andreaschandra

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)
        )

andreaschandra

cv = CountVectorizer()

X_train = cv.fit_transform(d_train.content_cleansing)

X_test = cv.transform(d_test.content_cleansing)

andreaschandra

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

andreaschandra

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]})

andreaschandra

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

andreaschandra

for x in test train; do
	echo $x
done

andreaschandra

if [ $1 -le 10 ]; then
    echo "less than"
    echo "$0"
else
    echo "Too large $1"
fi

andreaschandra

## Arithmetic Operators

## Addition
$x + $y

## Subtraction
$x - $y

## Multiplication
$x * $y

andreaschandra

city="Jakarta"
echo $city

andreaschandra

echo Hello world
echo "Hello"

## you can also print out the file without specifically open it
cat nama_file.txt