[英]How to raise a matrix to the power of elements in an array that is increasing in an ascending order?
Currently I have a C matrix generated by: 目前,我有一个C矩阵,它由以下生成:
def c_matrix(n):
exp = np.exp(1j*np.pi/n)
exp_n = np.array([[exp, 0], [0, exp.conj()]], dtype=complex)
c_matrix = np.array([exp_n**i for i in range(1, n, 1)], dtype=complex)
return c_matrix
What this does is basically generate a list of number from 0 to n-1 using list comprehension, then returns a list of the matrix exp_n
being raised to the elements of the ascendingly increasing list. 这样做基本上是使用列表exp_n
生成一个从0到n-1的数字列表,然后将矩阵exp_n
的列表返回到递增列表中的元素。 ie 即
exp_n**[0, 1, ..., n-1] = [exp_n**0, exp_n**1, ..., exp_n**(n-1)]
So I was wondering if there's a more numpythonic way of doing it(in order to make use of Numpy's broadcasting ability) like: 所以我想知道是否还有更多的numpythonic方式(以便利用Numpy的广播功能),例如:
exp_n**np.arange(1,n,1) = np.array(exp_n**0, exp_n**1, ..., exp_n**(n-1))
You're speaking of a Vandermonde matrix. 您说的是范德蒙矩阵。 Numpy has numpy.vander
numpy.vander
有numpy.vander
def c_matrix_vander(n):
exp = np.exp(1j*np.pi/n)
exp_n = np.array([[exp, 0], [0, exp.conj()]], dtype=complex)
return np.vander(exp_n.ravel(), n, increasing=True)[:, 1:].swapaxes(0, 1).reshape(n-1, 2, 2)
Performance 性能
In [184]: %timeit c_matrix_vander(10_000)
849 µs ± 14.4 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)
In [185]: %timeit c_matrix(10_000)
41.5 ms ± 549 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
Validation 验证方式
>>> np.isclose(c_matrix(10_000), c_matrix_vander(10_000)).all()
True
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