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echo Hello world | |
echo "Hello" | |
## you can also print out the file without specifically open it | |
cat nama_file.txt |
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city="Jakarta" | |
echo $city |
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## Arithmetic Operators | |
## Addition | |
$x + $y | |
## Subtraction | |
$x - $y | |
## Multiplication | |
$x * $y |
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if [ $1 -le 10 ]; then | |
echo "less than" | |
echo "$0" | |
else | |
echo "Too large $1" | |
fi |
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for x in test train; do | |
echo $x | |
done |
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import pandas as pd | |
import numpy as np | |
import glob | |
import re | |
import pickle | |
import seaborn as sns | |
import string | |
from nltk.tokenize import word_tokenize |
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data_path = 'data' | |
files = glob.glob(data_path + '/*') | |
train = pickle.load(open(files[1], 'rb')) | |
test = pickle.load(open(files[0], 'rb')) | |
d_train = pd.DataFrame(data={'content': train[0], 'label': train[1]}) | |
d_test = pd.DataFrame(data={'content': test[0], 'label': test[1]}) |
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def cleansing(text): | |
word_list = word_tokenize(text) | |
word_list = [word for word in word_list if len(word) > 2 and word.isalnum()] | |
word_list = [word for word in word_list if string.punctuation not in word] | |
text = ' '.join(word_list) | |
return text |
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cv = CountVectorizer() | |
X_train = cv.fit_transform(d_train.content_cleansing) | |
X_test = cv.transform(d_test.content_cleansing) |
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class Classifier(nn.Module): | |
def __init__(self): | |
super(Classifier, self).__init__() | |
self.NeurlNet = nn.Sequential( | |
nn.Linear(50, 40), | |
nn.ReLU(), | |
nn.Linear(40, 30), | |
nn.ReLU(), | |
nn.Linear(30, 1) | |
) |