matrix-inverse

问题 Given n images and a projection matrix for each image, how can i calculate the ray (line) emitted by each pixel of the images, which is intersecting one of the three planes of the real-world coordinate system? The object captured by the camera is at the same position, just the camera's position is different for each image. That's why there is a separate projection matrix for each image. As far as my research suggests, this is the inverse of the 3D to 2D projection. Since information is lost

问题 Given n images and a projection matrix for each image, how can i calculate the ray (line) emitted by each pixel of the images, which is intersecting one of the three planes of the real-world coordinate system? The object captured by the camera is at the same position, just the camera's position is different for each image. That's why there is a separate projection matrix for each image. As far as my research suggests, this is the inverse of the 3D to 2D projection. Since information is lost

问题 I was trying to explore the option of "solveInPlace()" function while using LLT in Eigen3.3.7 to speed up the matrix inverse computation in my application. I used the following code to test it. int main() { const int M=3; Eigen::Matrix<MyType,Eigen::Dynamic,Eigen::Dynamic> R = Eigen::Matrix<MyType,Eigen::Dynamic,Eigen::Dynamic>::Zero(M,M); // to make sure full rank for(int i=0; i<M*2; i++) { const Eigen::Matrix<MyType, Eigen::Dynamic,1> tmp = Eigen::Matrix<MyType,Eigen::Dynamic,1>::Random(M);

问题 I have a time series model y(t)= h^T y(t-1) + n(t) where n(t) is a white Gaussian noise that excites and drives the process. y is the output of a linear regression model for t = 1,2,... denoting the number of data points. Question: If the Correlation matrix is Ryy = E[y(t)*y(t)^T], then is it possible to compute Correlation of the lagged random variables such as [E[y(t-1)*y(t-1)']] In general, these operators and expressions are also found in: Slide2 mentions the Autocorrelation matrix. In

问题 My real data is first input but inverse of result is so big. They are same data when you compare with first and second input. There is only difference decimal size. Why is there different result? Because they are same data. How can they have different result? You can see result and input. It is so strange. program test Implicit none double precision,allocatable,dimension(:,:) :: A double precision,allocatable,dimension(:) :: WORK integer ,allocatable,dimension(:) :: ipiv integer :: n,info,M

问题 My real data is first input but inverse of result is so big. They are same data when you compare with first and second input. There is only difference decimal size. Why is there different result? Because they are same data. How can they have different result? You can see result and input. It is so strange. program test Implicit none double precision,allocatable,dimension(:,:) :: A double precision,allocatable,dimension(:) :: WORK integer ,allocatable,dimension(:) :: ipiv integer :: n,info,M

问题 I am creating a javascript code for matrix inversion.But the function doesn't seem to run. I want my inverted matrix to be displayed where the input matrix was.I have tried alerting values of the invertedMatrix instead of putting them into s but that didn't work either.Would be grateful for any help html <div id = "table3"> <div class = "header">Macierz odwrotna [2x2]</div> <form id = "row1"> <input type = "text" class = "det2"/><!--first row--> <input type = "text" class = "det2"/> </form>

问题 The matrix below is singular, and AFAIK attempting to invert it should result in numpy.linalg.linalg.LinAlgError: Singular matrix but instead, I do get some output matrix. Note that output matrix is a non-sensical result, because it has a row of 0's (which is impossible, since an inverse of a matrix should itself be invertible)! Am I missing something here related to floating point precision, or the computation of a pseudoinverse as opposed to a true inverse? $ np.__version__ '1.13.1' $ np

问题 I am facing an issue of accuracy about my code which performs a number (128, 256, 512) of 4x4 matrix inversions. When I use the original version, i.e the numpy function np.linalg.inv or np.linalg.pinv , everything works fine. Unfortunately, with the CUDA code below, I get nan and inf values into inverted matrix. To be more explicit, I take this matrix to invert : 2.120771107884677649e+09 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 0.000000000000000000e+00 3

问题 in the example below, there is a 3d numpy matrix of size (4, 3, 3)+ a solution about how to calculate pinv of each of 4 of those 3*3 matrices in numpy. I also tried to use the same function worked in numpy, in theano hoping that it is implemented the same, but it failed. Any idea how to do it in theano? dt = np.dtype(np.float32) a=[[[12,3,1], [2,4,1], [2,4,2],], [[12,3,3], [2,4,4], [2,4,5],], [[12,3,6], [2,4,5], [2,4,4],], [[12,3,3], [2,4,5], [2,4,6]]] a=np.asarray(a,dtype=dt) print(a.shape)