scikit-learn求解多元线性回归问题

>>> from numpy.linalg import inv
>>> from numpy import dot,transpose
>>> x = [[1,1,1],[1,1,2],[1,2,1]]
>>> y = [[6],[9],[8]]
>>> print dot(inv(dot(transpose(x),x),dot(transpose(x),y)))
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: inv() takes exactly 1 argument (2 given)
>>> print dot(inv(dot(transpose(x),x)),dot(transpose(x),y))
[[ 1.]
[ 2.]
[ 3.]]
>>> from numpy.linalg import lstsq
>>> print lstsq(x,y)[0]
[[ 1.]
[ 2.]
[ 3.]]
>>> from sklearn.linear_model import LinearRegression
x>>> x = [[1,1,1],[1,1,2],[1,2,1]]
>>> y = [[6,[9],[8]]
... 
... 
... 
... ;
File "<stdin>", line 5
;
^
SyntaxError: invalid syntax
>>> y - [[6],[9],[8]]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: unsupported operand type(s) for -: 'list' and 'list'
>>> y = [[6],[9],[8]]
>>> model = LinearRegression()
>>> model.fit(x,y)
LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)
>>> x2 = [[1,3,5]]
>>> y2 = model.predict(x2)
>>> print y2
[[ 22.]]