matrix

问题 With Matlab, I have a matrix solution to find from 2 matricial equations (size matrix is 7x7). Here the 2 equations to solve with " a " and " b " are the unknow matrices and where F1, F2, P1 and P2, D, D2, D are known. Solving "a" and "b" would allow me to build a new matrix P = a . P1 + b . P2 . (remark : D matrix is equal to : D = a.a.D1 + b.b.D2 with D1and D2 diagonal matrices) : a.a + a.P1.b.P2^T + b.P2.a.P1^T + b.b - Id = 0 (equation 1) F1.a.P1 + F1.b.P2 + F2.a.P1 + F2.b.P2 − (a.P1 + b

问题 With Matlab, I have a matrix solution to find from 2 matricial equations (size matrix is 7x7). Here the 2 equations to solve with " a " and " b " are the unknow matrices and where F1, F2, P1 and P2, D, D2, D are known. Solving "a" and "b" would allow me to build a new matrix P = a . P1 + b . P2 . (remark : D matrix is equal to : D = a.a.D1 + b.b.D2 with D1and D2 diagonal matrices) : a.a + a.P1.b.P2^T + b.P2.a.P1^T + b.b - Id = 0 (equation 1) F1.a.P1 + F1.b.P2 + F2.a.P1 + F2.b.P2 − (a.P1 + b

问题 With Matlab, I have a matrix solution to find from 2 matricial equations (size matrix is 7x7). Here the 2 equations to solve with " a " and " b " are the unknow matrices and where F1, F2, P1 and P2, D, D2, D are known. Solving "a" and "b" would allow me to build a new matrix P = a . P1 + b . P2 . (remark : D matrix is equal to : D = a.a.D1 + b.b.D2 with D1and D2 diagonal matrices) : a.a + a.P1.b.P2^T + b.P2.a.P1^T + b.b - Id = 0 (equation 1) F1.a.P1 + F1.b.P2 + F2.a.P1 + F2.b.P2 − (a.P1 + b

问题 With Matlab, I have a matrix solution to find from 2 matricial equations (size matrix is 7x7). Here the 2 equations to solve with " a " and " b " are the unknow matrices and where F1, F2, P1 and P2, D, D2, D are known. Solving "a" and "b" would allow me to build a new matrix P = a . P1 + b . P2 . (remark : D matrix is equal to : D = a.a.D1 + b.b.D2 with D1and D2 diagonal matrices) : a.a + a.P1.b.P2^T + b.P2.a.P1^T + b.b - Id = 0 (equation 1) F1.a.P1 + F1.b.P2 + F2.a.P1 + F2.b.P2 − (a.P1 + b

问题 I want to use Eigen::Ref to have non-template functions using Eigen::Matrix arguments. My problem is that in these functions, I may have to resize the matrices referenced by the Eigen::Ref. I understand that for generality an Eigen::Ref should not be resized because it can map to an expression or a matrix block, but In my case, I am sure that what is behind my Eigen::Ref is an Eigen::Matrix. To illustrate this: #include "Eigen/Dense" void add(Eigen::Ref<Eigen::MatrixXd> M, const Eigen::Ref

问题 I want to use Eigen::Ref to have non-template functions using Eigen::Matrix arguments. My problem is that in these functions, I may have to resize the matrices referenced by the Eigen::Ref. I understand that for generality an Eigen::Ref should not be resized because it can map to an expression or a matrix block, but In my case, I am sure that what is behind my Eigen::Ref is an Eigen::Matrix. To illustrate this: #include "Eigen/Dense" void add(Eigen::Ref<Eigen::MatrixXd> M, const Eigen::Ref

问题 I want to use Eigen::Ref to have non-template functions using Eigen::Matrix arguments. My problem is that in these functions, I may have to resize the matrices referenced by the Eigen::Ref. I understand that for generality an Eigen::Ref should not be resized because it can map to an expression or a matrix block, but In my case, I am sure that what is behind my Eigen::Ref is an Eigen::Matrix. To illustrate this: #include "Eigen/Dense" void add(Eigen::Ref<Eigen::MatrixXd> M, const Eigen::Ref

问题 I have a matrix A = np.array([[1,1,1],[1,2,3],[4,4,4]]) and I want only the linearly independent rows in my new matrix. The answer might be A_new = np.array([1,1,1],[1,2,3]]) or A_new = np.array([1,2,3],[4,4,4]) Since I have a very large matrix so I need to decompose the matrix into smaller linearly independent full rank matrix. Can someone please help? 回答1: There are many ways to do this, and which way is best will depend on your needs. And, as you noted in your statement, there isn't even a

问题 I have a matrix A = np.array([[1,1,1],[1,2,3],[4,4,4]]) and I want only the linearly independent rows in my new matrix. The answer might be A_new = np.array([1,1,1],[1,2,3]]) or A_new = np.array([1,2,3],[4,4,4]) Since I have a very large matrix so I need to decompose the matrix into smaller linearly independent full rank matrix. Can someone please help? 回答1: There are many ways to do this, and which way is best will depend on your needs. And, as you noted in your statement, there isn't even a

问题 Looking at the comments for the code behind scipy.stats.wasserstein_distance which invokes a function called _cdf_distance(p, u_values, v_values, u_weights=None, v_weights=None) , it says this function implements the following formula: l_p(u, v) = \left( \int_{-\infty}^{+\infty} |U-V|^p \right)^{1/p} However, this is not the Wasserstein distance as I know it since, although I see the distance matrix |U-V| in the above formula comment, the transport matrix is noticeably absent. The transport