Oct-30-2019, 10:54 AM
I need to speed up a python code, I would like to avoid the use of the following for cycle, where "data" matrix has dimension [dim1xdim2]:
Thanks,
Luca
for i in range(int(dim1)): data_process = data[i,:].reshape((dim2, 1)) rxx = data_process * np.matrix.getH(np.asmatrix(data_process)) / dim2Using the 'for cycle' the dimension of the rxx matrix is [dim2xdim2], I would get a 3D "rxx" matrix [dim1xdim2xdim2]. I tried to use the following solution:
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)In this case I got 3D "y_out" matrix [dim1xdim2xdim2] but this doesn't work in my case.
Thanks,
Luca