In [3]:

    

from sklearn.datasets import load_iris
import numpy as np

X = np.array([[ 1., -1.,  2.],
              [ 2.,  0.,  0.],
              [ 0.,  1., -1.]])


    

In [7]:

    

from sklearn import preprocessing
X_scaled = preprocessing.scale(X)
print(X_scaled)
print(X_scaled.mean(axis=0))
print(X_scaled.std(axis=0))


    

    




[[ 0.         -1.22474487  1.33630621]
 [ 1.22474487  0.         -0.26726124]
 [-1.22474487  1.22474487 -1.06904497]]
[ 0.  0.  0.]
[ 1.  1.  1.]

In [9]:

    

scaler = preprocessing.StandardScaler().fit(X)
print(scaler)

print(scaler.mean_)                                     

print(scaler.scale_)                                     

print(scaler.transform(X))
scaler.transform([[-1.,  1., 0.]])


    

    




StandardScaler(copy=True, with_mean=True, with_std=True)
[ 1.          0.          0.33333333]
[ 0.81649658  0.81649658  1.24721913]
[[ 0.         -1.22474487  1.33630621]
 [ 1.22474487  0.         -0.26726124]
 [-1.22474487  1.22474487 -1.06904497]]






    
Out[9]:





array([[-2.44948974,  1.22474487, -0.26726124]])

In [12]:

    

X_train = np.array([[ 1., -1.,  2.],
                    [ 2.,  0.,  0.],
                    [ 0.,  1., -1.]])
min_max_scaler = preprocessing.MinMaxScaler()
X_train_minmax = min_max_scaler.fit_transform(X_train)
print(X_train_minmax)


    

    




[[ 0.5         0.          1.        ]
 [ 1.          0.5         0.33333333]
 [ 0.          1.          0.        ]]

In [16]:

    

X_normalized = preprocessing.normalize(X_train, norm='l2')
print(X_normalized)
normalizer = preprocessing.Normalizer().fit(X_train)
normalizer.transform(X_train)


    

    




[[ 0.40824829 -0.40824829  0.81649658]
 [ 1.          0.          0.        ]
 [ 0.          0.70710678 -0.70710678]]






    
Out[16]:





array([[ 0.40824829, -0.40824829,  0.81649658],
       [ 1.        ,  0.        ,  0.        ],
       [ 0.        ,  0.70710678, -0.70710678]])

In [18]:

    

import numpy as np
from sklearn.preprocessing import PolynomialFeatures
X = np.arange(6).reshape(3, 2)
PolynomialFeatures(2).fit_transform(X)


    

    
Out[18]:





array([[  1.,   0.,   1.,   0.,   0.,   1.],
       [  1.,   2.,   3.,   4.,   6.,   9.],
       [  1.,   4.,   5.,  16.,  20.,  25.]])

In [ ]: