eigenvector

问题 This question already has answers here : Sign difference in eigenvectors taken form Matlab and Python (1 answer) Conflicting eigen vector outputs between Matlab and Numpy (1 answer) Matlab VS Python - eig(A,B) VS sc.linalg.eig(A,B) (1 answer) eigenvalue and eigenvectors in python vs matlab (1 answer) Closed 2 years ago . I have stiffness matrix and mass matrix. I want to calculate my structure vibration shapes and period (eigenvalue/vector) so I am using NumPy for this. Eigenvalues are the

问题 In Matlab, I have to study the eventual existence of common eigenvectors basis between 2 Fisher matrices FISH_sp and FISH_xc of size 7x7 and diagonalisable. I get from my computation the following result: >> x=null(FISH_sp*FISH_xc-FISH_xc*FISH_sp) x = -0.0085 -0.0048 -0.2098 0.9776 -0.0089 -0.0026 0.0109 In this result, It appears that condition to get a common eigenvectors basis on commutator is true. But I need to further examine the mathematics. If one gets a single column vector, then

问题 In Matlab, I have to study the eventual existence of common eigenvectors basis between 2 Fisher matrices FISH_sp and FISH_xc of size 7x7 and diagonalisable. I get from my computation the following result: >> x=null(FISH_sp*FISH_xc-FISH_xc*FISH_sp) x = -0.0085 -0.0048 -0.2098 0.9776 -0.0089 -0.0026 0.0109 In this result, It appears that condition to get a common eigenvectors basis on commutator is true. But I need to further examine the mathematics. If one gets a single column vector, then

问题 According to https://docs.scipy.org/doc/numpy-1.15.0/user/numpy-for-matlab-users.html, the equivalent numpy expression for the MATLAB [V,D]=eig(a,b) is V,D = np.linalg.eig(a,b) . But when I try this I get the error: TypeError: eig() takes 1 positional argument but 2 were given I'm confused, the documentation says np.linalg.eig can take two arguments? Curiously, when I look at the linalg documentation at https://docs.scipy.org/doc/numpy-1.15.1/reference/routines.linalg.html, under the heading

问题 According to https://docs.scipy.org/doc/numpy-1.15.0/user/numpy-for-matlab-users.html, the equivalent numpy expression for the MATLAB [V,D]=eig(a,b) is V,D = np.linalg.eig(a,b) . But when I try this I get the error: TypeError: eig() takes 1 positional argument but 2 were given I'm confused, the documentation says np.linalg.eig can take two arguments? Curiously, when I look at the linalg documentation at https://docs.scipy.org/doc/numpy-1.15.1/reference/routines.linalg.html, under the heading

问题 I am trying to find the stationary distribution of a Markov chain. I have a transition probability matrix (TPM). Here is the code: [V, D] = eigs(double(TPM'),1); Py = abs(V)/sum(V); My problem is that sum(V) < 0 so it gives me some negative values in the vector Py . I've tested the code using another probability matrix and it gives sum(V) > 0 . I don't know what is the problem, is it the TPM or the code I am using? EDIT: Here is a more "elaborated" version of the code (including the answer of

问题 I am looking for finding or rather building common eigenvectors matrix X between 2 matrices A and B such as : AX=aX with "a" the diagonal matrix corresponding to the eigenvalues BX=bX with "b" the diagonal matrix corresponding to the eigenvalues where A and B are square and diagonalizable matrices. I took a look in a similar post but had not managed to conclude, i.e having valid results when I build the final wanted endomorphism F defined by : F = P D P^-1 I have also read the wikipedia topic

问题 I am looking for finding or rather building common eigenvectors matrix X between 2 matrices A and B such as : AX=aX with "a" the diagonal matrix corresponding to the eigenvalues BX=bX with "b" the diagonal matrix corresponding to the eigenvalues where A and B are square and diagonalizable matrices. I took a look in a similar post but had not managed to conclude, i.e having valid results when I build the final wanted endomorphism F defined by : F = P D P^-1 I have also read the wikipedia topic

问题 I am looking for finding or rather building common eigenvectors matrix X between 2 matrices A and B such as : AX=aX with "a" the diagonal matrix corresponding to the eigenvalues BX=bX with "b" the diagonal matrix corresponding to the eigenvalues where A and B are square and diagonalizable matrices. I took a look in a similar post but had not managed to conclude, i.e having valid results when I build the final wanted endomorphism F defined by : F = P D P^-1 I have also read the wikipedia topic

来源： https://stackoverflow.com/questions/62561548/different-results-in-eigenvector-centrality-numpy