In [42]:
import numpy as np
from sklearn import linear_model
In [43]:
k = 20
n = 4
m = 2
x = np.random.randn(k+1, n)
u = np.random.randn(k, m)
Y = x[1:,:]
X = np.ones((k, n+m))
X[:,:n] = x[:-1,:].copy()
X[:,n:] = u.copy()
regr = linear_model.LinearRegression()
regr.fit(X, Y)
Out[43]:
In [44]:
print(regr.coef_)
print(regr.intercept_)
print(regr.score(X, Y))
In [52]:
print(regr.predict(X[:2,:]))
print(np.dot(X[:2,:], regr.coef_.T) + regr.intercept_)