我需要加快python代码的速度,我希望避免使用以下for cycle,其中“data”矩阵有维度[dim1xdim2]:
for i in range(int(dim1)):
data_process = data[i,:].reshape((dim2, 1))
rxx = data_process * np.matrix.getH(np.asmatrix(data_process)) / dim2
使用“for cycle”,rxx矩阵的维数是[dim2xdim2],我将得到一个3D“rxx”矩阵[dim1xdim2xdim2]。我尝试使用以下解决方案:
data_new = repeat(data_process0[:, :, newaxis], dim2, axis=2)
N_2 = data_new.shape[2]
m1 = data_new - data_new.sum(2, keepdims=1) / N_2
y_out = einsum('ijk,ilk->ijl', m1, m1) / (N_2 - 1)
在这个例子中,我得到了3D“y\u out”矩阵[dim1xdim2xdim2],但在我的例子中这不起作用
谢谢
代表性样本数据:
from numpy import matrix, random, asmatrix, linalg, empty
B = random.random((156, 48))
A = B.shape
eig_val = empty(A, dtype=complex)
eig_vec = empty((A[0], A[1], A[1]), dtype=complex)
for i in range(int(A[0])):
data_process = B[i, :].reshape((A[1], 1))
rxx = data_process * matrix.getH(asmatrix(data_process)) / A[1]
eig_val[i:, ...], eig_vec[i:, ...] = linalg.eig(rxx)
目前没有回答
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