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one_hot_encoder.py
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# One hot encode all label training array.
from numpy import array
from numpy import argmax
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder
##################### Meta-Training set ##############################
# One hot encode all label train array.
# integer encode
label_encoder = LabelEncoder()
integer_encoded = label_encoder.fit_transform(new_y_train)
#print(integer_encoded)
# binary encode
onehot_encoder = OneHotEncoder(sparse_output=False)
integer_encoded = integer_encoded.reshape(len(integer_encoded), 1)
onehot_encoded = onehot_encoder.fit_transform(integer_encoded)
##################### Meta-Valid set ##############################
# One hot encode all label valid array.
from numpy import array
from numpy import argmax
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder
# integer encode
label_encoder = LabelEncoder()
integer_encodedval = label_encoder.fit_transform(new_y_val)
# binary encode. As of New ver, use sparse_output instead of sparse.
onehot_encoderval = OneHotEncoder(sparse_output = False)
integer_encodedval= integer_encodedval.reshape(len(integer_encodedval), 1)
onehot_encodedval= onehot_encoderval.fit_transform(integer_encodedval)