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numpy的常用函数reshape、matmul

2017-02-13 19:41 387 查看

1.矩阵重建

numpy.reshape(a,
newshape, order='C')

eg1:

>>> a = np.arange(6).reshape((3, 2))
>>> a
array([[0, 1],
[2, 3],
[4, 5]])

eg2:

>>> np.reshape(a, (2, 3)) # C-like index ordering
array([[0, 1, 2],
[3, 4, 5]])
>>> np.reshape(np.ravel(a), (2, 3)) # equivalent to C ravel then C reshape
array([[0, 1, 2],
[3, 4, 5]])
>>> np.reshape(a, (2, 3), order='F') # Fortran-like index ordering
array([[0, 4, 3],
[2, 1, 5]])
>>> np.reshape(np.ravel(a, order='F'), (2, 3), order='F')
array([[0, 4, 3],
[2, 1, 5]])

eg.3

>>> np.reshape(a, (3,-1))       # the unspecified value is inferred to be 2
array([[1, 2],
[3, 4],
[5, 6]])

2.矩阵相乘

numpy.matmul(a, b, out=None)

eg1：

For 2-D arrays it is the matrix product:

>>> a = [[1, 0], [0, 1]]
>>> b = [[4, 1], [2, 2]]
>>> np.matmul(a, b)
array([[4, 1],
[2, 2]])

eg2：

For 2-D mixed with 1-D, the result is the usual>>

>>> a = [[1, 0], [0, 1]]
>>> b = [1, 2]
>>> np.matmul(a, b)
array([1, 2])
>>> np.matmul(b, a)
array([1, 2])

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