In [1]:

    

# creating a GaussianProcess object directly


    

In [2]:

    

from sklearn.datasets import make_regression
X,y = make_regression(1000, 1,1)
from sklearn.gaussian_process import regression_models


    

In [3]:

    

regression_models.constant(X)[:5]


    

    
Out[3]:





array([[ 1.],
       [ 1.],
       [ 1.],
       [ 1.],
       [ 1.]])

In [7]:

    

regression_models.linear(X)[:5]


    

    
Out[7]:





array([[ 1.        ,  0.30946471],
       [ 1.        ,  1.77762774],
       [ 1.        ,  1.71482785],
       [ 1.        , -0.67049132],
       [ 1.        , -1.24408678]])

In [8]:

    

regression_models.quadratic(X)[:5]


    

    
Out[8]:





array([[ 1.        ,  0.30946471,  0.09576841],
       [ 1.        ,  1.77762774,  3.15996037],
       [ 1.        ,  1.71482785,  2.94063455],
       [ 1.        , -0.67049132,  0.44955861],
       [ 1.        , -1.24408678,  1.54775192]])

In [6]:

    

X[:5]


    

    
Out[6]:





array([[ 0.30946471],
       [ 1.77762774],
       [ 1.71482785],
       [-0.67049132],
       [-1.24408678]])

In [ ]: