pemagrg1

from keras.preprocessing.text import Tokenizer
from numpy import array
from numpy import argmax
from keras.utils import to_categorical


doc = "Can I eat the Pizza".lower().split()

def using_Tokenizer(doc):
    # create the tokenizer

pemagrg1

from numpy import array
from numpy import argmax
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder
# define example
# data = ['cold', 'cold', 'warm', 'cold', 'hot', 'hot', 'warm', 'cold', 'warm', 'hot']


doc1 = "Can I eat the Pizza".lower()
doc2 = "You can eat the Pizza".lower()

pemagrg1

import numpy as np
docs = "Can I eat the Pizza".lower().split()
doc1 = set(docs)
doc1 = sorted(doc1)
print ("\nvalues: ", doc1)

integer_encoded = []
for i in docs:
    v = np.where( np.array(doc1) == i)[0][0]
    integer_encoded.append(v)