import mxnet as mx
from mxnet import nd, autograd, gluon
import sys
import argparse
import numpy as np
import matplotlib.pyplot as plt

import data.train_data as train_data

parser = argparse.ArgumentParser()
parser.add_argument("section", help="The section for which you want to train a model")
section = parser.parse_args().section


train_features, train_labels, test_features, test_labels, data_size, num_outputs = train_data.get_class_labeled_data(section, 30)

train_numpy = np.array(train_features)

label_values = []
for row in range(len(train_labels)):
	index = train_labels[row].index(1)
	if(index != 2):
		index = 0
	else:
		index = 1
	label_values.append(index)


label_numpy = np.array(label_values)
print(label_numpy)

np.savetxt('./octave/data/labels.mtx', label_numpy)
np.savetxt('./octave/data/train.mtx', train_numpy)
fd6dee5f2408d0759f185d674980d7f4ce089559
<mask token>

<mask token>
parser.add_argument('section', help=
    'The section for which you want to train a model')
<mask token>
for row in range(len(train_labels)):
    index = train_labels[row].index(1)
    if index != 2:
        index = 0
    else:
        index = 1
    label_values.append(index)
<mask token>
print(label_numpy)
np.savetxt('./octave/data/labels.mtx', label_numpy)
np.savetxt('./octave/data/train.mtx', train_numpy)

<mask token>
parser = argparse.ArgumentParser()
parser.add_argument('section', help=
    'The section for which you want to train a model')
section = parser.parse_args().section
(train_features, train_labels, test_features, test_labels, data_size,
    num_outputs) = train_data.get_class_labeled_data(section, 30)
train_numpy = np.array(train_features)
label_values = []
for row in range(len(train_labels)):
    index = train_labels[row].index(1)
    if index != 2:
        index = 0
    else:
        index = 1
    label_values.append(index)
label_numpy = np.array(label_values)
print(label_numpy)
np.savetxt('./octave/data/labels.mtx', label_numpy)
np.savetxt('./octave/data/train.mtx', train_numpy)

import mxnet as mx
from mxnet import nd, autograd, gluon
import sys
import argparse
import numpy as np
import matplotlib.pyplot as plt
import data.train_data as train_data
parser = argparse.ArgumentParser()
parser.add_argument('section', help=
    'The section for which you want to train a model')
section = parser.parse_args().section
(train_features, train_labels, test_features, test_labels, data_size,
    num_outputs) = train_data.get_class_labeled_data(section, 30)
train_numpy = np.array(train_features)
label_values = []
for row in range(len(train_labels)):
    index = train_labels[row].index(1)
    if index != 2:
        index = 0
    else:
        index = 1
    label_values.append(index)
label_numpy = np.array(label_values)
print(label_numpy)
np.savetxt('./octave/data/labels.mtx', label_numpy)
np.savetxt('./octave/data/train.mtx', train_numpy)

import mxnet as mx
from mxnet import nd, autograd, gluon
import sys
import argparse
import numpy as np
import matplotlib.pyplot as plt

import data.train_data as train_data

parser = argparse.ArgumentParser()
parser.add_argument("section", help="The section for which you want to train a model")
section = parser.parse_args().section


train_features, train_labels, test_features, test_labels, data_size, num_outputs = train_data.get_class_labeled_data(section, 30)

train_numpy = np.array(train_features)

label_values = []
for row in range(len(train_labels)):
	index = train_labels[row].index(1)
	if(index != 2):
		index = 0
	else:
		index = 1
	label_values.append(index)


label_numpy = np.array(label_values)
print(label_numpy)

np.savetxt('./octave/data/labels.mtx', label_numpy)
np.savetxt('./octave/data/train.mtx', train_numpy)