In this project, you'll generate your own Simpsons TV scripts using RNNs. You'll be using part of the Simpsons dataset of scripts from 27 seasons. The Neural Network you'll build will generate a new TV script for a scene at Moe's Tavern.
The data is already provided for you. You'll be using a subset of the original dataset. It consists of only the scenes in Moe's Tavern. This doesn't include other versions of the tavern, like "Moe's Cavern", "Flaming Moe's", "Uncle Moe's Family Feed-Bag", etc..
In [2]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
data_dir = './data/simpsons/moes_tavern_lines.txt'
text = helper.load_data(data_dir)
# Ignore notice, since we don't use it for analysing the data
text = text[81:]
In [3]:
view_sentence_range = (0, 10)
"""
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"""
import numpy as np
print('Dataset Stats')
print('Roughly the number of unique words: {}'.format(len({word: None for word in text.split()})))
scenes = text.split('\n\n')
print('Number of scenes: {}'.format(len(scenes)))
sentence_count_scene = [scene.count('\n') for scene in scenes]
print('Average number of sentences in each scene: {}'.format(np.average(sentence_count_scene)))
sentences = [sentence for scene in scenes for sentence in scene.split('\n')]
print('Number of lines: {}'.format(len(sentences)))
word_count_sentence = [len(sentence.split()) for sentence in sentences]
print('Average number of words in each line: {}'.format(np.average(word_count_sentence)))
print()
print('The sentences {} to {}:'.format(*view_sentence_range))
print('\n'.join(text.split('\n')[view_sentence_range[0]:view_sentence_range[1]]))
Dataset Stats
Roughly the number of unique words: 11492
Number of scenes: 262
Average number of sentences in each scene: 15.248091603053435
Number of lines: 4257
Average number of words in each line: 11.50434578341555
The sentences 0 to 10:
Moe_Szyslak: (INTO PHONE) Moe's Tavern. Where the elite meet to drink.
Bart_Simpson: Eh, yeah, hello, is Mike there? Last name, Rotch.
Moe_Szyslak: (INTO PHONE) Hold on, I'll check. (TO BARFLIES) Mike Rotch. Mike Rotch. Hey, has anybody seen Mike Rotch, lately?
Moe_Szyslak: (INTO PHONE) Listen you little puke. One of these days I'm gonna catch you, and I'm gonna carve my name on your back with an ice pick.
Moe_Szyslak: What's the matter Homer? You're not your normal effervescent self.
Homer_Simpson: I got my problems, Moe. Give me another one.
Moe_Szyslak: Homer, hey, you should not drink to forget your problems.
Barney_Gumble: Yeah, you should only drink to enhance your social skills.
The first thing to do to any dataset is preprocessing. Implement the following preprocessing functions below:
To create a word embedding, you first need to transform the words to ids. In this function, create two dictionaries:
vocab_to_intint_to_vocabReturn these dictionaries in the following tuple (vocab_to_int, int_to_vocab)
In [4]:
import numpy as np
import problem_unittests as tests
from collections import Counter
def create_lookup_tables(text):
"""
Create lookup tables for vocabulary
:param text: The text of tv scripts split into words
:return: A tuple of dicts (vocab_to_int, int_to_vocab)
"""
counts = Counter(text)
vocab = sorted(counts, key=counts.get, reverse=True)
# TODO: Implement Function
vocab_to_int = {word.lower(): x for x, word in enumerate(vocab)}
int_to_vocab = {ndx: word for word, ndx in vocab_to_int.items()}
return vocab_to_int, int_to_vocab
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_create_lookup_tables(create_lookup_tables)
Tests Passed
We'll be splitting the script into a word array using spaces as delimiters. However, punctuations like periods and exclamation marks make it hard for the neural network to distinguish between the word "bye" and "bye!".
Implement the function token_lookup to return a dict that will be used to tokenize symbols like "!" into "||Exclamation_Mark||". Create a dictionary for the following symbols where the symbol is the key and value is the token:
This dictionary will be used to token the symbols and add the delimiter (space) around it. This separates the symbols as it's own word, making it easier for the neural network to predict on the next word. Make sure you don't use a token that could be confused as a word. Instead of using the token "dash", try using something like "||dash||".
In [5]:
def token_lookup():
"""
Generate a dict to turn punctuation into a token.
:return: Tokenize dictionary where the key is the punctuation and the value is the token
"""
# TODO: Implement Function
return {'.':'||period||',
',':'||comma||',
'"':'||quotation_mark||',
';':'||semicolon||',
'!':'||exclamation_mark||',
'?':'||question_mark||',
'(':'||left_parentheses||',
')':'||right_parentheses||',
'--':'||dash||',
'\n':'||return||'}
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_tokenize(token_lookup)
Tests Passed
In [6]:
"""
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"""
# Preprocess Training, Validation, and Testing Data
helper.preprocess_and_save_data(data_dir, token_lookup, create_lookup_tables)
In [7]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper
import numpy as np
import problem_unittests as tests
int_text, vocab_to_int, int_to_vocab, token_dict = helper.load_preprocess()
In [8]:
"""
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"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf
# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer'
print('TensorFlow Version: {}'.format(tf.__version__))
# Check for a GPU
if not tf.test.gpu_device_name():
warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.0.0
Default GPU Device: /gpu:0
Implement the get_inputs() function to create TF Placeholders for the Neural Network. It should create the following placeholders:
name parameter.Return the placeholders in the following the tuple (Input, Targets, LearingRate)
In [9]:
def get_inputs():
"""
Create TF Placeholders for input, targets, and learning rate.
:return: Tuple (input, targets, learning rate)
"""
# TODO: Implement Function
Input = tf.placeholder(tf.int32, [None,None], name='input')
Targets = tf.placeholder(tf.int32, [None,None], name='targets')
LearningRate = tf.placeholder(tf.float32, name='learning_rate')
return Input, Targets, LearningRate
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_get_inputs(get_inputs)
Tests Passed
Stack one or more BasicLSTMCells in a MultiRNNCell.
rnn_sizezero_state() functiontf.identity()Return the cell and initial state in the following tuple (Cell, InitialState)
In [10]:
def get_init_cell(batch_size, rnn_size):
"""
Create an RNN Cell and initialize it.
:param batch_size: Size of batches
:param rnn_size: Size of RNNs
:return: Tuple (cell, initialize state)
"""
# TODO: Implement Function
num_layers = 1
lstm = tf.contrib.rnn.BasicLSTMCell(rnn_size)
cell = tf.contrib.rnn.MultiRNNCell([lstm]*num_layers)
initial_state = cell.zero_state(batch_size, tf.float32)
initial_state = tf.identity(initial_state, name="initial_state")
return (cell, initial_state)
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_get_init_cell(get_init_cell)
Tests Passed
In [11]:
def get_embed(input_data, vocab_size, embed_dim):
"""
Create embedding for <input_data>.
:param input_data: TF placeholder for text input.
:param vocab_size: Number of words in vocabulary.
:param embed_dim: Number of embedding dimensions
:return: Embedded input.
"""
# TODO: Implement Function
#embedding = tf.Variable(tf.random_uniform((vocab_size, embed_dim), -1, 1))
#embed = tf.nn.embedding_lookup(embedding, input_data)
embed = tf.contrib.layers.embed_sequence(input_data, vocab_size, embed_dim)
return embed
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_get_embed(get_embed)
Tests Passed
You created a RNN Cell in the get_init_cell() function. Time to use the cell to create a RNN.
tf.nn.dynamic_rnn()tf.identity()Return the outputs and final_state state in the following tuple (Outputs, FinalState)
In [12]:
def build_rnn(cell, inputs):
"""
Create a RNN using a RNN Cell
:param cell: RNN Cell
:param inputs: Input text data
:return: Tuple (Outputs, Final State)
"""
# TODO: Implement Function
outputs, final_state = tf.nn.dynamic_rnn(cell, inputs, dtype=tf.float32)
final_state = tf.identity(final_state, "final_state")
return outputs, final_state
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_build_rnn(build_rnn)
Tests Passed
Apply the functions you implemented above to:
input_data using your get_embed(input_data, vocab_size, embed_dim) function.cell and your build_rnn(cell, inputs) function.vocab_size as the number of outputs.Return the logits and final state in the following tuple (Logits, FinalState)
In [13]:
def build_nn(cell, rnn_size, input_data, vocab_size):
"""
Build part of the neural network
:param cell: RNN cell
:param rnn_size: Size of rnns
:param input_data: Input data
:param vocab_size: Vocabulary size
:return: Tuple (Logits, FinalState)
"""
# TODO: Implement Function
batch_size = 500
embed_dim = 256
inputs = get_embed(input_data, vocab_size, embed_dim)
#cell, init_state = get_init_cell(batch_size, rnn_size)
outputs, final_state = build_rnn(cell, inputs)
logits = tf.contrib.layers.fully_connected(outputs, num_outputs=vocab_size, activation_fn=None)
return logits, final_state
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_build_nn(build_nn)
Tests Passed
Implement get_batches to create batches of input and targets using int_text. The batches should be a Numpy array with the shape (number of batches, 2, batch size, sequence length). Each batch contains two elements:
[batch size, sequence length][batch size, sequence length]If you can't fill the last batch with enough data, drop the last batch.
For exmple, get_batches([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], 2, 3) would return a Numpy array of the following:
[
# First Batch
[
# Batch of Input
[[ 1 2 3], [ 7 8 9]],
# Batch of targets
[[ 2 3 4], [ 8 9 10]]
],
# Second Batch
[
# Batch of Input
[[ 4 5 6], [10 11 12]],
# Batch of targets
[[ 5 6 7], [11 12 13]]
]
]
In [29]:
def get_batches(int_text, batch_size, seq_length):
"""
Return batches of input and target
:param int_text: Text with the words replaced by their ids
:param batch_size: The size of batch
:param seq_length: The length of sequence
:return: Batches as a Numpy array
"""
# TODO: Implement Function
retArr = []
entrySlice = batch_size*seq_length
int_text = np.array(int_text)
num_batches = len(int_text)//entrySlice
max_elements = (len(int_text)//seq_length)*seq_length
cur_ndx = {'start':0 , 'end':seq_length}
_slice = int_text[:entrySlice]
for val in range(0,len(int_text),entrySlice):
curResult = int_text[val:val+entrySlice]
if len(curResult) == entrySlice:
currArr = np.split(curResult , batch_size)
#print(currArr)
for i, batch in enumerate(currArr):
#print(i, batch)
try:
print("Retarr: ", retArr, batch)
print(i)
#print(type(retArr[i]))
retArr = np.concatenate(retArr,batch, dtype=np.int)#, axis=0)
#t = retArr[0:i]
#print("BEFORE: ", t)
#h = t.append(batch)
print("Ever here", retArr)
except (IndexError, AttributeError):
retArr = np.concatenate(retArr, batch, dtype=np.int)
#retArr.append(batch)
#retArr[i] = batch
print("Assigned: ", retArr[i])
print("Final")
print(retArr)
#print(np.split(_slice,batch_size))
#
#print(np.zeros(num_batches))
#for batch in outerBatches:
# innerBatch = np.array_split(batch, seq_length)
# print(innerBatch)
#st = np.split(np.array(truncated_arr), num_batches)
#print(st)
return None
get_batches([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], 2, 3)
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
#tests.test_get_batches(get_batches)
Retarr: [] [1 2 3]
0
---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-29-7d12de6028a4> in <module>()
55
56
---> 57 get_batches([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], 2, 3)
58 """
59 DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
<ipython-input-29-7d12de6028a4> in get_batches(int_text, batch_size, seq_length)
27 print(i)
28 #print(type(retArr[i]))
---> 29 retArr = np.concatenate(retArr,batch)#, axis=0)
30 #t = retArr[0:i]
31 #print("BEFORE: ", t)
TypeError: only integer scalar arrays can be converted to a scalar index
Tune the following parameters:
num_epochs to the number of epochs.batch_size to the batch size.rnn_size to the size of the RNNs.seq_length to the length of sequence.learning_rate to the learning rate.show_every_n_batches to the number of batches the neural network should print progress.
In [15]:
# Number of Epochs
num_epochs = 100
# Batch Size
batch_size = 100
# RNN Size
rnn_size = 512
# Sequence Length
seq_length = 15
# Learning Rate
learning_rate = 0.01
# Show stats for every n number of batches
show_every_n_batches = 25
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
save_dir = './save'
In [16]:
"""
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"""
from tensorflow.contrib import seq2seq
train_graph = tf.Graph()
with train_graph.as_default():
vocab_size = len(int_to_vocab)
input_text, targets, lr = get_inputs()
input_data_shape = tf.shape(input_text)
cell, initial_state = get_init_cell(input_data_shape[0], rnn_size)
logits, final_state = build_nn(cell, rnn_size, input_text, vocab_size)
# Probabilities for generating words
probs = tf.nn.softmax(logits, name='probs')
# Loss function
cost = seq2seq.sequence_loss(
logits,
targets,
tf.ones([input_data_shape[0], input_data_shape[1]]))
# Optimizer
optimizer = tf.train.AdamOptimizer(lr)
# Gradient Clipping
gradients = optimizer.compute_gradients(cost)
capped_gradients = [(tf.clip_by_value(grad, -1., 1.), var) for grad, var in gradients]
train_op = optimizer.apply_gradients(capped_gradients)
Train the neural network on the preprocessed data. If you have a hard time getting a good loss, check the forms to see if anyone is having the same problem.
In [17]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
batches = get_batches(int_text, batch_size, seq_length)
with tf.Session(graph=train_graph) as sess:
sess.run(tf.global_variables_initializer())
for epoch_i in range(num_epochs):
state = sess.run(initial_state, {input_text: batches[0][0]})
for batch_i, (x, y) in enumerate(batches):
feed = {
input_text: x,
targets: y,
initial_state: state,
lr: learning_rate}
train_loss, state, _ = sess.run([cost, final_state, train_op], feed)
# Show every <show_every_n_batches> batches
if (epoch_i * len(batches) + batch_i) % show_every_n_batches == 0:
print('Epoch {:>3} Batch {:>4}/{} train_loss = {:.3f}'.format(
epoch_i,
batch_i,
len(batches),
train_loss))
# Save Model
saver = tf.train.Saver()
saver.save(sess, save_dir)
print('Model Trained and Saved')
Retarr: []
Assigned: [ 9 3 121 178 4 175 341 0 143 5 4353 523 13 146 0]
Retarr: [array([ 9, 3, 121, 178, 4, 175, 341, 0, 143, 5, 4353,
523, 13, 146, 0])]
Assigned: [ 1 149 176 2 36 2 219 2 22 985 79 10 206 201 2]
Retarr: [array([ 9, 3, 121, 178, 4, 175, 341, 0, 143, 5, 4353,
523, 13, 146, 0]), array([ 1, 149, 176, 2, 36, 2, 219, 2, 22, 985, 79, 10, 206,
201, 2])]
Assigned: [1306 0 1 9 3 121 178 4 388 29 2 96 271 0 3]
Retarr: [array([ 9, 3, 121, 178, 4, 175, 341, 0, 143, 5, 4353,
523, 13, 146, 0]), array([ 1, 149, 176, 2, 36, 2, 219, 2, 22, 985, 79, 10, 206,
201, 2]), array([1306, 0, 1, 9, 3, 121, 178, 4, 388, 29, 2,
96, 271, 0, 3])]
Assigned: [ 13 615 4 985 1306 0 985 1306 0 28 2 180 690 301 985]
Retarr: [array([ 9, 3, 121, 178, 4, 175, 341, 0, 143, 5, 4353,
523, 13, 146, 0]), array([ 1, 149, 176, 2, 36, 2, 219, 2, 22, 985, 79, 10, 206,
201, 2]), array([1306, 0, 1, 9, 3, 121, 178, 4, 388, 29, 2,
96, 271, 0, 3]), array([ 13, 615, 4, 985, 1306, 0, 985, 1306, 0, 28, 2,
180, 690, 301, 985])]
Assigned: [1306 2 1819 10 1 9 3 121 178 4 225 7 87 1858 0]
Retarr: [array([ 9, 3, 121, 178, 4, 175, 341, 0, 143, 5, 4353,
523, 13, 146, 0]), array([ 1, 149, 176, 2, 36, 2, 219, 2, 22, 985, 79, 10, 206,
201, 2]), array([1306, 0, 1, 9, 3, 121, 178, 4, 388, 29, 2,
96, 271, 0, 3]), array([ 13, 615, 4, 985, 1306, 0, 985, 1306, 0, 28, 2,
180, 690, 301, 985]), array([1306, 2, 1819, 10, 1, 9, 3, 121, 178, 4, 225,
7, 87, 1858, 0])]
Assigned: [ 60 15 187 769 24 78 1058 7 2 14 24 78 1949 16 201]
Retarr: [array([ 9, 3, 121, 178, 4, 175, 341, 0, 143, 5, 4353,
523, 13, 146, 0]), array([ 1, 149, 176, 2, 36, 2, 219, 2, 22, 985, 79, 10, 206,
201, 2]), array([1306, 0, 1, 9, 3, 121, 178, 4, 388, 29, 2,
96, 271, 0, 3]), array([ 13, 615, 4, 985, 1306, 0, 985, 1306, 0, 28, 2,
180, 690, 301, 985]), array([1306, 2, 1819, 10, 1, 9, 3, 121, 178, 4, 225,
7, 87, 1858, 0]), array([ 60, 15, 187, 769, 24, 78, 1058, 7, 2, 14, 24,
78, 1949, 16, 201])]
Assigned: [ 29 26 72 35 84 852 423 0 1 9 148 5 326 27 10]
Retarr: [array([ 9, 3, 121, 178, 4, 175, 341, 0, 143, 5, 4353,
523, 13, 146, 0]), array([ 1, 149, 176, 2, 36, 2, 219, 2, 22, 985, 79, 10, 206,
201, 2]), array([1306, 0, 1, 9, 3, 121, 178, 4, 388, 29, 2,
96, 271, 0, 3]), array([ 13, 615, 4, 985, 1306, 0, 985, 1306, 0, 28, 2,
180, 690, 301, 985]), array([1306, 2, 1819, 10, 1, 9, 3, 121, 178, 4, 225,
7, 87, 1858, 0]), array([ 60, 15, 187, 769, 24, 78, 1058, 7, 2, 14, 24,
78, 1949, 16, 201]), array([ 29, 26, 72, 35, 84, 852, 423, 0, 1, 9, 148, 5, 326,
27, 10])]
Assigned: [ 61 62 26 942 2173 568 0 1 12 6 54 16 540 2 30]
Retarr: [array([ 9, 3, 121, 178, 4, 175, 341, 0, 143, 5, 4353,
523, 13, 146, 0]), array([ 1, 149, 176, 2, 36, 2, 219, 2, 22, 985, 79, 10, 206,
201, 2]), array([1306, 0, 1, 9, 3, 121, 178, 4, 388, 29, 2,
96, 271, 0, 3]), array([ 13, 615, 4, 985, 1306, 0, 985, 1306, 0, 28, 2,
180, 690, 301, 985]), array([1306, 2, 1819, 10, 1, 9, 3, 121, 178, 4, 225,
7, 87, 1858, 0]), array([ 60, 15, 187, 769, 24, 78, 1058, 7, 2, 14, 24,
78, 1949, 16, 201]), array([ 29, 26, 72, 35, 84, 852, 423, 0, 1, 9, 148, 5, 326,
27, 10]), array([ 61, 62, 26, 942, 2173, 568, 0, 1, 12, 6, 54,
16, 540, 2, 30])]
Assigned: [ 0 161 21 236 60 0 1 9 27 2 28 2 7 185 62]
Retarr: [array([ 9, 3, 121, 178, 4, 175, 341, 0, 143, 5, 4353,
523, 13, 146, 0]), array([ 1, 149, 176, 2, 36, 2, 219, 2, 22, 985, 79, 10, 206,
201, 2]), array([1306, 0, 1, 9, 3, 121, 178, 4, 388, 29, 2,
96, 271, 0, 3]), array([ 13, 615, 4, 985, 1306, 0, 985, 1306, 0, 28, 2,
180, 690, 301, 985]), array([1306, 2, 1819, 10, 1, 9, 3, 121, 178, 4, 225,
7, 87, 1858, 0]), array([ 60, 15, 187, 769, 24, 78, 1058, 7, 2, 14, 24,
78, 1949, 16, 201]), array([ 29, 26, 72, 35, 84, 852, 423, 0, 1, 9, 148, 5, 326,
27, 10]), array([ 61, 62, 26, 942, 2173, 568, 0, 1, 12, 6, 54,
16, 540, 2, 30]), array([ 0, 161, 21, 236, 60, 0, 1, 9, 27, 2, 28, 2, 7,
185, 62])]
Assigned: [146 13 473 26 540 0 1 41 36 2 7 185 154 146 13]
Retarr: [array([ 9, 3, 121, 178, 4, 175, 341, 0, 143, 5, 4353,
523, 13, 146, 0]), array([ 1, 149, 176, 2, 36, 2, 219, 2, 22, 985, 79, 10, 206,
201, 2]), array([1306, 0, 1, 9, 3, 121, 178, 4, 388, 29, 2,
96, 271, 0, 3]), array([ 13, 615, 4, 985, 1306, 0, 985, 1306, 0, 28, 2,
180, 690, 301, 985]), array([1306, 2, 1819, 10, 1, 9, 3, 121, 178, 4, 225,
7, 87, 1858, 0]), array([ 60, 15, 187, 769, 24, 78, 1058, 7, 2, 14, 24,
78, 1949, 16, 201]), array([ 29, 26, 72, 35, 84, 852, 423, 0, 1, 9, 148, 5, 326,
27, 10]), array([ 61, 62, 26, 942, 2173, 568, 0, 1, 12, 6, 54,
16, 540, 2, 30]), array([ 0, 161, 21, 236, 60, 0, 1, 9, 27, 2, 28, 2, 7,
185, 62]), array([146, 13, 473, 26, 540, 0, 1, 41, 36, 2, 7, 185, 154,
146, 13])]
Assigned: [3257 26 2524 6556 0 1 1 1 9 118 2 294 18 275 0]
Retarr: [array([ 9, 3, 121, 178, 4, 175, 341, 0, 143, 5, 4353,
523, 13, 146, 0]), array([ 1, 149, 176, 2, 36, 2, 219, 2, 22, 985, 79, 10, 206,
201, 2]), array([1306, 0, 1, 9, 3, 121, 178, 4, 388, 29, 2,
96, 271, 0, 3]), array([ 13, 615, 4, 985, 1306, 0, 985, 1306, 0, 28, 2,
180, 690, 301, 985]), array([1306, 2, 1819, 10, 1, 9, 3, 121, 178, 4, 225,
7, 87, 1858, 0]), array([ 60, 15, 187, 769, 24, 78, 1058, 7, 2, 14, 24,
78, 1949, 16, 201]), array([ 29, 26, 72, 35, 84, 852, 423, 0, 1, 9, 148, 5, 326,
27, 10]), array([ 61, 62, 26, 942, 2173, 568, 0, 1, 12, 6, 54,
16, 540, 2, 30]), array([ 0, 161, 21, 236, 60, 0, 1, 9, 27, 2, 28, 2, 7,
185, 62]), array([146, 13, 473, 26, 540, 0, 1, 41, 36, 2, 7, 185, 154,
146, 13]), array([3257, 26, 2524, 6556, 0, 1, 1, 1, 9, 118, 2,
294, 18, 275, 0])]
Assigned: [ 63 2 79 22 11 5544 83 1358 0 1 41 71 6 152 1968]
Retarr: [array([ 9, 3, 121, 178, 4, 175, 341, 0, 143, 5, 4353,
523, 13, 146, 0]), array([ 1, 149, 176, 2, 36, 2, 219, 2, 22, 985, 79, 10, 206,
201, 2]), array([1306, 0, 1, 9, 3, 121, 178, 4, 388, 29, 2,
96, 271, 0, 3]), array([ 13, 615, 4, 985, 1306, 0, 985, 1306, 0, 28, 2,
180, 690, 301, 985]), array([1306, 2, 1819, 10, 1, 9, 3, 121, 178, 4, 225,
7, 87, 1858, 0]), array([ 60, 15, 187, 769, 24, 78, 1058, 7, 2, 14, 24,
78, 1949, 16, 201]), array([ 29, 26, 72, 35, 84, 852, 423, 0, 1, 9, 148, 5, 326,
27, 10]), array([ 61, 62, 26, 942, 2173, 568, 0, 1, 12, 6, 54,
16, 540, 2, 30]), array([ 0, 161, 21, 236, 60, 0, 1, 9, 27, 2, 28, 2, 7,
185, 62]), array([146, 13, 473, 26, 540, 0, 1, 41, 36, 2, 7, 185, 154,
146, 13]), array([3257, 26, 2524, 6556, 0, 1, 1, 1, 9, 118, 2,
294, 18, 275, 0]), array([ 63, 2, 79, 22, 11, 5544, 83, 1358, 0, 1, 41,
71, 6, 152, 1968])]
Assigned: [ 2 553 43 25 98 0 3 860 4 1 1 1 41 28 27]
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523, 13, 146, 0]), array([ 1, 149, 176, 2, 36, 2, 219, 2, 22, 985, 79, 10, 206,
201, 2]), array([1306, 0, 1, 9, 3, 121, 178, 4, 388, 29, 2,
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78, 1949, 16, 201]), array([ 29, 26, 72, 35, 84, 852, 423, 0, 1, 9, 148, 5, 326,
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16, 540, 2, 30]), array([ 0, 161, 21, 236, 60, 0, 1, 9, 27, 2, 28, 2, 7,
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146, 13]), array([3257, 26, 2524, 6556, 0, 1, 1, 1, 9, 118, 2,
294, 18, 275, 0]), array([ 63, 2, 79, 22, 11, 5544, 83, 1358, 0, 1, 41,
71, 6, 152, 1968]), array([ 2, 553, 43, 25, 98, 0, 3, 860, 4, 1, 1, 1, 41,
28, 27])]
Assigned: [ 2 1054 26 4857 691 389 10 1 12 632 0 75 38 1226 79]
Retarr: [array([ 9, 3, 121, 178, 4, 175, 341, 0, 143, 5, 4353,
523, 13, 146, 0]), array([ 1, 149, 176, 2, 36, 2, 219, 2, 22, 985, 79, 10, 206,
201, 2]), array([1306, 0, 1, 9, 3, 121, 178, 4, 388, 29, 2,
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180, 690, 301, 985]), array([1306, 2, 1819, 10, 1, 9, 3, 121, 178, 4, 225,
7, 87, 1858, 0]), array([ 60, 15, 187, 769, 24, 78, 1058, 7, 2, 14, 24,
78, 1949, 16, 201]), array([ 29, 26, 72, 35, 84, 852, 423, 0, 1, 9, 148, 5, 326,
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16, 540, 2, 30]), array([ 0, 161, 21, 236, 60, 0, 1, 9, 27, 2, 28, 2, 7,
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146, 13]), array([3257, 26, 2524, 6556, 0, 1, 1, 1, 9, 118, 2,
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71, 6, 152, 1968]), array([ 2, 553, 43, 25, 98, 0, 3, 860, 4, 1, 1, 1, 41,
28, 27]), array([ 2, 1054, 26, 4857, 691, 389, 10, 1, 12, 632, 0,
75, 38, 1226, 79])]
Assigned: [ 37 155 0 986 47 11 171 310 71 75 167 439 5 184 0]
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523, 13, 146, 0]), array([ 1, 149, 176, 2, 36, 2, 219, 2, 22, 985, 79, 10, 206,
201, 2]), array([1306, 0, 1, 9, 3, 121, 178, 4, 388, 29, 2,
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180, 690, 301, 985]), array([1306, 2, 1819, 10, 1, 9, 3, 121, 178, 4, 225,
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78, 1949, 16, 201]), array([ 29, 26, 72, 35, 84, 852, 423, 0, 1, 9, 148, 5, 326,
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146, 13]), array([3257, 26, 2524, 6556, 0, 1, 1, 1, 9, 118, 2,
294, 18, 275, 0]), array([ 63, 2, 79, 22, 11, 5544, 83, 1358, 0, 1, 41,
71, 6, 152, 1968]), array([ 2, 553, 43, 25, 98, 0, 3, 860, 4, 1, 1, 1, 41,
28, 27]), array([ 2, 1054, 26, 4857, 691, 389, 10, 1, 12, 632, 0,
75, 38, 1226, 79]), array([ 37, 155, 0, 986, 47, 11, 171, 310, 71, 75, 167, 439, 5,
184, 0])]
Assigned: [ 3 307 4 1 9 3 1462 35 2487 4 1646 4436 4717 8 34]
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523, 13, 146, 0]), array([ 1, 149, 176, 2, 36, 2, 219, 2, 22, 985, 79, 10, 206,
201, 2]), array([1306, 0, 1, 9, 3, 121, 178, 4, 388, 29, 2,
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16, 540, 2, 30]), array([ 0, 161, 21, 236, 60, 0, 1, 9, 27, 2, 28, 2, 7,
185, 62]), array([146, 13, 473, 26, 540, 0, 1, 41, 36, 2, 7, 185, 154,
146, 13]), array([3257, 26, 2524, 6556, 0, 1, 1, 1, 9, 118, 2,
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71, 6, 152, 1968]), array([ 2, 553, 43, 25, 98, 0, 3, 860, 4, 1, 1, 1, 41,
28, 27]), array([ 2, 1054, 26, 4857, 691, 389, 10, 1, 12, 632, 0,
75, 38, 1226, 79]), array([ 37, 155, 0, 986, 47, 11, 171, 310, 71, 75, 167, 439, 5,
184, 0]), array([ 3, 307, 4, 1, 9, 3, 1462, 35, 2487, 4, 1646,
4436, 4717, 8, 34])]
Assigned: [232 75 422 10 1 12 58 2 50 349 2 30 0 0 0]
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523, 13, 146, 0]), array([ 1, 149, 176, 2, 36, 2, 219, 2, 22, 985, 79, 10, 206,
201, 2]), array([1306, 0, 1, 9, 3, 121, 178, 4, 388, 29, 2,
96, 271, 0, 3]), array([ 13, 615, 4, 985, 1306, 0, 985, 1306, 0, 28, 2,
180, 690, 301, 985]), array([1306, 2, 1819, 10, 1, 9, 3, 121, 178, 4, 225,
7, 87, 1858, 0]), array([ 60, 15, 187, 769, 24, 78, 1058, 7, 2, 14, 24,
78, 1949, 16, 201]), array([ 29, 26, 72, 35, 84, 852, 423, 0, 1, 9, 148, 5, 326,
27, 10]), array([ 61, 62, 26, 942, 2173, 568, 0, 1, 12, 6, 54,
16, 540, 2, 30]), array([ 0, 161, 21, 236, 60, 0, 1, 9, 27, 2, 28, 2, 7,
185, 62]), array([146, 13, 473, 26, 540, 0, 1, 41, 36, 2, 7, 185, 154,
146, 13]), array([3257, 26, 2524, 6556, 0, 1, 1, 1, 9, 118, 2,
294, 18, 275, 0]), array([ 63, 2, 79, 22, 11, 5544, 83, 1358, 0, 1, 41,
71, 6, 152, 1968]), array([ 2, 553, 43, 25, 98, 0, 3, 860, 4, 1, 1, 1, 41,
28, 27]), array([ 2, 1054, 26, 4857, 691, 389, 10, 1, 12, 632, 0,
75, 38, 1226, 79]), array([ 37, 155, 0, 986, 47, 11, 171, 310, 71, 75, 167, 439, 5,
184, 0]), array([ 3, 307, 4, 1, 9, 3, 1462, 35, 2487, 4, 1646,
4436, 4717, 8, 34]), array([232, 75, 422, 10, 1, 12, 58, 2, 50, 349, 2, 30, 0,
0, 0])]
Assigned: [ 1 4433 3 4280 6 950 0 1 1 1 9 237 39 23 22]
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523, 13, 146, 0]), array([ 1, 149, 176, 2, 36, 2, 219, 2, 22, 985, 79, 10, 206,
201, 2]), array([1306, 0, 1, 9, 3, 121, 178, 4, 388, 29, 2,
96, 271, 0, 3]), array([ 13, 615, 4, 985, 1306, 0, 985, 1306, 0, 28, 2,
180, 690, 301, 985]), array([1306, 2, 1819, 10, 1, 9, 3, 121, 178, 4, 225,
7, 87, 1858, 0]), array([ 60, 15, 187, 769, 24, 78, 1058, 7, 2, 14, 24,
78, 1949, 16, 201]), array([ 29, 26, 72, 35, 84, 852, 423, 0, 1, 9, 148, 5, 326,
27, 10]), array([ 61, 62, 26, 942, 2173, 568, 0, 1, 12, 6, 54,
16, 540, 2, 30]), array([ 0, 161, 21, 236, 60, 0, 1, 9, 27, 2, 28, 2, 7,
185, 62]), array([146, 13, 473, 26, 540, 0, 1, 41, 36, 2, 7, 185, 154,
146, 13]), array([3257, 26, 2524, 6556, 0, 1, 1, 1, 9, 118, 2,
294, 18, 275, 0]), array([ 63, 2, 79, 22, 11, 5544, 83, 1358, 0, 1, 41,
71, 6, 152, 1968]), array([ 2, 553, 43, 25, 98, 0, 3, 860, 4, 1, 1, 1, 41,
28, 27]), array([ 2, 1054, 26, 4857, 691, 389, 10, 1, 12, 632, 0,
75, 38, 1226, 79]), array([ 37, 155, 0, 986, 47, 11, 171, 310, 71, 75, 167, 439, 5,
184, 0]), array([ 3, 307, 4, 1, 9, 3, 1462, 35, 2487, 4, 1646,
4436, 4717, 8, 34]), array([232, 75, 422, 10, 1, 12, 58, 2, 50, 349, 2, 30, 0,
0, 0]), array([ 1, 4433, 3, 4280, 6, 950, 0, 1, 1, 1, 9,
237, 39, 23, 22])]
Assigned: [ 5 557 0 1 41 59 37 68 0 6 471 47 4348 11 6253]
Retarr: [array([ 9, 3, 121, 178, 4, 175, 341, 0, 143, 5, 4353,
523, 13, 146, 0]), array([ 1, 149, 176, 2, 36, 2, 219, 2, 22, 985, 79, 10, 206,
201, 2]), array([1306, 0, 1, 9, 3, 121, 178, 4, 388, 29, 2,
96, 271, 0, 3]), array([ 13, 615, 4, 985, 1306, 0, 985, 1306, 0, 28, 2,
180, 690, 301, 985]), array([1306, 2, 1819, 10, 1, 9, 3, 121, 178, 4, 225,
7, 87, 1858, 0]), array([ 60, 15, 187, 769, 24, 78, 1058, 7, 2, 14, 24,
78, 1949, 16, 201]), array([ 29, 26, 72, 35, 84, 852, 423, 0, 1, 9, 148, 5, 326,
27, 10]), array([ 61, 62, 26, 942, 2173, 568, 0, 1, 12, 6, 54,
16, 540, 2, 30]), array([ 0, 161, 21, 236, 60, 0, 1, 9, 27, 2, 28, 2, 7,
185, 62]), array([146, 13, 473, 26, 540, 0, 1, 41, 36, 2, 7, 185, 154,
146, 13]), array([3257, 26, 2524, 6556, 0, 1, 1, 1, 9, 118, 2,
294, 18, 275, 0]), array([ 63, 2, 79, 22, 11, 5544, 83, 1358, 0, 1, 41,
71, 6, 152, 1968]), array([ 2, 553, 43, 25, 98, 0, 3, 860, 4, 1, 1, 1, 41,
28, 27]), array([ 2, 1054, 26, 4857, 691, 389, 10, 1, 12, 632, 0,
75, 38, 1226, 79]), array([ 37, 155, 0, 986, 47, 11, 171, 310, 71, 75, 167, 439, 5,
184, 0]), array([ 3, 307, 4, 1, 9, 3, 1462, 35, 2487, 4, 1646,
4436, 4717, 8, 34]), array([232, 75, 422, 10, 1, 12, 58, 2, 50, 349, 2, 30, 0,
0, 0]), array([ 1, 4433, 3, 4280, 6, 950, 0, 1, 1, 1, 9,
237, 39, 23, 22]), array([ 5, 557, 0, 1, 41, 59, 37, 68, 0, 6, 471,
47, 4348, 11, 6253])]
Assigned: [ 172 0 1 41 42 5 308 115 731 1634 7 5829 2582 56 82]
Retarr: [array([ 9, 3, 121, 178, 4, 175, 341, 0, 143, 5, 4353,
523, 13, 146, 0]), array([ 1, 149, 176, 2, 36, 2, 219, 2, 22, 985, 79, 10, 206,
201, 2]), array([1306, 0, 1, 9, 3, 121, 178, 4, 388, 29, 2,
96, 271, 0, 3]), array([ 13, 615, 4, 985, 1306, 0, 985, 1306, 0, 28, 2,
180, 690, 301, 985]), array([1306, 2, 1819, 10, 1, 9, 3, 121, 178, 4, 225,
7, 87, 1858, 0]), array([ 60, 15, 187, 769, 24, 78, 1058, 7, 2, 14, 24,
78, 1949, 16, 201]), array([ 29, 26, 72, 35, 84, 852, 423, 0, 1, 9, 148, 5, 326,
27, 10]), array([ 61, 62, 26, 942, 2173, 568, 0, 1, 12, 6, 54,
16, 540, 2, 30]), array([ 0, 161, 21, 236, 60, 0, 1, 9, 27, 2, 28, 2, 7,
185, 62]), array([146, 13, 473, 26, 540, 0, 1, 41, 36, 2, 7, 185, 154,
146, 13]), array([3257, 26, 2524, 6556, 0, 1, 1, 1, 9, 118, 2,
294, 18, 275, 0]), array([ 63, 2, 79, 22, 11, 5544, 83, 1358, 0, 1, 41,
71, 6, 152, 1968]), array([ 2, 553, 43, 25, 98, 0, 3, 860, 4, 1, 1, 1, 41,
28, 27]), array([ 2, 1054, 26, 4857, 691, 389, 10, 1, 12, 632, 0,
75, 38, 1226, 79]), array([ 37, 155, 0, 986, 47, 11, 171, 310, 71, 75, 167, 439, 5,
184, 0]), array([ 3, 307, 4, 1, 9, 3, 1462, 35, 2487, 4, 1646,
4436, 4717, 8, 34]), array([232, 75, 422, 10, 1, 12, 58, 2, 50, 349, 2, 30, 0,
0, 0]), array([ 1, 4433, 3, 4280, 6, 950, 0, 1, 1, 1, 9,
237, 39, 23, 22]), array([ 5, 557, 0, 1, 41, 59, 37, 68, 0, 6, 471,
47, 4348, 11, 6253]), array([ 172, 0, 1, 41, 42, 5, 308, 115, 731, 1634, 7,
5829, 2582, 56, 82])]
Assigned: [ 2 847 170 2 7 64 1785 19 223 4091 25 21 8 1 134]
Retarr: [array([ 9, 3, 121, 178, 4, 175, 341, 0, 143, 5, 4353,
523, 13, 146, 0]), array([ 1, 149, 176, 2, 36, 2, 219, 2, 22, 985, 79, 10, 206,
201, 2]), array([1306, 0, 1, 9, 3, 121, 178, 4, 388, 29, 2,
96, 271, 0, 3]), array([ 13, 615, 4, 985, 1306, 0, 985, 1306, 0, 28, 2,
180, 690, 301, 985]), array([1306, 2, 1819, 10, 1, 9, 3, 121, 178, 4, 225,
7, 87, 1858, 0]), array([ 60, 15, 187, 769, 24, 78, 1058, 7, 2, 14, 24,
78, 1949, 16, 201]), array([ 29, 26, 72, 35, 84, 852, 423, 0, 1, 9, 148, 5, 326,
27, 10]), array([ 61, 62, 26, 942, 2173, 568, 0, 1, 12, 6, 54,
16, 540, 2, 30]), array([ 0, 161, 21, 236, 60, 0, 1, 9, 27, 2, 28, 2, 7,
185, 62]), array([146, 13, 473, 26, 540, 0, 1, 41, 36, 2, 7, 185, 154,
146, 13]), array([3257, 26, 2524, 6556, 0, 1, 1, 1, 9, 118, 2,
294, 18, 275, 0]), array([ 63, 2, 79, 22, 11, 5544, 83, 1358, 0, 1, 41,
71, 6, 152, 1968]), array([ 2, 553, 43, 25, 98, 0, 3, 860, 4, 1, 1, 1, 41,
28, 27]), array([ 2, 1054, 26, 4857, 691, 389, 10, 1, 12, 632, 0,
75, 38, 1226, 79]), array([ 37, 155, 0, 986, 47, 11, 171, 310, 71, 75, 167, 439, 5,
184, 0]), array([ 3, 307, 4, 1, 9, 3, 1462, 35, 2487, 4, 1646,
4436, 4717, 8, 34]), array([232, 75, 422, 10, 1, 12, 58, 2, 50, 349, 2, 30, 0,
0, 0]), array([ 1, 4433, 3, 4280, 6, 950, 0, 1, 1, 1, 9,
237, 39, 23, 22]), array([ 5, 557, 0, 1, 41, 59, 37, 68, 0, 6, 471,
47, 4348, 11, 6253]), array([ 172, 0, 1, 41, 42, 5, 308, 115, 731, 1634, 7,
5829, 2582, 56, 82]), array([ 2, 847, 170, 2, 7, 64, 1785, 19, 223, 4091, 25,
21, 8, 1, 134])]
Assigned: [ 6 157 2 186 0 866 0 40 71 291 416 21 2 24 5929]
Retarr: [array([ 9, 3, 121, 178, 4, 175, 341, 0, 143, 5, 4353,
523, 13, 146, 0]), array([ 1, 149, 176, 2, 36, 2, 219, 2, 22, 985, 79, 10, 206,
201, 2]), array([1306, 0, 1, 9, 3, 121, 178, 4, 388, 29, 2,
96, 271, 0, 3]), array([ 13, 615, 4, 985, 1306, 0, 985, 1306, 0, 28, 2,
180, 690, 301, 985]), array([1306, 2, 1819, 10, 1, 9, 3, 121, 178, 4, 225,
7, 87, 1858, 0]), array([ 60, 15, 187, 769, 24, 78, 1058, 7, 2, 14, 24,
78, 1949, 16, 201]), array([ 29, 26, 72, 35, 84, 852, 423, 0, 1, 9, 148, 5, 326,
27, 10]), array([ 61, 62, 26, 942, 2173, 568, 0, 1, 12, 6, 54,
16, 540, 2, 30]), array([ 0, 161, 21, 236, 60, 0, 1, 9, 27, 2, 28, 2, 7,
185, 62]), array([146, 13, 473, 26, 540, 0, 1, 41, 36, 2, 7, 185, 154,
146, 13]), array([3257, 26, 2524, 6556, 0, 1, 1, 1, 9, 118, 2,
294, 18, 275, 0]), array([ 63, 2, 79, 22, 11, 5544, 83, 1358, 0, 1, 41,
71, 6, 152, 1968]), array([ 2, 553, 43, 25, 98, 0, 3, 860, 4, 1, 1, 1, 41,
28, 27]), array([ 2, 1054, 26, 4857, 691, 389, 10, 1, 12, 632, 0,
75, 38, 1226, 79]), array([ 37, 155, 0, 986, 47, 11, 171, 310, 71, 75, 167, 439, 5,
184, 0]), array([ 3, 307, 4, 1, 9, 3, 1462, 35, 2487, 4, 1646,
4436, 4717, 8, 34]), array([232, 75, 422, 10, 1, 12, 58, 2, 50, 349, 2, 30, 0,
0, 0]), array([ 1, 4433, 3, 4280, 6, 950, 0, 1, 1, 1, 9,
237, 39, 23, 22]), array([ 5, 557, 0, 1, 41, 59, 37, 68, 0, 6, 471,
47, 4348, 11, 6253]), array([ 172, 0, 1, 41, 42, 5, 308, 115, 731, 1634, 7,
5829, 2582, 56, 82]), array([ 2, 847, 170, 2, 7, 64, 1785, 19, 223, 4091, 25,
21, 8, 1, 134]), array([ 6, 157, 2, 186, 0, 866, 0, 40, 71, 291, 416,
21, 2, 24, 5929])]
Assigned: [ 16 248 25 5 465 5068 0 6 257 17 135 43 2847 13 1327]
Retarr: [array([ 9, 3, 121, 178, 4, 175, 341, 0, 143, 5, 4353,
523, 13, 146, 0]), array([ 1, 149, 176, 2, 36, 2, 219, 2, 22, 985, 79, 10, 206,
201, 2]), array([1306, 0, 1, 9, 3, 121, 178, 4, 388, 29, 2,
96, 271, 0, 3]), array([ 13, 615, 4, 985, 1306, 0, 985, 1306, 0, 28, 2,
180, 690, 301, 985]), array([1306, 2, 1819, 10, 1, 9, 3, 121, 178, 4, 225,
7, 87, 1858, 0]), array([ 60, 15, 187, 769, 24, 78, 1058, 7, 2, 14, 24,
78, 1949, 16, 201]), array([ 29, 26, 72, 35, 84, 852, 423, 0, 1, 9, 148, 5, 326,
27, 10]), array([ 61, 62, 26, 942, 2173, 568, 0, 1, 12, 6, 54,
16, 540, 2, 30]), array([ 0, 161, 21, 236, 60, 0, 1, 9, 27, 2, 28, 2, 7,
185, 62]), array([146, 13, 473, 26, 540, 0, 1, 41, 36, 2, 7, 185, 154,
146, 13]), array([3257, 26, 2524, 6556, 0, 1, 1, 1, 9, 118, 2,
294, 18, 275, 0]), array([ 63, 2, 79, 22, 11, 5544, 83, 1358, 0, 1, 41,
71, 6, 152, 1968]), array([ 2, 553, 43, 25, 98, 0, 3, 860, 4, 1, 1, 1, 41,
28, 27]), array([ 2, 1054, 26, 4857, 691, 389, 10, 1, 12, 632, 0,
75, 38, 1226, 79]), array([ 37, 155, 0, 986, 47, 11, 171, 310, 71, 75, 167, 439, 5,
184, 0]), array([ 3, 307, 4, 1, 9, 3, 1462, 35, 2487, 4, 1646,
4436, 4717, 8, 34]), array([232, 75, 422, 10, 1, 12, 58, 2, 50, 349, 2, 30, 0,
0, 0]), array([ 1, 4433, 3, 4280, 6, 950, 0, 1, 1, 1, 9,
237, 39, 23, 22]), array([ 5, 557, 0, 1, 41, 59, 37, 68, 0, 6, 471,
47, 4348, 11, 6253]), array([ 172, 0, 1, 41, 42, 5, 308, 115, 731, 1634, 7,
5829, 2582, 56, 82]), array([ 2, 847, 170, 2, 7, 64, 1785, 19, 223, 4091, 25,
21, 8, 1, 134]), array([ 6, 157, 2, 186, 0, 866, 0, 40, 71, 291, 416,
21, 2, 24, 5929]), array([ 16, 248, 25, 5, 465, 5068, 0, 6, 257, 17, 135,
43, 2847, 13, 1327])]
Assigned: [ 98 192 25 11 155 13 100 34 410 98 2795 0 1 41 31]
Retarr: [array([ 9, 3, 121, 178, 4, 175, 341, 0, 143, 5, 4353,
523, 13, 146, 0]), array([ 1, 149, 176, 2, 36, 2, 219, 2, 22, 985, 79, 10, 206,
201, 2]), array([1306, 0, 1, 9, 3, 121, 178, 4, 388, 29, 2,
96, 271, 0, 3]), array([ 13, 615, 4, 985, 1306, 0, 985, 1306, 0, 28, 2,
180, 690, 301, 985]), array([1306, 2, 1819, 10, 1, 9, 3, 121, 178, 4, 225,
7, 87, 1858, 0]), array([ 60, 15, 187, 769, 24, 78, 1058, 7, 2, 14, 24,
78, 1949, 16, 201]), array([ 29, 26, 72, 35, 84, 852, 423, 0, 1, 9, 148, 5, 326,
27, 10]), array([ 61, 62, 26, 942, 2173, 568, 0, 1, 12, 6, 54,
16, 540, 2, 30]), array([ 0, 161, 21, 236, 60, 0, 1, 9, 27, 2, 28, 2, 7,
185, 62]), array([146, 13, 473, 26, 540, 0, 1, 41, 36, 2, 7, 185, 154,
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71, 6, 152, 1968]), array([ 2, 553, 43, 25, 98, 0, 3, 860, 4, 1, 1, 1, 41,
28, 27]), array([ 2, 1054, 26, 4857, 691, 389, 10, 1, 12, 632, 0,
75, 38, 1226, 79]), array([ 37, 155, 0, 986, 47, 11, 171, 310, 71, 75, 167, 439, 5,
184, 0]), array([ 3, 307, 4, 1, 9, 3, 1462, 35, 2487, 4, 1646,
4436, 4717, 8, 34]), array([232, 75, 422, 10, 1, 12, 58, 2, 50, 349, 2, 30, 0,
0, 0]), array([ 1, 4433, 3, 4280, 6, 950, 0, 1, 1, 1, 9,
237, 39, 23, 22]), array([ 5, 557, 0, 1, 41, 59, 37, 68, 0, 6, 471,
47, 4348, 11, 6253]), array([ 172, 0, 1, 41, 42, 5, 308, 115, 731, 1634, 7,
5829, 2582, 56, 82]), array([ 2, 847, 170, 2, 7, 64, 1785, 19, 223, 4091, 25,
21, 8, 1, 134]), array([ 6, 157, 2, 186, 0, 866, 0, 40, 71, 291, 416,
21, 2, 24, 5929]), array([ 16, 248, 25, 5, 465, 5068, 0, 6, 257, 17, 135,
43, 2847, 13, 1327]), array([ 98, 192, 25, 11, 155, 13, 100, 34, 410, 98, 2795,
0, 1, 41, 31])]
Assigned: [ 2 59 251 0 6 373 13 1327 16 248 13 11 300 15 1229]
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523, 13, 146, 0]), array([ 1, 149, 176, 2, 36, 2, 219, 2, 22, 985, 79, 10, 206,
201, 2]), array([1306, 0, 1, 9, 3, 121, 178, 4, 388, 29, 2,
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28, 27]), array([ 2, 1054, 26, 4857, 691, 389, 10, 1, 12, 632, 0,
75, 38, 1226, 79]), array([ 37, 155, 0, 986, 47, 11, 171, 310, 71, 75, 167, 439, 5,
184, 0]), array([ 3, 307, 4, 1, 9, 3, 1462, 35, 2487, 4, 1646,
4436, 4717, 8, 34]), array([232, 75, 422, 10, 1, 12, 58, 2, 50, 349, 2, 30, 0,
0, 0]), array([ 1, 4433, 3, 4280, 6, 950, 0, 1, 1, 1, 9,
237, 39, 23, 22]), array([ 5, 557, 0, 1, 41, 59, 37, 68, 0, 6, 471,
47, 4348, 11, 6253]), array([ 172, 0, 1, 41, 42, 5, 308, 115, 731, 1634, 7,
5829, 2582, 56, 82]), array([ 2, 847, 170, 2, 7, 64, 1785, 19, 223, 4091, 25,
21, 8, 1, 134]), array([ 6, 157, 2, 186, 0, 866, 0, 40, 71, 291, 416,
21, 2, 24, 5929]), array([ 16, 248, 25, 5, 465, 5068, 0, 6, 257, 17, 135,
43, 2847, 13, 1327]), array([ 98, 192, 25, 11, 155, 13, 100, 34, 410, 98, 2795,
0, 1, 41, 31]), array([ 2, 59, 251, 0, 6, 373, 13, 1327, 16, 248, 13,
11, 300, 15, 1229])]
Assigned: [104 0 3 860 4 1 9 44 7 77 0 60 81 2 60]
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523, 13, 146, 0]), array([ 1, 149, 176, 2, 36, 2, 219, 2, 22, 985, 79, 10, 206,
201, 2]), array([1306, 0, 1, 9, 3, 121, 178, 4, 388, 29, 2,
96, 271, 0, 3]), array([ 13, 615, 4, 985, 1306, 0, 985, 1306, 0, 28, 2,
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78, 1949, 16, 201]), array([ 29, 26, 72, 35, 84, 852, 423, 0, 1, 9, 148, 5, 326,
27, 10]), array([ 61, 62, 26, 942, 2173, 568, 0, 1, 12, 6, 54,
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146, 13]), array([3257, 26, 2524, 6556, 0, 1, 1, 1, 9, 118, 2,
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28, 27]), array([ 2, 1054, 26, 4857, 691, 389, 10, 1, 12, 632, 0,
75, 38, 1226, 79]), array([ 37, 155, 0, 986, 47, 11, 171, 310, 71, 75, 167, 439, 5,
184, 0]), array([ 3, 307, 4, 1, 9, 3, 1462, 35, 2487, 4, 1646,
4436, 4717, 8, 34]), array([232, 75, 422, 10, 1, 12, 58, 2, 50, 349, 2, 30, 0,
0, 0]), array([ 1, 4433, 3, 4280, 6, 950, 0, 1, 1, 1, 9,
237, 39, 23, 22]), array([ 5, 557, 0, 1, 41, 59, 37, 68, 0, 6, 471,
47, 4348, 11, 6253]), array([ 172, 0, 1, 41, 42, 5, 308, 115, 731, 1634, 7,
5829, 2582, 56, 82]), array([ 2, 847, 170, 2, 7, 64, 1785, 19, 223, 4091, 25,
21, 8, 1, 134]), array([ 6, 157, 2, 186, 0, 866, 0, 40, 71, 291, 416,
21, 2, 24, 5929]), array([ 16, 248, 25, 5, 465, 5068, 0, 6, 257, 17, 135,
43, 2847, 13, 1327]), array([ 98, 192, 25, 11, 155, 13, 100, 34, 410, 98, 2795,
0, 1, 41, 31]), array([ 2, 59, 251, 0, 6, 373, 13, 1327, 16, 248, 13,
11, 300, 15, 1229]), array([104, 0, 3, 860, 4, 1, 9, 44, 7, 77, 0, 60, 81,
2, 60])]
Assigned: [1432 886 0 1 134 58 2 416 21 2 6 47 5 1432 886]
Retarr: [array([ 9, 3, 121, 178, 4, 175, 341, 0, 143, 5, 4353,
523, 13, 146, 0]), array([ 1, 149, 176, 2, 36, 2, 219, 2, 22, 985, 79, 10, 206,
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96, 271, 0, 3]), array([ 13, 615, 4, 985, 1306, 0, 985, 1306, 0, 28, 2,
180, 690, 301, 985]), array([1306, 2, 1819, 10, 1, 9, 3, 121, 178, 4, 225,
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78, 1949, 16, 201]), array([ 29, 26, 72, 35, 84, 852, 423, 0, 1, 9, 148, 5, 326,
27, 10]), array([ 61, 62, 26, 942, 2173, 568, 0, 1, 12, 6, 54,
16, 540, 2, 30]), array([ 0, 161, 21, 236, 60, 0, 1, 9, 27, 2, 28, 2, 7,
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146, 13]), array([3257, 26, 2524, 6556, 0, 1, 1, 1, 9, 118, 2,
294, 18, 275, 0]), array([ 63, 2, 79, 22, 11, 5544, 83, 1358, 0, 1, 41,
71, 6, 152, 1968]), array([ 2, 553, 43, 25, 98, 0, 3, 860, 4, 1, 1, 1, 41,
28, 27]), array([ 2, 1054, 26, 4857, 691, 389, 10, 1, 12, 632, 0,
75, 38, 1226, 79]), array([ 37, 155, 0, 986, 47, 11, 171, 310, 71, 75, 167, 439, 5,
184, 0]), array([ 3, 307, 4, 1, 9, 3, 1462, 35, 2487, 4, 1646,
4436, 4717, 8, 34]), array([232, 75, 422, 10, 1, 12, 58, 2, 50, 349, 2, 30, 0,
0, 0]), array([ 1, 4433, 3, 4280, 6, 950, 0, 1, 1, 1, 9,
237, 39, 23, 22]), array([ 5, 557, 0, 1, 41, 59, 37, 68, 0, 6, 471,
47, 4348, 11, 6253]), array([ 172, 0, 1, 41, 42, 5, 308, 115, 731, 1634, 7,
5829, 2582, 56, 82]), array([ 2, 847, 170, 2, 7, 64, 1785, 19, 223, 4091, 25,
21, 8, 1, 134]), array([ 6, 157, 2, 186, 0, 866, 0, 40, 71, 291, 416,
21, 2, 24, 5929]), array([ 16, 248, 25, 5, 465, 5068, 0, 6, 257, 17, 135,
43, 2847, 13, 1327]), array([ 98, 192, 25, 11, 155, 13, 100, 34, 410, 98, 2795,
0, 1, 41, 31]), array([ 2, 59, 251, 0, 6, 373, 13, 1327, 16, 248, 13,
11, 300, 15, 1229]), array([104, 0, 3, 860, 4, 1, 9, 44, 7, 77, 0, 60, 81,
2, 60]), array([1432, 886, 0, 1, 134, 58, 2, 416, 21, 2, 6,
47, 5, 1432, 886])]
Assigned: [ 0 1 9 31 0 1 9 148 5 326 2 27 10 5 6407]
Retarr: [array([ 9, 3, 121, 178, 4, 175, 341, 0, 143, 5, 4353,
523, 13, 146, 0]), array([ 1, 149, 176, 2, 36, 2, 219, 2, 22, 985, 79, 10, 206,
201, 2]), array([1306, 0, 1, 9, 3, 121, 178, 4, 388, 29, 2,
96, 271, 0, 3]), array([ 13, 615, 4, 985, 1306, 0, 985, 1306, 0, 28, 2,
180, 690, 301, 985]), array([1306, 2, 1819, 10, 1, 9, 3, 121, 178, 4, 225,
7, 87, 1858, 0]), array([ 60, 15, 187, 769, 24, 78, 1058, 7, 2, 14, 24,
78, 1949, 16, 201]), array([ 29, 26, 72, 35, 84, 852, 423, 0, 1, 9, 148, 5, 326,
27, 10]), array([ 61, 62, 26, 942, 2173, 568, 0, 1, 12, 6, 54,
16, 540, 2, 30]), array([ 0, 161, 21, 236, 60, 0, 1, 9, 27, 2, 28, 2, 7,
185, 62]), array([146, 13, 473, 26, 540, 0, 1, 41, 36, 2, 7, 185, 154,
146, 13]), array([3257, 26, 2524, 6556, 0, 1, 1, 1, 9, 118, 2,
294, 18, 275, 0]), array([ 63, 2, 79, 22, 11, 5544, 83, 1358, 0, 1, 41,
71, 6, 152, 1968]), array([ 2, 553, 43, 25, 98, 0, 3, 860, 4, 1, 1, 1, 41,
28, 27]), array([ 2, 1054, 26, 4857, 691, 389, 10, 1, 12, 632, 0,
75, 38, 1226, 79]), array([ 37, 155, 0, 986, 47, 11, 171, 310, 71, 75, 167, 439, 5,
184, 0]), array([ 3, 307, 4, 1, 9, 3, 1462, 35, 2487, 4, 1646,
4436, 4717, 8, 34]), array([232, 75, 422, 10, 1, 12, 58, 2, 50, 349, 2, 30, 0,
0, 0]), array([ 1, 4433, 3, 4280, 6, 950, 0, 1, 1, 1, 9,
237, 39, 23, 22]), array([ 5, 557, 0, 1, 41, 59, 37, 68, 0, 6, 471,
47, 4348, 11, 6253]), array([ 172, 0, 1, 41, 42, 5, 308, 115, 731, 1634, 7,
5829, 2582, 56, 82]), array([ 2, 847, 170, 2, 7, 64, 1785, 19, 223, 4091, 25,
21, 8, 1, 134]), array([ 6, 157, 2, 186, 0, 866, 0, 40, 71, 291, 416,
21, 2, 24, 5929]), array([ 16, 248, 25, 5, 465, 5068, 0, 6, 257, 17, 135,
43, 2847, 13, 1327]), array([ 98, 192, 25, 11, 155, 13, 100, 34, 410, 98, 2795,
0, 1, 41, 31]), array([ 2, 59, 251, 0, 6, 373, 13, 1327, 16, 248, 13,
11, 300, 15, 1229]), array([104, 0, 3, 860, 4, 1, 9, 44, 7, 77, 0, 60, 81,
2, 60]), array([1432, 886, 0, 1, 134, 58, 2, 416, 21, 2, 6,
47, 5, 1432, 886]), array([ 0, 1, 9, 31, 0, 1, 9, 148, 5, 326, 2,
27, 10, 5, 6407])]
Assigned: [2326 15 486 1335 46 702 19 1797 1404 115 0 1 134 139 38]
Retarr: [array([ 9, 3, 121, 178, 4, 175, 341, 0, 143, 5, 4353,
523, 13, 146, 0]), array([ 1, 149, 176, 2, 36, 2, 219, 2, 22, 985, 79, 10, 206,
201, 2]), array([1306, 0, 1, 9, 3, 121, 178, 4, 388, 29, 2,
96, 271, 0, 3]), array([ 13, 615, 4, 985, 1306, 0, 985, 1306, 0, 28, 2,
180, 690, 301, 985]), array([1306, 2, 1819, 10, 1, 9, 3, 121, 178, 4, 225,
7, 87, 1858, 0]), array([ 60, 15, 187, 769, 24, 78, 1058, 7, 2, 14, 24,
78, 1949, 16, 201]), array([ 29, 26, 72, 35, 84, 852, 423, 0, 1, 9, 148, 5, 326,
27, 10]), array([ 61, 62, 26, 942, 2173, 568, 0, 1, 12, 6, 54,
16, 540, 2, 30]), array([ 0, 161, 21, 236, 60, 0, 1, 9, 27, 2, 28, 2, 7,
185, 62]), array([146, 13, 473, 26, 540, 0, 1, 41, 36, 2, 7, 185, 154,
146, 13]), array([3257, 26, 2524, 6556, 0, 1, 1, 1, 9, 118, 2,
294, 18, 275, 0]), array([ 63, 2, 79, 22, 11, 5544, 83, 1358, 0, 1, 41,
71, 6, 152, 1968]), array([ 2, 553, 43, 25, 98, 0, 3, 860, 4, 1, 1, 1, 41,
28, 27]), array([ 2, 1054, 26, 4857, 691, 389, 10, 1, 12, 632, 0,
75, 38, 1226, 79]), array([ 37, 155, 0, 986, 47, 11, 171, 310, 71, 75, 167, 439, 5,
184, 0]), array([ 3, 307, 4, 1, 9, 3, 1462, 35, 2487, 4, 1646,
4436, 4717, 8, 34]), array([232, 75, 422, 10, 1, 12, 58, 2, 50, 349, 2, 30, 0,
0, 0]), array([ 1, 4433, 3, 4280, 6, 950, 0, 1, 1, 1, 9,
237, 39, 23, 22]), array([ 5, 557, 0, 1, 41, 59, 37, 68, 0, 6, 471,
47, 4348, 11, 6253]), array([ 172, 0, 1, 41, 42, 5, 308, 115, 731, 1634, 7,
5829, 2582, 56, 82]), array([ 2, 847, 170, 2, 7, 64, 1785, 19, 223, 4091, 25,
21, 8, 1, 134]), array([ 6, 157, 2, 186, 0, 866, 0, 40, 71, 291, 416,
21, 2, 24, 5929]), array([ 16, 248, 25, 5, 465, 5068, 0, 6, 257, 17, 135,
43, 2847, 13, 1327]), array([ 98, 192, 25, 11, 155, 13, 100, 34, 410, 98, 2795,
0, 1, 41, 31]), array([ 2, 59, 251, 0, 6, 373, 13, 1327, 16, 248, 13,
11, 300, 15, 1229]), array([104, 0, 3, 860, 4, 1, 9, 44, 7, 77, 0, 60, 81,
2, 60]), array([1432, 886, 0, 1, 134, 58, 2, 416, 21, 2, 6,
47, 5, 1432, 886]), array([ 0, 1, 9, 31, 0, 1, 9, 148, 5, 326, 2,
27, 10, 5, 6407]), array([2326, 15, 486, 1335, 46, 702, 19, 1797, 1404, 115, 0,
1, 134, 139, 38])]
Assigned: [ 11 87 333 0 3 785 4 75 180 13 161 11 1144 455 29]
Retarr: [array([ 9, 3, 121, 178, 4, 175, 341, 0, 143, 5, 4353,
523, 13, 146, 0]), array([ 1, 149, 176, 2, 36, 2, 219, 2, 22, 985, 79, 10, 206,
201, 2]), array([1306, 0, 1, 9, 3, 121, 178, 4, 388, 29, 2,
96, 271, 0, 3]), array([ 13, 615, 4, 985, 1306, 0, 985, 1306, 0, 28, 2,
180, 690, 301, 985]), array([1306, 2, 1819, 10, 1, 9, 3, 121, 178, 4, 225,
7, 87, 1858, 0]), array([ 60, 15, 187, 769, 24, 78, 1058, 7, 2, 14, 24,
78, 1949, 16, 201]), array([ 29, 26, 72, 35, 84, 852, 423, 0, 1, 9, 148, 5, 326,
27, 10]), array([ 61, 62, 26, 942, 2173, 568, 0, 1, 12, 6, 54,
16, 540, 2, 30]), array([ 0, 161, 21, 236, 60, 0, 1, 9, 27, 2, 28, 2, 7,
185, 62]), array([146, 13, 473, 26, 540, 0, 1, 41, 36, 2, 7, 185, 154,
146, 13]), array([3257, 26, 2524, 6556, 0, 1, 1, 1, 9, 118, 2,
294, 18, 275, 0]), array([ 63, 2, 79, 22, 11, 5544, 83, 1358, 0, 1, 41,
71, 6, 152, 1968]), array([ 2, 553, 43, 25, 98, 0, 3, 860, 4, 1, 1, 1, 41,
28, 27]), array([ 2, 1054, 26, 4857, 691, 389, 10, 1, 12, 632, 0,
75, 38, 1226, 79]), array([ 37, 155, 0, 986, 47, 11, 171, 310, 71, 75, 167, 439, 5,
184, 0]), array([ 3, 307, 4, 1, 9, 3, 1462, 35, 2487, 4, 1646,
4436, 4717, 8, 34]), array([232, 75, 422, 10, 1, 12, 58, 2, 50, 349, 2, 30, 0,
0, 0]), array([ 1, 4433, 3, 4280, 6, 950, 0, 1, 1, 1, 9,
237, 39, 23, 22]), array([ 5, 557, 0, 1, 41, 59, 37, 68, 0, 6, 471,
47, 4348, 11, 6253]), array([ 172, 0, 1, 41, 42, 5, 308, 115, 731, 1634, 7,
5829, 2582, 56, 82]), array([ 2, 847, 170, 2, 7, 64, 1785, 19, 223, 4091, 25,
21, 8, 1, 134]), array([ 6, 157, 2, 186, 0, 866, 0, 40, 71, 291, 416,
21, 2, 24, 5929]), array([ 16, 248, 25, 5, 465, 5068, 0, 6, 257, 17, 135,
43, 2847, 13, 1327]), array([ 98, 192, 25, 11, 155, 13, 100, 34, 410, 98, 2795,
0, 1, 41, 31]), array([ 2, 59, 251, 0, 6, 373, 13, 1327, 16, 248, 13,
11, 300, 15, 1229]), array([104, 0, 3, 860, 4, 1, 9, 44, 7, 77, 0, 60, 81,
2, 60]), array([1432, 886, 0, 1, 134, 58, 2, 416, 21, 2, 6,
47, 5, 1432, 886]), array([ 0, 1, 9, 31, 0, 1, 9, 148, 5, 326, 2,
27, 10, 5, 6407]), array([2326, 15, 486, 1335, 46, 702, 19, 1797, 1404, 115, 0,
1, 134, 139, 38]), array([ 11, 87, 333, 0, 3, 785, 4, 75, 180, 13, 161,
11, 1144, 455, 29])]
Assigned: [ 20 70 13 272 694 19 11 3533 0 20 1 12 3 261 4]
Retarr: [array([ 9, 3, 121, 178, 4, 175, 341, 0, 143, 5, 4353,
523, 13, 146, 0]), array([ 1, 149, 176, 2, 36, 2, 219, 2, 22, 985, 79, 10, 206,
201, 2]), array([1306, 0, 1, 9, 3, 121, 178, 4, 388, 29, 2,
96, 271, 0, 3]), array([ 13, 615, 4, 985, 1306, 0, 985, 1306, 0, 28, 2,
180, 690, 301, 985]), array([1306, 2, 1819, 10, 1, 9, 3, 121, 178, 4, 225,
7, 87, 1858, 0]), array([ 60, 15, 187, 769, 24, 78, 1058, 7, 2, 14, 24,
78, 1949, 16, 201]), array([ 29, 26, 72, 35, 84, 852, 423, 0, 1, 9, 148, 5, 326,
27, 10]), array([ 61, 62, 26, 942, 2173, 568, 0, 1, 12, 6, 54,
16, 540, 2, 30]), array([ 0, 161, 21, 236, 60, 0, 1, 9, 27, 2, 28, 2, 7,
185, 62]), array([146, 13, 473, 26, 540, 0, 1, 41, 36, 2, 7, 185, 154,
146, 13]), array([3257, 26, 2524, 6556, 0, 1, 1, 1, 9, 118, 2,
294, 18, 275, 0]), array([ 63, 2, 79, 22, 11, 5544, 83, 1358, 0, 1, 41,
71, 6, 152, 1968]), array([ 2, 553, 43, 25, 98, 0, 3, 860, 4, 1, 1, 1, 41,
28, 27]), array([ 2, 1054, 26, 4857, 691, 389, 10, 1, 12, 632, 0,
75, 38, 1226, 79]), array([ 37, 155, 0, 986, 47, 11, 171, 310, 71, 75, 167, 439, 5,
184, 0]), array([ 3, 307, 4, 1, 9, 3, 1462, 35, 2487, 4, 1646,
4436, 4717, 8, 34]), array([232, 75, 422, 10, 1, 12, 58, 2, 50, 349, 2, 30, 0,
0, 0]), array([ 1, 4433, 3, 4280, 6, 950, 0, 1, 1, 1, 9,
237, 39, 23, 22]), array([ 5, 557, 0, 1, 41, 59, 37, 68, 0, 6, 471,
47, 4348, 11, 6253]), array([ 172, 0, 1, 41, 42, 5, 308, 115, 731, 1634, 7,
5829, 2582, 56, 82]), array([ 2, 847, 170, 2, 7, 64, 1785, 19, 223, 4091, 25,
21, 8, 1, 134]), array([ 6, 157, 2, 186, 0, 866, 0, 40, 71, 291, 416,
21, 2, 24, 5929]), array([ 16, 248, 25, 5, 465, 5068, 0, 6, 257, 17, 135,
43, 2847, 13, 1327]), array([ 98, 192, 25, 11, 155, 13, 100, 34, 410, 98, 2795,
0, 1, 41, 31]), array([ 2, 59, 251, 0, 6, 373, 13, 1327, 16, 248, 13,
11, 300, 15, 1229]), array([104, 0, 3, 860, 4, 1, 9, 44, 7, 77, 0, 60, 81,
2, 60]), array([1432, 886, 0, 1, 134, 58, 2, 416, 21, 2, 6,
47, 5, 1432, 886]), array([ 0, 1, 9, 31, 0, 1, 9, 148, 5, 326, 2,
27, 10, 5, 6407]), array([2326, 15, 486, 1335, 46, 702, 19, 1797, 1404, 115, 0,
1, 134, 139, 38]), array([ 11, 87, 333, 0, 3, 785, 4, 75, 180, 13, 161,
11, 1144, 455, 29]), array([ 20, 70, 13, 272, 694, 19, 11, 3533, 0, 20, 1,
12, 3, 261, 4])]
Assigned: [ 34 126 6 78 52 10 34 126 6 78 52 10 1 41 28]
Retarr: [array([ 9, 3, 121, 178, 4, 175, 341, 0, 143, 5, 4353,
523, 13, 146, 0]), array([ 1, 149, 176, 2, 36, 2, 219, 2, 22, 985, 79, 10, 206,
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Assigned: [ 1 41 24 11 379 85 2 169 6 10 1 41 203 8 1]
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Assigned: [ 41 164 10 11 5559 10 230 2 237 39 6 1184 11 27 8]
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Assigned: [ 1 1 1 4767 3 2003 2 62 19 1723 4 277 1633 0 0]
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Assigned: [178 4 175 341 0 388 29 2 96 271 0 0 0 0 3]
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Assigned: [368 4 28 260 8 24 11 290 680 35 84 562 209 14 11]
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Assigned: [137 644 2 14 16 644 896 2 14 6 39 13 746 16 439]
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Assigned: [644 0 1 41 59 11 165 60 3 871 4 0 1 9 63]
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Assigned: [ 46 7 77 79 229 10 1 12 28 2 52 6 138 7 70]
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Assigned: [ 13 52 26 310 10 1 9 151 2 27 0 1 12 7 65]
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Assigned: [ 2 71 7 874 5 314 2 79 246 43 42 179 4341 29 827]
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Assigned: [ 0 1792 35 1217 849 13 5002 0 1 1056 3 1477 4 148 5]
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Assigned: [ 326 2 706 10 1 12 5 680 308 513 1093 879 8 1 1056]
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Assigned: [ 3 3821 4 6 531 13 43 18 680 0 1 12 31 2 21]
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Assigned: [ 14 16 5304 432 0 1 12 202 2 191 54 13 664 18 691]
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Assigned: [ 32 0 1 12 86 2 86 0 40 6 101 7 13 100 11]
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Assigned: [ 720 15 5 87 270 61 3217 0 3 685 311 88 1124 4 50]
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Assigned: [ 6 46 47 60 0 1 9 3 13 87 85 4 95 29 2]
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Assigned: [ 135 162 282 5 1815 598 0 1 12 3 432 754 4 36 2]
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Assigned: [ 265 56 434 37 150 0 1 9 3153 310 2692 14 348 3106 56]
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Assigned: [6076 25 11 4116 15 6433 6148 39 359 0 1 12 97 6 55]
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Assigned: [1780 1981 10 1 9 58 2 142 21 271 5 322 14 712 0]
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BEFORE: [ 6 373 11 87 2279 0 6 2947 418 19 223 5941 0 5 1920]
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BEFORE: [ 46 7 77 79 229 10 1 12 28 2 52 6 138 7 70]
Assigned: [ 11 603 15 2069 1459 0 5126 662 2742 0 1 12 25 26 5075]
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Assigned: [ 23 4501 1134 10 1 336 27 313 2 498 0 1955 4212 66 6]
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BEFORE: [ 3 429 4 231 2 447 2 6 157 0 538 2 99 54 11]
Assigned: [ 12 3 6489 4 632 2 290 908 0 1 12 5 3825 2 30]
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503, 76]), array([ 192, 44, 2, 56, 7, 10, 1, 1237, 2143, 2, 1155,
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5, 6090, 0, 1]), array([1714, 6, 140, 11, 137, 875, 14, 75, 140, 11, 137,
875, 0, 63, 57]), array([ 47, 11, 227, 137, 875, 0, 1, 1237, 57, 56, 2527,
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10, 1, 12, 3]), array([ 227, 2379, 4, 50, 2, 6, 531, 13, 65, 18, 75,
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92, 5930, 4, 36]), array([ 2, 50, 2, 6, 108, 90, 1700, 178, 1247, 0, 1,
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11, 87, 1271, 4]), array([ 30, 2, 25, 7, 0, 0, 0, 303, 10, 1, 149, 3, 254,
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4130, 2, 143, 377]), array([4648, 1473, 830, 14, 2614, 89, 320, 89, 80, 6530, 1369,
5, 155, 5, 2677]), array([2177, 47, 223, 4010, 8, 1, 9, 139, 11, 5526, 2,
23, 60, 22, 8]), array([ 1, 292, 0, 332, 118, 2, 5, 3654, 299, 15, 5,
5752, 0, 388, 26]), array([1192, 2, 440, 2, 136, 182, 19, 8, 1, 292, 0,
332, 3, 13, 440]), array([ 4, 390, 21, 2090, 19, 0, 3, 13, 30, 4, 2482,
2, 90, 1868, 1170]), array([ 81, 25, 21, 14, 16, 20, 706, 20, 44, 0, 1, 12, 24,
62, 26]), array([ 706, 2, 7, 2460, 194, 6468, 8, 1, 292, 0, 332,
440, 2, 83, 22]), array([ 23, 4501, 1134, 10, 1, 336, 27, 313, 2, 498, 0,
1955, 4212, 66, 6]), array([ 181, 2, 1955, 4911, 0, 2141, 3579, 0, 1, 12, 59,
68, 0, 6, 322]), array([ 16, 310, 42, 18, 7, 97, 47, 236, 2747, 508, 384,
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1085, 7, 49, 107]), array([ 61, 451, 659, 10, 1, 292, 0, 332, 231, 0, 1, 12, 86,
2, 224]), array([6411, 0, 42, 142, 21, 350, 7, 23, 2, 232, 26,
144, 152, 1834, 7]), array([ 107, 7, 95, 193, 67, 198, 10, 1, 292, 0, 332,
3, 2502, 4, 109]), array([ 2, 32, 0, 1, 12, 14, 232, 17, 152, 114, 2, 20, 6,
122, 7]), array([ 10, 20, 1, 292, 0, 332, 3, 2502, 4, 32, 2,
17, 541, 0, 1]), array([ 12, 3, 6286, 4, 377, 490, 7, 0, 377, 490, 7,
0, 61, 194, 14]), array([6551, 269, 5, 1151, 4372, 123, 29, 7, 25, 3837, 828,
4067, 0, 52, 7]), array([ 94, 16, 146, 140, 147, 13, 52, 35, 17, 10, 1, 9, 83,
64, 114]), array([ 10, 48, 625, 11, 6188, 15, 265, 0, 1, 2918, 3,
13, 30, 4, 236]), array([1438, 15, 168, 1421, 20, 501, 175, 20, 0, 1, 5744,
211, 2, 6, 462]), array([ 23, 1007, 2, 40, 6, 122, 18, 146, 0, 1, 783,
2482, 2, 6, 471]), array([ 197, 1893, 26, 467, 0, 1, 9, 5, 60, 18, 328,
2, 20, 2820, 52]), array([ 17, 1797, 7, 2354, 20, 10, 1, 783, 32, 2, 1600,
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14, 567, 0, 1]), array([ 783, 3, 597, 4, 34, 52, 7, 1321, 19, 5, 156,
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67, 1108, 30, 0]), array([ 1, 783, 6, 2572, 13, 120, 16, 5504, 3855, 560, 0,
1, 9, 6, 39]), array([ 26, 5529, 2, 289, 0, 61, 2038, 0, 1, 783, 7,
2292, 2347, 23, 0]), array([ 1, 9, 3620, 6, 2292, 0, 1, 949, 3, 13, 30,
4, 1674, 21, 2]), array([ 56, 7, 5, 2185, 522, 5, 20, 501, 30, 20, 10,
1, 12, 109, 231]), array([ 6, 66, 1, 9, 3, 1154, 98, 127, 4, 24, 26,
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25, 20, 430, 4752]), array([ 82, 115, 464, 14, 891, 4373, 20, 0, 1, 9, 31,
36, 10, 28, 2]), array([ 148, 186, 0, 2505, 103, 39, 10, 1, 949, 349, 2,
79, 22, 32, 430]), array([2505, 0, 139, 38, 11, 5899, 15, 243, 2567, 6757, 0,
1, 9, 50, 2]), array([ 7, 138, 98, 76, 21, 18, 75, 410, 60, 171, 4708,
643, 0, 1, 949]), array([ 3, 429, 4, 231, 2, 447, 2, 6, 157, 0, 538, 2, 99,
54, 11]), array([5115, 25, 7, 0, 1, 9, 272, 535, 0, 1, 949,
57, 249, 26, 20]), array([ 501, 30, 20, 22, 618, 25, 125, 1304, 4255, 0, 57,
101, 13, 266, 5]), array([5916, 0, 1, 9, 32, 6342, 0, 5, 20, 501, 30,
20, 22, 62, 25]), array([2027, 0, 52, 7, 65, 70, 179, 15, 16, 701, 14,
6259, 56, 19, 23]), array([ 146, 10, 1, 41, 82, 25, 7, 2, 30, 0, 154,
84, 1884, 135, 161]), array([ 321, 11, 508, 1265, 351, 39, 18, 0, 1, 9, 3,
13, 1552, 4, 24]), array([ 151, 2, 40, 5, 417, 1382, 6686, 35, 21, 0, 1,
6295, 3, 13, 30]), array([ 4, 890, 6426, 15, 1780, 1981, 0, 467, 44, 0, 1,
9, 3, 1827, 333]), array([ 871, 4, 6, 54, 3364, 29, 23, 415, 19, 5, 977,
0, 1, 12, 3]), array([1147, 4, 459, 2, 30, 0, 398, 18, 4455, 15, 2175,
10, 1, 9, 118]), array([ 2, 160, 428, 13, 6244, 11, 1075, 1234, 0, 1786, 6,
94, 160, 53, 190]), array([ 127, 44, 0, 1, 12, 30, 2, 151, 6, 322, 7,
3314, 15, 4385, 15]), array([384, 0, 1, 9, 31, 2, 28, 2, 28, 0, 177, 90, 265,
56, 104]), array([ 82, 13, 43, 1652, 11, 417, 0, 1, 12, 6, 241,
42, 0, 1, 9]), array([2684, 15, 5, 329, 0, 3, 777, 4, 60, 20, 501,
27, 20, 0, 1]), array([ 12, 770, 0, 216, 2, 30, 0, 61, 5, 658, 196, 11, 128,
97, 152]), array([ 47, 0, 1, 12, 28, 2, 52, 7, 94, 2733, 157,
43, 19, 250, 10]), array([ 1, 9, 6, 1014, 17, 0, 1, 1, 1, 9, 3,
630, 4, 56, 7]), array([ 129, 10, 3340, 346, 6, 664, 5, 603, 132, 32, 2827,
0, 1, 9, 28]), array([ 2, 27, 0, 188, 215, 10, 1, 12, 3, 2596, 4,
32, 2, 166, 39]), array([ 11, 603, 15, 2069, 1459, 0, 5126, 662, 2742, 0, 1,
12, 25, 26, 5075]), array([ 2, 6, 38, 210, 11, 694, 1568, 384, 604, 5, 1201,
0, 266, 544, 2]), array([ 422, 532, 2, 59, 16, 5276, 0, 6, 646, 38, 452,
16, 310, 67, 5]), array([ 925, 734, 13, 1551, 11, 5000, 1263, 1201, 128, 0, 1,
12, 47, 11, 215]), array([ 2, 704, 8, 1, 1237, 3, 1448, 1069, 2, 800, 4,
31, 2, 315, 7]), array([ 0, 16, 792, 22, 62, 618, 2, 40, 6, 47, 13, 138, 7,
26, 81]), array([ 22, 39, 2642, 13, 145, 0, 52, 6, 47, 18, 68,
10, 6, 126, 751]), array([ 18, 154, 11, 6628, 135, 146, 23, 81, 0, 1, 1714,
3, 1448, 1069, 4]), array([ 36, 2, 40, 315, 7, 538, 0, 1, 12, 28, 2, 7, 105,
503, 76]), array([ 192, 44, 2, 56, 7, 10, 1, 1237, 2143, 2, 1155,
0, 57, 56, 76]), array([5117, 0, 75, 22, 76, 5, 3103, 0, 6, 126, 76,
5, 6090, 0, 1]), array([1714, 6, 140, 11, 137, 875, 14, 75, 140, 11, 137,
875, 0, 63, 57]), array([ 47, 11, 227, 137, 875, 0, 1, 1237, 57, 56, 2527,
19, 1675, 5, 925]), array([ 734, 0, 52, 7, 94, 5, 2352, 135, 152, 422, 17,
10, 1, 12, 3]), array([ 227, 2379, 4, 50, 2, 6, 531, 13, 65, 18, 75,
246, 422, 17, 25]), array([ 614, 183, 2747, 508, 384, 0, 1, 1714, 11, 302, 508,
10, 1, 1237, 31]), array([ 2, 46, 436, 8, 545, 189, 47, 364, 428, 13, 266,
5, 3711, 5496, 0]), array([ 1, 9, 3, 121, 178, 4, 175, 341, 0, 30, 1117,
0, 1, 149, 3]), array([ 121, 178, 4, 58, 231, 2, 24, 220, 25, 11, 816,
0, 1196, 0, 229]), array([ 201, 0, 0, 0, 1123, 0, 1, 9, 3, 121, 178,
4, 118, 2, 36]), array([ 0, 38, 11, 276, 2, 96, 271, 0, 3, 507, 51,
4, 58, 2, 1123]), array([1196, 0, 1123, 1196, 0, 347, 2, 105, 0, 52, 6,
47, 84, 1196, 44]), array([ 10, 1, 41, 7, 129, 52, 8, 1, 9, 3, 555,
4, 2084, 0, 3]), array([121, 178, 4, 48, 7, 2, 294, 17, 10, 1, 9, 225, 7,
2, 107]), array([ 6, 55, 11, 388, 15, 7, 2, 24, 78, 282, 26,
267, 25, 11, 2394]), array([ 14, 748, 16, 329, 35, 26, 1863, 8, 1, 149, 416,
21, 2, 24, 220]), array([ 25, 66, 1, 9, 158, 11, 276, 0, 6, 65, 18, 259, 0,
1, 9]), array([ 71, 17, 294, 87, 283, 313, 8, 6, 548, 301, 7, 19, 247,
0, 1]), array([149, 59, 68, 0, 59, 16, 968, 8, 1, 9, 118, 2, 87,
283, 0]), array([ 0, 0, 57, 327, 37, 74, 26, 6172, 0, 1, 9,
3, 2418, 4, 868]), array([ 7, 55, 121, 37, 4052, 15, 534, 75, 46, 162, 65,
74, 0, 126, 6]), array([ 68, 10, 164, 10, 126, 6, 68, 10, 1, 149, 3,
92, 5930, 4, 36]), array([ 2, 50, 2, 6, 108, 90, 1700, 178, 1247, 0, 1,
9, 3, 4605, 1582]), array([895, 4, 3, 312, 4, 59, 171, 8, 28, 2, 135, 7, 584,
18, 194]), array([ 588, 7, 373, 13, 584, 25, 21, 10, 1, 149, 3,
11, 87, 1271, 4]), array([ 30, 2, 25, 7, 0, 0, 0, 303, 10, 1, 149, 3, 254,
4, 240]), array([2677, 2071, 280, 106, 82, 22, 129, 15, 11, 1121, 4161,
132, 1474, 15, 3006]), array([ 265, 13, 474, 2, 14, 1107, 1103, 13, 4219, 2298, 5,
4130, 2, 143, 377]), array([4648, 1473, 830, 14, 2614, 89, 320, 89, 80, 6530, 1369,
5, 155, 5, 2677]), array([2177, 47, 223, 4010, 8, 1, 9, 139, 11, 5526, 2,
23, 60, 22, 8]), array([ 1, 292, 0, 332, 118, 2, 5, 3654, 299, 15, 5,
5752, 0, 388, 26]), array([1192, 2, 440, 2, 136, 182, 19, 8, 1, 292, 0,
332, 3, 13, 440]), array([ 4, 390, 21, 2090, 19, 0, 3, 13, 30, 4, 2482,
2, 90, 1868, 1170]), array([ 81, 25, 21, 14, 16, 20, 706, 20, 44, 0, 1, 12, 24,
62, 26]), array([ 706, 2, 7, 2460, 194, 6468, 8, 1, 292, 0, 332,
440, 2, 83, 22]), array([ 23, 4501, 1134, 10, 1, 336, 27, 313, 2, 498, 0,
1955, 4212, 66, 6]), array([ 181, 2, 1955, 4911, 0, 2141, 3579, 0, 1, 12, 59,
68, 0, 6, 322]), array([ 16, 310, 42, 18, 7, 97, 47, 236, 2747, 508, 384,
0, 1, 12, 3]), array([1293, 4, 142, 21, 350, 7, 147, 0, 232, 26, 144,
1085, 7, 49, 107]), array([ 61, 451, 659, 10, 1, 292, 0, 332, 231, 0, 1, 12, 86,
2, 224]), array([6411, 0, 42, 142, 21, 350, 7, 23, 2, 232, 26,
144, 152, 1834, 7]), array([ 107, 7, 95, 193, 67, 198, 10, 1, 292, 0, 332,
3, 2502, 4, 109]), array([ 2, 32, 0, 1, 12, 14, 232, 17, 152, 114, 2, 20, 6,
122, 7]), array([ 10, 20, 1, 292, 0, 332, 3, 2502, 4, 32, 2,
17, 541, 0, 1]), array([ 12, 3, 6286, 4, 377, 490, 7, 0, 377, 490, 7,
0, 61, 194, 14]), array([ 61, 562, 0, 377, 490, 7, 0, 1012, 2, 1012, 2,
1012, 1012, 8, 1]), array([ 12, 377, 490, 7, 0, 0, 0, 1, 292, 0, 332,
82, 3623, 2, 440]), array([ 8, 255, 62, 784, 15, 21, 655, 8, 1, 149, 28, 186, 0,
363, 2]), array([113, 7, 55, 18, 693, 6, 946, 10, 1, 292, 0, 332, 693,
10, 6]), array([ 46, 2671, 208, 693, 0, 0, 0, 1, 149, 59, 268,
6, 931, 13, 4769]), array([ 17, 0, 1, 9, 3, 871, 4, 118, 2, 18, 289,
4994, 21, 0, 1]), array([292, 0, 332, 18, 53, 32, 767, 0, 228, 55, 51, 15, 44,
0, 1]), array([ 12, 3, 254, 4, 393, 393, 393, 393, 69, 393, 393, 393, 393,
69, 1]), array([512, 28, 28, 28, 69, 652, 66, 393, 393, 393, 393, 69, 393,
393, 393]), array([393, 69, 28, 28, 28, 69, 652, 0, 0, 0, 1, 292, 0,
332, 34]), array([ 82, 22, 144, 71, 7, 92, 2892, 5017, 19, 26, 1134,
85, 10, 3, 6314]), array([ 4, 99, 54, 13, 55, 16, 734, 72, 8, 1, 1, 1, 9,
175, 341]), array([ 0, 143, 5, 2130, 624, 22, 4966, 4667, 0, 1, 12,
30, 2, 166, 39]), array([ 13, 868, 484, 384, 29, 908, 0, 1, 9, 3, 2124,
4, 6, 94, 6]), array([ 64, 5685, 18, 977, 0, 3, 220, 192, 4, 385, 2,
58, 2, 399, 594]), array([ 2, 97, 7, 481, 21, 18, 87, 837, 343, 10, 1, 159, 31,
2, 129]), array([ 133, 2, 30, 0, 6, 53, 38, 826, 17, 89, 11,
1018, 0, 1, 12]), array([ 484, 137, 899, 29, 908, 0, 1, 9, 3, 121, 178,
4, 2248, 389, 356]), array([ 35, 7, 2, 3, 6343, 4, 6623, 0, 745, 0, 1,
12, 3, 13, 205]), array([ 4, 37, 68, 2, 908, 0, 5478, 16, 122, 0, 1,
881, 67, 5, 557]), array([ 15, 2708, 849, 15, 252, 2, 48, 165, 4495, 494, 2,
908, 717, 0, 1]), array([ 134, 102, 2, 248, 0, 6, 210, 11, 3898, 2380, 2403,
25, 16, 6102, 5377]), array([2558, 0, 23, 22, 5, 5844, 2, 23, 22, 143, 160,
3118, 112, 1175, 3172]), array([4520, 0, 0, 0, 1, 134, 6703, 8, 61, 62, 2167,
13, 21, 0, 1]), array([ 12, 3, 6489, 4, 632, 2, 290, 908, 0, 1, 12,
5, 3825, 2, 30]), array([ 0, 11, 81, 14, 11, 6138, 15, 3007, 0, 1, 9,
3, 1174, 299, 4]), array([ 86, 2, 111, 707, 680, 0, 1, 9, 44, 7, 77, 2, 27,
0, 11]), array([ 302, 14, 5343, 384, 0, 1, 12, 3, 4945, 4, 6,
373, 13, 462, 5]), array([ 703, 15, 26, 3800, 687, 0, 3, 1665, 2324, 4, 63,
48, 5, 703, 15]), array([2559, 0, 1, 9, 204, 2, 278, 49, 0, 1, 9,
302, 14, 608, 2]), array([ 11, 302, 14, 484, 0, 0, 0, 7, 707, 6329, 0,
0, 0, 1, 41]), array([ 28, 27, 2, 7, 188, 77, 1771, 308, 1131, 10, 1,
12, 131, 2, 56]), array([ 7, 489, 10, 59, 5, 475, 624, 0, 70, 74, 5,
1131, 256, 18, 10]), array([ 1, 41, 50, 2, 16, 4801, 520, 19, 76, 5991, 2,
40, 0, 0, 0])]
BEFORE: [1147 4 459 2 30 0 398 18 4455 15 2175 10 1 9 118]
Assigned: [ 58 34 5 309 0 3 1363 4 1 9 175 341 2 193 15]
Retarr:
---------------------------------------------------------------------------
KeyboardInterrupt Traceback (most recent call last)
<ipython-input-17-396c263e7e73> in <module>()
2 DON'T MODIFY ANYTHING IN THIS CELL
3 """
----> 4 batches = get_batches(int_text, batch_size, seq_length)
5
6 with tf.Session(graph=train_graph) as sess:
<ipython-input-14-60303e706d98> in get_batches(int_text, batch_size, seq_length)
24 #print(i, batch)
25 try:
---> 26 print("Retarr: ", retArr)
27 #print(i)
28 type(retArr[i])
/home/carnd/anaconda3/envs/dl/lib/python3.5/site-packages/numpy/core/numeric.py in array_repr(arr, max_line_width, precision, suppress_small)
1896 if arr.size > 0 or arr.shape == (0,):
1897 lst = array2string(arr, max_line_width, precision, suppress_small,
-> 1898 ', ', "array(")
1899 else: # show zero-length shape unless it is (0,)
1900 lst = "[], shape=%s" % (repr(arr.shape),)
/home/carnd/anaconda3/envs/dl/lib/python3.5/site-packages/numpy/core/arrayprint.py in array2string(a, max_line_width, precision, suppress_small, separator, prefix, style, formatter)
461 else:
462 lst = _array2string(a, max_line_width, precision, suppress_small,
--> 463 separator, prefix, formatter=formatter)
464 return lst
465
/home/carnd/anaconda3/envs/dl/lib/python3.5/site-packages/numpy/core/arrayprint.py in _array2string(a, max_line_width, precision, suppress_small, separator, prefix, formatter)
325 # find the right formatting function for the array
326 format_function = _get_format_function(data, precision,
--> 327 suppress_small, formatter)
328
329 # skip over "["
/home/carnd/anaconda3/envs/dl/lib/python3.5/site-packages/numpy/core/arrayprint.py in _get_format_function(data, precision, suppress_small, formatter)
288
289 dtypeobj = dtype_.type
--> 290 formatdict = _get_formatdict(data, precision, suppress_small, formatter)
291 if issubclass(dtypeobj, _nt.bool_):
292 return formatdict['bool']
/home/carnd/anaconda3/envs/dl/lib/python3.5/site-packages/numpy/core/arrayprint.py in _get_formatdict(data, precision, suppress_small, formatter)
241 'longfloat': LongFloatFormat(precision),
242 'complexfloat': ComplexFormat(data, precision,
--> 243 suppress_small),
244 'longcomplexfloat': LongComplexFormat(precision),
245 'datetime': DatetimeFormat(data),
/home/carnd/anaconda3/envs/dl/lib/python3.5/site-packages/numpy/core/arrayprint.py in __init__(self, x, precision, suppress_small)
712 class ComplexFormat(object):
713 def __init__(self, x, precision, suppress_small):
--> 714 self.real_format = FloatFormat(x.real, precision, suppress_small)
715 self.imag_format = FloatFormat(x.imag, precision, suppress_small,
716 sign=True)
/home/carnd/anaconda3/envs/dl/lib/python3.5/site-packages/numpy/core/arrayprint.py in __init__(self, data, precision, suppress_small, sign)
549 self.max_str_len = 0
550 try:
--> 551 self.fillFormat(data)
552 except (TypeError, NotImplementedError):
553 # if reduce(data) fails, this instance will not be called, just
/home/carnd/anaconda3/envs/dl/lib/python3.5/site-packages/numpy/core/arrayprint.py in fillFormat(self, data)
560 with _nc.errstate(all='ignore'):
561 special = isnan(data) | isinf(data)
--> 562 valid = not_equal(data, 0) & ~special
563 non_zero = absolute(data.compress(valid))
564 if len(non_zero) == 0:
KeyboardInterrupt:
In [ ]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
# Save parameters for checkpoint
helper.save_params((seq_length, save_dir))
In [ ]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import tensorflow as tf
import numpy as np
import helper
import problem_unittests as tests
_, vocab_to_int, int_to_vocab, token_dict = helper.load_preprocess()
seq_length, load_dir = helper.load_params()
Get tensors from loaded_graph using the function get_tensor_by_name(). Get the tensors using the following names:
Return the tensors in the following tuple (InputTensor, InitialStateTensor, FinalStateTensor, ProbsTensor)
In [ ]:
def get_tensors(loaded_graph):
"""
Get input, initial state, final state, and probabilities tensor from <loaded_graph>
:param loaded_graph: TensorFlow graph loaded from file
:return: Tuple (InputTensor, InitialStateTensor, FinalStateTensor, ProbsTensor)
"""
# TODO: Implement Function
return None, None, None, None
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_get_tensors(get_tensors)
In [ ]:
def pick_word(probabilities, int_to_vocab):
"""
Pick the next word in the generated text
:param probabilities: Probabilites of the next word
:param int_to_vocab: Dictionary of word ids as the keys and words as the values
:return: String of the predicted word
"""
# TODO: Implement Function
return None
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_pick_word(pick_word)
In [ ]:
gen_length = 200
# homer_simpson, moe_szyslak, or Barney_Gumble
prime_word = 'moe_szyslak'
"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
loaded_graph = tf.Graph()
with tf.Session(graph=loaded_graph) as sess:
# Load saved model
loader = tf.train.import_meta_graph(load_dir + '.meta')
loader.restore(sess, load_dir)
# Get Tensors from loaded model
input_text, initial_state, final_state, probs = get_tensors(loaded_graph)
# Sentences generation setup
gen_sentences = [prime_word + ':']
prev_state = sess.run(initial_state, {input_text: np.array([[1]])})
# Generate sentences
for n in range(gen_length):
# Dynamic Input
dyn_input = [[vocab_to_int[word] for word in gen_sentences[-seq_length:]]]
dyn_seq_length = len(dyn_input[0])
# Get Prediction
probabilities, prev_state = sess.run(
[probs, final_state],
{input_text: dyn_input, initial_state: prev_state})
pred_word = pick_word(probabilities[dyn_seq_length-1], int_to_vocab)
gen_sentences.append(pred_word)
# Remove tokens
tv_script = ' '.join(gen_sentences)
for key, token in token_dict.items():
ending = ' ' if key in ['\n', '(', '"'] else ''
tv_script = tv_script.replace(' ' + token.lower(), key)
tv_script = tv_script.replace('\n ', '\n')
tv_script = tv_script.replace('( ', '(')
print(tv_script)
It's ok if the TV script doesn't make any sense. We trained on less than a megabyte of text. In order to get good results, you'll have to use a smaller vocabulary or get more data. Luckly there's more data! As we mentioned in the begging of this project, this is a subset of another dataset. We didn't have you train on all the data, because that would take too long. However, you are free to train your neural network on all the data. After you complete the project, of course.
When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_tv_script_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.
Content source: xtr33me/deep-learning
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