numpy的常用函数reshape、matmul

1.矩阵重建

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

eg1:

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

eg2:

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