077638864517212tensor(-1.4000)
0.17687810957431793tensor(-1.4000)
0.23631763458251953tensor(-1.4000)
0.2224597930908203tensor(-1.4000)
0.16279521584510803tensor(-1.4000)
0.20861902832984924tensor(-1.4000)
0.19428031146526337tensor(-1.4000)
0.1852155476808548tensor(-1.4000)
0.19692738354206085tensor(-1.4000)
0.2331806868314743tensor(-1.4000)
0.20189082622528076tensor(-1.4000)
0.17031361162662506tensor(-1.4000)
0.20533233880996704tensor(-1.4000)
0.15550191700458527tensor(-1.4000)
0.2256239354610443tensor(-1.4000)
0.21529383957386017tensor(-1.4000)
0.18069782853126526tensor(-1.4000)
0.14541462063789368tensor(-1.4000)
0.21599259972572327tensor(-1.4000)
0.21599259972572327tensor(-1.4000)
0.2069883495569229tensor(-1.4000)
0.1851908266544342tensor(-1.4000)
0.16911713778972626tensor(-1.4000)
0.2171664535999298tensor(-1.4000)
0.1594383269548416tensor(-1.4000)
0.180593341588974tensor(-1.4000)
0.16420647501945496tensor(-1.4000)
0.15279051661491394tensor(-1.4000)
0.09750346839427948tensor(-1.4000)
0.16652554273605347tensor(-1.4000)
0.18958143889904022tensor(-1.4000)
0.22490668296813965tensor(-1.4000)
0.15860016644001007tensor(-1.4000)
0.23365338146686554tensor(-1.4000)
0.1903165578842163tensor(-1.4000)
0.22607670724391937tensor(-1.4000)
0.15624482929706573tensor(-1.4000)
0.12420438975095749tensor(-1.4000)
0.18737858533859253tensor(-1.4000)
0.17222647368907928tensor(-1.4000)
0.1737389713525772tensor(-1.4000)
0.1654207855463028tensor(-1.4000)
0.14568406343460083tensor(-1.4000)
0.13159967958927155tensor(-1.4000)
0.18405094742774963tensor(-1.4000)
0.19084298610687256tensor(-1.4000)
0.11296116560697556tensor(-1.4000)
0.15322040021419525tensor(-1.4000)
0.21236975491046906tensor(-1.4000)
0.14905492961406708tensor(-1.4000)
0.20053456723690033tensor(-1.4000)
0.15028166770935059tensor(-1.4000)
0.18290261924266815tensor(-1.4000)
0.13522477447986603tensor(-1.4000)
0.18556255102157593tensor(-1.4000)
0.13279326260089874tensor(-1.4000)
0.20115810632705688tensor(-1.4000)
0.19145990908145905tensor(-1.4000)
0.18366636335849762tensor(-1.4000)
0.10780733823776245tensor(-1.4000)
0.0662863627076149tensor(-1.4000)
0.04578249156475067tensor(-1.4000)
0.1616746336221695tensor(-1.4000)
0.10677435994148254tensor(-1.4000)
0.11771609634160995tensor(-1.4000)
0.15953612327575684tensor(-1.4000)
0.16951172053813934tensor(-1.4000)
0.15730082988739014tensor(-1.4000)
0.15939617156982422tensor(-1.4000)
0.16309814155101776tensor(-1.4000)
0.1473211646080017tensor(-1.4000)
0.17378203570842743tensor(-1.4000)
0.20432664453983307tensor(-1.4000)
0.14664790034294128tensor(-1.4000)
0.11232484132051468tensor(-1.4000)
0.14590740203857422tensor(-1.4000)
0.15599730610847473tensor(-1.4000)
0.11660754680633545tensor(-1.4000)
0.08391863107681274tensor(-1.4000)
0.16716623306274414tensor(-1.4000)
0.09616769105195999tensor(-1.4000)
0.14762063324451447tensor(-1.4000)
0.1633053719997406tensor(-1.4000)
0.12984055280685425tensor(-1.4000)
0.1279616504907608tensor(-1.4000)
0.23606006801128387tensor(-1.4000)
0.11203592270612717tensor(-1.4000)
-0.008198619820177555tensor(-1.4000)
0.13516129553318024tensor(-1.4000)
0.1513819545507431tensor(-1.4000)
0.259361207485199tensor(-1.4000)
0.18584290146827698tensor(-1.4000)
0.15237346291542053tensor(-1.4000)
0.1681416630744934tensor(-1.4000)
0.1603836864233017tensor(-1.4000)
0.1322885900735855tensor(-1.4000)
0.21557626128196716tensor(-1.4000)
0.17198190093040466tensor(-1.4000)
0.09137286990880966tensor(-1.4000)
0.21580669283866882tensor(-1.4000)
0.23251944780349731tensor(-1.4000)
0.20849022269248962tensor(-1.4000)
0.23583835363388062tensor(-1.4000)
0.1496191918849945tensor(-1.4000)
0.13725006580352783tensor(-1.4000)
0.16857993602752686tensor(-1.4000)
0.157929927110672tensor(-1.4000)
0.17869709432125092tensor(-1.4000)
0.23827561736106873tensor(-1.4000)
0.23646235466003418tensor(-1.4000)
0.1027638241648674tensor(-1.4000)
0.11315282434225082tensor(-1.4000)
0.22934730350971222tensor(-1.4000)
0.2154492288827896tensor(-1.4000)
0.243323415517807tensor(-1.4000)
0.12110251188278198tensor(-1.4000)
0.07173313200473785tensor(-1.4000)
0.20263925194740295tensor(-1.4000)
0.1748565286397934tensor(-1.4000)
0.13511362671852112tensor(-1.4000)
0.16232356429100037tensor(-1.4000)
0.18183457851409912tensor(-1.4000)
0.2049543410539627tensor(-1.4000)
0.21871618926525116tensor(-1.4000)
0.19740945100784302tensor(-1.4000)
0.15655070543289185tensor(-1.4000)
0.20085249841213226tensor(-1.4000)
0.2165992558002472tensor(-1.4000)
0.15411002933979034tensor(-1.4000)
0.20700494945049286tensor(-1.4000)
0.18412742018699646tensor(-1.4000)
0.1818612962961197tensor(-1.4000)
0.22424818575382233tensor(-1.4000)
0.10780799388885498tensor(-1.4000)
0.13377255201339722tensor(-1.4000)
0.12389082461595535tensor(-1.4000)
0.1980009227991104tensor(-1.4000)
0.13524550199508667tensor(-1.4000)
0.22192196547985077tensor(-1.4000)
0.24206295609474182tensor(-1.4000)
0.1698169857263565tensor(-1.4000)
0.20943960547447205tensor(-1.4000)
0.23343591392040253tensor(-1.4000)
0.16567693650722504tensor(-1.4000)
0.23138493299484253tensor(-1.4000)
0.2315031886100769tensor(-1.4000)
0.156260147690773tensor(-1.4000)
0.1580478847026825tensor(-1.4000)
0.1679186224937439tensor(-1.4000)
0.1936146467924118tensor(-1.4000)
0.09144247323274612tensor(-1.4000)
0.18764960765838623tensor(-1.4000)
0.18957878649234772tensor(-1.4000)
0.17434433102607727tensor(-1.4000)
0.22929997742176056tensor(-1.4000)
0.12542036175727844tensor(-1.4000)
0.2214912623167038tensor(-1.4000)
0.12656782567501068tensor(-1.4000)
0.07263223081827164tensor(-1.4000)
0.2263449877500534tensor(-1.4000)
0.20292994379997253tensor(-1.4000)
0.2173692137002945tensor(-1.4000)
0.1253381222486496tensor(-1.4000)
0.21584835648536682tensor(-1.4000)
0.08426444977521896tensor(-1.4000)
0.17400068044662476tensor(-1.4000)
0.21936006844043732tensor(-1.4000)
0.18636713922023773tensor(-1.4000)
0.21967056393623352tensor(-1.4000)
0.25717681646347046tensor(-1.4000)
0.2117634266614914tensor(-1.4000)
0.2322400063276291tensor(-1.4000)
0.2009655088186264tensor(-1.4000)
0.1322115957736969tensor(-1.4000)
0.15802228450775146tensor(-1.4000)
0.18350817263126373tensor(-1.4000)
0.22433945536613464tensor(-1.4000)
0.15762904286384583tensor(-1.4000)
0.16275732219219208tensor(-1.4000)
0.23839055001735687tensor(-1.4000)
0.23813249170780182tensor(-1.4000)
0.2396651804447174tensor(-1.4000)
0.20577022433280945tensor(-1.4000)
0.1614716351032257tensor(-1.4000)
0.13721473515033722tensor(-1.4000)
0.15896283090114594tensor(-1.4000)
0.14977015554904938tensor(-1.4000)
0.11308485269546509tensor(-1.4000)
0.13073666393756866tensor(-1.4000)
0.20640824735164642tensor(-1.4000)
0.25216466188430786tensor(-1.4000)
0.20025581121444702tensor(-1.4000)
0.2133374959230423tensor(-1.4000)
0.2138317972421646tensor(-1.4000)
0.11872367560863495tensor(-1.4000)
0.12323302030563354tensor(-1.4000)
0.2292805016040802tensor(-1.4000)
0.14205454289913177tensor(-1.4000)
0.22319677472114563tensor(-1.4000)
0.2474231868982315tensor(-1.4000)
0.1992388814687729tensor(-1.4000)
0.18295495212078094tensor(-1.4000)
0.1832294911146164tensor(-1.4000)
0.21713154017925262tensor(-1.4000)
0.07779016345739365tensor(-1.4000)
0.1640482097864151tensor(-1.4000)
0.165852889418602tensor(-1.4000)
0.23575137555599213tensor(-1.4000)
0.2620519697666168tensor(-1.4000)
0.21806886792182922tensor(-1.4000)
0.21759822964668274tensor(-1.4000)
0.07294249534606934tensor(-1.4000)
0.21010427176952362tensor(-1.4000)
0.1692211627960205tensor(-1.4000)
0.18402232229709625tensor(-1.4000)
0.19821617007255554tensor(-1.4000)
0.09678635001182556tensor(-1.4000)
0.16628345847129822tensor(-1.4000)
0.21388252079486847tensor(-1.4000)
0.22395621240139008tensor(-1.4000)
0.20664876699447632tensor(-1.4000)
0.18364295363426208tensor(-1.4000)
0.13048028945922852tensor(-1.4000)
0.13742993772029877tensor(-1.4000)
0.21346892416477203tensor(-1.4000)
0.23436251282691956tensor(-1.4000)
0.16494490206241608tensor(-1.4000)
0.20347177982330322tensor(-1.4000)
0.13828200101852417tensor(-1.4000)
0.2524191737174988tensor(-1.4000)
0.20652875304222107tensor(-1.4000)
0.1374225914478302tensor(-1.4000)
0.15962493419647217tensor(-1.4000)
0.24343223869800568tensor(-1.4000)
0.14552877843379974tensor(-1.4000)
0.1393662840127945tensor(-1.4000)
0.2205611765384674tensor(-1.4000)
0.20675089955329895tensor(-1.4000)
0.1526237577199936tensor(-1.4000)
0.15721984207630157tensor(-1.4000)
0.16557054221630096tensor(-1.4000)
0.17453430593013763tensor(-1.4000)
0.15517491102218628tensor(-1.4000)
0.17969566583633423tensor(-1.4000)
0.23740370571613312tensor(-1.4000)
0.11686547100543976tensor(-1.4000)
0.15169252455234528tensor(-1.4000)
0.17205213010311127tensor(-1.4000)
0.18135303258895874tensor(-1.4000)
0.18777568638324738tensor(-1.4000)
0.19901907444000244tensor(-1.4000)
0.17184333503246307tensor(-1.4000)
0.19801457226276398tensor(-1.4000)
0.10606352239847183tensor(-1.4000)
0.1865454465150833tensor(-1.4000)
0.07999757677316666tensor(-1.4000)
0.2166835516691208tensor(-1.4000)
0.2225121259689331tensor(-1.4000)
0.19717693328857422tensor(-1.4000)
0.2392384260892868tensor(-1.4000)
0.13733424246311188tensor(-1.4000)
0.24946032464504242tensor(-1.4000)
0.14494556188583374tensor(-1.4000)
0.22551022469997406tensor(-1.4000)
0.17476943135261536tensor(-1.4000)
0.13844610750675201tensor(-1.4000)
0.231386199593544tensor(-1.4000)
0.20411317050457tensor(-1.4000)
0.18295419216156006tensor(-1.4000)
0.14971642196178436tensor(-1.4000)
0.22978238761425018tensor(-1.4000)
0.15520226955413818tensor(-1.4000)
0.23841798305511475tensor(-1.4000)
0.1599179357290268tensor(-1.4000)
0.2018052637577057tensor(-1.4000)
0.1876402199268341tensor(-1.4000)
0.23378123342990875tensor(-1.4000)
0.22421282529830933tensor(-1.4000)
0.20499108731746674tensor(-1.4000)
0.15991656482219696tensor(-1.4000)
0.1541794240474701tensor(-1.4000)
0.1535450518131256tensor(-1.4000)
0.14035996794700623tensor(-1.4000)
0.18986549973487854tensor(-1.4000)
0.12935666739940643tensor(-1.4000)
0.1596607118844986tensor(-1.4000)
0.1731691211462021tensor(-1.4000)
0.16821688413619995tensor(-1.4000)
0.1854250431060791tensor(-1.4000)
0.19980056583881378tensor(-1.4000)
0.1956007331609726tensor(-1.4000)
0.15022461116313934tensor(-1.4000)
0.0588139072060585tensor(-1.4000)
0.2350245863199234tensor(-1.4000)
0.09627455472946167tensor(-1.4000)
0.12375010550022125tensor(-1.4000)
0.17775939404964447tensor(-1.4000)
0.1525425761938095tensor(-1.4000)
0.21241100132465363tensor(-1.4000)
0.13072480261325836tensor(-1.4000)
0.19140176475048065tensor(-1.4000)
0.11176194995641708tensor(-1.4000)
0.22308912873268127tensor(-1.4000)
0.21646013855934143tensor(-1.4000)
0.1590280532836914tensor(-1.4000)
0.2098729908466339tensor(-1.4000)
0.22494813799858093tensor(-1.4000)
0.21603268384933472tensor(-1.4000)
0.14638787508010864tensor(-1.4000)
0.1806362420320511tensor(-1.4000)
0.17439185082912445tensor(-1.4000)
0.23826606571674347tensor(-1.4000)
0.23248505592346191tensor(-1.4000)
0.15304391086101532tensor(-1.4000)
0.19739678502082825tensor(-1.4000)
0.1939697265625tensor(-1.4000)
0.14719216525554657tensor(-1.4000)
0.1866556853055954tensor(-1.4000)
0.14605939388275146tensor(-1.4000)
0.22434329986572266tensor(-1.4000)
0.14372576773166656tensor(-1.4000)
0.19069616496562958tensor(-1.4000)
0.14030450582504272tensor(-1.4000)
0.24387988448143005tensor(-1.4000)
0.1728994995355606tensor(-1.4000)
0.20801912248134613tensor(-1.4000)
0.2449246197938919tensor(-1.4000)
0.15172003209590912tensor(-1.4000)
0.23377437889575958tensor(-1.4000)
0.23587588965892792tensor(-1.4000)
0.20272520184516907tensor(-1.4000)
0.2142658829689026tensor(-1.4000)
0.23105759918689728tensor(-1.4000)
0.1083371564745903tensor(-1.4000)
0.21816657483577728tensor(-1.4000)
0.16193217039108276tensor(-1.4000)
0.1743137389421463tensor(-1.4000)
0.10574766993522644tensor(-1.4000)
0.21632404625415802tensor(-1.4000)
0.22419238090515137tensor(-1.4000)
0.1974412202835083tensor(-1.4000)
0.108305923640728tensor(-1.4000)
0.25001809000968933tensor(-1.4000)
0.1878732293844223tensor(-1.4000)
0.24707838892936707tensor(-1.4000)
0.20610038936138153tensor(-1.4000)
0.1801663041114807tensor(-1.4000)
0.1975591480731964tensor(-1.4000)
0.16505636274814606tensor(-1.4000)
0.15457093715667725tensor(-1.4000)
0.18136124312877655tensor(-1.4000)
0.22377146780490875tensor(-1.4000)
0.20653241872787476tensor(-1.4000)
0.23904263973236084tensor(-1.4000)
0.1474790722131729tensor(-1.4000)
0.21506522595882416tensor(-1.4000)
0.20082217454910278tensor(-1.4000)
0.09814280271530151tensor(-1.4000)
0.2211722433567047tensor(-1.4000)
0.17234480381011963tensor(-1.4000)
0.16349093616008759tensor(-1.4000)
0.2152538299560547tensor(-1.4000)
0.13058429956436157tensor(-1.4000)
0.17615649104118347tensor(-1.4000)
0.16282092034816742tensor(-1.4000)
0.22230765223503113tensor(-1.4000)
0.1188245639204979tensor(-1.4000)
0.20327353477478027tensor(-1.4000)
0.15574190020561218tensor(-1.4000)
0.13046494126319885tensor(-1.4000)
0.16170312464237213tensor(-1.4000)
0.07697577774524689tensor(-1.4000)
0.17028944194316864tensor(-1.4000)
0.16383790969848633tensor(-1.4000)
0.19581209123134613tensor(-1.4000)
0.19352583587169647tensor(-1.4000)
0.16005603969097137tensor(-1.4000)
0.17348459362983704tensor(-1.4000)
0.15073668956756592tensor(-1.4000)
0.2616230547428131tensor(-1.4000)
0.17027047276496887tensor(-1.4000)
0.21412967145442963tensor(-1.4000)
0.2099747210741043tensor(-1.4000)
0.20709410309791565tensor(-1.4000)
0.19823119044303894tensor(-1.4000)
0.19190870225429535tensor(-1.4000)
0.2321850061416626tensor(-1.4000)
0.23039808869361877tensor(-1.4000)
0.19099858403205872tensor(-1.4000)
0.23663823306560516tensor(-1.4000)
0.18859972059726715tensor(-1.4000)
0.20288525521755219tensor(-1.4000)
0.25029444694519043tensor(-1.4000)
0.15074703097343445tensor(-1.4000)
0.2114260494709015tensor(-1.4000)
0.22026696801185608tensor(-1.4000)
0.1465217024087906tensor(-1.4000)
0.20054249465465546tensor(-1.4000)
0.22116494178771973tensor(-1.4000)
0.1937815099954605tensor(-1.4000)
0.14585624635219574tensor(-1.4000)
0.20545527338981628tensor(-1.4000)
0.20163246989250183tensor(-1.4000)
0.23020713031291962tensor(-1.4000)
0.1762525886297226tensor(-1.4000)
0.2619001567363739tensor(-1.4000)
0.1599022001028061tensor(-1.4000)
0.1061270460486412tensor(-1.4000)
0.257782518863678tensor(-1.4000)
0.24086642265319824tensor(-1.4000)
0.1604774296283722tensor(-1.4000)
0.1359712928533554tensor(-1.4000)
0.2010449469089508tensor(-1.4000)
0.2273634523153305tensor(-1.4000)
0.2340317964553833tensor(-1.4000)
0.2151031345129013tensor(-1.4000)
0.16882240772247314tensor(-1.4000)
0.2153768241405487tensor(-1.4000)
0.19445084035396576tensor(-1.4000)
0.1029045432806015tensor(-1.4000)
0.24094919860363007tensor(-1.4000)
0.18207234144210815tensor(-1.4000)
0.19043931365013123tensor(-1.4000)
0.18888969719409943tensor(-1.4000)
0.16511358320713043tensor(-1.4000)
0.11655538529157639tensor(-1.4000)
0.18882296979427338tensor(-1.4000)
0.14504072070121765tensor(-1.4000)
0.2326812744140625tensor(-1.4000)
0.17817342281341553tensor(-1.4000)
0.2348317801952362tensor(-1.4000)
0.24459195137023926tensor(-1.4000)
0.15062116086483002tensor(-1.4000)
0.09102634340524673tensor(-1.4000)
0.23442363739013672tensor(-1.4000)
0.19811101257801056tensor(-1.4000)
0.10710987448692322tensor(-1.4000)
0.16338546574115753tensor(-1.4000)
0.25191530585289tensor(-1.4000)
0.18941351771354675tensor(-1.4000)
0.1255074143409729tensor(-1.4000)
0.21026049554347992tensor(-1.4000)
0.24447064101696014tensor(-1.4000)
0.20741049945354462tensor(-1.4000)
0.18218165636062622tensor(-1.4000)
0.24792659282684326tensor(-1.4000)
0.20852558314800262tensor(-1.4000)
0.25179630517959595tensor(-1.4000)
0.2523632049560547tensor(-1.4000)
0.25348517298698425tensor(-1.4000)
0.2321215718984604tensor(-1.4000)
0.19688092172145844tensor(-1.4000)
0.19102652370929718tensor(-1.4000)
0.20091286301612854tensor(-1.4000)
0.228019580245018tensor(-1.4000)
0.1944364756345749tensor(-1.4000)
0.1700524538755417tensor(-1.4000)
0.1624617725610733tensor(-1.4000)
0.22650016844272614tensor(-1.4000)
0.19974921643733978tensor(-1.4000)
0.2126191109418869tensor(-1.4000)
0.05313323065638542tensor(-1.4000)
0.2619292140007019tensor(-1.4000)
0.19851404428482056tensor(-1.4000)
0.24617940187454224tensor(-1.4000)
0.17482730746269226tensor(-1.4000)
0.12986363470554352tensor(-1.4000)
0.1553184688091278tensor(-1.4000)
0.1965426653623581tensor(-1.4000)
0.1682707816362381tensor(-1.4000)
0.20011624693870544tensor(-1.4000)
0.23920801281929016tensor(-1.4000)
0.1936015784740448tensor(-1.4000)
0.14686788618564606tensor(-1.4000)
0.17490245401859283tensor(-1.4000)
0.2177003175020218tensor(-1.4000)
0.22804053127765656tensor(-1.4000)
0.23370108008384705tensor(-1.4000)
0.2506176233291626tensor(-1.4000)
0.13608665764331818tensor(-1.4000)
0.17190629243850708tensor(-1.4000)
0.19046154618263245tensor(-1.4000)
0.22394923865795135tensor(-1.4000)
0.18345168232917786tensor(-1.4000)
0.23388975858688354tensor(-1.4000)
0.16027836501598358tensor(-1.4000)
0.13541558384895325tensor(-1.4000)
0.20772895216941833tensor(-1.4000)
0.1833905130624771tensor(-1.4000)
0.21706175804138184tensor(-1.4000)
0.21258914470672607tensor(-1.4000)
0.21826349198818207tensor(-1.4000)
0.1910492479801178tensor(-1.4000)
0.1834004670381546tensor(-1.4000)
0.23593786358833313tensor(-1.4000)
0.21714019775390625tensor(-1.4000)
0.17359396815299988tensor(-1.4000)
0.1689077913761139tensor(-1.4000)
0.13843291997909546tensor(-1.4000)
0.22417287528514862tensor(-1.4000)
0.18256217241287231tensor(-1.4000)
0.1788729876279831tensor(-1.4000)
0.17812997102737427tensor(-1.4000)
0.23002633452415466tensor(-1.4000)
0.1509026139974594tensor(-1.4000)
0.2356109470129013tensor(-1.4000)
0.15953873097896576tensor(-1.4000)
0.2420549839735031tensor(-1.4000)
0.18026567995548248tensor(-1.4000)
0.1797371208667755tensor(-1.4000)
0.2468455284833908tensor(-1.4000)
0.16782918572425842tensor(-1.4000)
0.23489052057266235tensor(-1.4000)
0.2407422512769699tensor(-1.4000)
0.19632136821746826tensor(-1.4000)
0.15396904945373535tensor(-1.4000)
0.17803288996219635tensor(-1.4000)
0.1549152433872223tensor(-1.4000)
0.1698884218931198tensor(-1.4000)
0.1530388444662094tensor(-1.4000)
0.1740005761384964tensor(-1.4000)
0.23714478313922882tensor(-1.4000)
0.210626021027565tensor(-1.4000)
0.1495581716299057tensor(-1.4000)
0.16140827536582947tensor(-1.4000)
0.2154608964920044tensor(-1.4000)
0.18434500694274902tensor(-1.4000)
0.1744769811630249tensor(-1.4000)
0.25083285570144653tensor(-1.4000)
0.2530995011329651tensor(-1.4000)
0.20664100348949432tensor(-1.4000)
0.25062212347984314tensor(-1.4000)
0.13941040635108948tensor(-1.4000)
0.2445995956659317tensor(-1.4000)
0.17288947105407715tensor(-1.4000)
0.17772223055362701tensor(-1.4000)
0.23733261227607727tensor(-1.4000)
0.21042566001415253tensor(-1.4000)
0.2328610122203827tensor(-1.4000)
0.21783314645290375tensor(-1.4000)
0.1510167121887207tensor(-1.4000)
0.2257612645626068tensor(-1.4000)
0.23964178562164307tensor(-1.4000)
0.20773747563362122tensor(-1.4000)
0.11623647809028625tensor(-1.4000)
0.2283732146024704tensor(-1.4000)
0.13705256581306458tensor(-1.4000)
0.2210860401391983tensor(-1.4000)
0.21433348953723907tensor(-1.4000)
0.23609507083892822tensor(-1.4000)
0.20118463039398193tensor(-1.4000)
0.17968083918094635tensor(-1.4000)
0.19479423761367798tensor(-1.4000)
0.21111488342285156tensor(-1.4000)
0.20020760595798492tensor(-1.4000)
0.2331591695547104tensor(-1.4000)
0.2437729388475418tensor(-1.4000)
0.2116737961769104tensor(-1.4000)
0.22200612723827362tensor(-1.4000)
0.21169929206371307tensor(-1.4000)
0.2033049762248993tensor(-1.4000)
0.1967785507440567tensor(-1.4000)
0.1902615875005722tensor(-1.4000)
0.2128700166940689tensor(-1.4000)
0.196517676115036tensor(-1.4000)
0.20470893383026123tensor(-1.4000)
0.23039771616458893tensor(-1.4000)
0.19221089780330658tensor(-1.4000)
0.20628325641155243tensor(-1.4000)
0.1665232926607132tensor(-1.4000)
0.1785939484834671tensor(-1.4000)
0.18408367037773132tensor(-1.4000)
0.144108384847641tensor(-1.4000)
0.17158228158950806tensor(-1.4000)
0.21789555251598358tensor(-1.4000)
0.16982458531856537tensor(-1.4000)
0.2530663013458252tensor(-1.4000)
0.16258658468723297tensor(-1.4000)
0.2507081627845764tensor(-1.4000)
0.22887305915355682tensor(-1.4000)
0.15279167890548706tensor(-1.4000)
0.1786508709192276tensor(-1.4000)
0.23188625276088715tensor(-1.4000)
0.19041001796722412tensor(-1.4000)
0.21845771372318268tensor(-1.4000)
0.23538382351398468tensor(-1.4000)
0.16202965378761292tensor(-1.4000)
0.2113114446401596tensor(-1.4000)
0.19248849153518677tensor(-1.4000)
0.23840588331222534tensor(-1.4000)
0.1893419772386551tensor(-1.4000)
0.24329346418380737tensor(-1.4000)
0.20965488255023956tensor(-1.4000)
0.23388031125068665tensor(-1.4000)
0.23026986420154572tensor(-1.4000)
0.1884394735097885tensor(-1.4000)
0.21279354393482208tensor(-1.4000)
0.18325386941432953tensor(-1.4000)
0.25271061062812805tensor(-1.4000)
0.22513625025749207tensor(-1.4000)
0.20599006116390228tensor(-1.4000)
0.21468979120254517tensor(-1.4000)
0.1395479142665863tensor(-1.4000)
0.19125649333000183tensor(-1.4000)
0.17864160239696503tensor(-1.4000)
0.246071919798851tensor(-1.4000)
0.18496476113796234tensor(-1.4000)
0.22014500200748444tensor(-1.4000)
0.2187165468931198tensor(-1.4000)
0.1631406545639038tensor(-1.4000)
0.15407690405845642tensor(-1.4000)
0.18326333165168762tensor(-1.4000)
0.2556092143058777tensor(-1.4000)
0.1761041134595871tensor(-1.4000)
0.22911378741264343tensor(-1.4000)
0.15708261728286743tensor(-1.4000)
0.18726839125156403tensor(-1.4000)
0.18987935781478882tensor(-1.4000)
0.17377835512161255tensor(-1.4000)
0.1437741219997406tensor(-1.4000)
0.21098734438419342tensor(-1.4000)
0.24363093078136444tensor(-1.4000)
0.22977297008037567tensor(-1.4000)
0.2127877026796341tensor(-1.4000)
0.19787150621414185tensor(-1.4000)
0.16145610809326172tensor(-1.4000)
0.21832279860973358tensor(-1.4000)
0.2285822480916977tensor(-1.4000)
0.20105594396591187tensor(-1.4000)
0.14870138466358185tensor(-1.4000)
0.2069259136915207tensor(-1.4000)
0.18046410381793976tensor(-1.4000)
0.17980651557445526tensor(-1.4000)
0.18052168190479279tensor(-1.4000)
0.1968732476234436tensor(-1.4000)
0.1594647765159607tensor(-1.4000)
0.22436034679412842tensor(-1.4000)
0.17446844279766083tensor(-1.4000)
0.1753850281238556tensor(-1.4000)
0.153499037027359tensor(-1.4000)
0.19356341660022736tensor(-1.4000)
0.13711057603359222tensor(-1.4000)
0.19018018245697021tensor(-1.4000)
0.1691700667142868tensor(-1.4000)
0.24714358150959015tensor(-1.4000)
0.19778525829315186tensor(-1.4000)
0.20669494569301605tensor(-1.4000)
0.22243306040763855tensor(-1.4000)
0.22231444716453552tensor(-1.4000)
0.15379153192043304tensor(-1.4000)
0.16656716167926788tensor(-1.4000)
0.17824368178844452tensor(-1.4000)
0.23468215763568878tensor(-1.4000)
0.22111093997955322tensor(-1.4000)
0.138853520154953tensor(-1.4000)
0.1792006492614746tensor(-1.4000)
0.21858268976211548tensor(-1.4000)
0.2530324459075928tensor(-1.4000)
0.20878081023693085tensor(-1.4000)
0.19627931714057922tensor(-1.4000)
0.18911212682724tensor(-1.4000)
0.18276870250701904tensor(-1.4000)
0.21672075986862183tensor(-1.4000)
0.17158953845500946tensor(-1.4000)
0.22744019329547882tensor(-1.4000)
0.21499907970428467tensor(-1.4000)
0.21916620433330536tensor(-1.4000)
0.22975954413414001tensor(-1.4000)
0.22718709707260132tensor(-1.4000)
0.1910461038351059tensor(-1.4000)
0.16307444870471954tensor(-1.4000)
0.19047988951206207tensor(-1.4000)
0.200912207365036tensor(-1.4000)
0.197919100522995tensor(-1.4000)
0.1627911627292633tensor(-1.4000)
0.2363564521074295tensor(-1.4000)
0.20635461807250977tensor(-1.4000)
0.16418474912643433tensor(-1.4000)
0.197912335395813tensor(-1.4000)
0.22085046768188477tensor(-1.4000)
0.2568252980709076tensor(-1.4000)
0.2165840119123459tensor(-1.4000)
0.15778186917304993tensor(-1.4000)
0.23743470013141632tensor(-1.4000)
0.19069571793079376tensor(-1.4000)
0.22599636018276215tensor(-1.4000)
0.16986806690692902tensor(-1.4000)
0.18578818440437317tensor(-1.4000)
0.22688235342502594tensor(-1.4000)
0.21040304005146027tensor(-1.4000)
0.21322503685951233tensor(-1.4000)
0.260331928730011tensor(-1.4000)
0.21850645542144775tensor(-1.4000)
0.2513972222805023tensor(-1.4000)
0.1781981736421585tensor(-1.4000)
0.17026031017303467tensor(-1.4000)
0.20793874561786652tensor(-1.4000)
0.2575323283672333tensor(-1.4000)
0.19034455716609955tensor(-1.4000)
0.2161027044057846tensor(-1.4000)
0.23598341643810272tensor(-1.4000)
0.20406363904476166tensor(-1.4000)
0.18966734409332275tensor(-1.4000)
0.16281758248806tensor(-1.4000)
0.2313983142375946tensor(-1.4000)
0.19093771278858185tensor(-1.4000)
0.18726995587348938tensor(-1.4000)
0.18355712294578552tensor(-1.4000)
0.20544672012329102tensor(-1.4000)
0.20145145058631897tensor(-1.4000)
0.16991077363491058tensor(-1.4000)
0.13321955502033234tensor(-1.4000)
0.18867939710617065tensor(-1.4000)
0.1616208255290985tensor(-1.4000)
0.2529752850532532tensor(-1.4000)
0.20534898340702057tensor(-1.4000)
0.21686293184757233tensor(-1.4000)
0.2486358880996704tensor(-1.4000)
0.1661246120929718tensor(-1.4000)
0.21098209917545319tensor(-1.4000)
0.18923956155776978tensor(-1.4000)
0.17764581739902496tensor(-1.4000)
0.23165661096572876tensor(-1.4000)
0.1358717828989029tensor(-1.4000)
0.216399148106575tensor(-1.4000)
0.17856362462043762tensor(-1.4000)
0.20058496296405792tensor(-1.4000)
0.182124525308609tensor(-1.4000)
0.16087403893470764tensor(-1.4000)
0.1975012868642807tensor(-1.4000)
0.1852225512266159tensor(-1.4000)
0.1434483677148819tensor(-1.4000)
0.199505016207695tensor(-1.4000)
0.24832679331302643tensor(-1.4000)
0.2362777292728424tensor(-1.4000)
0.1405862271785736tensor(-1.4000)
0.16770564019680023tensor(-1.4000)
0.12609638273715973tensor(-1.4000)
0.14469808340072632tensor(-1.4000)
0.21460919082164764tensor(-1.4000)
0.172663152217865tensor(-1.4000)
0.15650254487991333tensor(-1.4000)
0.22217871248722076tensor(-1.4000)
0.23748381435871124tensor(-1.4000)
0.16684427857398987tensor(-1.4000)
0.11870356649160385tensor(-1.4000)
0.23180577158927917tensor(-1.4000)
0.1537405401468277tensor(-1.4000)
0.17201857268810272tensor(-1.4000)
0.20310209691524506tensor(-1.4000)
0.137125626206398tensor(-1.4000)
0.1431446224451065tensor(-1.4000)
0.19330725073814392tensor(-1.4000)
0.14550498127937317tensor(-1.4000)
0.22678211331367493tensor(-1.4000)
0.18765445053577423tensor(-1.4000)
0.19982050359249115tensor(-1.4000)
0.21961839497089386tensor(-1.4000)
0.18822620809078217tensor(-1.4000)
0.18743044137954712tensor(-1.4000)
0.18145926296710968tensor(-1.4000)
0.1971980333328247tensor(-1.4000)
0.20526030659675598tensor(-1.4000)
0.16585294902324677tensor(-1.4000)
0.14887480437755585tensor(-1.4000)
0.18627533316612244tensor(-1.4000)
0.15285007655620575tensor(-1.4000)
0.16448718309402466tensor(-1.4000)
0.18916451930999756tensor(-1.4000)
0.20995058119297028tensor(-1.4000)
0.2183295637369156tensor(-1.4000)
0.21291711926460266tensor(-1.4000)
0.15636759996414185tensor(-1.4000)
0.16383177042007446tensor(-1.4000)
0.18324460089206696tensor(-1.4000)
0.1670553982257843tensor(-1.4000)
0.16070234775543213tensor(-1.4000)
0.16042408347129822tensor(-1.4000)
0.16921250522136688tensor(-1.4000)
0.2100733518600464tensor(-1.4000)
0.1516515016555786tensor(-1.4000)
0.1772017478942871tensor(-1.4000)
0.18177740275859833tensor(-1.4000)
0.1734127402305603tensor(-1.4000)
0.2180105447769165tensor(-1.4000)
0.15777073800563812tensor(-1.4000)
0.1709175854921341tensor(-1.4000)
0.1812416911125183tensor(-1.4000)
0.1897643357515335tensor(-1.4000)
0.1919667273759842tensor(-1.4000)
0.17690509557724tensor(-1.4000)
0.2004481554031372tensor(-1.4000)
0.17937587201595306tensor(-1.4000)
0.1926102191209793tensor(-1.4000)
0.16383595764636993tensor(-1.4000)
0.2041756957769394tensor(-1.4000)
0.18342822790145874tensor(-1.4000)
0.17631636559963226tensor(-1.4000)
0.1698649525642395tensor(-1.4000)
0.21114447712898254tensor(-1.4000)
0.2120722383260727tensor(-1.4000)
0.1698961853981018tensor(-1.4000)
0.21731676161289215tensor(-1.4000)
0.18889684975147247tensor(-1.4000)
0.1525249183177948tensor(-1.4000)
0.22409404814243317tensor(-1.4000)
0.1630428433418274tensor(-1.4000)
0.19933098554611206tensor(-1.4000)
0.1384163647890091tensor(-1.4000)
0.23387061059474945tensor(-1.4000)
0.17365390062332153tensor(-1.4000)
0.18642432987689972tensor(-1.4000)
0.11872759461402893tensor(-1.4000)
0.1651703119277954tensor(-1.4000)
0.14086435735225677tensor(-1.4000)
0.16754521429538727tensor(-1.4000)
0.2171323448419571tensor(-1.4000)
0.19869427382946014tensor(-1.4000)
0.2237125039100647tensor(-1.4000)
0.15858431160449982tensor(-1.4000)
0.23704463243484497tensor(-1.4000)
0.13235925137996674tensor(-1.4000)
0.19673356413841248tensor(-1.4000)
0.1545514166355133tensor(-1.4000)
0.16989582777023315tensor(-1.4000)
0.21180765330791473tensor(-1.4000)
0.20280808210372925tensor(-1.4000)
0.15087836980819702tensor(-1.4000)
0.20274856686592102tensor(-1.4000)
0.23436222970485687tensor(-1.4000)
0.17277438938617706tensor(-1.4000)
0.1535673886537552tensor(-1.4000)
0.188127338886261tensor(-1.4000)
0.14478106796741486tensor(-1.4000)
0.1730610728263855tensor(-1.4000)
0.17327286303043365tensor(-1.4000)
0.19863882660865784tensor(-1.4000)
0.12883561849594116tensor(-1.4000)
0.10730590671300888tensor(-1.4000)
0.1933862864971161tensor(-1.4000)
0.15976984798908234tensor(-1.4000)
0.18191397190093994tensor(-1.4000)
0.2085813581943512tensor(-1.4000)
0.11617257446050644tensor(-1.4000)
0.1806708723306656tensor(-1.4000)
0.2038748413324356tensor(-1.4000)
0.1703106164932251tensor(-1.4000)
0.1869269609451294tensor(-1.4000)
0.18340416252613068tensor(-1.4000)
0.18087835609912872tensor(-1.4000)
0.2076762169599533tensor(-1.4000)
0.22831769287586212tensor(-1.4000)
0.18882213532924652tensor(-1.4000)
0.20515233278274536tensor(-1.4000)
0.14807409048080444tensor(-1.4000)
0.2012169063091278tensor(-1.4000)
0.20618247985839844tensor(-1.4000)
0.24252410233020782tensor(-1.4000)
0.19543766975402832tensor(-1.4000)
0.192985400557518tensor(-1.4000)
0.14022190868854523tensor(-1.4000)
0.19547872245311737tensor(-1.4000)
0.1764545738697052tensor(-1.4000)
0.14577209949493408tensor(-1.4000)
0.19039775431156158tensor(-1.4000)
0.15937578678131104tensor(-1.4000)
0.1654968112707138tensor(-1.4000)
0.22324170172214508tensor(-1.4000)
0.14461469650268555tensor(-1.4000)
0.1916940063238144tensor(-1.4000)
0.22100146114826202tensor(-1.4000)
0.20422080159187317tensor(-1.4000)
0.14186277985572815tensor(-1.4000)
0.22731831669807434tensor(-1.4000)
0.18117515742778778tensor(-1.4000)
0.24580946564674377tensor(-1.4000)
0.19535481929779053tensor(-1.4000)
0.17158286273479462tensor(-1.4000)
0.24574531614780426tensor(-1.4000)
0.2178047150373459tensor(-1.4000)
0.13275282084941864tensor(-1.4000)
0.21666796505451202tensor(-1.4000)
0.1772407740354538tensor(-1.4000)
0.19283534586429596tensor(-1.4000)
0.1293940544128418tensor(-1.4000)
0.14554977416992188tensor(-1.4000)
0.19556990265846252tensor(-1.4000)
0.2355385422706604tensor(-1.4000)
0.1848045140504837tensor(-1.4000)
0.25272318720817566tensor(-1.4000)
0.2389572113752365tensor(-1.4000)
0.22011497616767883tensor(-1.4000)
0.16674257814884186tensor(-1.4000)
0.19784942269325256tensor(-1.4000)
0.2271832674741745tensor(-1.4000)
0.204009011387825tensor(-1.4000)
0.2550947964191437tensor(-1.4000)
0.23024973273277283tensor(-1.4000)
0.18396127223968506tensor(-1.4000)
0.14721204340457916tensor(-1.4000)
0.19050715863704681tensor(-1.4000)
0.15678662061691284tensor(-1.4000)
0.16435734927654266tensor(-1.4000)
0.16964532434940338tensor(-1.4000)
0.16612794995307922tensor(-1.4000)
0.21568720042705536tensor(-1.4000)
0.204022616147995tensor(-1.4000)
0.15134403109550476tensor(-1.4000)
0.21145397424697876tensor(-1.4000)
0.20200596749782562tensor(-1.4000)
0.16504986584186554tensor(-1.4000)
0.16510175168514252tensor(-1.4000)
0.13233773410320282tensor(-1.4000)
0.16062770783901215tensor(-1.4000)
0.19481590390205383tensor(-1.4000)
0.25873708724975586tensor(-1.4000)
0.17912712693214417tensor(-1.4000)
0.1835862696170807tensor(-1.4000)
0.1634383201599121tensor(-1.4000)
0.15271799266338348tensor(-1.4000)
0.22736875712871552tensor(-1.4000)
0.17157220840454102tensor(-1.4000)
0.11419299989938736tensor(-1.4000)
0.1652996689081192tensor(-1.4000)
0.1875506192445755tensor(-1.4000)
0.17843952775001526tensor(-1.4000)
0.16387860476970673tensor(-1.4000)
0.156889408826828tensor(-1.4000)
0.24857407808303833tensor(-1.4000)
0.17120875418186188tensor(-1.4000)
0.24315060675144196tensor(-1.4000)
0.220265194773674tensor(-1.4000)
0.20366311073303223tensor(-1.4000)
0.20777857303619385tensor(-1.4000)
0.1217728853225708tensor(-1.4000)
0.17767389118671417tensor(-1.4000)
0.16550970077514648tensor(-1.4000)
0.12488286197185516tensor(-1.4000)
0.22715292870998383tensor(-1.4000)
0.1636105328798294tensor(-1.4000)
0.141145259141922tensor(-1.4000)
0.16126377880573273tensor(-1.4000)
0.19492758810520172tensor(-1.4000)
0.19200310111045837tensor(-1.4000)
0.15120698511600494tensor(-1.4000)
0.19806157052516937tensor(-1.4000)
0.2053111046552658tensor(-1.4000)
0.15609821677207947tensor(-1.4000)
0.17414484918117523tensor(-1.4000)
0.15349788963794708tensor(-1.4000)
0.1894805133342743tensor(-1.4000)
0.1884358823299408tensor(-1.4000)
0.22679878771305084tensor(-1.4000)
0.15601545572280884tensor(-1.4000)
0.1855309158563614tensor(-1.4000)
0.22720426321029663tensor(-1.4000)
0.19963254034519196tensor(-1.4000)
0.12287198752164841tensor(-1.4000)
0.14516253769397736tensor(-1.4000)
0.18562915921211243tensor(-1.4000)
0.1680903285741806tensor(-1.4000)
0.21560117602348328tensor(-1.4000)
0.1902918815612793tensor(-1.4000)
0.15899445116519928tensor(-1.4000)
0.23457001149654388tensor(-1.4000)
0.16738282144069672tensor(-1.4000)
0.2100963294506073tensor(-1.4000)
0.2135733664035797tensor(-1.4000)
0.21221411228179932tensor(-1.4000)
0.1787315458059311tensor(-1.4000)
0.14357030391693115tensor(-1.4000)
0.19952678680419922tensor(-1.4000)
0.0340559259057045tensor(-1.4000)
0.20010346174240112tensor(-1.4000)
0.1327422559261322tensor(-1.4000)
0.12070447951555252tensor(-1.4000)
0.12073937058448792tensor(-1.4000)
0.19632773101329803tensor(-1.4000)
0.22938281297683716tensor(-1.4000)
0.16012395918369293tensor(-1.4000)
0.07705783098936081tensor(-1.4000)
0.12057927995920181tensor(-1.4000)
0.18442970514297485tensor(-1.4000)
0.08594398200511932tensor(-1.4000)
0.2050175666809082tensor(-1.4000)
0.14901499450206757tensor(-1.4000)
0.19206953048706055tensor(-1.4000)
0.18188036978244781tensor(-1.4000)
0.23754027485847473tensor(-1.4000)
0.022610455751419067tensor(-1.4000)
0.025151005014777184tensor(-1.4000)
0.23842330276966095tensor(-1.4000)
0.20551976561546326tensor(-1.4000)
0.247052401304245tensor(-1.4000)
0.1970101296901703tensor(-1.4000)
0.10351932048797607tensor(-1.4000)
-0.006736127659678459tensor(-1.4000)
0.12601491808891296tensor(-1.4000)
0.0985279530286789tensor(-1.4000)
0.2076616883277893tensor(-1.4000)
0.19697895646095276tensor(-1.4000)
0.146495059132576tensor(-1.4000)
0.19824497401714325tensor(-1.4000)
0.22204528748989105tensor(-1.4000)
-0.002258731983602047tensor(-1.4000)
0.12712278962135315tensor(-1.4000)
0.14500769972801208tensor(-1.4000)
0.2177596092224121tensor(-1.4000)
0.06146633252501488tensor(-1.4000)
0.18936406075954437tensor(-1.4000)
0.23241430521011353tensor(-1.4000)
0.10061290860176086tensor(-1.4000)
0.20638981461524963tensor(-1.4000)
0.11950136721134186tensor(-1.4000)
-0.01070451084524393tensor(-1.4000)
0.21413107216358185tensor(-1.4000)
0.10753166675567627tensor(-1.4000)
0.2595307230949402tensor(-1.4000)
0.07921183854341507tensor(-1.4000)
0.1516534686088562tensor(-1.4000)
0.16583320498466492tensor(-1.4000)
0.25373005867004395tensor(-1.4000)
0.1389901489019394tensor(-1.4000)
0.09368916600942612tensor(-1.4000)
0.1946215033531189tensor(-1.4000)
0.22359564900398254tensor(-1.4000)
0.21453358232975006tensor(-1.4000)
0.157292902469635tensor(-1.4000)
0.15786482393741608tensor(-1.4000)
0.17615078389644623tensor(-1.4000)
0.177358478307724tensor(-1.4000)
0.18982848525047302tensor(-1.4000)
0.11250356584787369tensor(-1.4000)
0.10029029846191406tensor(-1.4000)
0.20764242112636566tensor(-1.4000)
0.24434669315814972tensor(-1.4000)
0.2105565369129181tensor(-1.4000)
0.14037643373012543tensor(-1.4000)
0.24028757214546204tensor(-1.4000)
0.09345024824142456tensor(-1.4000)
0.15237806737422943tensor(-1.4000)
0.12420177459716797tensor(-1.4000)
0.22272337973117828tensor(-1.4000)
0.22805270552635193tensor(-1.4000)
0.10344910621643066tensor(-1.4000)
0.24677674472332tensor(-1.4000)
0.1049804836511612tensor(-1.4000)
0.23844309151172638tensor(-1.4000)
0.11710364371538162tensor(-1.4000)
0.09025827050209045tensor(-1.4000)
0.13164407014846802tensor(-1.4000)
0.2161635309457779tensor(-1.4000)
0.1173747107386589tensor(-1.4000)
0.22476129233837128tensor(-1.4000)
0.2164711356163025tensor(-1.4000)
0.17284907400608063tensor(-1.4000)
0.11613447964191437tensor(-1.4000)
0.1322343945503235tensor(-1.4000)
0.07818208634853363tensor(-1.4000)
0.1590670794248581tensor(-1.4000)
0.1278531551361084tensor(-1.4000)
0.25561287999153137tensor(-1.4000)
0.15640778839588165tensor(-1.4000)
0.1604805290699005tensor(-1.4000)
0.18119247257709503tensor(-1.4000)
0.2122970074415207tensor(-1.4000)
0.20926786959171295tensor(-1.4000)
0.19664229452610016tensor(-1.4000)
0.10105139762163162tensor(-1.4000)
0.22328048944473267tensor(-1.4000)
0.12157509475946426tensor(-1.4000)
0.18239395320415497tensor(-1.4000)
0.19555701315402985tensor(-1.4000)
0.16839411854743958tensor(-1.4000)
0.10808739811182022tensor(-1.4000)
0.1893589347600937tensor(-1.4000)
0.1959211379289627tensor(-1.4000)
0.23569686710834503tensor(-1.4000)
0.16088590025901794tensor(-1.4000)
0.15652777254581451tensor(-1.4000)
0.15186144411563873tensor(-1.4000)
0.171722412109375tensor(-1.4000)
0.15258018672466278tensor(-1.4000)
0.0036517668049782515tensor(-1.4000)
0.20676204562187195tensor(-1.4000)
0.22830666601657867tensor(-1.4000)
0.06791062653064728tensor(-1.4000)
0.05178697407245636tensor(-1.4000)
0.22105059027671814tensor(-1.4000)
0.1068749949336052tensor(-1.4000)
0.24726004898548126tensor(-1.4000)
0.19305817782878876tensor(-1.4000)
0.1952791064977646tensor(-1.4000)
0.057869136333465576tensor(-1.4000)
0.20309258997440338tensor(-1.4000)
0.1019628494977951tensor(-1.4000)
0.19072271883487701tensor(-1.4000)
0.16306497156620026tensor(-1.4000)
0.255033940076828tensor(-1.4000)
0.11277575045824051tensor(-1.4000)
0.1834629774093628tensor(-1.4000)
0.2123236209154129tensor(-1.4000)
0.21153272688388824tensor(-1.4000)
0.1819947510957718tensor(-1.4000)
0.22807522118091583tensor(-1.4000)
0.25492063164711tensor(-1.4000)
0.14400605857372284tensor(-1.4000)
0.1177901178598404tensor(-1.4000)
0.05289604514837265tensor(-1.4000)
0.12926360964775085tensor(-1.4000)
0.1985255479812622tensor(-1.4000)
0.16846822202205658tensor(-1.4000)
0.1315501481294632tensor(-1.4000)
0.1327163428068161tensor(-1.4000)
0.18882274627685547tensor(-1.4000)
0.12785108387470245tensor(-1.4000)
0.2617390751838684tensor(-1.4000)
0.11208908259868622tensor(-1.4000)
0.21057505905628204tensor(-1.4000)
0.029147831723093987tensor(-1.4000)
0.028685282915830612tensor(-1.4000)
0.154188334941864tensor(-1.4000)
0.17033088207244873tensor(-1.4000)
0.11963847279548645tensor(-1.4000)
0.21074433624744415tensor(-1.4000)
0.22716106474399567tensor(-1.4000)
0.1033785492181778tensor(-1.4000)
0.008538630791008472tensor(-1.4000)
0.12353048473596573tensor(-1.4000)
0.1951850950717926tensor(-1.4000)
0.10456414520740509tensor(-1.4000)
0.15231630206108093tensor(-1.4000)
0.21942035853862762tensor(-1.4000)
0.15017782151699066tensor(-1.4000)
0.09755057096481323tensor(-1.4000)
0.21414875984191895tensor(-1.4000)
0.07415670901536942tensor(-1.4000)
0.00733835157006979tensor(-1.4000)
0.20016923546791077tensor(-1.4000)
0.21801534295082092tensor(-1.4000)
0.20763497054576874tensor(-1.4000)
0.2464318424463272tensor(-1.4000)
0.20076152682304382tensor(-1.4000)
0.21152502298355103tensor(-1.4000)
0.1520642191171646tensor(-1.4000)
0.1964855045080185tensor(-1.4000)
0.1653665155172348tensor(-1.4000)
0.05248218774795532tensor(-1.4000)
0.12573620676994324tensor(-1.4000)
0.11853691190481186tensor(-1.4000)
0.16260622441768646tensor(-1.4000)
0.11795502156019211tensor(-1.4000)
0.06348079442977905tensor(-1.4000)
0.15390551090240479tensor(-1.4000)
0.23662206530570984tensor(-1.4000)
0.21925663948059082tensor(-1.4000)
0.2149646282196045tensor(-1.4000)
0.24944651126861572tensor(-1.4000)
0.01906793750822544tensor(-1.4000)
0.18949420750141144tensor(-1.4000)
0.10530447959899902tensor(-1.4000)
0.137837216258049tensor(-1.4000)
0.2111908346414566tensor(-1.4000)
0.26621347665786743tensor(-1.4000)
0.22961397469043732tensor(-1.4000)
0.225243479013443tensor(-1.4000)
0.18926583230495453tensor(-1.4000)
0.1437719166278839tensor(-1.4000)
0.2080170065164566tensor(-1.4000)
0.20486824214458466tensor(-1.4000)
0.23150202631950378tensor(-1.4000)
0.09665564447641373tensor(-1.4000)
0.21358337998390198tensor(-1.4000)
0.22334463894367218tensor(-1.4000)
0.16611281037330627tensor(-1.4000)
0.17027199268341064tensor(-1.4000)
0.14686992764472961tensor(-1.4000)
0.15187466144561768tensor(-1.4000)
0.14878679811954498tensor(-1.4000)
0.12209044396877289tensor(-1.4000)
0.2239512801170349tensor(-1.4000)
0.10856243222951889tensor(-1.4000)
0.1626124083995819tensor(-1.4000)
0.10180304944515228tensor(-1.4000)
0.24868743121623993tensor(-1.4000)
0.19903284311294556tensor(-1.4000)
0.2141726016998291tensor(-1.4000)
0.2406112402677536tensor(-1.4000)
0.21508455276489258tensor(-1.4000)
0.253208726644516tensor(-1.4000)
0.22986939549446106tensor(-1.4000)
0.23646898567676544tensor(-1.4000)
0.18985329568386078tensor(-1.4000)
0.2063402235507965tensor(-1.4000)
0.14961250126361847tensor(-1.4000)
0.20829027891159058tensor(-1.4000)
0.2492445409297943tensor(-1.4000)
0.1415177881717682tensor(-1.4000)
0.13647550344467163tensor(-1.4000)
0.1051720455288887tensor(-1.4000)
0.19575954973697662tensor(-1.4000)
-0.003012025961652398tensor(-1.4000)
0.08144436031579971tensor(-1.4000)
0.24201712012290955tensor(-1.4000)
0.19226516783237457tensor(-1.4000)
0.16107578575611115tensor(-1.4000)
0.20979903638362885tensor(-1.4000)
0.02796982415020466tensor(-1.4000)
0.1574384570121765tensor(-1.4000)
0.04240145534276962tensor(-1.4000)
0.04960082471370697tensor(-1.4000)
0.1615912765264511tensor(-1.4000)
0.08560123294591904tensor(-1.4000)
0.0461542047560215tensor(-1.4000)
0.1179116815328598tensor(-1.4000)
0.039939094334840775tensor(-1.4000)
0.06365920603275299tensor(-1.4000)
0.14884041249752045tensor(-1.4000)
assignment2/output/Q3_crossing_val.txt 0 → 100644
View file @ 98dae565
0.23883649706840515tensor(1.)
0.2634292244911194tensor(0.9000)
0.24769198894500732tensor(1.)
0.21910716593265533tensor(-0.0400)
0.22409991919994354tensor(0.1000)
0.2056102752685547tensor(0.0460)
0.22836345434188843tensor(1.)
0.21680667996406555tensor(-0.0668)
0.24246317148208618tensor(0.5000)
0.18968431651592255tensor(1.)
0.24829697608947754tensor(0.2800)
0.25037500262260437tensor(-0.3668)
0.23249347507953644tensor(1.)
0.23787018656730652tensor(1.)
0.22234490513801575tensor(0.9636)
0.21976006031036377tensor(-0.6800)
0.2215922325849533tensor(-0.0400)
0.22781778872013092tensor(1.)
0.23643748462200165tensor(0.6000)
0.2323775291442871tensor(-0.7456)
0.21678359806537628tensor(0.2000)
0.2526857554912567tensor(-0.3144)
0.22897067666053772tensor(0.2800)
0.22920602560043335tensor(-0.1332)
0.22967015206813812tensor(-0.6000)
0.254603773355484tensor(-0.2332)
0.22490441799163818tensor(0.7000)
0.20066514611244202tensor(0.2000)
0.19296328723430634tensor(-0.6000)
0.2051520049571991tensor(-0.7600)
0.17810660600662231tensor(0.0400)
0.21438510715961456tensor(1.)
0.22506657242774963tensor(0.8400)
0.21135574579238892tensor(1.)
0.22472751140594482tensor(0.9200)
0.2331400364637375tensor(0.5200)
0.20050625503063202tensor(1.)
0.21052533388137817tensor(1.)
0.21431730687618256tensor(0.6800)
0.2348039150238037tensor(-0.4400)
0.16487076878547668tensor(0.4400)
0.22959187626838684tensor(0.1200)
0.18921184539794922tensor(-0.3600)
0.23561854660511017tensor(0.2000)
0.22648195922374725tensor(-0.4400)
0.1176544576883316tensor(-0.9000)
0.13491632044315338tensor(-0.9000)
0.12763166427612305tensor(-1.)
0.21797975897789001tensor(0.6000)
0.20942528545856476tensor(0.8000)
0.13702814280986786tensor(-0.8000)
0.2181217521429062tensor(0.5200)
0.1930132508277893tensor(0.9200)
0.20866529643535614tensor(1.)
0.16370415687561035tensor(-0.9000)
0.21746066212654114tensor(-0.5200)
0.1768663227558136tensor(-0.7600)
0.15572123229503632tensor(-0.6800)
0.19658486545085907tensor(0.5200)
0.12223505228757858tensor(-1.)
0.17176160216331482tensor(0.4000)
0.16827552020549774tensor(0.8000)
0.11823496967554092tensor(0.1200)
0.23599618673324585tensor(0.5200)
0.22209390997886658tensor(0.5200)
0.18761660158634186tensor(-1.)
0.1947704404592514tensor(0.6000)
0.21887809038162231tensor(0.7000)
0.1728212535381317tensor(0.1248)
0.23405911028385162tensor(0.7000)
0.19589288532733917tensor(0.2000)
0.12112277746200562tensor(-0.6000)
0.16802281141281128tensor(0.5000)
0.18592169880867004tensor(-1.)
0.06700412184000015tensor(-0.8400)
0.19754068553447723tensor(0.6000)
0.17850232124328613tensor(0.1200)
0.1860058754682541tensor(0.5000)
0.18726396560668945tensor(-0.5384)
0.1995716243982315tensor(0.1000)
0.20977000892162323tensor(0.1200)
0.10094377398490906tensor(-1.)
0.1483609974384308tensor(-1.)
0.15598687529563904tensor(0.3600)
0.22285933792591095tensor(1.)
0.1563321202993393tensor(-0.6800)
0.16115133464336395tensor(-0.1000)
0.14246152341365814tensor(0.1000)
0.1248432919383049tensor(0.8000)
0.20757102966308594tensor(0.0400)
0.17447999119758606tensor(0.5200)
0.16051438450813293tensor(0.1200)
0.1418909877538681tensor(-0.3600)
0.1458435207605362tensor(-1.)
0.16654814779758453tensor(-0.4668)
0.1672174334526062tensor(0.0400)
0.17925521731376648tensor(-0.7600)
0.14450959861278534tensor(-1.)
0.10989005863666534tensor(-1.)
0.16021223366260529tensor(0.2000)
0.12756973505020142tensor(-1.)
0.16685283184051514tensor(-0.9200)
0.2014315277338028tensor(0.2800)
0.169684499502182tensor(-0.2000)
0.12297119200229645tensor(-1.)
0.15959320962429047tensor(0.2000)
0.22006359696388245tensor(0.2364)
0.17873041331768036tensor(0.1000)
0.1626030057668686tensor(-0.5200)
0.03899049013853073tensor(-0.8000)
0.13728435337543488tensor(-0.9000)
0.13723896443843842tensor(-1.)
-0.009411471895873547tensor(-1.)
0.13420343399047852tensor(-0.8000)
0.13910812139511108tensor(-1.)
0.11301042139530182tensor(-0.7600)
0.15226693451404572tensor(0.6000)
0.16815489530563354tensor(0.3600)
0.10644876956939697tensor(-1.)
0.10908190160989761tensor(-1.)
0.11506210267543793tensor(-0.6000)
0.15467076003551483tensor(-1.)
0.13319885730743408tensor(-0.0400)
0.11126483231782913tensor(-0.8400)
0.14984849095344543tensor(-0.2800)
0.07273491472005844tensor(0.)
0.08771047741174698tensor(-0.9668)
0.054164379835128784tensor(-1.)
0.1259259581565857tensor(-0.6000)
0.11546790599822998tensor(0.8000)
0.03922658786177635tensor(-1.)
0.2332790344953537tensor(0.4400)
0.2595544755458832tensor(1.)
0.2278873324394226tensor(1.)
0.23148246109485626tensor(1.)
0.23148246109485626tensor(1.)
0.24571551382541656tensor(0.5200)
0.20902760326862335tensor(1.)
0.23024310171604156tensor(0.)
0.24696451425552368tensor(0.5296)
0.2231873720884323tensor(-0.3600)
0.2583218514919281tensor(1.)
0.23286397755146027tensor(1.)
0.2097988724708557tensor(-0.0500)
0.21907742321491241tensor(1.)
0.24394388496875763tensor(-0.5000)
0.20567718148231506tensor(-0.5000)
0.19768580794334412tensor(0.2000)
0.22920602560043335tensor(-0.1332)
0.24119055271148682tensor(-0.4400)
0.21778832376003265tensor(0.6000)
0.24986545741558075tensor(0.4400)
0.2294471710920334tensor(0.9428)
0.20982006192207336tensor(-0.8400)
0.21889396011829376tensor(-0.9200)
0.2345019280910492tensor(-0.4800)
0.23261518776416779tensor(-0.5200)
0.23261518776416779tensor(-0.5200)
0.22859905660152435tensor(0.7600)
0.23496325314044952tensor(-0.2800)
0.23513494431972504tensor(-0.4400)
0.2204827219247818tensor(-0.8400)
0.17679092288017273tensor(0.7600)
0.18401433527469635tensor(0.3000)
0.20886246860027313tensor(-0.6888)
0.19805099070072174tensor(-0.1000)
0.21497011184692383tensor(0.1000)
0.19897261261940002tensor(-0.2000)
0.18783089518547058tensor(-0.6668)
0.203958198428154tensor(0.5000)
0.22106513381004333tensor(-0.2800)
0.1453423947095871tensor(0.7332)
0.21268507838249207tensor(0.6856)
0.11561288684606552tensor(-1.)
0.20891129970550537tensor(0.3600)
0.20663852989673615tensor(0.6800)
0.17600028216838837tensor(-0.6800)
0.21353287994861603tensor(0.6800)
0.1326994150876999tensor(0.1000)
0.20489907264709473tensor(-0.1200)
0.16826368868350983tensor(-0.5200)
0.19959907233715057tensor(-0.6000)
0.20024918019771576tensor(-0.3868)
0.21795061230659485tensor(-0.9200)
0.13818654417991638tensor(0.2000)
0.20358681678771973tensor(0.5200)
0.15856806933879852tensor(-0.7000)
0.21944329142570496tensor(0.2000)
0.1659984290599823tensor(-0.8400)
0.1635873019695282tensor(0.3600)
0.18585234880447388tensor(0.5200)
0.2209325134754181tensor(-0.4400)
0.23221346735954285tensor(0.3600)
0.1245313510298729tensor(0.2800)
0.18131214380264282tensor(-0.0668)
0.17317889630794525tensor(-0.6800)
0.15347920358181tensor(-1.)
0.18837933242321014tensor(0.2000)
0.20408481359481812tensor(0.5000)
0.1176665872335434tensor(-1.)
0.16682852804660797tensor(-1.)
0.14912065863609314tensor(-1.)
0.12786506116390228tensor(-0.8000)
0.2106740027666092tensor(-0.8000)
0.16542737185955048tensor(-0.8400)
0.10393405705690384tensor(-0.7000)
0.12444327026605606tensor(0.)
0.2280828207731247tensor(-0.2800)
0.05579777806997299tensor(0.)
0.15934506058692932tensor(-0.8000)
0.11055240780115128tensor(-0.8400)
0.18386809527873993tensor(-0.5200)
0.1886022984981537tensor(0.1000)
0.14977003633975983tensor(-0.8400)
0.19496363401412964tensor(-0.7600)
0.13946868479251862tensor(-0.6800)
0.14371825754642487tensor(0.2000)
0.12665335834026337tensor(-0.8400)
0.20768487453460693tensor(0.2800)
0.22326767444610596tensor(-0.5000)
0.1916244924068451tensor(1.)
0.15927471220493317tensor(-0.9200)
0.19337469339370728tensor(0.2000)
0.15728195011615753tensor(-0.4400)
0.19691753387451172tensor(0.5200)
0.16173088550567627tensor(0.1000)
0.09761864691972733tensor(-1.)
0.12787482142448425tensor(0.1200)
0.1375417858362198tensor(-1.)
0.1003948450088501tensor(-1.)
0.13872112333774567tensor(0.0400)
0.15812407433986664tensor(-0.9600)
0.12468641251325607tensor(-1.)
0.09093260765075684tensor(-0.2000)
0.16102628409862518tensor(0.5000)
0.11757706105709076tensor(0.2400)
0.18657942116260529tensor(0.0768)
0.18469439446926117tensor(0.7716)
0.201075479388237tensor(1.)
0.1328566074371338tensor(-1.)
0.11088640987873077tensor(-0.6800)
0.15724240243434906tensor(-0.4668)
0.13240307569503784tensor(-1.)
0.10667887330055237tensor(-0.7600)
0.07101195305585861tensor(-1.)
0.09573091566562653tensor(-1.)
0.11985678225755692tensor(-1.)
0.12858137488365173tensor(0.3600)
0.19435322284698486tensor(-0.1200)
0.12556740641593933tensor(-1.)
0.20728600025177002tensor(0.4400)
0.10494787991046906tensor(-1.)
0.17594827711582184tensor(-0.4400)
0.1435348391532898tensor(-0.1200)
0.15132085978984833tensor(0.5200)
0.20456120371818542tensor(0.0400)
0.0914267748594284tensor(-0.8400)
0.2142968475818634tensor(0.6000)
0.20350536704063416tensor(-0.1000)
0.16632547974586487tensor(0.2800)
0.15138961374759674tensor(-1.)
0.14559553563594818tensor(-0.8400)
0.18289007246494293tensor(0.5200)
0.22243942320346832tensor(-0.6800)
0.2379876971244812tensor(0.6800)
0.22145527601242065tensor(0.4400)
0.13506068289279938tensor(-0.2000)
0.17745231091976166tensor(0.6800)
0.1328679472208023tensor(0.3600)
0.17911361157894135tensor(0.2000)
0.2064313292503357tensor(0.3600)
0.24639199674129486tensor(0.4400)
0.17933425307273865tensor(0.2000)
0.21074460446834564tensor(-0.0400)
0.1873215287923813tensor(-0.2000)
0.22304664552211761tensor(0.6000)
0.030172310769557953tensor(-1.)
0.20582543313503265tensor(0.6000)
0.19668009877204895tensor(0.6800)
0.16379238665103912tensor(-0.6800)
0.15731480717658997tensor(-0.1200)
0.2014332115650177tensor(0.5200)
0.2344430685043335tensor(0.2800)
0.218793585896492tensor(0.7600)
0.1385260820388794tensor(-1.)
0.10287252068519592tensor(-0.4400)
0.14756374061107635tensor(0.0400)
0.1665956974029541tensor(-0.8400)
0.08067911118268967tensor(-1.)
0.1950966715812683tensor(0.2800)
0.21617458760738373tensor(0.8400)
0.09676118195056915tensor(-1.)
0.13574925065040588tensor(-0.7600)
0.16762188076972961tensor(-0.5200)
0.24538424611091614tensor(0.5200)
0.1951674371957779tensor(0.2800)
0.1817006915807724tensor(0.0400)
0.20498986542224884tensor(1.)
0.12778592109680176tensor(-1.)
0.19334101676940918tensor(0.2800)
0.21010662615299225tensor(0.6000)
0.198759526014328tensor(-0.5200)
0.2315809577703476tensor(0.7600)
0.18992391228675842tensor(0.3600)
0.2324196696281433tensor(0.7600)
0.23420315980911255tensor(-0.6800)
0.24425391852855682tensor(0.8400)
0.23211707174777985tensor(0.6800)
0.07516022771596909tensor(-1.)
0.17729640007019043tensor(0.2800)
0.13516579568386078tensor(-1.)
0.16532908380031586tensor(-0.4400)
0.13940612971782684tensor(0.2800)
0.17990073561668396tensor(-0.2800)
0.17033730447292328tensor(-0.2800)
0.08068107068538666tensor(-1.)
0.17924249172210693tensor(0.5200)
0.16009719669818878tensor(0.5200)
0.21538090705871582tensor(0.6000)
0.1084502711892128tensor(-1.)
0.22078974545001984tensor(0.2800)
0.1855853646993637tensor(-0.3600)
0.13307234644889832tensor(0.4400)
0.1873137205839157tensor(-0.6000)
0.18192216753959656tensor(0.4400)
0.15868274867534637tensor(-0.2000)
0.2131693959236145tensor(0.4400)
0.24158446490764618tensor(0.6000)
0.1988273411989212tensor(0.4400)
0.11482968926429749tensor(0.2800)
0.19709599018096924tensor(0.1200)
0.13040131330490112tensor(-0.0400)
0.1835963875055313tensor(0.4400)
0.15780821442604065tensor(-0.2800)
0.11408533900976181tensor(-0.9200)
0.1799275130033493tensor(0.3600)
0.14542415738105774tensor(-0.5200)
0.12384457141160965tensor(-0.4400)
0.19439388811588287tensor(0.0400)
0.20656681060791016tensor(-0.0400)
0.15763024985790253tensor(-1.)
0.19331730902194977tensor(0.6000)
0.17075814306735992tensor(0.2000)
0.25957924127578735tensor(0.6000)
0.1861203908920288tensor(0.6000)
0.20322366058826447tensor(-0.2000)
0.17107251286506653tensor(0.6000)
0.18847611546516418tensor(-0.1200)
0.18798840045928955tensor(-0.1200)
0.15407119691371918tensor(0.3600)
0.2373436689376831tensor(0.2800)
0.22738958895206451tensor(0.6000)
0.21705131232738495tensor(0.2800)
0.2247776985168457tensor(0.5200)
0.22230029106140137tensor(0.2000)
0.1801161915063858tensor(0.6000)
0.2414536327123642tensor(-0.1200)
0.24137024581432343tensor(0.3600)
0.2490820586681366tensor(0.5200)
0.10617118328809738tensor(-1.)
0.11954577267169952tensor(-1.)
0.24069270491600037tensor(0.9200)
0.12540999054908752tensor(0.2000)
0.1367240697145462tensor(-0.6800)
0.214602530002594tensor(0.6800)
0.23050548136234283tensor(0.6800)
0.14962947368621826tensor(-1.)
0.19818729162216187tensor(0.6800)
0.21154603362083435tensor(0.8400)
0.22119179368019104tensor(1.)
0.12505733966827393tensor(-1.)
0.11908163875341415tensor(0.2800)
0.23035109043121338tensor(0.0400)
0.20333075523376465tensor(-0.1200)
0.25732579827308655tensor(0.8400)
0.20457744598388672tensor(1.)
0.20578698813915253tensor(0.8400)
0.20269283652305603tensor(0.8400)
0.241116002202034tensor(0.9200)
0.15874113142490387tensor(-0.7600)
0.07779521495103836tensor(-0.9200)
0.21057315170764923tensor(-0.1200)
0.1857912242412567tensor(0.9200)
0.12566427886486053tensor(-0.5200)
0.18986420333385468tensor(-0.1200)
0.21259929239749908tensor(0.8400)
0.19141162931919098tensor(0.0400)
0.2289283573627472tensor(0.4400)
0.12499474734067917tensor(-0.6800)
0.24636885523796082tensor(-0.0400)
0.22287915647029877tensor(0.9200)
0.2127474993467331tensor(1.)
0.13829025626182556tensor(-0.8400)
0.17139415442943573tensor(0.2800)
0.17371033132076263tensor(-0.2800)
0.24929079413414001tensor(0.2800)
0.2298174351453781tensor(0.3600)
0.24741952121257782tensor(0.9200)
0.2244790494441986tensor(0.6800)
0.21967802941799164tensor(0.0400)
0.17220132052898407tensor(-0.1200)
0.10971330851316452tensor(-0.4400)
0.18990558385849tensor(0.0400)
0.14040228724479675tensor(-0.5200)
0.19010697305202484tensor(-0.2800)
0.20672380924224854tensor(0.6000)
0.12436924129724503tensor(-0.8400)
0.08009512722492218tensor(-1.)
0.12715117633342743tensor(-0.8400)
0.23234647512435913tensor(0.2000)
0.24785280227661133tensor(0.6000)
0.16591238975524902tensor(-0.4400)
0.09735382348299026tensor(-0.8400)
0.11465800553560257tensor(-0.0400)
0.10739684104919434tensor(0.1200)
0.23035064339637756tensor(0.8400)
0.23529693484306335tensor(0.6800)
0.17318834364414215tensor(0.2800)
0.16090022027492523tensor(-0.5200)
0.2087727040052414tensor(-0.2800)
0.1566709727048874tensor(0.0400)
0.2313794046640396tensor(-0.3600)
0.13643968105316162tensor(-1.)
0.2231973558664322tensor(0.5200)
0.2220652848482132tensor(0.6000)
0.2506154477596283tensor(0.9200)
0.21773435175418854tensor(-0.0400)
0.21425069868564606tensor(1.)
0.13877905905246735tensor(-1.)
0.2320035696029663tensor(0.4400)
0.19333615899085999tensor(-0.0400)
0.2365417331457138tensor(0.6800)
0.15238569676876068tensor(-0.9200)
0.20085056126117706tensor(0.6800)
0.2384820431470871tensor(0.2800)
0.14245496690273285tensor(-0.7600)
0.16300739347934723tensor(-0.3600)
0.11028555780649185tensor(-1.)
0.191949263215065tensor(-0.4400)
0.24416391551494598tensor(0.9200)
0.22497543692588806tensor(0.6000)
0.19785438477993011tensor(0.2000)
0.09666256606578827tensor(-0.9200)
0.11695896834135056tensor(-0.3600)
0.18405920267105103tensor(0.7600)
0.2555636465549469tensor(0.9200)
0.15292923152446747tensor(-0.8400)
0.1481347233057022tensor(-0.0400)
0.2141149342060089tensor(0.2000)
0.18221816420555115tensor(-0.2000)
0.23714855313301086tensor(0.8400)
0.1717878133058548tensor(-0.6000)
0.20191232860088348tensor(1.)
0.10621268302202225tensor(-0.4400)
0.16798105835914612tensor(-1.)
0.1927870810031891tensor(0.2000)
0.11400746554136276tensor(-0.7600)
0.08776235580444336tensor(-1.)
0.21430952847003937tensor(0.0400)
0.16798363626003265tensor(-0.5200)
0.2291853129863739tensor(1.)
0.16978314518928528tensor(0.3600)
0.12329485267400742tensor(-0.6800)
0.10730718076229095tensor(-0.6800)
0.13146871328353882tensor(-0.6000)
0.18575076758861542tensor(-1.)
0.13386324048042297tensor(-1.)
0.14770172536373138tensor(-0.2000)
0.18180805444717407tensor(-0.0400)
0.1878451257944107tensor(-0.1200)
0.19095169007778168tensor(0.5200)
0.166290283203125tensor(-0.1200)
0.22026550769805908tensor(0.8400)
0.1107202097773552tensor(0.1200)
0.13507553935050964tensor(-0.2000)
0.23373451828956604tensor(0.6800)
0.22517819702625275tensor(0.8000)
0.10498058050870895tensor(-1.)
0.18761548399925232tensor(-0.2800)
0.24302002787590027tensor(0.6800)
0.20422033965587616tensor(0.6800)
0.10139433294534683tensor(-0.4400)
0.14960600435733795tensor(0.4400)
0.14301587641239166tensor(-0.7600)
0.2281097173690796tensor(1.)
0.16080443561077118tensor(-0.6800)
0.10457948595285416tensor(-1.)
0.10645248740911484tensor(-1.)
0.18363310396671295tensor(-0.2800)
0.13817888498306274tensor(-0.6800)
0.22330063581466675tensor(0.1200)
0.07177810370922089tensor(-1.)
0.20156408846378326tensor(-0.6800)
0.13733622431755066tensor(-0.2800)
0.17992572486400604tensor(-0.4400)
0.17717435956001282tensor(-0.2800)
0.1904361993074417tensor(-0.0400)
0.2206955999135971tensor(-0.3600)
0.21727967262268066tensor(0.5200)
0.22162500023841858tensor(0.9200)
0.0987548902630806tensor(-0.9200)
0.17467689514160156tensor(-0.6000)
0.12216344475746155tensor(-0.8400)
0.13666130602359772tensor(-0.8400)
0.22542133927345276tensor(0.6000)
0.194701686501503tensor(0.3600)
0.18853768706321716tensor(0.2000)
0.24123355746269226tensor(-0.2800)
0.14190635085105896tensor(-0.1200)
0.24243547022342682tensor(0.4400)
0.16332003474235535tensor(-0.8400)
0.101925328373909tensor(-0.9200)
0.258565217256546tensor(0.5200)
0.25337907671928406tensor(0.6800)
0.13816316425800323tensor(-0.5200)
0.23556412756443024tensor(0.3600)
0.19433490931987762tensor(-0.0400)
0.2564026117324829tensor(0.2800)
0.24291972815990448tensor(-0.1200)
0.2599056363105774tensor(0.7600)
0.21228589117527008tensor(0.0400)
0.14604435861110687tensor(-0.9200)
0.20897969603538513tensor(0.2800)
0.1532488465309143tensor(-0.1200)
0.21359427273273468tensor(1.)
0.23385463654994965tensor(1.)
0.17877909541130066tensor(-0.1200)
0.1471109837293625tensor(-0.8400)
0.15351642668247223tensor(-0.6000)
0.12382306903600693tensor(-0.1200)
0.14296479523181915tensor(-0.2000)
0.19682975113391876tensor(-0.5200)
0.18949641287326813tensor(0.6000)
0.24458791315555573tensor(-0.6000)
0.26106998324394226tensor(0.6800)
0.1935487687587738tensor(0.3600)
0.22582122683525085tensor(0.6000)
0.26391109824180603tensor(0.9200)
0.13099399209022522tensor(-0.8400)
0.23303763568401337tensor(0.7600)
0.1056613177061081tensor(-0.8400)
0.13104790449142456tensor(-1.)
0.2131401002407074tensor(0.2000)
0.24668993055820465tensor(0.8400)
0.1405731737613678tensor(-0.8400)
0.17304331064224243tensor(-0.4400)
0.2030046433210373tensor(0.2000)
0.2228040248155594tensor(-0.3600)
0.22278647124767303tensor(-0.0400)
0.11859387159347534tensor(-1.)
0.19761665165424347tensor(-0.3600)
0.18409991264343262tensor(-0.3600)
0.13116520643234253tensor(-0.9200)
0.2200571596622467tensor(-0.1200)
0.21554744243621826tensor(-0.2000)
0.16996124386787415tensor(-0.8400)
0.24609699845314026tensor(0.9200)
0.19464509189128876tensor(-0.2000)
0.08822507411241531tensor(-0.9200)
0.1971699744462967tensor(0.5200)
0.19387724995613098tensor(-0.1200)
0.1754349023103714tensor(-0.2000)
0.2600603997707367tensor(0.6000)
0.19308798015117645tensor(0.8400)
0.16215921938419342tensor(-0.4400)
0.13535523414611816tensor(-0.6800)
0.20221242308616638tensor(-0.7600)
0.14620809257030487tensor(-0.5200)
0.17898549139499664tensor(-0.3600)
0.09899970889091492tensor(-1.)
0.1361166089773178tensor(-0.7600)
0.16634447872638702tensor(0.2000)
0.2412349134683609tensor(-0.0400)
0.12922945618629456tensor(-0.7600)
0.2101016789674759tensor(0.8400)
0.1623978316783905tensor(-0.7600)
0.23541119694709778tensor(0.9200)
0.23907844722270966tensor(1.)
0.15764671564102173tensor(-0.9200)
0.2006053626537323tensor(-0.7600)
0.18682867288589478tensor(-0.6800)
0.1760459542274475tensor(0.4400)
0.007802315056324005tensor(-1.)
0.2413487434387207tensor(0.7600)
0.2478158324956894tensor(0.8400)
0.12349536269903183tensor(0.1200)
0.22148647904396057tensor(0.6000)
0.14877834916114807tensor(-0.6000)
0.25677046179771423tensor(1.)
0.12368160486221313tensor(-0.8400)
0.07941172271966934tensor(-0.6800)
0.15699461102485657tensor(-0.8400)
0.1476559042930603tensor(-1.)
0.08857952058315277tensor(-0.3600)
0.16205017268657684tensor(-0.3600)
0.23449014127254486tensor(0.4400)
0.22602297365665436tensor(0.2000)
0.20275670289993286tensor(0.1200)
0.15441489219665527tensor(-0.9200)
0.11732136458158493tensor(-0.6000)
0.17041166126728058tensor(-0.4400)
0.24416500329971313tensor(0.1200)
0.13727568089962006tensor(0.2000)
0.13186372816562653tensor(-0.3600)
0.23392131924629211tensor(0.1200)
0.24644045531749725tensor(0.6000)
0.1823490411043167tensor(-0.6800)
0.1993671953678131tensor(-0.2000)
0.26268666982650757tensor(0.7600)
0.20945978164672852tensor(0.8400)
0.23196494579315186tensor(-0.0400)
0.2374938279390335tensor(0.7600)
0.12864407896995544tensor(-0.6800)
0.1858026385307312tensor(0.5200)
0.1400374174118042tensor(-0.4400)
0.17185673117637634tensor(0.1200)
0.16217146813869476tensor(0.2000)
0.2571495771408081tensor(-0.3600)
0.1745690107345581tensor(-0.6000)
0.17226429283618927tensor(0.0400)
0.14995935559272766tensor(-0.3600)
0.14901088178157806tensor(-0.9200)
0.23406656086444855tensor(0.2000)
0.24554437398910522tensor(0.2000)
0.19506697356700897tensor(0.2800)
0.14218571782112122tensor(0.2000)
0.07058925181627274tensor(-0.7600)
0.06644435226917267tensor(-0.6000)
0.07103103399276733tensor(-0.7600)
0.12157418578863144tensor(-0.6800)
0.12137312442064285tensor(-0.0400)
0.06939082592725754tensor(-0.6000)
0.10197985917329788tensor(-0.9200)
0.0705563947558403tensor(-0.7600)
0.14987584948539734tensor(0.4400)
0.06473671644926071tensor(-0.9200)
0.0611693412065506tensor(-0.9200)
0.043195102363824844tensor(-1.)
0.08355451375246048tensor(-1.)
0.03644150123000145tensor(-0.5200)
0.013716006651520729tensor(-1.)
0.05963391810655594tensor(-0.8400)
0.11580066382884979tensor(-0.8400)
0.1245032474398613tensor(0.2000)
0.1266009360551834tensor(0.7600)
0.11523131281137466tensor(0.2800)
0.11381582915782928tensor(0.3600)
0.11550690233707428tensor(0.3600)
0.12079204618930817tensor(0.4400)
0.12648509442806244tensor(-0.6800)
0.06902772933244705tensor(-0.6800)
0.07490697503089905tensor(-0.6000)
0.1336248219013214tensor(-0.3600)
0.10660600662231445tensor(-0.6000)
0.11712979525327682tensor(0.2800)
0.13489846885204315tensor(-0.2000)
0.01826905831694603tensor(-1.)
0.07404862344264984tensor(-0.7600)
0.04822993278503418tensor(-1.)
0.10275211185216904tensor(-0.4400)
0.13558444380760193tensor(-0.2000)
0.11023972928524017tensor(0.0400)
0.13375826179981232tensor(0.6800)
0.112209752202034tensor(0.1200)
0.13962996006011963tensor(-1.)
0.10941451787948608tensor(-1.)
0.07459208369255066tensor(-0.2000)
0.03734223172068596tensor(-1.)
0.10226895660161972tensor(0.0400)
0.07494811713695526tensor(0.0400)
0.07430128753185272tensor(-1.)
0.07352160662412643tensor(0.0400)
0.04857417568564415tensor(-1.)
0.11221456527709961tensor(-0.3600)
0.05100655555725098tensor(-1.)
0.059519458562135696tensor(-0.7600)
0.04190336540341377tensor(-0.2800)
0.1593642383813858tensor(0.5200)
0.09686625748872757tensor(-0.3600)
0.14185066521167755tensor(0.2800)
0.08471451699733734tensor(0.0400)
0.07954203337430954tensor(0.2000)
0.08496863394975662tensor(-0.4400)
0.1260102540254593tensor(-0.3600)
0.14569030702114105tensor(-0.3600)
0.06428686529397964tensor(-0.6000)
0.11693976074457169tensor(-0.2800)
0.07944457978010178tensor(-0.2800)
0.1153256893157959tensor(-0.4400)
0.066503144800663tensor(0.0400)
0.06297992169857025tensor(-0.2000)
0.07947488874197006tensor(-0.6000)
0.05657222121953964tensor(-0.4400)
0.06172759085893631tensor(-0.7600)
0.08602449297904968tensor(-0.4400)
0.1474069058895111tensor(-0.8400)
0.10208017379045486tensor(-0.2800)
0.1560882180929184tensor(0.6000)
0.11018851399421692tensor(-0.7600)
0.06942640244960785tensor(0.1200)
0.08318150788545609tensor(0.0400)
0.09144289046525955tensor(0.0400)
0.0692664384841919tensor(-0.7600)
0.15114624798297882tensor(0.7600)
0.13380707800388336tensor(-0.1200)
0.08890481293201447tensor(-0.6000)
0.08525536954402924tensor(-1.)
0.030400792136788368tensor(-0.3600)
0.11366510391235352tensor(-0.2800)
0.14855538308620453tensor(0.5200)
0.11693520843982697tensor(0.2000)
0.11651136726140976tensor(-0.4400)
0.14779101312160492tensor(0.2000)
0.05435940995812416tensor(-0.8400)
0.07472337037324905tensor(0.4400)
0.07798245549201965tensor(0.3600)
0.07664166390895844tensor(0.1200)
0.15035709738731384tensor(0.6800)
0.1443939208984375tensor(0.6800)
0.06339746713638306tensor(-0.2800)
0.06009259447455406tensor(-0.6000)
0.06747507303953171tensor(-0.2000)
0.06471705436706543tensor(-0.6000)
0.07547137141227722tensor(-0.3600)
0.04891772195696831tensor(0.0400)
0.10209226608276367tensor(0.2800)
0.04397403448820114tensor(-0.8400)
0.026155535131692886tensor(-1.)
0.11597445607185364tensor(-1.)
0.042372170835733414tensor(-0.1200)
0.09172951430082321tensor(-0.4000)
0.09745321422815323tensor(-0.7600)
0.1140652671456337tensor(0.6000)
0.0654485896229744tensor(-0.2800)
0.06296443939208984tensor(-0.7600)
0.048890773206949234tensor(-0.3600)
0.08706580102443695tensor(-0.0400)
0.046574391424655914tensor(-0.6000)
0.09259592741727829tensor(-0.2000)
0.10857295989990234tensor(-0.3600)
0.11044124513864517tensor(-0.9200)
0.027248095721006393tensor(-0.9200)
0.09695940464735031tensor(-0.2000)
0.057137228548526764tensor(-1.)
0.051639582961797714tensor(0.2800)
0.0685737207531929tensor(-0.6000)
0.08718140423297882tensor(-0.2000)
0.0885692909359932tensor(-0.0400)
0.03779252991080284tensor(-1.)
0.040828920900821686tensor(-0.3600)
0.08549230545759201tensor(0.5200)
0.11947478353977203tensor(-0.5200)
0.12629134953022003tensor(-0.2000)
0.0842604711651802tensor(-0.6000)
0.1361195147037506tensor(-0.2000)
0.0870189443230629tensor(-0.7600)
0.0535833016037941tensor(-1.)
0.07669621706008911tensor(-1.)
0.06761319935321808tensor(0.3600)
0.09941578656435013tensor(0.0400)
0.037284862250089645tensor(-0.8400)
0.0666985735297203tensor(0.1200)
0.1183740496635437tensor(-0.2000)
0.07790014147758484tensor(-0.3600)
0.08289571851491928tensor(-0.4400)
0.04258183017373085tensor(-0.5200)
0.05690782889723778tensor(-0.1200)
0.07409649342298508tensor(-0.7600)
0.17232130467891693tensor(0.0400)
0.11489707231521606tensor(0.2000)
0.11798305809497833tensor(-0.1200)
0.10302631556987762tensor(-0.2800)
0.13503603637218475tensor(-0.7600)
0.03349830210208893tensor(-1.)
0.07324038445949554tensor(-0.3600)
0.06803380697965622tensor(-0.6800)
0.03420095518231392tensor(-0.9200)
0.054190121591091156tensor(-0.6000)
0.12131816148757935tensor(0.1200)
0.04158560186624527tensor(-0.7600)
0.13733652234077454tensor(0.1200)
0.06552104651927948tensor(-0.3600)
0.09785976260900497tensor(-0.4400)
0.11119430512189865tensor(-0.4400)
0.12811744213104248tensor(0.2000)
0.13220447301864624tensor(0.2800)
0.07994359731674194tensor(0.1200)
0.11438997834920883tensor(0.3600)
0.11477482318878174tensor(-0.2000)
0.05871271714568138tensor(0.1200)
0.034341391175985336tensor(-1.)
0.049131665378808975tensor(-0.9200)
0.015159002505242825tensor(-0.6800)
0.06723984330892563tensor(-1.)
0.031098563224077225tensor(0.6800)
0.07876765727996826tensor(-0.9200)
0.06339585036039352tensor(0.2800)
0.018482565879821777tensor(-1.)
0.06709397584199905tensor(-0.7600)
0.030981194227933884tensor(-1.)
0.21603892743587494tensor(0.9200)
0.12988361716270447tensor(0.5200)
0.12076138705015182tensor(-0.2800)
0.09217088669538498tensor(-0.8400)
0.12497887760400772tensor(0.0400)
0.11481313407421112tensor(-0.3600)
0.06525868922472tensor(-0.4400)
0.1383008062839508tensor(-0.2000)
0.0899210274219513tensor(-0.2800)
0.08447924256324768tensor(-0.5200)
0.08350051939487457tensor(-0.6800)
0.06336416304111481tensor(-0.1200)
0.12651962041854858tensor(0.2800)
0.0648212730884552tensor(-0.4400)
0.011158115230500698tensor(-0.4400)
0.055534303188323975tensor(-1.)
0.04920241981744766tensor(-0.3600)
0.06460210680961609tensor(-0.6800)
0.1218312606215477tensor(0.1200)
0.04256731644272804tensor(-0.2800)
0.07777909189462662tensor(-0.7320)
0.10587369650602341tensor(0.0400)
0.04304809123277664tensor(-0.8400)
0.09747195243835449tensor(-0.0400)
0.1027800589799881tensor(0.1200)
0.08738840371370316tensor(-0.2000)
0.09977127611637115tensor(0.2000)
0.08394841104745865tensor(0.0400)
0.06895825266838074tensor(-0.2800)
0.18735535442829132tensor(0.5200)
0.040359675884246826tensor(0.2000)
0.06505391001701355tensor(-0.4400)
0.14380468428134918tensor(-0.3600)
0.05310261249542236tensor(-1.)
0.07102308422327042tensor(-0.7600)
0.06797351688146591tensor(-1.)
0.08408632129430771tensor(0.3600)
0.1034964770078659tensor(0.4400)
0.133595809340477tensor(-0.1200)
0.09673020988702774tensor(0.2000)
0.04668289050459862tensor(-0.2800)
0.11159495264291763tensor(-0.0400)
0.08035659044981003tensor(0.0400)
0.08486039936542511tensor(-0.4400)
0.051487118005752563tensor(-0.3600)
0.06425265222787857tensor(-0.3600)
0.06684920191764832tensor(-0.4400)
0.1491187959909439tensor(0.1200)
0.1433217078447342tensor(0.5200)
0.1292785257101059tensor(0.3600)
0.06638586521148682tensor(-1.)
0.11445010453462601tensor(-0.2000)
0.06769540905952454tensor(-1.)
0.12844769656658173tensor(0.5200)
0.049464695155620575tensor(-0.9200)
0.07024577260017395tensor(-0.7600)
0.09731533378362656tensor(-0.2800)
0.1042184829711914tensor(-0.2800)
0.05229082331061363tensor(-0.5200)
0.0958152562379837tensor(-1.)
0.11353608220815659tensor(-0.6000)
0.04634065553545952tensor(-0.8400)
0.06847186386585236tensor(-0.6800)
0.12074004858732224tensor(0.2000)
0.13390368223190308tensor(0.5200)
0.1367955356836319tensor(0.2800)
0.11025972664356232tensor(0.0400)
0.13028275966644287tensor(0.2000)
0.1044793352484703tensor(0.0400)
0.07094454765319824tensor(-0.2800)
0.06959766149520874tensor(0.0400)
0.04211428016424179tensor(-0.6800)
0.07788194715976715tensor(-0.3600)
0.07145070284605026tensor(-0.7600)
0.13303104043006897tensor(0.0400)
0.04864194616675377tensor(-0.5200)
0.07834327220916748tensor(-1.)
0.07202418148517609tensor(-0.0400)
0.08557067811489105tensor(-0.8400)
0.03971174731850624tensor(-0.7600)
0.06476326286792755tensor(-0.6000)
0.08390415459871292tensor(0.0400)
0.19613192975521088tensor(0.6000)
0.14829899370670319tensor(0.4400)
0.11382590234279633tensor(0.0400)
0.09074366092681885tensor(-0.3600)
0.15947964787483215tensor(0.6000)
0.15804684162139893tensor(0.2000)
0.07169698923826218tensor(-0.7600)
0.03018626570701599tensor(-1.)
0.06515097618103027tensor(-0.8400)
0.09169001132249832tensor(-0.8400)
0.10663565248250961tensor(-0.2000)
0.024413790553808212tensor(-0.7600)
0.002539665438234806tensor(-1.)
0.09409443289041519tensor(0.2800)
0.13566555082798004tensor(0.1200)
0.0945127010345459tensor(0.0400)
0.0997123196721077tensor(0.1200)
0.043044235557317734tensor(-0.9200)
0.18053841590881348tensor(0.2800)
0.08214808255434036tensor(-0.7600)
0.12030339241027832tensor(0.4400)
0.05950808525085449tensor(-1.)
0.08825886994600296tensor(-1.)
0.06429310142993927tensor(-1.)
0.08375382423400879tensor(-1.)
0.07733141630887985tensor(-0.1200)
0.09431204199790955tensor(-1.)
0.05865887179970741tensor(-0.8400)
0.05902040749788284tensor(-1.)
0.0708828717470169tensor(0.0400)
0.08817368000745773tensor(-0.6800)
0.07944139838218689tensor(-0.8400)
0.1409682184457779tensor(0.2000)
0.10599537193775177tensor(-0.1200)
0.0822686180472374tensor(-0.7600)
0.07071268558502197tensor(0.3600)
0.10173303633928299tensor(-0.1200)
0.012198674492537975tensor(-0.2800)
0.06560389697551727tensor(-0.6800)
0.01785562001168728tensor(-1.)
0.04006023705005646tensor(-0.3600)
0.11621806025505066tensor(0.1200)
0.106129489839077tensor(-0.3320)
0.10282976180315018tensor(-0.2800)
0.13356249034404755tensor(0.1200)
0.028605325147509575tensor(-1.)
0.0838429182767868tensor(-0.7600)
0.030469490215182304tensor(-0.2800)
0.09449147433042526tensor(-0.8400)
0.02237876132130623tensor(-0.9200)
0.04864023998379707tensor(-1.)
0.05646052211523056tensor(-0.4400)
0.04989892244338989tensor(-1.)
0.11215642839670181tensor(-0.7600)
0.08332288265228271tensor(-0.6800)
0.10673876851797104tensor(-0.9200)
0.07088746130466461tensor(-0.1200)
0.11798045784235tensor(-0.2800)
0.07844741642475128tensor(-0.9200)
0.2061358392238617tensor(0.6000)
0.1499703824520111tensor(0.6800)
0.17007552087306976tensor(0.7600)
0.13328267633914948tensor(0.2000)
0.11654883623123169tensor(0.0400)
0.14980752766132355tensor(-0.7600)
0.15486225485801697tensor(0.2800)
0.05592994764447212tensor(0.0400)
0.05784675106406212tensor(-0.5200)
0.10076519101858139tensor(0.2000)
0.044576965272426605tensor(-1.)
0.10608398914337158tensor(0.0400)
0.06624511629343033tensor(-0.5200)
0.09772160649299622tensor(0.1200)
0.024734605103731155tensor(-0.9200)
0.08786969631910324tensor(-0.2800)
0.06109968200325966tensor(-1.)
0.0037666666321456432tensor(-1.)
0.10167794674634933tensor(0.2000)
0.09031669795513153tensor(-1.)
0.11449047178030014tensor(-0.0400)
0.017715033143758774tensor(-0.8400)
0.13205143809318542tensor(0.0400)
0.06554045528173447tensor(-1.)
0.1727537363767624tensor(0.4400)
0.11088057607412338tensor(-0.2800)
0.12117131054401398tensor(-0.3600)
0.09422838687896729tensor(-0.2000)
0.08671793341636658tensor(-0.2000)
0.09192178398370743tensor(-1.)
0.12451498955488205tensor(0.0400)
0.03501713648438454tensor(-0.8400)
0.09300778806209564tensor(-1.)
0.025954239070415497tensor(-0.9200)
0.08092455565929413tensor(-0.2800)
0.03485749661922455tensor(-0.3600)
0.04103070870041847tensor(-0.6000)
0.06572984158992767tensor(-0.7600)
0.04368977248668671tensor(-0.6000)
0.0653967410326004tensor(0.1200)
0.1108386367559433tensor(0.0400)
0.12551598250865936tensor(0.3600)
0.0974801704287529tensor(-0.3600)
0.1189851313829422tensor(0.6000)
0.08887366205453873tensor(-0.2800)
0.08545608073472977tensor(-0.9200)
0.05183620750904083tensor(-1.)
0.09930006414651871tensor(-0.4400)
0.07222088426351547tensor(-1.)
0.08194008469581604tensor(-0.5200)
0.13541854918003082tensor(0.4400)
0.08712105453014374tensor(0.2000)
0.11590263992547989tensor(-0.5200)
0.019702665507793427tensor(-0.2800)
0.08864373713731766tensor(-0.6800)
0.07993534207344055tensor(-0.5200)
0.08493421971797943tensor(-1.)
0.08997293561697006tensor(0.2800)
0.02348772995173931tensor(-0.9200)
0.16576865315437317tensor(-0.6800)
0.21004316210746765tensor(0.4400)
0.21787171065807343tensor(1.)
0.1378079354763031tensor(-0.4400)
0.21009647846221924tensor(0.0400)
0.15549160540103912tensor(-0.2000)
0.2578662037849426tensor(0.5000)
0.1996760070323944tensor(0.6000)
0.20028996467590332tensor(0.5000)
0.17620208859443665tensor(0.2800)
0.21263447403907776tensor(0.6000)
0.17477117478847504tensor(-0.0400)
0.2083018571138382tensor(0.7600)
0.2375628501176834tensor(0.6800)
0.16668422520160675tensor(0.2800)
0.19382253289222717tensor(0.2000)
0.15242139995098114tensor(-0.6800)
0.16340912878513336tensor(0.2800)
0.20043286681175232tensor(0.5200)
0.17858636379241943tensor(0.2000)
0.19202281534671783tensor(-0.0400)
0.17313194274902344tensor(1.)
0.21706770360469818tensor(0.5200)
0.14191275835037231tensor(0.2800)
0.18645082414150238tensor(0.5200)
0.21978254616260529tensor(0.5200)
0.1617477983236313tensor(0.5000)
0.22088931500911713tensor(0.2800)
0.20096072554588318tensor(0.3600)
0.22396409511566162tensor(0.8400)
0.15419964492321014tensor(0.3332)
0.19690068066120148tensor(0.4000)
0.11273182183504105tensor(-0.3000)
0.15072111785411835tensor(0.2000)
0.22666212916374207tensor(0.7600)
0.17645835876464844tensor(0.5200)
0.19009053707122803tensor(0.2000)
0.24233558773994446tensor(0.8000)
0.1805526614189148tensor(-0.1200)
0.23658223450183868tensor(0.6000)
0.22637078166007996tensor(0.8400)
0.2034008502960205tensor(0.4000)
0.2322131246328354tensor(0.2000)
0.2055450826883316tensor(0.6000)
0.20950032770633698tensor(0.7332)
0.20580072700977325tensor(0.3000)
0.16459472477436066tensor(0.3000)
0.13987627625465393tensor(-0.3000)
0.15483741462230682tensor(-0.6800)
0.1582651138305664tensor(0.7600)
0.2188141942024231tensor(0.2000)
0.17788825929164886tensor(0.2000)
0.15177644789218903tensor(0.3000)
0.1842876523733139tensor(0.7600)
0.22996889054775238tensor(1.)
0.1755458414554596tensor(1.)
0.24272234737873077tensor(0.2800)
0.16774632036685944tensor(-0.1200)
0.23097574710845947tensor(0.3000)
0.1930573582649231tensor(0.5000)
0.14816708862781525tensor(0.2800)
0.21239472925662994tensor(0.7600)
0.1639682948589325tensor(0.0400)
0.24393384158611298tensor(0.4000)
0.16646280884742737tensor(0.5000)
0.14579668641090393tensor(0.4000)
0.19810505211353302tensor(0.7000)
0.12587401270866394tensor(-0.0400)
0.23782575130462646tensor(0.3000)
0.1838981658220291tensor(0.5200)
0.15569917857646942tensor(0.3600)
0.15097080171108246tensor(0.4000)
0.1755966991186142tensor(-0.0400)
0.1964235007762909tensor(0.5200)
0.21511626243591309tensor(0.5200)
0.20703671872615814tensor(0.6000)
0.2076202780008316tensor(0.3600)
0.20992104709148407tensor(0.3000)
0.19484873116016388tensor(0.8400)
0.22416450083255768tensor(0.4856)
0.23837725818157196tensor(0.3332)
0.2428455352783203tensor(0.3600)
0.08402958512306213tensor(0.1200)
0.19674356281757355tensor(0.2000)
0.2143011838197708tensor(0.2000)
0.1847347766160965tensor(0.5000)
0.2005581557750702tensor(0.7600)
0.16991811990737915tensor(-0.6000)
0.175054132938385tensor(0.1200)
0.20137645304203033tensor(0.3000)
0.15482543408870697tensor(-0.3000)
0.20023253560066223tensor(-0.3000)
0.17051857709884644tensor(1.)
0.258169561624527tensor(0.4400)
0.20845690369606018tensor(0.2000)
0.1474219709634781tensor(0.1200)
0.18825167417526245tensor(0.4400)
0.15284039080142975tensor(0.2000)
0.1613214761018753tensor(-0.6000)
0.22150178253650665tensor(1.)
0.177613765001297tensor(0.4000)
0.1987687349319458tensor(0.8000)
0.21789628267288208tensor(0.9200)
0.16363990306854248tensor(0.4000)
0.11054205149412155tensor(0.4768)
0.18394091725349426tensor(0.6000)
0.18391212821006775tensor(0.4000)
0.2235785573720932tensor(0.9000)
0.19329942762851715tensor(0.2800)
0.16350191831588745tensor(0.0400)
0.15127111971378326tensor(0.2000)
0.2388690561056137tensor(0.3000)
0.19944597780704498tensor(-0.1200)
0.1981014609336853tensor(0.4000)
0.24514883756637573tensor(0.9000)
0.1613687127828598tensor(0.2800)
0.16495801508426666tensor(0.3000)
0.21643240749835968tensor(0.5000)
0.16875889897346497tensor(0.2000)
0.17974424362182617tensor(0.0400)
0.18528899550437927tensor(0.3000)
0.24903802573680878tensor(0.3600)
0.11683571338653564tensor(-0.4668)
0.20408444106578827tensor(1.)
0.1712293028831482tensor(0.6000)
0.11939360201358795tensor(0.4400)
0.14530940353870392tensor(-0.6800)
0.19082194566726685tensor(0.4000)
0.2237338274717331tensor(0.2000)
0.14396661520004272tensor(-0.3332)
0.25409385561943054tensor(0.5000)
0.16767528653144836tensor(-0.1000)
0.21128953993320465tensor(0.6800)
0.17673131823539734tensor(0.4400)
0.2063160240650177tensor(0.3600)
0.16162574291229248tensor(0.2000)
0.2485411912202835tensor(0.3000)
0.22331970930099487tensor(0.4400)
0.1769036054611206tensor(0.2000)
0.1584656536579132tensor(-0.3000)
0.12181255221366882tensor(0.3000)
0.23502327501773834tensor(0.7600)
0.17991189658641815tensor(0.1200)
0.18620069324970245tensor(0.1000)
0.23111136257648468tensor(0.2000)
0.17596067488193512tensor(0.4400)
0.14094169437885284tensor(0.2000)
0.21951039135456085tensor(-0.2000)
0.17663845419883728tensor(0.2000)
0.22383153438568115tensor(0.7600)
0.21160179376602173tensor(-0.0400)
0.164578378200531tensor(0.7600)
0.20676620304584503tensor(0.7600)
0.1383640021085739tensor(0.0400)
0.1612565815448761tensor(0.2000)
0.1684630960226059tensor(0.8000)
0.21659868955612183tensor(0.2000)
0.18067330121994019tensor(-0.3000)
0.13058558106422424tensor(-0.2000)
0.15706247091293335tensor(-0.0400)
0.21029099822044373tensor(0.9200)
0.21892283856868744tensor(0.6000)
0.2015829086303711tensor(0.6000)
0.21501775085926056tensor(0.7600)
0.22835524380207062tensor(0.6800)
0.12882709503173828tensor(0.4400)
0.21454524993896484tensor(0.)
0.16011355817317963tensor(-0.4000)
0.21262045204639435tensor(1.)
0.16903017461299896tensor(0.5000)
0.1461588740348816tensor(-0.3000)
0.20996367931365967tensor(0.7600)
0.2275831550359726tensor(0.2000)
0.19370563328266144tensor(0.5200)
0.1679193675518036tensor(0.3000)
0.14134809374809265tensor(0.1000)
0.1758425235748291tensor(0.3600)
0.20359203219413757tensor(0.2800)
0.15329661965370178tensor(-0.2000)
0.1814989447593689tensor(0.2000)
0.206404909491539tensor(0.3000)
0.1642349809408188tensor(1.)
0.1777140200138092tensor(0.3332)
0.15863262116909027tensor(0.4400)
0.21815648674964905tensor(0.5200)
0.22688405215740204tensor(0.2000)
0.17491628229618073tensor(0.7000)
0.15676647424697876tensor(0.4000)
0.19606800377368927tensor(0.2800)
0.20987039804458618tensor(-0.0400)
0.23391921818256378tensor(0.6000)
0.19386757910251617tensor(0.8668)
0.18065245449543tensor(0.3332)
0.20595082640647888tensor(0.2000)
0.2212468981742859tensor(0.3000)
0.23555371165275574tensor(0.6000)
0.14614437520503998tensor(0.4000)
0.13959112763404846tensor(-0.2000)
0.2447255700826645tensor(0.4400)
0.17761018872261047tensor(0.3600)
0.18208561837673187tensor(0.4000)
0.1465015560388565tensor(-0.0400)
0.1712564080953598tensor(0.4000)
0.18918028473854065tensor(0.1200)
0.21133899688720703tensor(0.9200)
0.1729835569858551tensor(0.2000)
0.21886558830738068tensor(0.3600)
0.15881673991680145tensor(0.3000)
0.19391275942325592tensor(0.9000)
0.19235116243362427tensor(0.4400)
0.16412298381328583tensor(0.6800)
0.18205133080482483tensor(-0.2000)
0.17860820889472961tensor(0.5200)
0.1881839632987976tensor(-0.3600)
0.21263164281845093tensor(0.3000)
0.19130541384220123tensor(1.)
0.22973351180553436tensor(0.3668)
0.14502470195293427tensor(0.1000)
0.22170096635818481tensor(0.6444)
0.1731899529695511tensor(0.4400)
0.1915930211544037tensor(0.4400)
0.1989670842885971tensor(0.7332)
0.17495563626289368tensor(0.2800)
0.15562407672405243tensor(-0.1000)
0.1740008145570755tensor(0.8000)
0.17976422607898712tensor(0.1000)
0.1754639446735382tensor(0.6000)
0.23461148142814636tensor(0.3600)
0.14871083199977875tensor(-0.1200)
0.2173781394958496tensor(0.3000)
0.19288744032382965tensor(0.4668)
0.23676908016204834tensor(0.9000)
0.1843303143978119tensor(0.4000)
0.1098991334438324tensor(0.2000)
0.21319350600242615tensor(0.3000)
0.20418666303157806tensor(0.4400)
0.22520746290683746tensor(0.5200)
0.16374865174293518tensor(-0.6000)
0.23573856055736542tensor(0.3332)
0.18133661150932312tensor(0.7000)
0.19673936069011688tensor(-0.3000)
0.20525598526000977tensor(0.1000)
0.20237712562084198tensor(0.3000)
0.18248118460178375tensor(0.)
0.20162971317768097tensor(0.2364)
0.2057035267353058tensor(0.7600)
0.2265021800994873tensor(0.9200)
0.24094459414482117tensor(1.)
0.20652644336223602tensor(0.2000)
0.13830505311489105tensor(0.2800)
0.2432192862033844tensor(0.7600)
0.14798183739185333tensor(-0.2800)
0.2388157993555069tensor(0.6000)
0.1628802865743637tensor(-0.6000)
0.1550823301076889tensor(-0.7600)
0.23498748242855072tensor(0.8400)
0.13788451254367828tensor(-0.2800)
0.0776844471693039tensor(-0.5200)
0.18950441479682922tensor(0.8400)
0.1819283813238144tensor(-0.4400)
0.207198828458786tensor(0.2800)
0.08294688165187836tensor(0.1200)
0.09264472872018814tensor(-0.6800)
0.19066748023033142tensor(0.5200)
0.24647127091884613tensor(0.5200)
0.11874434351921082tensor(0.6800)
0.18877379596233368tensor(-0.3600)
0.21499769389629364tensor(0.5200)
0.11150805652141571tensor(-0.4400)
0.2377368062734604tensor(0.4400)
0.1654309779405594tensor(-0.2800)
0.12938298285007477tensor(0.5200)
0.11628491431474686tensor(-0.9200)
0.16155071556568146tensor(-0.3600)
0.08047331869602203tensor(-0.8400)
0.10113882273435593tensor(-0.0667)
0.06561930477619171tensor(-0.8400)
0.17074882984161377tensor(0.4400)
0.13673584163188934tensor(0.3600)
0.25099048018455505tensor(0.0400)
0.2144835740327835tensor(-0.1200)
0.13524265587329865tensor(0.0400)
0.23934923112392426tensor(0.9200)
0.16283820569515228tensor(0.9200)
0.2022310048341751tensor(0.9200)
0.1877928525209427tensor(0.0400)
0.12390630692243576tensor(-0.3600)
0.07604077458381653tensor(-0.1200)
0.08821219205856323tensor(-0.6000)
0.1261739879846573tensor(-0.5200)
0.16625653207302094tensor(0.3600)
0.18275785446166992tensor(-0.7600)
0.1784115731716156tensor(0.2800)
0.12288951128721237tensor(0.2800)
0.12011665105819702tensor(-0.2800)
0.21632131934165955tensor(-0.5200)
0.13365685939788818tensor(-0.3600)
0.21775472164154053tensor(0.9200)
0.1763528436422348tensor(0.4400)
0.24108335375785828tensor(0.5200)
0.11483419686555862tensor(0.2000)
0.18649865686893463tensor(0.4400)
0.19848434627056122tensor(0.7600)
0.18381257355213165tensor(-0.2000)
0.1460091918706894tensor(-0.0400)
0.14195416867733002tensor(0.2800)
0.18249690532684326tensor(0.3600)
0.16682569682598114tensor(0.1200)
0.16008614003658295tensor(0.2800)
0.1376914530992508tensor(-0.2800)
0.1411648541688919tensor(0.1200)
0.18154378235340118tensor(0.3600)
0.2302873134613037tensor(0.8400)
0.2592073678970337tensor(0.5200)
0.07612112164497375tensor(-0.3600)
0.07761155068874359tensor(-0.6800)
0.11701276898384094tensor(-0.6000)
0.14020824432373047tensor(0.0400)
0.19302788376808167tensor(0.6800)
0.227419912815094tensor(0.7600)
0.14767779409885406tensor(-0.1200)
0.24399487674236298tensor(0.0400)
0.1953979730606079tensor(0.1200)
0.2394864410161972tensor(0.6000)
0.13883644342422485tensor(0.2800)
-0.0028023417107760906tensor(-1.)
0.11400347948074341tensor(-0.2800)
0.02336515486240387tensor(-1.)
0.23235195875167847tensor(0.0400)
0.1774962842464447tensor(0.1000)
0.15806420147418976tensor(0.2800)
0.20758667588233948tensor(-0.2800)
0.14577144384384155tensor(0.6800)
0.25478416681289673tensor(0.6800)
0.15235355496406555tensor(0.0400)
0.20707671344280243tensor(0.6800)
0.12985961139202118tensor(-0.2000)
0.2540379762649536tensor(1.)
0.12012737989425659tensor(-0.6800)
0.1729334145784378tensor(0.5200)
0.21360555291175842tensor(-0.3600)
0.1282355636358261tensor(0.3600)
0.1229422464966774tensor(-0.7600)
0.21077466011047363tensor(-0.4400)
0.09920705109834671tensor(0.5200)
0.06701800227165222tensor(-0.5200)
0.18947090208530426tensor(0.2000)
0.18419447541236877tensor(-0.1200)
0.07807426899671555tensor(-0.6000)
0.21356552839279175tensor(-0.1200)
0.14395557343959808tensor(-0.2800)
0.0408463254570961tensor(-1.)
0.22409023344516754tensor(0.6800)
0.0996900349855423tensor(-0.8400)
0.25068390369415283tensor(0.0400)
0.07919822633266449tensor(-0.7600)
0.1727748066186905tensor(0.5200)
0.06673573702573776tensor(-1.)
0.23640766739845276tensor(1.)
0.09010164439678192tensor(-0.6000)
0.18551993370056152tensor(0.2800)
0.1859516054391861tensor(0.1200)
0.25456491112709045tensor(0.8400)
0.16283108294010162tensor(0.3600)
0.10620807856321335tensor(-0.5200)
0.1981305032968521tensor(0.6000)
0.25540101528167725tensor(-0.2800)
0.18861599266529083tensor(0.6000)
0.2544138729572296tensor(-0.0400)
0.24094460904598236tensor(-0.2800)
0.2210618108510971tensor(0.5200)
0.05834884196519852tensor(-0.4400)
0.2043565809726715tensor(0.0400)
0.09192346036434174tensor(-0.4400)
0.16233162581920624tensor(-0.3600)
0.20111432671546936tensor(0.9200)
0.05451052263379097tensor(-0.6800)
0.12567955255508423tensor(0.3600)
0.1817416101694107tensor(0.2800)
0.22372478246688843tensor(-0.1200)
0.07638280093669891tensor(-0.5200)
0.18817470967769623tensor(0.2000)
0.2192181497812271tensor(0.0400)
0.041186410933732986tensor(-0.8400)
0.1610386073589325tensor(0.8400)
0.1738058626651764tensor(0.6000)
0.10780755430459976tensor(0.1200)
0.22329074144363403tensor(0.5200)
0.03530355915427208tensor(-1.)
0.17693237960338593tensor(0.3600)
0.17630940675735474tensor(0.8400)
0.07398223876953125tensor(-0.6800)
0.15199358761310577tensor(0.5200)
0.12705524265766144tensor(-0.1200)
0.15833988785743713tensor(0.2800)
0.20127613842487335tensor(1.)
0.12462035566568375tensor(-0.3600)
0.1370929628610611tensor(-0.1200)
0.17383545637130737tensor(-0.6800)
0.15225093066692352tensor(0.4400)
0.215582013130188tensor(0.)
0.22214528918266296tensor(0.2000)
0.14588844776153564tensor(-0.1200)
0.017210498452186584tensor(-1.)
0.08514882624149323tensor(-0.9200)
0.16900286078453064tensor(0.2800)
0.21311454474925995tensor(1.)
0.14080829918384552tensor(-0.1200)
0.11725953966379166tensor(0.7600)
0.2497994601726532tensor(0.5200)
0.11195125430822372tensor(-0.5200)
0.20443633198738098tensor(0.8400)
0.12481625378131866tensor(0.5200)
0.219122052192688tensor(-0.4400)
0.23464976251125336tensor(0.5200)
0.05165162310004234tensor(-1.)
0.1509837657213211tensor(-0.3600)
0.17130468785762787tensor(1.)
0.028811248019337654tensor(-0.8400)
0.16445313394069672tensor(-0.0400)
0.10629483312368393tensor(0.2800)
0.1850476711988449tensor(0.5200)
0.21007263660430908tensor(0.0400)
0.2392447590827942tensor(-0.6000)
0.1673896610736847tensor(0.6000)
0.13118742406368256tensor(-0.2000)
0.09308581799268723tensor(-0.1200)
0.22653841972351074tensor(0.8400)
0.19450998306274414tensor(0.7600)
0.24417555332183838tensor(-0.2800)
0.08656783401966095tensor(-1.)
0.018847070634365082tensor(-1.)
0.12913350760936737tensor(-0.2000)
0.1339655965566635tensor(0.3600)
0.027945619076490402tensor(-0.9200)
0.14900967478752136tensor(-0.0400)
0.13601404428482056tensor(-1.)
0.15177421271800995tensor(-0.8400)
0.14379896223545074tensor(-0.2800)
0.14465464651584625tensor(-0.6800)
0.08611379563808441tensor(-0.8400)
0.1312280148267746tensor(-0.1200)
0.14942720532417297tensor(-0.2800)
0.25640615820884705tensor(0.8400)
0.13383416831493378tensor(-0.1200)
0.07764671742916107tensor(-0.6000)
0.09886015951633453tensor(-0.6000)
0.20268449187278748tensor(0.6800)
0.04090186208486557tensor(-0.8400)
0.05581318587064743tensor(-0.9200)
0.019179724156856537tensor(-1.)
-0.003586236387491226tensor(-1.)
0.09156043827533722tensor(-1.)
0.09233567863702774tensor(-0.7600)
0.04690423607826233tensor(-0.7600)
0.1777048110961914tensor(0.6000)
0.04600697010755539tensor(-1.)
0.12033494561910629tensor(-0.6800)
0.1568203866481781tensor(0.2800)
0.20544297993183136tensor(0.9200)
0.17817284166812897tensor(0.6800)
0.07912871241569519tensor(-0.1200)
0.09261202812194824tensor(-0.6000)
0.22019076347351074tensor(0.4400)
0.08994771540164948tensor(-0.3600)
0.14301364123821259tensor(0.2000)
0.23485711216926575tensor(-0.0400)
0.06050720810890198tensor(-0.8400)
0.10471851378679276tensor(-1.)
0.2703113257884979tensor(1.)
0.0780714824795723tensor(-0.5200)
0.1529168039560318tensor(-0.4400)
0.2512882947921753tensor(0.6000)
0.2225179523229599tensor(0.8400)
0.14802931249141693tensor(0.3600)
0.0649455115199089tensor(-0.8400)
0.017419487237930298tensor(-1.)
0.24869704246520996tensor(0.9200)
0.21712370216846466tensor(1.)
0.19059205055236816tensor(0.6000)
0.2106933444738388tensor(0.6000)
0.23742283880710602tensor(0.2000)
0.20776034891605377tensor(1.)
0.15605928003787994tensor(-0.2000)
0.12639650702476501tensor(0.2000)
0.12051364779472351tensor(0.2000)
0.10933669656515121tensor(-0.6000)
0.25926315784454346tensor(1.)
0.221638485789299tensor(-0.2000)
0.26269179582595825tensor(-0.2000)
0.1676587015390396tensor(-0.2000)
0.21519775688648224tensor(1.)
0.16297608613967896tensor(0.2000)
0.2147519886493683tensor(0.2000)
0.12294083088636398tensor(-1.)
0.1606927216053009tensor(-0.2000)
0.06416651606559753tensor(-1.)
0.06324557960033417tensor(-0.2000)
0.01951243169605732tensor(-1.)
0.08898743987083435tensor(-1.)
assignment2/output/Q3_non_crossing_test.txt 0 → 100644
View file @ 98dae565
0.21857832372188568tensor(-1.4000)
0.2122490406036377tensor(-1.4000)
0.2117069810628891tensor(-1.4000)
0.22571498155593872tensor(-1.4000)
0.21976815164089203tensor(-1.4000)
0.19206903874874115tensor(-1.4000)
0.2274087369441986tensor(-1.4000)
0.23585760593414307tensor(-1.4000)
0.19625437259674072tensor(-1.4000)
0.2298237532377243tensor(-1.4000)
0.2298237532377243tensor(-1.4000)
0.20891006290912628tensor(-1.4000)
0.19567641615867615tensor(-1.4000)
0.21489886939525604tensor(-1.4000)
0.21897998452186584tensor(-1.4000)
0.22662441432476044tensor(-1.4000)
0.2016517072916031tensor(-1.4000)
0.22347082197666168tensor(-1.4000)
0.20980983972549438tensor(-1.4000)
0.1940148025751114tensor(-1.4000)
0.16752268373966217tensor(-1.4000)
0.1628485471010208tensor(-1.4000)
0.17249839007854462tensor(-1.4000)
0.2071806639432907tensor(-1.4000)
0.18479815125465393tensor(-1.4000)
0.22398829460144043tensor(-1.4000)
0.21825934946537018tensor(-1.4000)
0.19243356585502625tensor(-1.4000)
0.20634010434150696tensor(-1.4000)
0.21353280544281006tensor(-1.4000)
0.1953456848859787tensor(-1.4000)
0.209268257021904tensor(-1.4000)
0.147725448012352tensor(-1.4000)
0.20238898694515228tensor(-1.4000)
0.2039065808057785tensor(-1.4000)
0.1557430624961853tensor(-1.4000)
0.1748470813035965tensor(-1.4000)
0.1763172298669815tensor(-1.4000)
0.15163695812225342tensor(-1.4000)
0.18100498616695404tensor(-1.4000)
0.19969674944877625tensor(-1.4000)
0.1465882956981659tensor(-1.4000)
0.20108728110790253tensor(-1.4000)
0.17117810249328613tensor(-1.4000)
0.1895805299282074tensor(-1.4000)
0.14796218276023865tensor(-1.4000)
0.1595875471830368tensor(-1.4000)
0.18130241334438324tensor(-1.4000)
0.18141598999500275tensor(-1.4000)
0.17238712310791016tensor(-1.4000)
0.20066070556640625tensor(-1.4000)
0.17966708540916443tensor(-1.4000)
0.1749783605337143tensor(-1.4000)
0.15700320899486542tensor(-1.4000)
0.19744090735912323tensor(-1.4000)
0.15556490421295166tensor(-1.4000)
0.1812317967414856tensor(-1.4000)
0.18750442564487457tensor(-1.4000)
0.14899009466171265tensor(-1.4000)
0.18571484088897705tensor(-1.4000)
0.20963570475578308tensor(-1.4000)
0.13293229043483734tensor(-1.4000)
0.1426672786474228tensor(-1.4000)
0.1447056084871292tensor(-1.4000)
0.20073944330215454tensor(-1.4000)
0.17720720171928406tensor(-1.4000)
0.1847742199897766tensor(-1.4000)
0.18986693024635315tensor(-1.4000)
0.1375056654214859tensor(-1.4000)
0.19869302213191986tensor(-1.4000)
0.15755943953990936tensor(-1.4000)
0.18596979975700378tensor(-1.4000)
0.13094159960746765tensor(-1.4000)
0.15094032883644104tensor(-1.4000)
0.15190882980823517tensor(-1.4000)
0.1550188809633255tensor(-1.4000)
0.15845271944999695tensor(-1.4000)
0.10525491088628769tensor(-1.4000)
0.10380858182907104tensor(-1.4000)
0.17835628986358643tensor(-1.4000)
0.16696317493915558tensor(-1.4000)
0.1738957166671753tensor(-1.4000)
0.17193947732448578tensor(-1.4000)
0.16113391518592834tensor(-1.4000)
0.1411997675895691tensor(-1.4000)
0.1298888623714447tensor(-1.4000)
0.1345503330230713tensor(-1.4000)
0.1810731291770935tensor(-1.4000)
0.1817420870065689tensor(-1.4000)
0.15960240364074707tensor(-1.4000)
0.13735391199588776tensor(-1.4000)
0.20892907679080963tensor(-1.4000)
0.1541341096162796tensor(-1.4000)
0.14924907684326172tensor(-1.4000)
0.18013228476047516tensor(-1.4000)
0.07558099925518036tensor(-1.4000)
0.18393835425376892tensor(-1.4000)
0.15579591691493988tensor(-1.4000)
0.14041468501091003tensor(-1.4000)
0.1481448858976364tensor(-1.4000)
0.13349443674087524tensor(-1.4000)
0.13251107931137085tensor(-1.4000)
0.1843564510345459tensor(-1.4000)
0.19864891469478607tensor(-1.4000)
0.17914636433124542tensor(-1.4000)
0.1438188999891281tensor(-1.4000)
0.1467726230621338tensor(-1.4000)
0.154560849070549tensor(-1.4000)
0.10634467005729675tensor(-1.4000)
0.13090646266937256tensor(-1.4000)
0.1437601000070572tensor(-1.4000)
0.13052091002464294tensor(-1.4000)
0.12715420126914978tensor(-1.4000)
0.17816685140132904tensor(-1.4000)
0.1267554610967636tensor(-1.4000)
0.10770697146654129tensor(-1.4000)
0.15819117426872253tensor(-1.4000)
0.18060870468616486tensor(-1.4000)
0.16455811262130737tensor(-1.4000)
0.16608791053295135tensor(-1.4000)
0.11552099138498306tensor(-1.4000)
0.11717726290225983tensor(-1.4000)
0.1122841015458107tensor(-1.4000)
0.13443760573863983tensor(-1.4000)
0.09241592884063721tensor(-1.4000)
0.22764775156974792tensor(-1.4000)
0.21098855137825012tensor(-1.4000)
0.2067919224500656tensor(-1.4000)
0.2174922525882721tensor(-1.4000)
0.18071016669273376tensor(-1.4000)
0.22611476480960846tensor(-1.4000)
0.22072653472423553tensor(-1.4000)
0.23108217120170593tensor(-1.4000)
0.21366584300994873tensor(-1.4000)
0.22302450239658356tensor(-1.4000)
0.16099059581756592tensor(-1.4000)
0.22099430859088898tensor(-1.4000)
0.22163225710391998tensor(-1.4000)
0.22811055183410645tensor(-1.4000)
0.2195269763469696tensor(-1.4000)
0.22011910378932953tensor(-1.4000)
0.22244909405708313tensor(-1.4000)
0.22279894351959229tensor(-1.4000)
0.22477446496486664tensor(-1.4000)
0.17856235802173615tensor(-1.4000)
0.21240368485450745tensor(-1.4000)
0.18067699670791626tensor(-1.4000)
0.2150598168373108tensor(-1.4000)
0.22012746334075928tensor(-1.4000)
0.20547406375408173tensor(-1.4000)
0.22378838062286377tensor(-1.4000)
0.18633714318275452tensor(-1.4000)
0.22683365643024445tensor(-1.4000)
0.23080699145793915tensor(-1.4000)
0.21708810329437256tensor(-1.4000)
0.21274712681770325tensor(-1.4000)
0.17558352649211884tensor(-1.4000)
0.21623468399047852tensor(-1.4000)
0.22677312791347504tensor(-1.4000)
0.20772795379161835tensor(-1.4000)
0.20091795921325684tensor(-1.4000)
0.1987101286649704tensor(-1.4000)
0.17365781962871552tensor(-1.4000)
0.2176741361618042tensor(-1.4000)
0.20734867453575134tensor(-1.4000)
0.16329984366893768tensor(-1.4000)
0.19715937972068787tensor(-1.4000)
0.1865229606628418tensor(-1.4000)
0.15267403423786163tensor(-1.4000)
0.17479561269283295tensor(-1.4000)
0.21536141633987427tensor(-1.4000)
0.1921403408050537tensor(-1.4000)
0.16307581961154938tensor(-1.4000)
0.18140597641468048tensor(-1.4000)
0.15465494990348816tensor(-1.4000)
0.2097509503364563tensor(-1.4000)
0.2020963579416275tensor(-1.4000)
0.15201054513454437tensor(-1.4000)
0.15048448741436005tensor(-1.4000)
0.2025504857301712tensor(-1.4000)
0.2025504857301712tensor(-1.4000)
0.19597122073173523tensor(-1.4000)
0.13432449102401733tensor(-1.4000)
0.16797660291194916tensor(-1.4000)
0.20343998074531555tensor(-1.4000)
0.16076374053955078tensor(-1.4000)
0.17639997601509094tensor(-1.4000)
0.1425207406282425tensor(-1.4000)
0.13517987728118896tensor(-1.4000)
0.10177095234394073tensor(-1.4000)
0.16047634184360504tensor(-1.4000)
0.17456479370594025tensor(-1.4000)
0.20921887457370758tensor(-1.4000)
0.15981975197792053tensor(-1.4000)
0.21568270027637482tensor(-1.4000)
0.17632655799388885tensor(-1.4000)
0.21003012359142303tensor(-1.4000)
0.15749552845954895tensor(-1.4000)
0.1287974864244461tensor(-1.4000)
0.18141674995422363tensor(-1.4000)
0.16837821900844574tensor(-1.4000)
0.17139311134815216tensor(-1.4000)
0.16517865657806396tensor(-1.4000)
0.1507159322500229tensor(-1.4000)
0.13069821894168854tensor(-1.4000)
0.17894606292247772tensor(-1.4000)
0.17701265215873718tensor(-1.4000)
0.1265743523836136tensor(-1.4000)
0.14086420834064484tensor(-1.4000)
0.1998704969882965tensor(-1.4000)
0.14096376299858093tensor(-1.4000)
0.19115014374256134tensor(-1.4000)
0.1525927633047104tensor(-1.4000)
0.17818142473697662tensor(-1.4000)
0.13789969682693481tensor(-1.4000)
0.18012137711048126tensor(-1.4000)
0.13338157534599304tensor(-1.4000)
0.19160734117031097tensor(-1.4000)
0.18437914550304413tensor(-1.4000)
0.16970475018024445tensor(-1.4000)
0.11667285859584808tensor(-1.4000)
0.09041550010442734tensor(-1.4000)
0.043857745826244354tensor(-1.4000)
0.1547759771347046tensor(-1.4000)
0.11481858044862747tensor(-1.4000)
0.12804971635341644tensor(-1.4000)
0.14114855229854584tensor(-1.4000)
0.16825056076049805tensor(-1.4000)
0.15921907126903534tensor(-1.4000)
0.1608044058084488tensor(-1.4000)
0.1635299026966095tensor(-1.4000)
0.15186020731925964tensor(-1.4000)
0.152643084526062tensor(-1.4000)
0.19325153529644012tensor(-1.4000)
0.12704326212406158tensor(-1.4000)
0.12066233158111572tensor(-1.4000)
0.15083849430084229tensor(-1.4000)
0.15823495388031006tensor(-1.4000)
0.10820385813713074tensor(-1.4000)
0.09556758403778076tensor(-1.4000)
0.16649466753005981tensor(-1.4000)
0.09389680624008179tensor(-1.4000)
0.15207761526107788tensor(-1.4000)
0.16189703345298767tensor(-1.4000)
0.13684138655662537tensor(-1.4000)
0.11389777064323425tensor(-1.4000)
0.2174484133720398tensor(-1.4000)
0.12217171490192413tensor(-1.4000)
0.03232337906956673tensor(-1.4000)
0.12586960196495056tensor(-1.4000)
0.14861465990543365tensor(-1.4000)
0.23478983342647552tensor(-1.4000)
0.15929274260997772tensor(-1.4000)
0.1520121991634369tensor(-1.4000)
0.15255753695964813tensor(-1.4000)
0.14136216044425964tensor(-1.4000)
0.13487765192985535tensor(-1.4000)
0.18646471202373505tensor(-1.4000)
0.14779193699359894tensor(-1.4000)
0.10760416090488434tensor(-1.4000)
0.20158961415290833tensor(-1.4000)
0.21484747529029846tensor(-1.4000)
0.18989427387714386tensor(-1.4000)
0.21732814610004425tensor(-1.4000)
0.1366814523935318tensor(-1.4000)
0.14009013772010803tensor(-1.4000)
0.15557576715946198tensor(-1.4000)
0.12335117906332016tensor(-1.4000)
0.16962556540966034tensor(-1.4000)
0.21911577880382538tensor(-1.4000)
0.2177511751651764tensor(-1.4000)
0.10267522931098938tensor(-1.4000)
0.09262821823358536tensor(-1.4000)
0.12875060737133026tensor(-1.4000)
0.19494421780109406tensor(-1.4000)
0.22285842895507812tensor(-1.4000)
0.12214725464582443tensor(-1.4000)
0.0567181222140789tensor(-1.4000)
0.1900079846382141tensor(-1.4000)
0.163884699344635tensor(-1.4000)
0.13904911279678345tensor(-1.4000)
0.15386369824409485tensor(-1.4000)
0.1496361941099167tensor(-1.4000)
0.1829318404197693tensor(-1.4000)
0.1887091100215912tensor(-1.4000)
0.18396258354187012tensor(-1.4000)
0.1385122388601303tensor(-1.4000)
0.19053463637828827tensor(-1.4000)
0.20185618102550507tensor(-1.4000)
0.1429852694272995tensor(-1.4000)
0.16535311937332153tensor(-1.4000)
0.17898055911064148tensor(-1.4000)
0.15400074422359467tensor(-1.4000)
0.20866699516773224tensor(-1.4000)
0.10666870325803757tensor(-1.4000)
0.14162246882915497tensor(-1.4000)
0.11522886157035828tensor(-1.4000)
0.18926966190338135tensor(-1.4000)
0.13097600638866425tensor(-1.4000)
0.20417934656143188tensor(-1.4000)
0.1919148564338684tensor(-1.4000)
0.16428612172603607tensor(-1.4000)
0.19773845374584198tensor(-1.4000)
0.21553057432174683tensor(-1.4000)
0.15067271888256073tensor(-1.4000)
0.21400217711925507tensor(-1.4000)
0.21402262151241302tensor(-1.4000)
0.1393345296382904tensor(-1.4000)
0.12227022647857666tensor(-1.4000)
0.14161409437656403tensor(-1.4000)
0.1844896525144577tensor(-1.4000)
0.10607926547527313tensor(-1.4000)
0.167685404419899tensor(-1.4000)
0.16433319449424744tensor(-1.4000)
0.13576243817806244tensor(-1.4000)
0.21179969608783722tensor(-1.4000)
0.13578878343105316tensor(-1.4000)
0.2066604644060135tensor(-1.4000)
0.11315226554870605tensor(-1.4000)
0.08763381838798523tensor(-1.4000)
0.21021126210689545tensor(-1.4000)
0.1810208112001419tensor(-1.4000)
0.17787668108940125tensor(-1.4000)
0.11990435421466827tensor(-1.4000)
0.20250578224658966tensor(-1.4000)
0.09567370265722275tensor(-1.4000)
0.1278337836265564tensor(-1.4000)
0.2050991952419281tensor(-1.4000)
0.14011290669441223tensor(-1.4000)
0.20529359579086304tensor(-1.4000)
0.23316355049610138tensor(-1.4000)
0.19694963097572327tensor(-1.4000)
0.21460454165935516tensor(-1.4000)
0.18511752784252167tensor(-1.4000)
0.09934554249048233tensor(-1.4000)
0.15773604810237885tensor(-1.4000)
0.168302983045578tensor(-1.4000)
0.19996650516986847tensor(-1.4000)
0.15042291581630707tensor(-1.4000)
0.09388721734285355tensor(-1.4000)
0.2192242294549942tensor(-1.4000)
0.21900764107704163tensor(-1.4000)
0.19955158233642578tensor(-1.4000)
0.19038046896457672tensor(-1.4000)
0.16232998669147491tensor(-1.4000)
0.13641293346881866tensor(-1.4000)
0.13021187484264374tensor(-1.4000)
0.1298178881406784tensor(-1.4000)
0.11559568345546722tensor(-1.4000)
0.13840264081954956tensor(-1.4000)
0.1948850005865097tensor(-1.4000)
0.2294396311044693tensor(-1.4000)
0.19073590636253357tensor(-1.4000)
0.1861904263496399tensor(-1.4000)
0.19446253776550293tensor(-1.4000)
0.11116395890712738tensor(-1.4000)
0.11492528021335602tensor(-1.4000)
0.21234740316867828tensor(-1.4000)
0.13400788605213165tensor(-1.4000)
0.20791953802108765tensor(-1.4000)
0.20022358000278473tensor(-1.4000)
0.19014528393745422tensor(-1.4000)
0.17485104501247406tensor(-1.4000)
0.1738804280757904tensor(-1.4000)
0.2033933401107788tensor(-1.4000)
0.0632527768611908tensor(-1.4000)
0.14621654152870178tensor(-1.4000)
0.1520323008298874tensor(-1.4000)
0.19046759605407715tensor(-1.4000)
0.2368025779724121tensor(-1.4000)
0.16588042676448822tensor(-1.4000)
0.20374274253845215tensor(-1.4000)
0.093607597053051tensor(-1.4000)
0.19823694229125977tensor(-1.4000)
0.16806650161743164tensor(-1.4000)
0.17896856367588043tensor(-1.4000)
0.18941903114318848tensor(-1.4000)
0.10473352670669556tensor(-1.4000)
0.1489909440279007tensor(-1.4000)
0.2010277956724167tensor(-1.4000)
0.20849865674972534tensor(-1.4000)
0.19135530292987823tensor(-1.4000)
0.13656936585903168tensor(-1.4000)
0.13514187932014465tensor(-1.4000)
0.144539937376976tensor(-1.4000)
0.20071835815906525tensor(-1.4000)
0.21262209117412567tensor(-1.4000)
0.15776248276233673tensor(-1.4000)
0.16837753355503082tensor(-1.4000)
0.1322004497051239tensor(-1.4000)
0.22962911427021027tensor(-1.4000)
0.19100110232830048tensor(-1.4000)
0.1331186592578888tensor(-1.4000)
0.15279187262058258tensor(-1.4000)
0.22096623480319977tensor(-1.4000)
0.11345332115888596tensor(-1.4000)
0.1351073831319809tensor(-1.4000)
0.20398332178592682tensor(-1.4000)
0.19575008749961853tensor(-1.4000)
0.11188933998346329tensor(-1.4000)
0.14651095867156982tensor(-1.4000)
0.14999206364154816tensor(-1.4000)
0.13553418219089508tensor(-1.4000)
0.1525758057832718tensor(-1.4000)
0.14219053089618683tensor(-1.4000)
0.21720780432224274tensor(-1.4000)
0.10440674424171448tensor(-1.4000)
0.14045608043670654tensor(-1.4000)
0.17008446156978607tensor(-1.4000)
0.1760568916797638tensor(-1.4000)
0.16315478086471558tensor(-1.4000)
0.18542972207069397tensor(-1.4000)
0.14118650555610657tensor(-1.4000)
0.17957744002342224tensor(-1.4000)
0.09485538303852081tensor(-1.4000)
0.18082986772060394tensor(-1.4000)
0.08562956750392914tensor(-1.4000)
0.19854462146759033tensor(-1.4000)
0.20745161175727844tensor(-1.4000)
0.17174741625785828tensor(-1.4000)
0.21984152495861053tensor(-1.4000)
0.1090618371963501tensor(-1.4000)
0.22743114829063416tensor(-1.4000)
0.14892399311065674tensor(-1.4000)
0.20966272056102753tensor(-1.4000)
0.17177638411521912tensor(-1.4000)
0.1348162293434143tensor(-1.4000)
0.21401925384998322tensor(-1.4000)
0.19157306849956512tensor(-1.4000)
0.1667744368314743tensor(-1.4000)
0.140011265873909tensor(-1.4000)
0.21280744671821594tensor(-1.4000)
0.1548301726579666tensor(-1.4000)
0.21922506392002106tensor(-1.4000)
0.1228589192032814tensor(-1.4000)
0.1521133929491043tensor(-1.4000)
0.18157987296581268tensor(-1.4000)
0.21578052639961243tensor(-1.4000)
0.20871922373771667tensor(-1.4000)
0.1587958037853241tensor(-1.4000)
0.13097256422042847tensor(-1.4000)
0.10830245912075043tensor(-1.4000)
0.15092231333255768tensor(-1.4000)
0.11164955794811249tensor(-1.4000)
0.18217861652374268tensor(-1.4000)
0.13841605186462402tensor(-1.4000)
0.1554170697927475tensor(-1.4000)
0.16631175577640533tensor(-1.4000)
0.16089168190956116tensor(-1.4000)
0.17704229056835175tensor(-1.4000)
0.1888720840215683tensor(-1.4000)
0.16722577810287476tensor(-1.4000)
0.1482776403427124tensor(-1.4000)
0.059290967881679535tensor(-1.4000)
0.21547526121139526tensor(-1.4000)
0.09669394046068192tensor(-1.4000)
0.1001296266913414tensor(-1.4000)
0.17361518740653992tensor(-1.4000)
0.15572519600391388tensor(-1.4000)
0.19387704133987427tensor(-1.4000)
0.10918445140123367tensor(-1.4000)
0.1843576729297638tensor(-1.4000)
0.12568049132823944tensor(-1.4000)
0.2023557424545288tensor(-1.4000)
0.20290972292423248tensor(-1.4000)
0.12262700498104095tensor(-1.4000)
0.19040276110172272tensor(-1.4000)
0.2088402807712555tensor(-1.4000)
0.1970728039741516tensor(-1.4000)
0.14156772196292877tensor(-1.4000)
0.17508240044116974tensor(-1.4000)
0.16098269820213318tensor(-1.4000)
0.2191305309534073tensor(-1.4000)
0.21143658459186554tensor(-1.4000)
0.15320561826229095tensor(-1.4000)
0.16144850850105286tensor(-1.4000)
0.18357917666435242tensor(-1.4000)
0.13352857530117035tensor(-1.4000)
0.18085971474647522tensor(-1.4000)
0.1322302669286728tensor(-1.4000)
0.20797096192836761tensor(-1.4000)
0.1460716724395752tensor(-1.4000)
0.17960132658481598tensor(-1.4000)
0.13954588770866394tensor(-1.4000)
0.22305148839950562tensor(-1.4000)
0.16786500811576843tensor(-1.4000)
0.19514964520931244tensor(-1.4000)
0.22405289113521576tensor(-1.4000)
0.11732310801744461tensor(-1.4000)
0.1937175840139389tensor(-1.4000)
0.21729376912117004tensor(-1.4000)
0.18983836472034454tensor(-1.4000)
0.20134247839450836tensor(-1.4000)
0.2137376219034195tensor(-1.4000)
0.10087674111127853tensor(-1.4000)
0.20418532192707062tensor(-1.4000)
0.1575571894645691tensor(-1.4000)
0.14345914125442505tensor(-1.4000)
0.10295584052801132tensor(-1.4000)
0.2028219848871231tensor(-1.4000)
0.19016696512699127tensor(-1.4000)
0.18159152567386627tensor(-1.4000)
0.08289957791566849tensor(-1.4000)
0.2278912365436554tensor(-1.4000)
0.17502406239509583tensor(-1.4000)
0.22565822303295135tensor(-1.4000)
0.19525842368602753tensor(-1.4000)
0.17606499791145325tensor(-1.4000)
0.16717590391635895tensor(-1.4000)
0.15132193267345428tensor(-1.4000)
0.13189037144184113tensor(-1.4000)
0.17697738111019135tensor(-1.4000)
0.20829570293426514tensor(-1.4000)
0.19526523351669312tensor(-1.4000)
0.21967442333698273tensor(-1.4000)
0.1402534693479538tensor(-1.4000)
0.20188046991825104tensor(-1.4000)
0.1913021355867386tensor(-1.4000)
0.08566310256719589tensor(-1.4000)
0.20643794536590576tensor(-1.4000)
0.13196852803230286tensor(-1.4000)
0.12862081825733185tensor(-1.4000)
0.18592588603496552tensor(-1.4000)
0.13954375684261322tensor(-1.4000)
0.1390576809644699tensor(-1.4000)
0.14183130860328674tensor(-1.4000)
0.20725132524967194tensor(-1.4000)
0.12036241590976715tensor(-1.4000)
0.17606647312641144tensor(-1.4000)
0.15463502705097198tensor(-1.4000)
0.07455667853355408tensor(-1.4000)
0.16242662072181702tensor(-1.4000)
0.06342655420303345tensor(-1.4000)
0.15855151414871216tensor(-1.4000)
0.14732015132904053tensor(-1.4000)
0.18767009675502777tensor(-1.4000)
0.18107137084007263tensor(-1.4000)
0.14945471286773682tensor(-1.4000)
0.1486596316099167tensor(-1.4000)
0.12654732167720795tensor(-1.4000)
0.2365025132894516tensor(-1.4000)
0.13919496536254883tensor(-1.4000)
0.18707992136478424tensor(-1.4000)
0.17093238234519958tensor(-1.4000)
0.19609501957893372tensor(-1.4000)
0.15381354093551636tensor(-1.4000)
0.18478254973888397tensor(-1.4000)
0.21165630221366882tensor(-1.4000)
0.21329106390476227tensor(-1.4000)
0.16572105884552002tensor(-1.4000)
0.21789909899234772tensor(-1.4000)
0.16839507222175598tensor(-1.4000)
0.19291718304157257tensor(-1.4000)
0.22804932296276093tensor(-1.4000)
0.1499670296907425tensor(-1.4000)
0.19920577108860016tensor(-1.4000)
0.1564437747001648tensor(-1.4000)
0.15067355334758759tensor(-1.4000)
0.16264910995960236tensor(-1.4000)
0.2064204216003418tensor(-1.4000)
0.18515558540821075tensor(-1.4000)
0.13674437999725342tensor(-1.4000)
0.1947796642780304tensor(-1.4000)
0.19203346967697144tensor(-1.4000)
0.21311457455158234tensor(-1.4000)
0.16199374198913574tensor(-1.4000)
0.23668526113033295tensor(-1.4000)
0.1456340104341507tensor(-1.4000)
0.07518443465232849tensor(-1.4000)
0.2336304634809494tensor(-1.4000)
0.22106166183948517tensor(-1.4000)
0.14711298048496246tensor(-1.4000)
0.10931617766618729tensor(-1.4000)
0.18003933131694794tensor(-1.4000)
0.18600940704345703tensor(-1.4000)
0.2159629613161087tensor(-1.4000)
0.17434804141521454tensor(-1.4000)
0.15530386567115784tensor(-1.4000)
0.18039849400520325tensor(-1.4000)
0.1653163880109787tensor(-1.4000)
0.09238363802433014tensor(-1.4000)
0.22109562158584595tensor(-1.4000)
0.15685616433620453tensor(-1.4000)
0.14779655635356903tensor(-1.4000)
0.1825648993253708tensor(-1.4000)
0.15569540858268738tensor(-1.4000)
0.1291828155517578tensor(-1.4000)
0.14175696671009064tensor(-1.4000)
0.141828253865242tensor(-1.4000)
0.2149919718503952tensor(-1.4000)
0.16780684888362885tensor(-1.4000)
0.20732995867729187tensor(-1.4000)
0.22377923130989075tensor(-1.4000)
0.10624765604734421tensor(-1.4000)
0.0894605740904808tensor(-1.4000)
0.21629366278648376tensor(-1.4000)
0.18162812292575836tensor(-1.4000)
0.0978916585445404tensor(-1.4000)
0.12776248157024384tensor(-1.4000)
0.22924752533435822tensor(-1.4000)
0.18288883566856384tensor(-1.4000)
0.09482675790786743tensor(-1.4000)
0.19449879229068756tensor(-1.4000)
0.22371898591518402tensor(-1.4000)
0.1962183266878128tensor(-1.4000)
0.13172589242458344tensor(-1.4000)
0.2262820452451706tensor(-1.4000)
0.19298948347568512tensor(-1.4000)
0.22915945947170258tensor(-1.4000)
0.22958825528621674tensor(-1.4000)
0.230428546667099tensor(-1.4000)
0.2120015025138855tensor(-1.4000)
0.1885531097650528tensor(-1.4000)
0.17716458439826965tensor(-1.4000)
0.18133077025413513tensor(-1.4000)
0.21130017936229706tensor(-1.4000)
0.17859570682048798tensor(-1.4000)
0.14250551164150238tensor(-1.4000)
0.15992353856563568tensor(-1.4000)
0.21039563417434692tensor(-1.4000)
0.190512552857399tensor(-1.4000)
0.2001206874847412tensor(-1.4000)
0.08258712291717529tensor(-1.4000)
0.21639768779277802tensor(-1.4000)
0.17338722944259644tensor(-1.4000)
0.22503989934921265tensor(-1.4000)
0.1189175397157669tensor(-1.4000)
0.09249734878540039tensor(-1.4000)
0.13161005079746246tensor(-1.4000)
0.18824897706508636tensor(-1.4000)
0.11193976551294327tensor(-1.4000)
0.1690441519021988tensor(-1.4000)
0.2197778970003128tensor(-1.4000)
0.165262833237648tensor(-1.4000)
0.12764562666416168tensor(-1.4000)
0.13864177465438843tensor(-1.4000)
0.15910328924655914tensor(-1.4000)
0.21153602004051208tensor(-1.4000)
0.2138100117444992tensor(-1.4000)
0.22829972207546234tensor(-1.4000)
0.11594528704881668tensor(-1.4000)
0.12398488819599152tensor(-1.4000)
0.17913149297237396tensor(-1.4000)
0.20740099251270294tensor(-1.4000)
0.11240196228027344tensor(-1.4000)
0.2019961029291153tensor(-1.4000)
0.13713248074054718tensor(-1.4000)
0.10583074390888214tensor(-1.4000)
0.19517067074775696tensor(-1.4000)
0.16910101473331451tensor(-1.4000)
0.20331868529319763tensor(-1.4000)
0.1625627875328064tensor(-1.4000)
0.2042812556028366tensor(-1.4000)
0.18419495224952698tensor(-1.4000)
0.1525772213935852tensor(-1.4000)
0.21738922595977783tensor(-1.4000)
0.20339669287204742tensor(-1.4000)
0.1193498969078064tensor(-1.4000)
0.16786977648735046tensor(-1.4000)
0.11770115047693253tensor(-1.4000)
0.20685914158821106tensor(-1.4000)
0.14926689863204956tensor(-1.4000)
0.14123404026031494tensor(-1.4000)
0.1392875760793686tensor(-1.4000)
0.21059755980968475tensor(-1.4000)
0.11473307013511658tensor(-1.4000)
0.21697759628295898tensor(-1.4000)
0.1323014199733734tensor(-1.4000)
0.22193719446659088tensor(-1.4000)
0.15536019206047058tensor(-1.4000)
0.1352311223745346tensor(-1.4000)
0.2255011349916458tensor(-1.4000)
0.14388105273246765tensor(-1.4000)
0.21660710871219635tensor(-1.4000)
0.22093671560287476tensor(-1.4000)
0.1881154477596283tensor(-1.4000)
0.13382495939731598tensor(-1.4000)
0.1345716416835785tensor(-1.4000)
0.1336754709482193tensor(-1.4000)
0.14447717368602753tensor(-1.4000)
0.14315129816532135tensor(-1.4000)
0.12421973794698715tensor(-1.4000)
0.21834154427051544tensor(-1.4000)
0.18447549641132355tensor(-1.4000)
0.1535123586654663tensor(-1.4000)
0.1282728612422943tensor(-1.4000)
0.20215636491775513tensor(-1.4000)
0.17915193736553192tensor(-1.4000)
0.1262558400630951tensor(-1.4000)
0.2284800112247467tensor(-1.4000)
0.23014186322689056tensor(-1.4000)
0.18839295208454132tensor(-1.4000)
0.22829842567443848tensor(-1.4000)
0.08415868133306503tensor(-1.4000)
0.2237853854894638tensor(-1.4000)
0.13363100588321686tensor(-1.4000)
0.15848441421985626tensor(-1.4000)
0.21848434209823608tensor(-1.4000)
0.19846047461032867tensor(-1.4000)
0.15789255499839783tensor(-1.4000)
0.17731299996376038tensor(-1.4000)
0.10465121269226074tensor(-1.4000)
0.1829105168581009tensor(-1.4000)
0.2200913429260254tensor(-1.4000)
0.18906831741333008tensor(-1.4000)
0.11301983892917633tensor(-1.4000)
0.19814902544021606tensor(-1.4000)
0.09522158652544022tensor(-1.4000)
0.20635956525802612tensor(-1.4000)
0.19043497741222382tensor(-1.4000)
0.21751795709133148tensor(-1.4000)
0.15395165979862213tensor(-1.4000)
0.12981750071048737tensor(-1.4000)
0.18666650354862213tensor(-1.4000)
0.17918381094932556tensor(-1.4000)
0.18268446624279022tensor(-1.4000)
0.1727588027715683tensor(-1.4000)
0.22320041060447693tensor(-1.4000)
0.19882987439632416tensor(-1.4000)
0.1717677265405655tensor(-1.4000)
0.1994536966085434tensor(-1.4000)
0.18821951746940613tensor(-1.4000)
0.144261434674263tensor(-1.4000)
0.183565154671669tensor(-1.4000)
0.10610926896333694tensor(-1.4000)
0.1881866306066513tensor(-1.4000)
0.15971197187900543tensor(-1.4000)
0.21323691308498383tensor(-1.4000)
0.15729954838752747tensor(-1.4000)
0.19452644884586334tensor(-1.4000)
0.13717837631702423tensor(-1.4000)
0.12718406319618225tensor(-1.4000)
0.10740027576684952tensor(-1.4000)
0.15016233921051025tensor(-1.4000)
0.11073936522006989tensor(-1.4000)
0.18968036770820618tensor(-1.4000)
0.13596640527248383tensor(-1.4000)
0.23012831807136536tensor(-1.4000)
0.08267486095428467tensor(-1.4000)
0.22839097678661346tensor(-1.4000)
0.21213707327842712tensor(-1.4000)
0.1243210956454277tensor(-1.4000)
0.1508701741695404tensor(-1.4000)
0.21441316604614258tensor(-1.4000)
0.1221182718873024tensor(-1.4000)
0.20442785322666168tensor(-1.4000)
0.16666731238365173tensor(-1.4000)
0.15372911095619202tensor(-1.4000)
0.1836056113243103tensor(-1.4000)
0.14741192758083344tensor(-1.4000)
0.21928171813488007tensor(-1.4000)
0.1689823567867279tensor(-1.4000)
0.22281783819198608tensor(-1.4000)
0.1965879499912262tensor(-1.4000)
0.21586881577968597tensor(-1.4000)
0.2122880071401596tensor(-1.4000)
0.16868709027767181tensor(-1.4000)
0.17302748560905457tensor(-1.4000)
0.1107746809720993tensor(-1.4000)
0.2298479676246643tensor(-1.4000)
0.2094505876302719tensor(-1.4000)
0.19524872303009033tensor(-1.4000)
0.19332586228847504tensor(-1.4000)
0.11340152472257614tensor(-1.4000)
0.16343635320663452tensor(-1.4000)
0.17509180307388306tensor(-1.4000)
0.2248792052268982tensor(-1.4000)
0.09234432131052017tensor(-1.4000)
0.13059526681900024tensor(-1.4000)
0.20192746818065643tensor(-1.4000)
0.145684152841568tensor(-1.4000)
0.1038748025894165tensor(-1.4000)
0.12001030892133713tensor(-1.4000)
0.23198851943016052tensor(-1.4000)
0.11717568337917328tensor(-1.4000)
0.21234436333179474tensor(-1.4000)
0.11203180998563766tensor(-1.4000)
0.18140563368797302tensor(-1.4000)
0.10022895038127899tensor(-1.4000)
0.16583023965358734tensor(-1.4000)
0.09338483959436417tensor(-1.4000)
0.19884055852890015tensor(-1.4000)
0.22306498885154724tensor(-1.4000)
0.21279668807983398tensor(-1.4000)
0.20018118619918823tensor(-1.4000)
0.18540778756141663tensor(-1.4000)
0.12358224391937256tensor(-1.4000)
0.18317751586437225tensor(-1.4000)
0.21196532249450684tensor(-1.4000)
0.10735967010259628tensor(-1.4000)
0.13917265832424164tensor(-1.4000)
0.1813577562570572tensor(-1.4000)
0.11374416947364807tensor(-1.4000)
0.17580153048038483tensor(-1.4000)
0.10855116695165634tensor(-1.4000)
0.16011525690555573tensor(-1.4000)
0.1105467826128006tensor(-1.4000)
0.20877912640571594tensor(-1.4000)
0.161529541015625tensor(-1.4000)
0.15802282094955444tensor(-1.4000)
0.10464640706777573tensor(-1.4000)
0.18612079322338104tensor(-1.4000)
0.12330867350101471tensor(-1.4000)
0.17673076689243317tensor(-1.4000)
0.15007701516151428tensor(-1.4000)
0.22570288181304932tensor(-1.4000)
0.18482063710689545tensor(-1.4000)
0.13873164355754852tensor(-1.4000)
0.20739968121051788tensor(-1.4000)
0.20417620241641998tensor(-1.4000)
0.0918639525771141tensor(-1.4000)
0.12404977530241013tensor(-1.4000)
0.1746523231267929tensor(-1.4000)
0.21644479036331177tensor(-1.4000)
0.20638640224933624tensor(-1.4000)
0.13739757239818573tensor(-1.4000)
0.16805380582809448tensor(-1.4000)
0.19949419796466827tensor(-1.4000)
0.2301131635904312tensor(-1.4000)
0.19719429314136505tensor(-1.4000)
0.14434324204921722tensor(-1.4000)
0.14876718819141388tensor(-1.4000)
0.17811115086078644tensor(-1.4000)
0.15287016332149506tensor(-1.4000)
0.12965579330921173tensor(-1.4000)
0.21104012429714203tensor(-1.4000)
0.18522043526172638tensor(-1.4000)
0.19109240174293518tensor(-1.4000)
0.21281416714191437tensor(-1.4000)
0.21097250282764435tensor(-1.4000)
0.12690305709838867tensor(-1.4000)
0.1635219007730484tensor(-1.4000)
0.14916902780532837tensor(-1.4000)
0.19144363701343536tensor(-1.4000)
0.1864471137523651tensor(-1.4000)
0.10977989435195923tensor(-1.4000)
0.2176637351512909tensor(-1.4000)
0.19556894898414612tensor(-1.4000)
0.12926150858402252tensor(-1.4000)
0.1477491408586502tensor(-1.4000)
0.20442135632038116tensor(-1.4000)
0.232897087931633tensor(-1.4000)
0.2030470222234726tensor(-1.4000)
0.14399413764476776tensor(-1.4000)
0.2184533029794693tensor(-1.4000)
0.18172918260097504tensor(-1.4000)
0.2100704312324524tensor(-1.4000)
0.15265542268753052tensor(-1.4000)
0.14930903911590576tensor(-1.4000)
0.21021562814712524tensor(-1.4000)
0.1829775720834732tensor(-1.4000)
0.18515661358833313tensor(-1.4000)
0.23551324009895325tensor(-1.4000)
0.18727827072143555tensor(-1.4000)
0.2288680225610733tensor(-1.4000)
0.13616137206554413tensor(-1.4000)
0.10831242054700851tensor(-1.4000)
0.19662587344646454tensor(-1.4000)
0.23342685401439667tensor(-1.4000)
0.16122306883335114tensor(-1.4000)
0.20262247323989868tensor(-1.4000)
0.21738378703594208tensor(-1.4000)
0.19353780150413513tensor(-1.4000)
0.175338014960289tensor(-1.4000)
0.10250048339366913tensor(-1.4000)
0.21409198641777039tensor(-1.4000)
0.16675226390361786tensor(-1.4000)
0.18013793230056763tensor(-1.4000)
0.17866423726081848tensor(-1.4000)
0.18774791061878204tensor(-1.4000)
0.15458254516124725tensor(-1.4000)
0.11543962359428406tensor(-1.4000)
0.09120766818523407tensor(-1.4000)
0.1743016541004181tensor(-1.4000)
0.157244011759758tensor(-1.4000)
0.23005342483520508tensor(-1.4000)
0.18022018671035767tensor(-1.4000)
0.20325253903865814tensor(-1.4000)
0.22680987417697906tensor(-1.4000)
0.13966774940490723tensor(-1.4000)
0.10991325229406357tensor(-1.4000)
0.1802423745393753tensor(-1.4000)
0.08269645273685455tensor(-1.4000)
0.20633278787136078tensor(-1.4000)
0.058743610978126526tensor(-1.4000)
0.14869365096092224tensor(-1.4000)
0.13300548493862152tensor(-1.4000)
0.1721627116203308tensor(-1.4000)
0.10570331662893295tensor(-1.4000)
0.12073323130607605tensor(-1.4000)
0.11153355985879898tensor(-1.4000)
0.12913087010383606tensor(-1.4000)
0.08667073398828506tensor(-1.4000)
0.12714077532291412tensor(-1.4000)
0.18276341259479523tensor(-1.4000)
0.21576432883739471tensor(-1.4000)
0.07808668911457062tensor(-1.4000)
0.12896841764450073tensor(-1.4000)
0.12417279928922653tensor(-1.4000)
0.11210360378026962tensor(-1.4000)
0.16934794187545776tensor(-1.4000)
0.1325509399175644tensor(-1.4000)
0.14905285835266113tensor(-1.4000)
0.16717134416103363tensor(-1.4000)
0.19527198374271393tensor(-1.4000)
0.14825309813022614tensor(-1.4000)
0.08907674998044968tensor(-1.4000)
0.20878057181835175tensor(-1.4000)
0.07409847527742386tensor(-1.4000)
0.1362782120704651tensor(-1.4000)
0.16764576733112335tensor(-1.4000)
0.08191366493701935tensor(-1.4000)
0.06916934996843338tensor(-1.4000)
0.1607770025730133tensor(-1.4000)
0.07078529894351959tensor(-1.4000)
0.19689233601093292tensor(-1.4000)
0.11547506600618362tensor(-1.4000)
0.1685848832130432tensor(-1.4000)
0.20524147152900696tensor(-1.4000)
0.13281570374965668tensor(-1.4000)
0.13624851405620575tensor(-1.4000)
0.1525818407535553tensor(-1.4000)
0.1263934224843979tensor(-1.4000)
0.1260002851486206tensor(-1.4000)
0.07392691820859909tensor(-1.4000)
0.12447301298379898tensor(-1.4000)
0.09575530886650085tensor(-1.4000)
0.13745637238025665tensor(-1.4000)
0.07287677377462387tensor(-1.4000)
0.1742180585861206tensor(-1.4000)
0.1903548240661621tensor(-1.4000)
0.09730196744203568tensor(-1.4000)
0.1397203952074051tensor(-1.4000)
0.14987586438655853tensor(-1.4000)
0.10178626328706741tensor(-1.4000)
0.16989384591579437tensor(-1.4000)
0.15606209635734558tensor(-1.4000)
0.09309280663728714tensor(-1.4000)
0.0937931165099144tensor(-1.4000)
0.054637644439935684tensor(-1.4000)
0.12710106372833252tensor(-1.4000)
0.11765030771493912tensor(-1.4000)
0.12575235962867737tensor(-1.4000)
0.1392623484134674tensor(-1.4000)
0.1448742300271988tensor(-1.4000)
0.19818653166294098tensor(-1.4000)
0.09382133930921555tensor(-1.4000)
0.1299571841955185tensor(-1.4000)
0.15727263689041138tensor(-1.4000)
0.142579585313797tensor(-1.4000)
0.12035498768091202tensor(-1.4000)
0.1286185383796692tensor(-1.4000)
0.13171575963497162tensor(-1.4000)
0.09373664855957031tensor(-1.4000)
0.11750004440546036tensor(-1.4000)
0.12428650259971619tensor(-1.4000)
0.16156210005283356tensor(-1.4000)
0.12191543728113174tensor(-1.4000)
0.0724460706114769tensor(-1.4000)
0.05808062478899956tensor(-1.4000)
0.15209142863750458tensor(-1.4000)
0.1089499220252037tensor(-1.4000)
0.08856511861085892tensor(-1.4000)
0.11731745302677155tensor(-1.4000)
0.12726080417633057tensor(-1.4000)
0.08782550692558289tensor(-1.4000)
0.20655718445777893tensor(-1.4000)
0.11749865859746933tensor(-1.4000)
0.06668199598789215tensor(-1.4000)
0.10751613229513168tensor(-1.4000)
0.15840649604797363tensor(-1.4000)
0.10902724415063858tensor(-1.4000)
0.15669625997543335tensor(-1.4000)
0.07089757174253464tensor(-1.4000)
0.1071370542049408tensor(-1.4000)
0.10469434410333633tensor(-1.4000)
0.11831481009721756tensor(-1.4000)
0.1280352771282196tensor(-1.4000)
0.12578454613685608tensor(-1.4000)
0.1737992912530899tensor(-1.4000)
0.10377100110054016tensor(-1.4000)
0.19784532487392426tensor(-1.4000)
0.11169712990522385tensor(-1.4000)
0.14666160941123962tensor(-1.4000)
0.14767655730247498tensor(-1.4000)
0.14788132905960083tensor(-1.4000)
0.1871473342180252tensor(-1.4000)
0.1781642735004425tensor(-1.4000)
0.12153360247612tensor(-1.4000)
0.11823394149541855tensor(-1.4000)
0.18283964693546295tensor(-1.4000)
0.14526239037513733tensor(-1.4000)
0.08960909396409988tensor(-1.4000)
0.11077781766653061tensor(-1.4000)
0.13248564302921295tensor(-1.4000)
0.16004255414009094tensor(-1.4000)
0.1342594176530838tensor(-1.4000)
0.15356256067752838tensor(-1.4000)
0.125799760222435tensor(-1.4000)
0.04012390598654747tensor(-1.4000)
0.12548796832561493tensor(-1.4000)
0.12255613505840302tensor(-1.4000)
0.08175145834684372tensor(-1.4000)
0.16544446349143982tensor(-1.4000)
0.08514518290758133tensor(-1.4000)
0.14815977215766907tensor(-1.4000)
0.10551521927118301tensor(-1.4000)
0.13534072041511536tensor(-1.4000)
0.18105573952198029tensor(-1.4000)
0.1605674922466278tensor(-1.4000)
0.16720785200595856tensor(-1.4000)
0.16008689999580383tensor(-1.4000)
0.20674900710582733tensor(-1.4000)
0.10112307965755463tensor(-1.4000)
0.18272261321544647tensor(-1.4000)
0.06253935396671295tensor(-1.4000)
0.0948859229683876tensor(-1.4000)
0.15155279636383057tensor(-1.4000)
0.14570774137973785tensor(-1.4000)
0.16854004561901093tensor(-1.4000)
0.15583118796348572tensor(-1.4000)
0.09935377538204193tensor(-1.4000)
0.18741530179977417tensor(-1.4000)
0.15177814662456512tensor(-1.4000)
0.04050232470035553tensor(-1.4000)
0.17271412909030914tensor(-1.4000)
0.13092057406902313tensor(-1.4000)
0.1573503017425537tensor(-1.4000)
0.11557706445455551tensor(-1.4000)
0.10907793045043945tensor(-1.4000)
0.16391795873641968tensor(-1.4000)
0.13670073449611664tensor(-1.4000)
0.18140612542629242tensor(-1.4000)
0.09316433221101761tensor(-1.4000)
0.21061508357524872tensor(-1.4000)
0.1133950799703598tensor(-1.4000)
0.22288580238819122tensor(-1.4000)
0.14317601919174194tensor(-1.4000)
0.0652846023440361tensor(-1.4000)
0.18447822332382202tensor(-1.4000)
0.1405191570520401tensor(-1.4000)
0.13348491489887238tensor(-1.4000)
0.19464720785617828tensor(-1.4000)
0.15290850400924683tensor(-1.4000)
0.13956701755523682tensor(-1.4000)
0.11778585612773895tensor(-1.4000)
0.10642138123512268tensor(-1.4000)
0.15204279124736786tensor(-1.4000)
0.1800820678472519tensor(-1.4000)
0.04540224000811577tensor(-1.4000)
0.2298641949892044tensor(-1.4000)
0.1900278627872467tensor(-1.4000)
0.17598798871040344tensor(-1.4000)
0.1259310394525528tensor(-1.4000)
0.17257598042488098tensor(-1.4000)
0.15827925503253937tensor(-1.4000)
0.11231179535388947tensor(-1.4000)
0.22363930940628052tensor(-1.4000)
0.21313905715942383tensor(-1.4000)
0.08076973259449005tensor(-1.4000)
0.06972601264715195tensor(-1.4000)
0.18263421952724457tensor(-1.4000)
0.09331613034009933tensor(-1.4000)
0.11659184843301773tensor(-1.4000)
0.11129886656999588tensor(-1.4000)
0.14642903208732605tensor(-1.4000)
0.18581369519233704tensor(-1.4000)
0.11132613569498062tensor(-1.4000)
0.11875514686107635tensor(-1.4000)
0.16103146970272064tensor(-1.4000)
0.09411323070526123tensor(-1.4000)
0.09732020646333694tensor(-1.4000)
0.08377823233604431tensor(-1.4000)
0.0811314657330513tensor(-1.4000)
0.1037508100271225tensor(-1.4000)
0.18255458772182465tensor(-1.4000)
0.22746682167053223tensor(-1.4000)
0.10717920958995819tensor(-1.4000)
0.12805111706256866tensor(-1.4000)
0.12158481031656265tensor(-1.4000)
0.08888454735279083tensor(-1.4000)
0.1393083781003952tensor(-1.4000)
0.15532617270946503tensor(-1.4000)
0.0874590277671814tensor(-1.4000)
0.09026636183261871tensor(-1.4000)
0.13398271799087524tensor(-1.4000)
0.07933359593153tensor(-1.4000)
0.14776404201984406tensor(-1.4000)
0.157814621925354tensor(-1.4000)
0.1769123673439026tensor(-1.4000)
0.13705462217330933tensor(-1.4000)
0.1698627471923828tensor(-1.4000)
0.1380678415298462tensor(-1.4000)
0.14581727981567383tensor(-1.4000)
0.1439208835363388tensor(-1.4000)
0.08975666761398315tensor(-1.4000)
0.11304191499948502tensor(-1.4000)
0.1419670581817627tensor(-1.4000)
0.1313786655664444tensor(-1.4000)
0.15690186619758606tensor(-1.4000)
0.1148262768983841tensor(-1.4000)
0.12586024403572083tensor(-1.4000)
0.12715326249599457tensor(-1.4000)
0.14815889298915863tensor(-1.4000)
0.11421119421720505tensor(-1.4000)
0.11487998813390732tensor(-1.4000)
0.13066256046295166tensor(-1.4000)
0.14355848729610443tensor(-1.4000)
0.12726642191410065tensor(-1.4000)
0.11397000402212143tensor(-1.4000)
0.10614313185214996tensor(-1.4000)
0.1320706307888031tensor(-1.4000)
0.11888203769922256tensor(-1.4000)
0.18129153549671173tensor(-1.4000)
0.11131028831005096tensor(-1.4000)
0.13050267100334167tensor(-1.4000)
0.20807667076587677tensor(-1.4000)
0.13306596875190735tensor(-1.4000)
0.1235070750117302tensor(-1.4000)
0.11237034946680069tensor(-1.4000)
0.1635822355747223tensor(-1.4000)
0.15200866758823395tensor(-1.4000)
0.17544996738433838tensor(-1.4000)
0.13760051131248474tensor(-1.4000)
0.11738287657499313tensor(-1.4000)
0.1990521401166916tensor(-1.4000)
0.15479110181331635tensor(-1.4000)
0.15255829691886902tensor(-1.4000)
0.15087860822677612tensor(-1.4000)
0.19976496696472168tensor(-1.4000)
0.17507492005825043tensor(-1.4000)
0.125637024641037tensor(-1.4000)
0.1769763082265854tensor(-1.4000)
0.04790610820055008tensor(-1.4000)
0.18300165235996246tensor(-1.4000)
0.12009185552597046tensor(-1.4000)
0.11729300767183304tensor(-1.4000)
0.06246931478381157tensor(-1.4000)
0.18689319491386414tensor(-1.4000)
0.20785409212112427tensor(-1.4000)
0.16131772100925446tensor(-1.4000)
0.08270902186632156tensor(-1.4000)
0.12135625630617142tensor(-1.4000)
0.12739388644695282tensor(-1.4000)
0.05983321741223335tensor(-1.4000)
0.19224940240383148tensor(-1.4000)
0.13345639407634735tensor(-1.4000)
0.16301573812961578tensor(-1.4000)
0.1624426394701004tensor(-1.4000)
0.2186237871646881tensor(-1.4000)
0.05806908756494522tensor(-1.4000)
0.06047409772872925tensor(-1.4000)
0.21925219893455505tensor(-1.4000)
0.10072176158428192tensor(-1.4000)
0.2256234735250473tensor(-1.4000)
0.17242015898227692tensor(-1.4000)
0.10644505172967911tensor(-1.4000)
0.03388858959078789tensor(-1.4000)
0.12109746783971786tensor(-1.4000)
0.08180077373981476tensor(-1.4000)
0.18478001654148102tensor(-1.4000)
0.17222101986408234tensor(-1.4000)
0.12858158349990845tensor(-1.4000)
0.172793909907341tensor(-1.4000)
0.20703096687793732tensor(-1.4000)
0.0412348136305809tensor(-1.4000)
0.10424396395683289tensor(-1.4000)
0.1493203192949295tensor(-1.4000)
0.17871743440628052tensor(-1.4000)
0.0681457668542862tensor(-1.4000)
0.1782546490430832tensor(-1.4000)
0.1369394063949585tensor(-1.4000)
0.11137989908456802tensor(-1.4000)
0.19548767805099487tensor(-1.4000)
0.12105495482683182tensor(-1.4000)
0.026740673929452896tensor(-1.4000)
0.19058437645435333tensor(-1.4000)
0.12104886025190353tensor(-1.4000)
0.21182966232299805tensor(-1.4000)
0.09818160533905029tensor(-1.4000)
0.14953704178333282tensor(-1.4000)
0.1541689932346344tensor(-1.4000)
0.23060408234596252tensor(-1.4000)
0.11497103422880173tensor(-1.4000)
0.10117099434137344tensor(-1.4000)
0.18151327967643738tensor(-1.4000)
0.20596599578857422tensor(-1.4000)
0.18960553407669067tensor(-1.4000)
0.15605919063091278tensor(-1.4000)
0.12817341089248657tensor(-1.4000)
0.17213843762874603tensor(-1.4000)
0.1739881932735443tensor(-1.4000)
0.18321426212787628tensor(-1.4000)
0.10323300957679749tensor(-1.4000)
0.112564817070961tensor(-1.4000)
0.18355455994606018tensor(-1.4000)
0.22364473342895508tensor(-1.4000)
0.17790921032428741tensor(-1.4000)
0.12207290530204773tensor(-1.4000)
0.22065965831279755tensor(-1.4000)
0.09092392027378082tensor(-1.4000)
0.12618224322795868tensor(-1.4000)
0.1087140366435051tensor(-1.4000)
0.1693919152021408tensor(-1.4000)
0.21153569221496582tensor(-1.4000)
0.10600173473358154tensor(-1.4000)
0.22544962167739868tensor(-1.4000)
0.11111805588006973tensor(-1.4000)
0.21921537816524506tensor(-1.4000)
0.12450163066387177tensor(-1.4000)
0.06659575551748276tensor(-1.4000)
0.11515053361654282tensor(-1.4000)
0.20267286896705627tensor(-1.4000)
0.10856080055236816tensor(-1.4000)
0.1852840781211853tensor(-1.4000)
0.20300240814685822tensor(-1.4000)
0.15987399220466614tensor(-1.4000)
0.11501669883728027tensor(-1.4000)
0.11348457634449005tensor(-1.4000)
0.06676165759563446tensor(-1.4000)
0.158355250954628tensor(-1.4000)
0.11269920319318771tensor(-1.4000)
0.23200111091136932tensor(-1.4000)
0.15119963884353638tensor(-1.4000)
0.15526096522808075tensor(-1.4000)
0.166978120803833tensor(-1.4000)
0.1884099543094635tensor(-1.4000)
0.19763988256454468tensor(-1.4000)
0.1869359314441681tensor(-1.4000)
0.09274591505527496tensor(-1.4000)
0.20799069106578827tensor(-1.4000)
0.10648659616708755tensor(-1.4000)
0.1777172088623047tensor(-1.4000)
0.17580686509609222tensor(-1.4000)
0.15096545219421387tensor(-1.4000)
0.11707618832588196tensor(-1.4000)
0.16329284012317657tensor(-1.4000)
0.187707781791687tensor(-1.4000)
0.21725201606750488tensor(-1.4000)
0.16181087493896484tensor(-1.4000)
0.153451606631279tensor(-1.4000)
0.09571942687034607tensor(-1.4000)
0.13260146975517273tensor(-1.4000)
0.15036189556121826tensor(-1.4000)
0.044487129896879196tensor(-1.4000)
0.18588383495807648tensor(-1.4000)
0.21176715195178986tensor(-1.4000)
0.08251333981752396tensor(-1.4000)
0.07753701508045197tensor(-1.4000)
0.1768852323293686tensor(-1.4000)
0.09285800904035568tensor(-1.4000)
0.2095491886138916tensor(-1.4000)
0.18477150797843933tensor(-1.4000)
0.18724805116653442tensor(-1.4000)
0.047304317355155945tensor(-1.4000)
0.19302727282047272tensor(-1.4000)
0.10794131457805634tensor(-1.4000)
0.11905726045370102tensor(-1.4000)
0.15376873314380646tensor(-1.4000)
0.21390457451343536tensor(-1.4000)
0.0864318460226059tensor(-1.4000)
0.17852561175823212tensor(-1.4000)
0.19985905289649963tensor(-1.4000)
0.1977081596851349tensor(-1.4000)
0.16470207273960114tensor(-1.4000)
0.21152491867542267tensor(-1.4000)
0.21378009021282196tensor(-1.4000)
0.10376636683940887tensor(-1.4000)
0.11803744733333588tensor(-1.4000)
0.055335961282253265tensor(-1.4000)
0.1315871775150299tensor(-1.4000)
0.17133992910385132tensor(-1.4000)
0.13312910497188568tensor(-1.4000)
0.05271201208233833tensor(-1.4000)
0.10903250426054001tensor(-1.4000)
0.1781667321920395tensor(-1.4000)
0.1237858235836029tensor(-1.4000)
0.22020120918750763tensor(-1.4000)
0.12145473808050156tensor(-1.4000)
0.14897306263446808tensor(-1.4000)
0.04423157498240471tensor(-1.4000)
0.060520946979522705tensor(-1.4000)
0.12493184953927994tensor(-1.4000)
0.166920006275177tensor(-1.4000)
0.10804369300603867tensor(-1.4000)
0.19872327148914337tensor(-1.4000)
0.20443934202194214tensor(-1.4000)
0.1180497258901596tensor(-1.4000)
0.032196760177612305tensor(-1.4000)
0.12576888501644135tensor(-1.4000)
0.18522872030735016tensor(-1.4000)
0.12040889263153076tensor(-1.4000)
0.13335879147052765tensor(-1.4000)
0.2051149606704712tensor(-1.4000)
0.1492966115474701tensor(-1.4000)
0.11161718517541885tensor(-1.4000)
0.09832677245140076tensor(-1.4000)
0.08810219168663025tensor(-1.4000)
0.03268130123615265tensor(-1.4000)
0.18879888951778412tensor(-1.4000)
0.2041659653186798tensor(-1.4000)
0.1964092254638672tensor(-1.4000)
0.22516028583049774tensor(-1.4000)
0.18231448531150818tensor(-1.4000)
0.19337761402130127tensor(-1.4000)
0.12987753748893738tensor(-1.4000)
0.18814271688461304tensor(-1.4000)
0.16125118732452393tensor(-1.4000)
0.06999798864126205tensor(-1.4000)
0.11608658730983734tensor(-1.4000)
0.1043393611907959tensor(-1.4000)
0.15100082755088806tensor(-1.4000)
0.10932425409555435tensor(-1.4000)
0.07428095489740372tensor(-1.4000)
0.1494790017604828tensor(-1.4000)
0.2179160863161087tensor(-1.4000)
0.20505349338054657tensor(-1.4000)
0.20180398225784302tensor(-1.4000)
0.22445733845233917tensor(-1.4000)
0.0542246475815773tensor(-1.4000)
0.18299460411071777tensor(-1.4000)
0.11566068977117538tensor(-1.4000)
0.13244874775409698tensor(-1.4000)
0.19903533160686493tensor(-1.4000)
0.2354874610900879tensor(-1.4000)
0.1807301938533783tensor(-1.4000)
0.20942750573158264tensor(-1.4000)
0.1686490774154663tensor(-1.4000)
0.14575563371181488tensor(-1.4000)
0.18876169621944427tensor(-1.4000)
0.18914702534675598tensor(-1.4000)
0.21201974153518677tensor(-1.4000)
0.09771209210157394tensor(-1.4000)
0.1998753398656845tensor(-1.4000)
0.1961812674999237tensor(-1.4000)
0.15412884950637817tensor(-1.4000)
0.15901531279087067tensor(-1.4000)
0.12347440421581268tensor(-1.4000)
0.12675902247428894tensor(-1.4000)
0.14057211577892303tensor(-1.4000)
0.13066335022449493tensor(-1.4000)
0.19949178397655487tensor(-1.4000)
0.12330269813537598tensor(-1.4000)
0.15955939888954163tensor(-1.4000)
0.07387842983007431tensor(-1.4000)
0.22686630487442017tensor(-1.4000)
0.19000205397605896tensor(-1.4000)
0.19186943769454956tensor(-1.4000)
0.16586774587631226tensor(-1.4000)
0.19714605808258057tensor(-1.4000)
0.23020964860916138tensor(-1.4000)
0.20008771121501923tensor(-1.4000)
0.21777434647083282tensor(-1.4000)
0.09958566725254059tensor(-1.4000)
0.1954057663679123tensor(-1.4000)
0.14568611979484558tensor(-1.4000)
0.19697758555412292tensor(-1.4000)
0.22730258107185364tensor(-1.4000)
0.13214226067066193tensor(-1.4000)
0.1393117904663086tensor(-1.4000)
0.1172405257821083tensor(-1.4000)
0.17935402691364288tensor(-1.4000)
0.041207652539014816tensor(-1.4000)
0.09430637210607529tensor(-1.4000)
0.2218853235244751tensor(-1.4000)
0.181149423122406tensor(-1.4000)
0.15222740173339844tensor(-1.4000)
0.18960890173912048tensor(-1.4000)
0.05145599693059921tensor(-1.4000)
0.1526564508676529tensor(-1.4000)
0.057561151683330536tensor(-1.4000)
0.07141795009374619tensor(-1.4000)
0.1574305146932602tensor(-1.4000)
0.09459097683429718tensor(-1.4000)
0.07123980671167374tensor(-1.4000)
0.11760599166154861tensor(-1.4000)
0.03917185217142105tensor(-1.4000)
0.07128971070051193tensor(-1.4000)
0.1225258857011795tensor(-1.4000)
assignment2/output/Q3_non_crossing_val.txt 0 → 100644
View file @ 98dae565
0.21952109038829803tensor(1.)
0.23782777786254883tensor(0.9000)
0.22611626982688904tensor(1.)
0.20487292110919952tensor(-0.0400)
0.20857982337474823tensor(0.1000)
0.19488589465618134tensor(0.0460)
0.21176186203956604tensor(1.)
0.20323744416236877tensor(-0.0668)
0.22220832109451294tensor(0.5000)
0.17408636212348938tensor(1.)
0.22654947638511658tensor(0.2800)
0.22811055183410645tensor(-0.3668)
0.2148173451423645tensor(1.)
0.2188432663679123tensor(1.)
0.2072826772928238tensor(0.9636)
0.20535282790660858tensor(-0.6800)
0.20276841521263123tensor(-0.0400)
0.21137219667434692tensor(1.)
0.2177630066871643tensor(0.6000)
0.21471473574638367tensor(-0.7456)
0.20317435264587402tensor(0.2000)
0.2298237532377243tensor(-0.3144)
0.210434228181839tensor(0.2800)
0.21239811182022095tensor(-0.1332)
0.2127140313386917tensor(-0.6000)
0.2312510758638382tensor(-0.2332)
0.20917381346225739tensor(0.7000)
0.18962012231349945tensor(0.2000)
0.1855674386024475tensor(-0.6000)
0.19451506435871124tensor(-0.7600)
0.17294295132160187tensor(0.0400)
0.2014499306678772tensor(1.)
0.209279865026474tensor(0.8400)
0.19915391504764557tensor(1.)
0.20909467339515686tensor(0.9200)
0.2131439745426178tensor(0.5200)
0.19119079411029816tensor(1.)
0.19854183495044708tensor(1.)
0.2013477087020874tensor(0.6800)
0.21653999388217926tensor(-0.4400)
0.15919999778270721tensor(0.4400)
0.21264298260211945tensor(0.1200)
0.18273192644119263tensor(-0.3600)
0.21713757514953613tensor(0.2000)
0.21038496494293213tensor(-0.4400)
0.12800642848014832tensor(-0.9000)
0.14271655678749084tensor(-0.9000)
0.13734029233455658tensor(-1.)
0.18281681835651398tensor(0.6000)
0.1977405846118927tensor(0.8000)
0.14425887167453766tensor(-0.8000)
0.2041633576154709tensor(0.5200)
0.18560700118541718tensor(0.9200)
0.1734110414981842tensor(1.)
0.1612943559885025tensor(-0.9000)
0.20057038962841034tensor(-0.5200)
0.17363129556179047tensor(-0.7600)
0.15812665224075317tensor(-0.6800)
0.1882983297109604tensor(0.5200)
0.10606071352958679tensor(-1.)
0.12680551409721375tensor(0.4000)
0.1440865397453308tensor(0.8000)
0.12657012045383453tensor(0.1200)
0.21740467846393585tensor(0.5200)
0.20710618793964386tensor(0.5200)
0.18154871463775635tensor(-1.)
0.1863163709640503tensor(0.6000)
0.20473028719425201tensor(0.7000)
0.1706537902355194tensor(0.1248)
0.21361090242862701tensor(0.7000)
0.1792641431093216tensor(0.2000)
0.1275566965341568tensor(-0.6000)
0.16319316625595093tensor(0.5000)
0.1803625226020813tensor(-1.)
0.0989532545208931tensor(-0.8400)
0.18354813754558563tensor(0.6000)
0.16619978845119476tensor(0.1200)
0.1803804636001587tensor(0.5000)
0.18132324516773224tensor(-0.5384)
0.1719921976327896tensor(0.1000)
0.1980201154947281tensor(0.1200)
0.11704200506210327tensor(-1.)
0.1526172161102295tensor(-1.)
0.1510888934135437tensor(0.3600)
0.1829804629087448tensor(1.)
0.1584928333759308tensor(-0.6800)
0.1488611102104187tensor(-0.1000)
0.13696132600307465tensor(0.1000)
0.1341213881969452tensor(0.8000)
0.16979573667049408tensor(0.0400)
0.15451394021511078tensor(0.5200)
0.16003933548927307tensor(0.1200)
0.13384783267974854tensor(-0.3600)
0.15077005326747894tensor(-1.)
0.16401468217372894tensor(-0.4668)
0.16393303871154785tensor(0.0400)
0.17549139261245728tensor(-0.7600)
0.14981071650981903tensor(-1.)
0.11813931167125702tensor(-1.)
0.12317473441362381tensor(0.2000)
0.12132212519645691tensor(-1.)
0.15419535338878632tensor(-0.9200)
0.17724628746509552tensor(0.2800)
0.16117659211158752tensor(-0.2000)
0.13400578498840332tensor(-1.)
0.15760385990142822tensor(0.2000)
0.1972872018814087tensor(0.2364)
0.1749957650899887tensor(0.1000)
0.16320721805095673tensor(-0.5200)
0.06988326460123062tensor(-0.8000)
0.1444753259420395tensor(-0.9000)
0.14351274073123932tensor(-1.)
0.04320165142416954tensor(-1.)
0.1422291249036789tensor(-0.8000)
0.14577536284923553tensor(-1.)
0.12661631405353546tensor(-0.7600)
0.14432793855667114tensor(0.6000)
0.15439939498901367tensor(0.3600)
0.11530300974845886tensor(-1.)
0.12186361104249954tensor(-1.)
0.1254180371761322tensor(-0.6000)
0.15393532812595367tensor(-1.)
0.1406518518924713tensor(-0.0400)
0.12210708856582642tensor(-0.8400)
0.15302293002605438tensor(-0.2800)
0.08761551976203918tensor(0.)
0.09963203966617584tensor(-0.9668)
0.06123895198106766tensor(-1.)
0.1301640123128891tensor(-0.6000)
0.12276408821344376tensor(0.8000)
0.06715775281190872tensor(-1.)
0.2153923660516739tensor(0.4400)
0.23494695127010345tensor(1.)
0.21139885485172272tensor(1.)
0.2140115648508072tensor(1.)
0.2140115648508072tensor(1.)
0.22464847564697266tensor(0.5200)
0.1974422186613083tensor(1.)
0.21319794654846191tensor(0.)
0.2255629152059555tensor(0.5296)
0.20792533457279205tensor(-0.3600)
0.23402009904384613tensor(1.)
0.21507686376571655tensor(1.)
0.1925256848335266tensor(-0.0500)
0.20486827194690704tensor(1.)
0.22330422699451447tensor(-0.5000)
0.19493475556373596tensor(-0.5000)
0.18902906775474548tensor(0.2000)
0.21239811182022095tensor(-0.1332)
0.22126026451587677tensor(-0.4400)
0.2037319391965866tensor(0.6000)
0.22771777212619781tensor(0.4400)
0.20628510415554047tensor(0.9428)
0.19800761342048645tensor(-0.8400)
0.20475606620311737tensor(-0.9200)
0.21628090739250183tensor(-0.4800)
0.21490231156349182tensor(-0.5200)
0.21490231156349182tensor(-0.5200)
0.2119624763727188tensor(0.7600)
0.21669089794158936tensor(-0.2800)
0.2167542725801468tensor(-0.4400)
0.20590904355049133tensor(-0.8400)
0.17366120219230652tensor(0.7600)
0.17896521091461182tensor(0.3000)
0.1973838061094284tensor(-0.6888)
0.18931002914905548tensor(-0.1000)
0.17367437481880188tensor(0.1000)
0.1899791657924652tensor(-0.2000)
0.18172775208950043tensor(-0.6668)
0.17987360060214996tensor(0.5000)
0.20243671536445618tensor(-0.2800)
0.14789125323295593tensor(0.7332)
0.2000957429409027tensor(0.6856)
0.12851615250110626tensor(-1.)
0.1895725131034851tensor(0.3600)
0.19307856261730194tensor(0.6800)
0.17303505539894104tensor(-0.6800)
0.17950062453746796tensor(0.6800)
0.13775955140590668tensor(0.1000)
0.19438917934894562tensor(-0.1200)
0.15612705051898956tensor(-0.5200)
0.1904379427433014tensor(-0.6000)
0.19095955789089203tensor(-0.3868)
0.20404282212257385tensor(-0.9200)
0.14525079727172852tensor(0.2000)
0.18667253851890564tensor(0.5200)
0.16019761562347412tensor(-0.7000)
0.20511570572853088tensor(0.2000)
0.1653820127248764tensor(-0.8400)
0.16393809020519257tensor(0.3600)
0.15630830824375153tensor(0.5200)
0.20624804496765137tensor(-0.4400)
0.21458958089351654tensor(0.3600)
0.12776941061019897tensor(0.2800)
0.17695868015289307tensor(-0.0668)
0.170821875333786tensor(-0.6800)
0.15638796985149384tensor(-1.)
0.18088488280773163tensor(0.2000)
0.17886961996555328tensor(0.5000)
0.1300341635942459tensor(-1.)
0.1662473827600479tensor(-1.)
0.13839593529701233tensor(-1.)
0.1375056654214859tensor(-0.8000)
0.1986047476530075tensor(-0.8000)
0.16522769629955292tensor(-0.8400)
0.10768487304449081tensor(-0.7000)
0.13424314558506012tensor(0.)
0.21157892048358917tensor(-0.2800)
0.07559621334075928tensor(0.)
0.15435351431369781tensor(-0.8000)
0.1254611760377884tensor(-0.8400)
0.16923093795776367tensor(-0.5200)
0.18232788145542145tensor(0.1000)
0.14047151803970337tensor(-0.8400)
0.18700923025608063tensor(-0.7600)
0.14317211508750916tensor(-0.6800)
0.14336687326431274tensor(0.2000)
0.1366833746433258tensor(-0.8400)
0.18293538689613342tensor(0.2800)
0.20795214176177979tensor(-0.5000)
0.18053756654262543tensor(1.)
0.1593831479549408tensor(-0.9200)
0.18588058650493622tensor(0.2000)
0.14985297620296478tensor(-0.4400)
0.12510165572166443tensor(0.5200)
0.15611417591571808tensor(0.1000)
0.11351925879716873tensor(-1.)
0.1352771371603012tensor(0.1200)
0.14462806284427643tensor(-1.)
0.11183277517557144tensor(-1.)
0.14178472757339478tensor(0.0400)
0.1547503024339676tensor(-0.9600)
0.11967171728610992tensor(-1.)
0.11038559675216675tensor(-0.2000)
0.16192662715911865tensor(0.5000)
0.12391580641269684tensor(0.2400)
0.17649534344673157tensor(0.0768)
0.1597699522972107tensor(0.7716)
0.1679179072380066tensor(1.)
0.12027426809072495tensor(-1.)
0.12472488731145859tensor(-0.6800)
0.14868344366550446tensor(-0.4668)
0.134051114320755tensor(-1.)
0.10038960725069046tensor(-0.7600)
0.09572199732065201tensor(-1.)
0.09107469022274017tensor(-1.)
0.1316457837820053tensor(-1.)
0.10456763207912445tensor(0.3600)
0.1865563541650772tensor(-0.1200)
0.1358366310596466tensor(-1.)
0.19614967703819275tensor(0.4400)
0.11329727619886398tensor(-1.)
0.1729498654603958tensor(-0.4400)
0.14331288635730743tensor(-0.1200)
0.12418988347053528tensor(0.5200)
0.18807271122932434tensor(0.0400)
0.10656706243753433tensor(-0.8400)
0.2013329714536667tensor(0.6000)
0.18385165929794312tensor(-0.1000)
0.14565549790859222tensor(0.2800)
0.13471035659313202tensor(-1.)
0.14519040286540985tensor(-0.8400)
0.17722895741462708tensor(0.5200)
0.20739325881004333tensor(-0.6800)
0.21891023218631744tensor(0.6800)
0.19898292422294617tensor(0.4400)
0.1316789835691452tensor(-0.2000)
0.15652307868003845tensor(0.6800)
0.09026879072189331tensor(0.3600)
0.1540524661540985tensor(0.2000)
0.19549717009067535tensor(0.3600)
0.2251725196838379tensor(0.4400)
0.1755216270685196tensor(0.2000)
0.19446487724781036tensor(-0.0400)
0.17131613194942474tensor(-0.2000)
0.20776869356632233tensor(0.6000)
0.039152223616838455tensor(-1.)
0.17970803380012512tensor(0.6000)
0.18830449879169464tensor(0.6800)
0.15720151364803314tensor(-0.6800)
0.15758894383907318tensor(-0.1200)
0.1848653107881546tensor(0.5200)
0.18562225997447968tensor(0.2800)
0.20226624608039856tensor(0.7600)
0.14094308018684387tensor(-1.)
0.09520897269248962tensor(-0.4400)
0.15200679004192352tensor(0.0400)
0.1659354418516159tensor(-0.8400)
0.0856734961271286tensor(-1.)
0.1422804594039917tensor(0.2800)
0.18697458505630493tensor(0.8400)
0.10866742581129074tensor(-1.)
0.1433115303516388tensor(-0.7600)
0.1490950733423233tensor(-0.5200)
0.2243758738040924tensor(0.5200)
0.18240445852279663tensor(0.2800)
0.17145468294620514tensor(0.0400)
0.18332433700561523tensor(1.)
0.1365073323249817tensor(-1.)
0.1857757419347763tensor(0.2800)
0.1981777399778366tensor(0.6000)
0.18982474505901337tensor(-0.5200)
0.2125098705291748tensor(0.7600)
0.17283837497234344tensor(0.3600)
0.21480412781238556tensor(0.7600)
0.2108636498451233tensor(-0.6800)
0.18919900059700012tensor(0.8400)
0.20582839846611023tensor(0.6800)
0.09318532049655914tensor(-1.)
0.13205993175506592tensor(0.2800)
0.12721742689609528tensor(-1.)
0.16318939626216888tensor(-0.4400)
0.14603199064731598tensor(0.2800)
0.1759437918663025tensor(-0.2800)
0.16388745605945587tensor(-0.2800)
0.08655139058828354tensor(-1.)
0.17535506188869476tensor(0.5200)
0.16123969852924347tensor(0.5200)
0.20193597674369812tensor(0.6000)
0.11664671450853348tensor(-1.)
0.20437943935394287tensor(0.2800)
0.17186793684959412tensor(-0.3600)
0.12383332848548889tensor(0.4400)
0.167347714304924tensor(-0.6000)
0.16962990164756775tensor(0.4400)
0.12689244747161865tensor(-0.2000)
0.2004716992378235tensor(0.4400)
0.19749660789966583tensor(0.6000)
0.15616647899150848tensor(0.4400)
0.12055361270904541tensor(0.2800)
0.18859808146953583tensor(0.1200)
0.11611733585596085tensor(-0.0400)
0.17857807874679565tensor(0.4400)
0.10726254433393478tensor(-0.2800)
0.09590332955121994tensor(-0.9200)
0.17341817915439606tensor(0.3600)
0.15045017004013062tensor(-0.5200)
0.09832731634378433tensor(-0.4400)
0.15005944669246674tensor(0.0400)
0.19138266146183014tensor(-0.0400)
0.13605545461177826tensor(-1.)
0.18580646812915802tensor(0.6000)
0.13740459084510803tensor(0.2000)
0.23495334386825562tensor(0.6000)
0.1618029624223709tensor(0.6000)
0.1931174099445343tensor(-0.2000)
0.11645138263702393tensor(0.6000)
0.18135736882686615tensor(-0.1200)
0.18194840848445892tensor(-0.1200)
0.15510909259319305tensor(0.3600)
0.2184053659439087tensor(0.2800)
0.21103371679782867tensor(0.6000)
0.2033967673778534tensor(0.2800)
0.17762808501720428tensor(0.5200)
0.19069059193134308tensor(0.2000)
0.15333421528339386tensor(0.6000)
0.22145961225032806tensor(-0.1200)
0.2213836908340454tensor(0.3600)
0.22715269029140472tensor(0.5200)
0.11528938263654709tensor(-1.)
0.08748601377010345tensor(-1.)
0.22091121971607208tensor(0.9200)
0.11106610298156738tensor(0.2000)
0.13209114968776703tensor(-0.6800)
0.20159970223903656tensor(0.6800)
0.20199374854564667tensor(0.6800)
0.13471904397010803tensor(-1.)
0.1832374781370163tensor(0.6800)
0.19927720725536346tensor(0.8400)
0.15462526679039001tensor(1.)
0.12945470213890076tensor(-1.)
0.11942054331302643tensor(0.2800)
0.20199275016784668tensor(0.0400)
0.1908886432647705tensor(-0.1200)
0.23125487565994263tensor(0.8400)
0.12787982821464539tensor(1.)
0.1947147399187088tensor(0.8400)
0.16320012509822845tensor(0.8400)
0.22123058140277863tensor(0.9200)
0.1456812173128128tensor(-0.7600)
0.07407398521900177tensor(-0.9200)
0.19859476387500763tensor(-0.1200)
0.1360691487789154tensor(0.9200)
0.10157038271427155tensor(-0.5200)
0.17238971590995789tensor(-0.1200)
0.19093972444534302tensor(0.8400)
0.17866188287734985tensor(0.0400)
0.19757629930973053tensor(0.4400)
0.12266788631677628tensor(-0.6800)
0.22512292861938477tensor(-0.0400)
0.20251554250717163tensor(0.9200)
0.20013198256492615tensor(1.)
0.13609187304973602tensor(-0.8400)
0.15574251115322113tensor(0.2800)
0.16234798729419708tensor(-0.2800)
0.20681166648864746tensor(0.2800)
0.2104719877243042tensor(0.3600)
0.22338435053825378tensor(0.9200)
0.19375619292259216tensor(0.6800)
0.2052852064371109tensor(0.0400)
0.15243327617645264tensor(-0.1200)
0.11913357675075531tensor(-0.4400)
0.17727136611938477tensor(0.0400)
0.12745758891105652tensor(-0.5200)
0.18232029676437378tensor(-0.2800)
0.1626170426607132tensor(0.6000)
0.13294187188148499tensor(-0.8400)
0.08278171718120575tensor(-1.)
0.12398846447467804tensor(-0.8400)
0.21471112966537476tensor(0.2000)
0.2262236475944519tensor(0.6000)
0.15860918164253235tensor(-0.4400)
0.11407069116830826tensor(-0.8400)
0.1275598108768463tensor(-0.0400)
0.09927322715520859tensor(0.1200)
0.20882894098758698tensor(0.8400)
0.21688489615917206tensor(0.6800)
0.1647098809480667tensor(0.2800)
0.14783933758735657tensor(-0.5200)
0.19567745923995972tensor(-0.2800)
0.15859104692935944tensor(0.0400)
0.21403411030769348tensor(-0.3600)
0.10385552048683167tensor(-1.)
0.2079208940267563tensor(0.5200)
0.20125024020671844tensor(0.6000)
0.22828061878681183tensor(0.9200)
0.20388081669807434tensor(-0.0400)
0.2013065665960312tensor(1.)
0.14398251473903656tensor(-1.)
0.16815143823623657tensor(0.4400)
0.18155662715435028tensor(-0.0400)
0.2178577333688736tensor(0.6800)
0.14676328003406525tensor(-0.9200)
0.17797748744487762tensor(0.6800)
0.21926996111869812tensor(0.2800)
0.1411927342414856tensor(-0.7600)
0.15884166955947876tensor(-0.3600)
0.11957917362451553tensor(-1.)
0.17641808092594147tensor(-0.4400)
0.22348210215568542tensor(0.9200)
0.20924445986747742tensor(0.6000)
0.1582152098417282tensor(0.2000)
0.09179122000932693tensor(-0.9200)
0.12855854630470276tensor(-0.3600)
0.16475142538547516tensor(0.7600)
0.22978535294532776tensor(0.9200)
0.13445451855659485tensor(-0.8400)
0.10635241121053696tensor(-0.0400)
0.19978567957878113tensor(0.2000)
0.17759105563163757tensor(-0.2000)
0.20906542241573334tensor(0.8400)
0.15597577393054962tensor(-0.6000)
0.19239096343517303tensor(1.)
0.09750203043222427tensor(-0.4400)
0.14727512001991272tensor(-1.)
0.18051017820835114tensor(0.2000)
0.11252055317163467tensor(-0.7600)
0.08297224342823029tensor(-1.)
0.20134888589382172tensor(0.0400)
0.15197709202766418tensor(-0.5200)
0.1852095127105713tensor(1.)
0.157537043094635tensor(0.3600)
0.12354758381843567tensor(-0.6800)
0.11638432741165161tensor(-0.6800)
0.13662727177143097tensor(-0.6000)
0.17577864229679108tensor(-1.)
0.115805484354496tensor(-1.)
0.1514233946800232tensor(-0.2000)
0.16102229058742523tensor(-0.0400)
0.17351694405078888tensor(-0.1200)
0.1651153564453125tensor(0.5200)
0.14351250231266022tensor(-0.1200)
0.1311900019645691tensor(0.8400)
0.12280235439538956tensor(0.1200)
0.10939732939004898tensor(-0.2000)
0.2157556116580963tensor(0.6800)
0.20947593450546265tensor(0.8000)
0.11183619499206543tensor(-1.)
0.1797509640455246tensor(-0.2800)
0.22263923287391663tensor(0.6800)
0.1939178705215454tensor(0.6800)
0.10361939668655396tensor(-0.4400)
0.10807380080223083tensor(0.4400)
0.12068158388137817tensor(-0.7600)
0.1741792857646942tensor(1.)
0.1618080735206604tensor(-0.6800)
0.1190752163529396tensor(-1.)
0.11395204812288284tensor(-1.)
0.17862311005592346tensor(-0.2800)
0.12561814486980438tensor(-0.6800)
0.20182505249977112tensor(0.1200)
0.0751802995800972tensor(-1.)
0.1919555962085724tensor(-0.6800)
0.1356445699930191tensor(-0.2800)
0.1681707799434662tensor(-0.4400)
0.1738000065088272tensor(-0.2800)
0.1797473430633545tensor(-0.0400)
0.1637478619813919tensor(-0.3600)
0.19011719524860382tensor(0.5200)
0.2067737579345703tensor(0.9200)
0.10261224955320358tensor(-0.9200)
0.1367662400007248tensor(-0.6000)
0.11655715852975845tensor(-0.8400)
0.11579441279172897tensor(-0.8400)
0.20960988104343414tensor(0.6000)
0.1868198812007904tensor(0.3600)
0.17722055315971375tensor(0.2000)
0.21503762900829315tensor(-0.2800)
0.13673166930675507tensor(-0.1200)
0.22107824683189392tensor(0.4400)
0.1512995809316635tensor(-0.8400)
0.09790536761283875tensor(-0.9200)
0.2341938465833664tensor(0.5200)
0.2303488552570343tensor(0.6800)
0.1091434434056282tensor(-0.5200)
0.21080972254276276tensor(0.3600)
0.17831459641456604tensor(-0.0400)
0.23258942365646362tensor(0.2800)
0.22255149483680725tensor(-0.1200)
0.23520246148109436tensor(0.7600)
0.178996741771698tensor(0.0400)
0.12253978848457336tensor(-0.9200)
0.16016776859760284tensor(0.2800)
0.1513330638408661tensor(-0.1200)
0.17617714405059814tensor(1.)
0.16498157382011414tensor(1.)
0.12545861303806305tensor(-0.1200)
0.12865401804447174tensor(-0.8400)
0.12001704424619675tensor(-0.6000)
0.10244826227426529tensor(-0.1200)
0.12188634276390076tensor(-0.2000)
0.18493735790252686tensor(-0.5200)
0.15144576132297516tensor(0.6000)
0.22380149364471436tensor(-0.6000)
0.23608185350894928tensor(0.6800)
0.1693858951330185tensor(0.3600)
0.1806410551071167tensor(0.6000)
0.23818789422512054tensor(0.9200)
0.13107191026210785tensor(-0.8400)
0.21519477665424347tensor(0.7600)
0.09579803049564362tensor(-0.8400)
0.12460693717002869tensor(-1.)
0.19143891334533691tensor(0.2000)
0.22535160183906555tensor(0.8400)
0.1424633115530014tensor(-0.8400)
0.16589730978012085tensor(-0.4400)
0.19005964696407318tensor(0.2000)
0.20769357681274414tensor(-0.3600)
0.20768888294696808tensor(-0.0400)
0.11918934434652328tensor(-1.)
0.18896065652370453tensor(-0.3600)
0.1550711840391159tensor(-0.3600)
0.11359503865242004tensor(-0.9200)
0.18507231771945953tensor(-0.1200)
0.20225369930267334tensor(-0.2000)
0.13334433734416962tensor(-0.8400)
0.22490032017230988tensor(0.9200)
0.18165023624897003tensor(-0.2000)
0.08319410681724548tensor(-0.9200)
0.18868525326251984tensor(0.5200)
0.15806885063648224tensor(-0.1200)
0.1726353019475937tensor(-0.2000)
0.23531073331832886tensor(0.6000)
0.1808777004480362tensor(0.8400)
0.1411917805671692tensor(-0.4400)
0.10940553992986679tensor(-0.6800)
0.1769331693649292tensor(-0.7600)
0.10769849270582199tensor(-0.5200)
0.14710453152656555tensor(-0.3600)
0.11197832226753235tensor(-1.)
0.14165420830249786tensor(-0.7600)
0.1524682492017746tensor(0.2000)
0.22128701210021973tensor(-0.0400)
0.0970553457736969tensor(-0.7600)
0.1937704086303711tensor(0.8400)
0.14527584612369537tensor(-0.7600)
0.21699953079223633tensor(0.9200)
0.21442368626594543tensor(1.)
0.14488953351974487tensor(-0.9200)
0.1578524261713028tensor(-0.7600)
0.16005855798721313tensor(-0.6800)
0.11036648601293564tensor(0.4400)
0.045620452612638474tensor(-1.)
0.22140063345432281tensor(0.7600)
0.22342608869075775tensor(0.8400)
0.10446999967098236tensor(0.1200)
0.20271332561969757tensor(0.6000)
0.12388812005519867tensor(-0.6000)
0.21809136867523193tensor(1.)
0.1308685690164566tensor(-0.8400)
0.07442247867584229tensor(-0.6800)
0.13659796118736267tensor(-0.8400)
0.10851002484560013tensor(-1.)
0.10051233321428299tensor(-0.3600)
0.12606015801429749tensor(-0.3600)
0.2163524180650711tensor(0.4400)
0.1632906198501587tensor(0.2000)
0.16882477700710297tensor(0.1200)
0.1434861272573471tensor(-0.9200)
0.12150558829307556tensor(-0.6000)
0.1636759489774704tensor(-0.4400)
0.22352425754070282tensor(0.1200)
0.132660374045372tensor(0.2000)
0.10443982481956482tensor(-0.3600)
0.21587954461574554tensor(0.1200)
0.22517187893390656tensor(0.6000)
0.1714743971824646tensor(-0.6800)
0.17290149629116058tensor(-0.2000)
0.2372768223285675tensor(0.7600)
0.1682676523923874tensor(0.8400)
0.21441873908042908tensor(-0.0400)
0.21754103899002075tensor(0.7600)
0.12853461503982544tensor(-0.6800)
0.16926608979701996tensor(0.5200)
0.10601773858070374tensor(-0.4400)
0.1565149575471878tensor(0.1200)
0.14192146062850952tensor(0.2000)
0.23315051198005676tensor(-0.3600)
0.15998832881450653tensor(-0.6000)
0.1433938443660736tensor(0.0400)
0.1371707171201706tensor(-0.3600)
0.1512061506509781tensor(-0.9200)
0.20368361473083496tensor(0.2000)
0.22452247142791748tensor(0.2000)
0.17979709804058075tensor(0.2800)
0.11860068887472153tensor(0.2000)
0.06996510922908783tensor(-0.7600)
0.06677684932947159tensor(-0.6000)
0.06894636899232864tensor(-0.7600)
0.11733776330947876tensor(-0.6800)
0.08184415102005005tensor(-0.0400)
0.06686651706695557tensor(-0.6000)
0.06625086069107056tensor(-0.9200)
0.06002577766776085tensor(-0.7600)
0.10252383351325989tensor(0.4400)
0.059982120990753174tensor(-0.9200)
0.06066873297095299tensor(-0.9200)
0.05836592987179756tensor(-1.)
0.09061562269926071tensor(-1.)
0.042103856801986694tensor(-0.5200)
0.03993712365627289tensor(-1.)
0.058126289397478104tensor(-0.8400)
0.10311893373727798tensor(-0.8400)
0.10765204578638077tensor(0.2000)
0.09587886929512024tensor(0.7600)
0.0942838042974472tensor(0.2800)
0.09193766862154007tensor(0.3600)
0.09583122283220291tensor(0.3600)
0.10269835591316223tensor(0.4400)
0.10960717499256134tensor(-0.6800)
0.06526666134595871tensor(-0.6800)
0.05150081217288971tensor(-0.6000)
0.07131826877593994tensor(-0.3600)
0.06836497038602829tensor(-0.6000)
0.0889066681265831tensor(0.2800)
0.08366414159536362tensor(-0.2000)
0.02886185795068741tensor(-1.)
0.0781145766377449tensor(-0.7600)
0.037548284977674484tensor(-1.)
0.10341083258390427tensor(-0.4400)
0.11853298544883728tensor(-0.2000)
0.07571075111627579tensor(0.0400)
0.08094697445631027tensor(0.6800)
0.08510108292102814tensor(0.1200)
0.10280289500951767tensor(-1.)
0.11124327778816223tensor(-1.)
0.07694973796606064tensor(-0.2000)
0.05509539693593979tensor(-1.)
0.06896848231554031tensor(0.0400)
0.0667336955666542tensor(0.0400)
0.06385104358196259tensor(-1.)
0.06220762059092522tensor(0.0400)
0.06228742003440857tensor(-1.)
0.06991153210401535tensor(-0.3600)
0.03435347229242325tensor(-1.)
0.06068241596221924tensor(-0.7600)
0.04000449925661087tensor(-0.2800)
0.13515906035900116tensor(0.5200)
0.0687272697687149tensor(-0.3600)
0.10531941801309586tensor(0.2800)
0.07781021296977997tensor(0.0400)
0.06638915091753006tensor(0.2000)
0.0789942592382431tensor(-0.4400)
0.11414124816656113tensor(-0.3600)
0.10123205929994583tensor(-0.3600)
0.06554862856864929tensor(-0.6000)
0.09732712060213089tensor(-0.2800)
0.07254157215356827tensor(-0.2800)
0.08391833305358887tensor(-0.4400)
0.050708699971437454tensor(0.0400)
0.06146402657032013tensor(-0.2000)
0.06252795457839966tensor(-0.6000)
0.058784935623407364tensor(-0.4400)
0.061997510492801666tensor(-0.7600)
0.06873490661382675tensor(-0.4400)
0.12228299677371979tensor(-0.8400)
0.08709906786680222tensor(-0.2800)
0.11633183807134628tensor(0.6000)
0.060173794627189636tensor(-0.7600)
0.060295701026916504tensor(0.1200)
0.07968377321958542tensor(0.0400)
0.06444267928600311tensor(0.0400)
0.07102542370557785tensor(-0.7600)
0.114140085875988tensor(0.7600)
0.09305185824632645tensor(-0.1200)
0.08784745633602142tensor(-0.6000)
0.06774889677762985tensor(-1.)
0.04729451984167099tensor(-0.3600)
0.07227997481822968tensor(-0.2800)
0.09943866729736328tensor(0.5200)
0.08704831451177597tensor(0.2000)
0.07880393415689468tensor(-0.4400)
0.09158746153116226tensor(0.2000)
0.06772437691688538tensor(-0.8400)
0.07078664004802704tensor(0.4400)
0.055091388523578644tensor(0.3600)
0.06220021843910217tensor(0.1200)
0.12415582686662674tensor(0.6800)
0.11242426931858063tensor(0.6800)
0.05204285681247711tensor(-0.2800)
0.06860662996768951tensor(-0.6000)
0.07693121582269669tensor(-0.2000)
0.07411042600870132tensor(-0.6000)
0.07515888661146164tensor(-0.3600)
0.06729345768690109tensor(0.0400)
0.07676170021295547tensor(0.2800)
0.04842609912157059tensor(-0.8400)
0.025904301553964615tensor(-1.)
0.0895763635635376tensor(-1.)
0.06176883354783058tensor(-0.1200)
0.10238521546125412tensor(-0.4000)
0.10928881168365479tensor(-0.7600)
0.11947387456893921tensor(0.6000)
0.056273531168699265tensor(-0.2800)
0.0694270059466362tensor(-0.7600)
0.06031299754977226tensor(-0.3600)
0.0693625882267952tensor(-0.0400)
0.05002191290259361tensor(-0.6000)
0.10493462532758713tensor(-0.2000)
0.06909937411546707tensor(-0.3600)
0.10338281095027924tensor(-0.9200)
0.05198877304792404tensor(-0.9200)
0.07390774041414261tensor(-0.2000)
0.052589841187000275tensor(-1.)
0.06972728669643402tensor(0.2800)
0.07487358152866364tensor(-0.6000)
0.055521201342344284tensor(-0.2000)
0.07859823107719421tensor(-0.0400)
0.05287164822220802tensor(-1.)
0.03999995440244675tensor(-0.3600)
0.08749157935380936tensor(0.5200)
0.1148362010717392tensor(-0.5200)
0.1097012534737587tensor(-0.2000)
0.0798087865114212tensor(-0.6000)
0.10497570037841797tensor(-0.2000)
0.06502870470285416tensor(-0.7600)
0.04713524505496025tensor(-1.)
0.07184869050979614tensor(-1.)
0.07348126918077469tensor(0.3600)
0.07884491235017776tensor(0.0400)
0.04633929207921028tensor(-0.8400)
0.05538959428668022tensor(0.1200)
0.09666421264410019tensor(-0.2000)
0.0546647273004055tensor(-0.3600)
0.06871264427900314tensor(-0.4400)
0.035957373678684235tensor(-0.5200)
0.04265861585736275tensor(-0.1200)
0.046579811722040176tensor(-0.7600)
0.09314415603876114tensor(0.0400)
0.08005831390619278tensor(0.2000)
0.09821899980306625tensor(-0.1200)
0.08929144591093063tensor(-0.2800)
0.1131405159831047tensor(-0.7600)
0.040930040180683136tensor(-1.)
0.06799940019845963tensor(-0.3600)
0.060019660741090775tensor(-0.6800)
0.04054172709584236tensor(-0.9200)
0.057525552809238434tensor(-0.6000)
0.07972699403762817tensor(0.1200)
0.04932282865047455tensor(-0.7600)
0.09820957481861115tensor(0.1200)
0.05326586216688156tensor(-0.3600)
0.09182409197092056tensor(-0.4400)
0.08163481205701828tensor(-0.4400)
0.09714944660663605tensor(0.2000)
0.11192543059587479tensor(0.2800)
0.07395883649587631tensor(0.1200)
0.102455735206604tensor(0.3600)
0.0940299853682518tensor(-0.2000)
0.07512232661247253tensor(0.1200)
0.040880460292100906tensor(-1.)
0.05169679597020149tensor(-0.9200)
0.039053045213222504tensor(-0.6800)
0.06810827553272247tensor(-1.)
0.04321931302547455tensor(0.6800)
0.07210388034582138tensor(-0.9200)
0.0667327269911766tensor(0.2800)
0.02612116187810898tensor(-1.)
0.054275792092084885tensor(-0.7600)
0.052055757492780685tensor(-1.)
0.18893760442733765tensor(0.9200)
0.10465499013662338tensor(0.5200)
0.1263120472431183tensor(-0.2800)
0.08626026660203934tensor(-0.8400)
0.07763858139514923tensor(0.0400)
0.0930590108036995tensor(-0.3600)
0.0854049101471901tensor(-0.4400)
0.1266796886920929tensor(-0.2000)
0.0787000060081482tensor(-0.2800)
0.06963452696800232tensor(-0.5200)
0.07694792747497559tensor(-0.6800)
0.06689947843551636tensor(-0.1200)
0.1098942905664444tensor(0.2800)
0.06960266083478928tensor(-0.4400)
0.024806426838040352tensor(-0.4400)
0.059299614280462265tensor(-1.)
0.05080275610089302tensor(-0.3600)
0.06684742867946625tensor(-0.6800)
0.09075115621089935tensor(0.1200)
0.05016402155160904tensor(-0.2800)
0.087657131254673tensor(-0.7320)
0.10311480611562729tensor(0.0400)
0.050186317414045334tensor(-0.8400)
0.10118679702281952tensor(-0.0400)
0.105742909014225tensor(0.1200)
0.06864495575428009tensor(-0.2000)
0.08212641626596451tensor(0.2000)
0.08215388655662537tensor(0.0400)
0.05493345856666565tensor(-0.2800)
0.13965703547000885tensor(0.5200)
0.04774272441864014tensor(0.2000)
0.0679595097899437tensor(-0.4400)
0.09421931952238083tensor(-0.3600)
0.061095379292964935tensor(-1.)
0.06720376014709473tensor(-0.7600)
0.0413409024477005tensor(-1.)
0.07987009733915329tensor(0.3600)
0.10968008637428284tensor(0.4400)
0.09381632506847382tensor(-0.1200)
0.08442386239767075tensor(0.2000)
0.06470995396375656tensor(-0.2800)
0.08564872294664383tensor(-0.0400)
0.060080256313085556tensor(0.0400)
0.0743197575211525tensor(-0.4400)
0.043422192335128784tensor(-0.3600)
0.07293049991130829tensor(-0.3600)
0.053508684039115906tensor(-0.4400)
0.1377849280834198tensor(0.1200)
0.12796175479888916tensor(0.5200)
0.11923372745513916tensor(0.3600)
0.06998847424983978tensor(-1.)
0.0985039696097374tensor(-0.2000)
0.05805971100926399tensor(-1.)
0.08363897353410721tensor(0.5200)
0.04255245253443718tensor(-0.9200)
0.06938347220420837tensor(-0.7600)
0.09247715771198273tensor(-0.2800)
0.08193735033273697tensor(-0.2800)
0.0648047924041748tensor(-0.5200)
0.06512439996004105tensor(-1.)
0.09104029089212418tensor(-0.6000)
0.06442565470933914tensor(-0.8400)
0.06772330403327942tensor(-0.6800)
0.09068232774734497tensor(0.2000)
0.09576278924942017tensor(0.5200)
0.11076266318559647tensor(0.2800)
0.10133455693721771tensor(0.0400)
0.1100044846534729tensor(0.2000)
0.07638396322727203tensor(0.0400)
0.0664571076631546tensor(-0.2800)
0.06774033606052399tensor(0.0400)
0.0471884161233902tensor(-0.6800)
0.06978265941143036tensor(-0.3600)
0.05041122063994408tensor(-0.7600)
0.06600528210401535tensor(0.0400)
0.04360955208539963tensor(-0.5200)
0.07044138759374619tensor(-1.)
0.06676562130451202tensor(-0.0400)
0.0676833763718605tensor(-0.8400)
0.046294692903757095tensor(-0.7600)
0.06076645106077194tensor(-0.6000)
0.06590636074542999tensor(0.0400)
0.14694027602672577tensor(0.6000)
0.07471433281898499tensor(0.4400)
0.09933678060770035tensor(0.0400)
0.08197019249200821tensor(-0.3600)
0.10719726234674454tensor(0.6000)
0.13904182612895966tensor(0.2000)
0.05992332845926285tensor(-0.7600)
0.04423932358622551tensor(-1.)
0.05406465753912926tensor(-0.8400)
0.06763502210378647tensor(-0.8400)
0.10083623975515366tensor(-0.2000)
0.04661809653043747tensor(-0.7600)
0.03044629469513893tensor(-1.)
0.08558646589517593tensor(0.2800)
0.1116381511092186tensor(0.1200)
0.07112627476453781tensor(0.0400)
0.0681232288479805tensor(0.1200)
0.0494864247739315tensor(-0.9200)
0.1608443409204483tensor(0.2800)
0.0872381180524826tensor(-0.7600)
0.11643831431865692tensor(0.4400)
0.06294696033000946tensor(-1.)
0.09097111225128174tensor(-1.)
0.07186342775821686tensor(-1.)
0.07192344963550568tensor(-1.)
0.05368124321103096tensor(-0.1200)
0.08338689804077148tensor(-1.)
0.06914117932319641tensor(-0.8400)
0.05286329239606857tensor(-1.)
0.0678638145327568tensor(0.0400)
0.09470223635435104tensor(-0.6800)
0.06944020837545395tensor(-0.8400)
0.11074554920196533tensor(0.2000)
0.08947308361530304tensor(-0.1200)
0.08694350719451904tensor(-0.7600)
0.057572104036808014tensor(0.3600)
0.09664343297481537tensor(-0.1200)
0.03533110395073891tensor(-0.2800)
0.06326062977313995tensor(-0.6800)
0.043962202966213226tensor(-1.)
0.052044257521629333tensor(-0.3600)
0.05824817717075348tensor(0.1200)
0.06353865563869476tensor(-0.3320)
0.08843818306922913tensor(-0.2800)
0.10251210629940033tensor(0.1200)
0.04374433681368828tensor(-1.)
0.06960179656744003tensor(-0.7600)
0.04846859350800514tensor(-0.2800)
0.09125281870365143tensor(-0.8400)
0.02776302583515644tensor(-0.9200)
0.05578514188528061tensor(-1.)
0.055710259824991226tensor(-0.4400)
0.0446867011487484tensor(-1.)
0.07649753242731094tensor(-0.7600)
0.0725017562508583tensor(-0.6800)
0.09512197971343994tensor(-0.9200)
0.06360553950071335tensor(-0.1200)
0.0858076959848404tensor(-0.2800)
0.059990882873535156tensor(-0.9200)
0.1793230026960373tensor(0.6000)
0.12273914366960526tensor(0.6800)
0.11833781749010086tensor(0.7600)
0.09270551055669785tensor(0.2000)
0.0877777487039566tensor(0.0400)
0.10608988255262375tensor(-0.7600)
0.11943556368350983tensor(0.2800)
0.07804727554321289tensor(0.0400)
0.06379834562540054tensor(-0.5200)
0.08363361656665802tensor(0.2000)
0.044273633509874344tensor(-1.)
0.09778593480587006tensor(0.0400)
0.08075778931379318tensor(-0.5200)
0.09018567949533463tensor(0.1200)
0.04117107391357422tensor(-0.9200)
0.09086763113737106tensor(-0.2800)
0.05036270618438721tensor(-1.)
0.03497250750660896tensor(-1.)
0.07593423873186111tensor(0.2000)
0.06348763406276703tensor(-1.)
0.08138018101453781tensor(-0.0400)
0.021082788705825806tensor(-0.8400)
0.12244702130556107tensor(0.0400)
0.06323377043008804tensor(-1.)
0.1353948414325714tensor(0.4400)
0.11679327487945557tensor(-0.2800)
0.08360980451107025tensor(-0.3600)
0.08744142204523087tensor(-0.2000)
0.08412223309278488tensor(-0.2000)
0.0670066773891449tensor(-1.)
0.11312022060155869tensor(0.0400)
0.05129019543528557tensor(-0.8400)
0.08004879951477051tensor(-1.)
0.04183156415820122tensor(-0.9200)
0.060414962470531464tensor(-0.2800)
0.04807211086153984tensor(-0.3600)
0.05797523260116577tensor(-0.6000)
0.05401058867573738tensor(-0.7600)
0.0495246984064579tensor(-0.6000)
0.055092208087444305tensor(0.1200)
0.10671265423297882tensor(0.0400)
0.11502908915281296tensor(0.3600)
0.07264292240142822tensor(-0.3600)
0.10378343611955643tensor(0.6000)
0.07152608782052994tensor(-0.2800)
0.06015247851610184tensor(-0.9200)
0.0629473477602005tensor(-1.)
0.08918797224760056tensor(-0.4400)
0.05601724237203598tensor(-1.)
0.06840457022190094tensor(-0.5200)
0.06456799060106277tensor(0.4400)
0.08867625892162323tensor(0.2000)
0.09267663955688477tensor(-0.5200)
0.04077684506773949tensor(-0.2800)
0.08825524151325226tensor(-0.6800)
0.05735380947589874tensor(-0.5200)
0.07401784509420395tensor(-1.)
0.054056696593761444tensor(0.2800)
0.04266321286559105tensor(-0.9200)
0.15657617151737213tensor(-0.6800)
0.15109382569789886tensor(0.4400)
0.1890057474374771tensor(1.)
0.10820557177066803tensor(-0.4400)
0.18578271567821503tensor(0.0400)
0.11017512530088425tensor(-0.2000)
0.22362756729125977tensor(0.5000)
0.09653349220752716tensor(0.6000)
0.15083901584148407tensor(0.5000)
0.14551357924938202tensor(0.2800)
0.18451565504074097tensor(0.6000)
0.16223224997520447tensor(-0.0400)
0.08025643974542618tensor(0.7600)
0.2161441594362259tensor(0.6800)
0.11155086755752563tensor(0.2800)
0.14659753441810608tensor(0.2000)
0.11268409341573715tensor(-0.6800)
0.11299097537994385tensor(0.2800)
0.15749360620975494tensor(0.5200)
0.14664573967456818tensor(0.2000)
0.1774851381778717tensor(-0.0400)
0.1308962106704712tensor(1.)
0.19349651038646698tensor(0.5200)
0.1107378676533699tensor(0.2800)
0.14942310750484467tensor(0.5200)
0.1346442848443985tensor(0.5200)
0.08724971115589142tensor(0.5000)
0.15339265763759613tensor(0.2800)
0.14741270244121552tensor(0.3600)
0.17419348657131195tensor(0.8400)
0.06649467349052429tensor(0.3332)
0.1416660100221634tensor(0.4000)
0.09487863630056381tensor(-0.3000)
0.1044016182422638tensor(0.2000)
0.09938265383243561tensor(0.7600)
0.12405756860971451tensor(0.5200)
0.16103951632976532tensor(0.2000)
0.20939156413078308tensor(0.8000)
0.15363965928554535tensor(-0.1200)
0.19852818548679352tensor(0.6000)
0.1838451325893402tensor(0.8400)
0.086475670337677tensor(0.4000)
0.2056141495704651tensor(0.2000)
0.1890709549188614tensor(0.6000)
0.10467807203531265tensor(0.7332)
0.19242490828037262tensor(0.3000)
0.14345195889472961tensor(0.3000)
0.11138688772916794tensor(-0.3000)
0.09883628040552139tensor(-0.6800)
0.11967958509922028tensor(0.7600)
0.20112472772598267tensor(0.2000)
0.10264305770397186tensor(0.2000)
0.1360296905040741tensor(0.3000)
0.153983011841774tensor(0.7600)
0.19370009005069733tensor(1.)
0.13406595587730408tensor(1.)
0.1820715218782425tensor(0.2800)
0.11317534744739532tensor(-0.1200)
0.17688260972499847tensor(0.3000)
0.08018606156110764tensor(0.5000)
0.10509025305509567tensor(0.2800)
0.10396265238523483tensor(0.7600)
0.12781377136707306tensor(0.0400)
0.21836653351783752tensor(0.4000)
0.06224902346730232tensor(0.5000)
0.10156750679016113tensor(0.4000)
0.17532825469970703tensor(0.7000)
0.10599994659423828tensor(-0.0400)
0.2053963840007782tensor(0.3000)
0.11079281568527222tensor(0.5200)
0.08032440394163132tensor(0.3600)
0.14339572191238403tensor(0.4000)
0.10316693037748337tensor(-0.0400)
0.08395222574472427tensor(0.5200)
0.19721107184886932tensor(0.5200)
0.1645456701517105tensor(0.6000)
0.14485467970371246tensor(0.3600)
0.1910422444343567tensor(0.3000)
0.12681792676448822tensor(0.8400)
0.19953301548957825tensor(0.4856)
0.21738339960575104tensor(0.3332)
0.22097046673297882tensor(0.3600)
0.09543994069099426tensor(0.1200)
0.1662885993719101tensor(0.2000)
0.16900548338890076tensor(0.2000)
0.09730332344770432tensor(0.5000)
0.1216694712638855tensor(0.7600)
0.12623728811740875tensor(-0.6000)
0.15742705762386322tensor(0.1200)
0.1513732522726059tensor(0.3000)
0.07737997174263tensor(-0.3000)
0.12131720036268234tensor(-0.3000)
0.10318685322999954tensor(1.)
0.23377388715744019tensor(0.4400)
0.193138986825943tensor(0.2000)
0.1424471139907837tensor(0.1200)
0.11600998044013977tensor(0.4400)
0.1494482159614563tensor(0.2000)
0.09752153605222702tensor(-0.6000)
0.11977273970842361tensor(1.)
0.0755121111869812tensor(0.4000)
0.16602164506912231tensor(0.8000)
0.08897612988948822tensor(0.9200)
0.12666456401348114tensor(0.4000)
0.07313497364521027tensor(0.4768)
0.1247655600309372tensor(0.6000)
0.12537236511707306tensor(0.4000)
0.15713953971862793tensor(0.9000)
0.12801477313041687tensor(0.2800)
0.15784595906734467tensor(0.0400)
0.09747076034545898tensor(0.2000)
0.15055043995380402tensor(0.3000)
0.16015571355819702tensor(-0.1200)
0.17614807188510895tensor(0.4000)
0.21875032782554626tensor(0.9000)
0.15658637881278992tensor(0.2800)
0.13288664817810059tensor(0.3000)
0.1912735402584076tensor(0.5000)
0.1437816470861435tensor(0.2000)
0.05408409610390663tensor(0.0400)
0.15570668876171112tensor(0.3000)
0.22565680742263794tensor(0.3600)
0.08290725946426392tensor(-0.4668)
0.14099358022212982tensor(1.)
0.16457973420619965tensor(0.6000)
0.05822291970252991tensor(0.4400)
0.10797110944986343tensor(-0.6800)
0.14171220362186432tensor(0.4000)
0.16532541811466217tensor(0.2000)
0.1032351553440094tensor(-0.3332)
0.22634689509868622tensor(0.5000)
0.14377464354038239tensor(-0.1000)
0.14435285329818726tensor(0.6800)
0.05724632367491722tensor(0.4400)
0.15176990628242493tensor(0.3600)
0.07804319262504578tensor(0.2000)
0.22064118087291718tensor(0.3000)
0.20326218008995056tensor(0.4400)
0.08385902643203735tensor(0.2000)
0.09899023175239563tensor(-0.3000)
0.06069619581103325tensor(0.3000)
0.1688353717327118tensor(0.7600)
0.15891560912132263tensor(0.1200)
0.1575726717710495tensor(0.1000)
0.20663386583328247tensor(0.2000)
0.07285406440496445tensor(0.4400)
0.09862026572227478tensor(0.2000)
0.18765997886657715tensor(-0.2000)
0.0989244282245636tensor(0.2000)
0.15241041779518127tensor(0.7600)
0.13361875712871552tensor(-0.0400)
0.049809280782938004tensor(0.7600)
0.1752464473247528tensor(0.7600)
0.08381223678588867tensor(0.0400)
0.13384230434894562tensor(0.2000)
0.08544216305017471tensor(0.8000)
0.19339069724082947tensor(0.2000)
0.14345568418502808tensor(-0.3000)
0.13473612070083618tensor(-0.2000)
0.10077577829360962tensor(-0.0400)
0.11041254550218582tensor(0.9200)
0.14151988923549652tensor(0.6000)
0.15229271352291107tensor(0.6000)
0.16229631006717682tensor(0.7600)
0.1366676241159439tensor(0.6800)
0.06109293922781944tensor(0.4400)
0.15251389145851135tensor(0.)
0.11711303889751434tensor(-0.4000)
0.15497668087482452tensor(1.)
0.14478479325771332tensor(0.5000)
0.12450239807367325tensor(-0.3000)
0.15787293016910553tensor(0.7600)
0.19996334612369537tensor(0.2000)
0.14208637177944183tensor(0.5200)
0.10430789738893509tensor(0.3000)
0.1380041539669037tensor(0.1000)
0.07597842067480087tensor(0.3600)
0.09282626211643219tensor(0.2800)
0.11957515776157379tensor(-0.2000)
0.1325601190328598tensor(0.2000)
0.19287727773189545tensor(0.3000)
0.06373829394578934tensor(1.)
0.08102250099182129tensor(0.3332)
0.08325854688882828tensor(0.4400)
0.1730927973985672tensor(0.5200)
0.16426338255405426tensor(0.2000)
0.12436502426862717tensor(0.7000)
0.11336473375558853tensor(0.4000)
0.16846415400505066tensor(0.2800)
0.16852280497550964tensor(-0.0400)
0.15378515422344208tensor(0.6000)
0.12810969352722168tensor(0.8668)
0.15636086463928223tensor(0.3332)
0.19025345146656036tensor(0.2000)
0.20419247448444366tensor(0.3000)
0.1943768709897995tensor(0.6000)
0.12115341424942017tensor(0.4000)
0.10460444539785385tensor(-0.2000)
0.2143198847770691tensor(0.4400)
0.1544017195701599tensor(0.3600)
0.12378369271755219tensor(0.4000)
0.07778675854206085tensor(-0.0400)
0.13199037313461304tensor(0.4000)
0.08631624281406403tensor(0.1200)
0.10466421395540237tensor(0.9200)
0.10585516691207886tensor(0.2000)
0.131882905960083tensor(0.3600)
0.07703190296888351tensor(0.3000)
0.11392991989850998tensor(0.9000)
0.14755365252494812tensor(0.4400)
0.08432557433843613tensor(0.6800)
0.12755604088306427tensor(-0.2000)
0.1157873347401619tensor(0.5200)
0.14258073270320892tensor(-0.3600)
0.14870283007621765tensor(0.3000)
0.1697574257850647tensor(1.)
0.1836908608675003tensor(0.3668)
0.0883650854229927tensor(0.1000)
0.11782419681549072tensor(0.6444)
0.14568206667900085tensor(0.4400)
0.13894906640052795tensor(0.4400)
0.15033972263336182tensor(0.7332)
0.16444166004657745tensor(0.2800)
0.11512230336666107tensor(-0.1000)
0.13051758706569672tensor(0.8000)
0.10153035819530487tensor(0.1000)
0.10893924534320831tensor(0.6000)
0.21641847491264343tensor(0.3600)
0.13814422488212585tensor(-0.1200)
0.14533795416355133tensor(0.3000)
0.12098712474107742tensor(0.4668)
0.13985329866409302tensor(0.9000)
0.15166310966014862tensor(0.4000)
0.08744220435619354tensor(0.2000)
0.11820767819881439tensor(0.3000)
0.09811855852603912tensor(0.4400)
0.20947793126106262tensor(0.5200)
0.10916900634765625tensor(-0.6000)
0.15209904313087463tensor(0.3332)
0.08199092745780945tensor(0.7000)
0.18676885962486267tensor(-0.3000)
0.17459356784820557tensor(0.1000)
0.17737925052642822tensor(0.3000)
0.12449584156274796tensor(0.)
0.17388708889484406tensor(0.2364)
0.07412558794021606tensor(0.7600)
0.16710856556892395tensor(0.9200)
0.18633432686328888tensor(1.)
0.1436946988105774tensor(0.2000)
0.12311311811208725tensor(0.2800)
0.22276312112808228tensor(0.7600)
0.1517471969127655tensor(-0.2800)
0.21950820088386536tensor(0.6000)
0.16205386817455292tensor(-0.6000)
0.15489503741264343tensor(-0.7600)
0.2166478931903839tensor(0.8400)
0.14486069977283478tensor(-0.2800)
0.08900722116231918tensor(-0.5200)
0.17153024673461914tensor(0.8400)
0.13165244460105896tensor(-0.4400)
0.17769549787044525tensor(0.2800)
0.09428092837333679tensor(0.1200)
0.10391625016927719tensor(-0.6800)
0.18381324410438538tensor(0.5200)
0.21973678469657898tensor(0.5200)
0.1112796887755394tensor(0.6800)
0.18250557780265808tensor(-0.3600)
0.20135216414928436tensor(0.5200)
0.11555842310190201tensor(-0.4400)
0.21877463161945343tensor(0.4400)
0.16276273131370544tensor(-0.2800)
0.1333620697259903tensor(0.5200)
0.1077733263373375tensor(-0.9200)
0.13967247307300568tensor(-0.3600)
0.08006605505943298tensor(-0.8400)
0.0741242840886116tensor(-0.0667)
0.08563303202390671tensor(-0.8400)
0.11555031687021255tensor(0.4400)
0.12015656381845474tensor(0.3600)
0.2285747081041336tensor(0.0400)
0.19923947751522064tensor(-0.1200)
0.1341344118118286tensor(0.0400)
0.21990036964416504tensor(0.9200)
0.1633370965719223tensor(0.9200)
0.19237682223320007tensor(0.9200)
0.18173480033874512tensor(0.0400)
0.11641912162303925tensor(-0.3600)
0.05557055398821831tensor(-0.1200)
0.095156230032444tensor(-0.6000)
0.11426493525505066tensor(-0.5200)
0.1583193987607956tensor(0.3600)
0.17095281183719635tensor(-0.7600)
0.16034354269504547tensor(0.2800)
0.12270230799913406tensor(0.2800)
0.11638446897268295tensor(-0.2800)
0.20286811888217926tensor(-0.5200)
0.1418055146932602tensor(-0.3600)
0.20391175150871277tensor(0.9200)
0.17074044048786163tensor(0.4400)
0.18920071423053741tensor(0.5200)
0.10190127044916153tensor(0.2000)
0.1450309157371521tensor(0.4400)
0.18959221243858337tensor(0.7600)
0.17498749494552612tensor(-0.2000)
0.14569894969463348tensor(-0.0400)
0.0889558345079422tensor(0.2800)
0.1678875833749771tensor(0.3600)
0.14129741489887238tensor(0.1200)
0.12274348735809326tensor(0.2800)
0.1300591081380844tensor(-0.2800)
0.14726880192756653tensor(0.1200)
0.1436905413866043tensor(0.3600)
0.21260082721710205tensor(0.8400)
0.2336854785680771tensor(0.5200)
0.08735974878072739tensor(-0.3600)
0.07879571616649628tensor(-0.6800)
0.10740429162979126tensor(-0.6000)
0.1274515986442566tensor(0.0400)
0.18568502366542816tensor(0.6800)
0.20646396279335022tensor(0.7600)
0.14601661264896393tensor(-0.1200)
0.21923623979091644tensor(0.0400)
0.17510734498500824tensor(0.1200)
0.21999911963939667tensor(0.6000)
0.1409997195005417tensor(0.2800)
0.016632884740829468tensor(-1.)
0.12351001799106598tensor(-0.2800)
0.04984692856669426tensor(-1.)
0.2147400677204132tensor(0.0400)
0.17414090037345886tensor(0.1000)
0.12355341762304306tensor(0.2800)
0.19583721458911896tensor(-0.2800)
0.137812539935112tensor(0.6800)
0.2313961386680603tensor(0.6800)
0.14011843502521515tensor(0.0400)
0.1950841099023819tensor(0.6800)
0.13254280388355255tensor(-0.2000)
0.23083597421646118tensor(1.)
0.12517835199832916tensor(-0.6800)
0.1689414083957672tensor(0.5200)
0.20080220699310303tensor(-0.3600)
0.135813370347023tensor(0.3600)
0.1338849514722824tensor(-0.7600)
0.19881272315979004tensor(-0.4400)
0.11475562304258347tensor(0.5200)
0.07478567957878113tensor(-0.5200)
0.17905288934707642tensor(0.2000)
0.17614097893238068tensor(-0.1200)
0.10090915113687515tensor(-0.6000)
0.1625327467918396tensor(-0.1200)
0.09729336202144623tensor(-0.2800)
0.07025746256113052tensor(-1.)
0.19574730098247528tensor(0.6800)
0.11373120546340942tensor(-0.8400)
0.22839391231536865tensor(0.0400)
0.08030565083026886tensor(-0.7600)
0.15079976618289948tensor(0.5200)
0.07450550049543381tensor(-1.)
0.20532074570655823tensor(1.)
0.10243158787488937tensor(-0.6000)
0.16154275834560394tensor(0.2800)
0.1795278936624527tensor(0.1200)
0.2312384843826294tensor(0.8400)
0.140786275267601tensor(0.3600)
0.09174133837223053tensor(-0.5200)
0.1444629579782486tensor(0.6000)
0.2319001704454422tensor(-0.2800)
0.12746240198612213tensor(0.6000)
0.21679434180259705tensor(-0.0400)
0.2211339771747589tensor(-0.2800)
0.20638172328472137tensor(0.5200)
0.07490963488817215tensor(-0.4400)
0.18688809871673584tensor(0.0400)
0.06705497205257416tensor(-0.4400)
0.14384311437606812tensor(-0.3600)
0.18554188311100006tensor(0.9200)
0.06351140141487122tensor(-0.6800)
0.1320352703332901tensor(0.3600)
0.1526670604944229tensor(0.2800)
0.16251379251480103tensor(-0.1200)
0.09304572641849518tensor(-0.5200)
0.1486445665359497tensor(0.2000)
0.20497310161590576tensor(0.0400)
0.062324680387973785tensor(-0.8400)
0.15489621460437775tensor(0.8400)
0.15112543106079102tensor(0.6000)
0.08310254663228989tensor(0.1200)
0.20763856172561646tensor(0.5200)
0.049428991973400116tensor(-1.)
0.11956185102462769tensor(0.3600)
0.16460809111595154tensor(0.8400)
0.0865495577454567tensor(-0.6800)
0.1230904683470726tensor(0.5200)
0.13707344233989716tensor(-0.1200)
0.14319519698619843tensor(0.2800)
0.19177186489105225tensor(1.)
0.09899164736270905tensor(-0.3600)
0.13245351612567902tensor(-0.1200)
0.16978345811367035tensor(-0.6800)
0.08471869677305222tensor(0.4400)
0.20225238800048828tensor(0.)
0.17614831030368805tensor(0.2000)
0.13594719767570496tensor(-0.1200)
0.04175996035337448tensor(-1.)
0.10021861642599106tensor(-0.9200)
0.1390247792005539tensor(0.2800)
0.19965331256389618tensor(1.)
0.1405506581068039tensor(-0.1200)
0.12870007753372192tensor(0.7600)
0.21323177218437195tensor(0.5200)
0.11423824727535248tensor(-0.5200)
0.18192993104457855tensor(0.8400)
0.08951079845428467tensor(0.5200)
0.20494012534618378tensor(-0.4400)
0.21643801033496857tensor(0.5200)
0.07868974655866623tensor(-1.)
0.14644894003868103tensor(-0.3600)
0.16568389534950256tensor(1.)
0.0556023083627224tensor(-0.8400)
0.16450826823711395tensor(-0.0400)
0.10914310812950134tensor(0.2800)
0.17779237031936646tensor(0.5200)
0.19513386487960815tensor(0.0400)
0.21644727885723114tensor(-0.6000)
0.15602996945381165tensor(0.6000)
0.12433335930109024tensor(-0.2000)
0.08738037198781967tensor(-0.1200)
0.21014969050884247tensor(0.8400)
0.14182449877262115tensor(0.7600)
0.2234821319580078tensor(-0.2800)
0.09754414856433868tensor(-1.)
0.03975173458456993tensor(-1.)
0.11738212406635284tensor(-0.2000)
0.14112338423728943tensor(0.3600)
0.04956607148051262tensor(-0.9200)
0.14288471639156342tensor(-0.0400)
0.12547267973423004tensor(-1.)
0.13475678861141205tensor(-0.8400)
0.14451809227466583tensor(-0.2800)
0.1475871056318283tensor(-0.6800)
0.09716036170721054tensor(-0.8400)
0.1283160299062729tensor(-0.1200)
0.12961049377918243tensor(-0.2800)
0.23258794844150543tensor(0.8400)
0.12285372614860535tensor(-0.1200)
0.06777477264404297tensor(-0.6000)
0.09527100622653961tensor(-0.6000)
0.13936251401901245tensor(0.6800)
0.06017792224884033tensor(-0.8400)
0.07299642264842987tensor(-0.9200)
0.04060047119855881tensor(-1.)
0.033767759799957275tensor(-1.)
0.08858264237642288tensor(-1.)
0.09627792984247208tensor(-0.7600)
0.06775067001581192tensor(-0.7600)
0.17392368614673615tensor(0.6000)
0.05780477821826935tensor(-1.)
0.12707191705703735tensor(-0.6800)
0.1420026421546936tensor(0.2800)
0.1948411911725998tensor(0.9200)
0.15434472262859344tensor(0.6800)
0.0902843177318573tensor(-0.1200)
0.08240611851215363tensor(-0.6000)
0.1924615502357483tensor(0.4400)
0.07384775578975677tensor(-0.3600)
0.144351527094841tensor(0.2000)
0.21657899022102356tensor(-0.0400)
0.07839164137840271tensor(-0.8400)
0.11633919179439545tensor(-1.)
0.24297372996807098tensor(1.)
0.06886332482099533tensor(-0.5200)
0.15490524470806122tensor(-0.4400)
0.22832036018371582tensor(0.6000)
0.20743070542812347tensor(0.8400)
0.10694000869989395tensor(0.3600)
0.08265257626771927tensor(-0.8400)
0.0327269546687603tensor(-1.)
0.2268715500831604tensor(0.9200)
0.20337727665901184tensor(1.)
0.15413744747638702tensor(0.6000)
0.13964933156967163tensor(0.6000)
0.21173201501369476tensor(0.2000)
0.1933497190475464tensor(1.)
0.14066237211227417tensor(-0.2000)
0.13604919612407684tensor(0.2000)
0.09899909794330597tensor(0.2000)
0.11352479457855225tensor(-0.6000)
0.22438283264636993tensor(1.)
0.2048407793045044tensor(-0.2000)
0.23727907240390778tensor(-0.2000)
0.14679262042045593tensor(-0.2000)
0.20197315514087677tensor(1.)
0.16213366389274597tensor(0.2000)
0.19847168028354645tensor(0.2000)
0.12821368873119354tensor(-1.)
0.12000229209661484tensor(-0.2000)
0.06947322934865952tensor(-1.)
0.07184262573719025tensor(-0.2000)
0.03750338405370712tensor(-1.)
0.08032049983739853tensor(-1.)
assignment2/requirements.txt 0 → 100644
View file @ 98dae565
datasets==2.10.1
torch==1.13.1
torch==1.12.1
tqdm==4.65.0
transformers==4.24.0
transformers==4.27.0.dev0
transformers.egg==info
Markdown is supported
0% or
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment