import numpy as np

from sklearn import preprocessing

data = np.array([[ 3, -1.5, 2, -5.4],

[ 0, 4, -0.3, 2.1],

[ 1, 3.3, -1.9, -4.3]])

# mean removal

data_standardized = preprocessing.scale(data)

print "\nMean =", data_standardized.mean(axis=0)

print "Std deviation =", data_standardized.std(axis=0)

# min max scaling

data_scaler = preprocessing.MinMaxScaler(feature_range=(0, 1))

data_scaled = data_scaler.fit_transform(data)

print "\nMin max scaled data:\n", data_scaled

# normalization

data_normalized = preprocessing.normalize(data, norm='l1')

print "\nL1 normalized data:\n", data_normalized

# binarization

data_binarized = preprocessing.Binarizer(threshold=1.4).transform(data)

print "\nBinarized data:\n", data_binarized

# one hot encoding

encoder = preprocessing.OneHotEncoder()

encoder.fit([[0, 2, 1, 12], [1, 3, 5, 3], [2, 3, 2, 12], [1, 2, 4, 3]])

encoded_vector = encoder.transform([[2, 3, 5, 3]]).toarray()

print "\nEncoded vector:\n", encoded_vector