python,c++,r,armadillo,eigenvector

That is answered by help(eigen) in R: Value: The spectral decomposition of ‘x’ is returned as components of a list with components values: a vector containing the p eigenvalues of ‘x’, sorted in _decreasing_ order, according to ‘Mod(values)’ in the asymmetric case when they might be complex (even for real...

Pass the second, optional parameter to eigs, which controls how many eigenvectors are returned.

algorithm,matrix,eigenvector,wolframalpha

Eigenvectors of a matrix are not unique, and there are multiple possible decompositions; in fact, only eigenspaces can be defined uniquely. Both results that you are receiving are valid. You can easily see that by asking Wolfram Alpha to orthogonalize the second matrix. Run the following query: Orthogonalize[{{1.13168, 0.969831, 1.},...

c,lapack,eigenvalue,eigenvector

You can not port directly from zgeev to dgeev. The zgeev gets a complex matrix and computes complex eigenvalues. While dgeev gets a real matrix and computes complex eigenvalues. In order to be consistent LAPACK uses WR and WI which is used for the real and imaginary part of each...

c++,opengl,matrix,pca,eigenvector

I believe I found your problem. You need to check again the theory! As I may recall, you have the covariance defined in the theory as: C=1/M \Sum ( (p-pmean)*(p-pmean)^t ) Well, you may notice that C is a 3x3 matrix, NOT a value. Therefore, when you call Compute_EigenV and...

eigen,face-recognition,pca,eigenvector

The accuracy would depend on the classifier you are using once you have the data in the PCA projected space. In the original Turk/Pentland eigenface paper http://www.face-rec.org/algorithms/PCA/jcn.pdf they just use kNN / Euclidean distance but a modern implementation might use SVMs e.g. with an rbf kernel as the classifier in...

This behaviour is correct. To understand the reason, we need to look at the definition of eigenvectors (source: wikipedia): An eigenvector or characteristic vector of a square matrix A is a non-zero vector v that, when multiplied with A, yields a scalar multiple of itself. [...] That is: Av =...

Adapting a previous answer, you can do perp.segment.coord <- function(x0, y0, a=0,b=1){ #finds endpoint for a perpendicular segment from the point (x0,y0) to the line # defined by lm.mod as y=a+b*x x1 <- (x0+b*y0-a*b)/(1+b^2) y1 <- a + b*x1 list(x0=x0, y0=y0, x1=x1, y1=y1) } ss<-perp.segment.coord(df$Person1, df$Person2,0,eigen$vectors.scaled[1,1]) g + geom_segment(data=as.data.frame(ss), aes(x...

python,numpy,linear-algebra,eigenvector,markov-chains

Is the transition matrix symmetric? If not, consider checking for T.T (the transpose), because you need to make sure you're looking at the right state transitions: you need the left eigenvector of your stochastic matrix, but almost all out-of-the-box scientific packages (numpy included) default to computing the right eigenvectors (this...

java,matrix,eigenvector,eigenvalue,jama

Note the return type of PopulateMatrix(): public void PopulateMatrix() { ... } You say it returns nothing, by making it void so when you try to return a Matrix you get an error message saying this is an unexpected return type. If you want to return a Matrix from PopulateMatrix(),...

c++,opencv,complex-numbers,eigenvector,eigenvalue

So I solved the problem using the 'ComplexEigenSolver' from the Eigen library. //create a multichannel matrix Mat a_com = Mat::zeros(4,4,CV_32FC2); for(int i = 0; i<4; i++) { for(int j = 0; j<4; j++) { a_com.at<Vec2f>(i,j)[0] = a.at<double>(i,j); a_com.at<Vec2f>(i,j)[1] = 0; } } MatrixXcf eigenA; cv2eigen(a_com,eigenA); //convert OpenCV to Eigen ComplexEigenSolver<MatrixXcf>...

c++,cuda,eigenvalue,eigenvector,cusolver

It appears (to me) that the cuSolver documentation may be incorrect with respect to the mu parameter. The documentation appears to indicate that this is in the host memory space, i.e. the 2nd to last parameter should be a host pointer. If I change it to be a device pointer:...

swift,matrix,lapack,eigenvector,accelerate-framework

The problem’s with your lwork variable. This is supposed to be the size of the workspace you supply, with -1 meaning you’re performing a “workspace query”: LWORK (input) INTEGER The dimension of the array WORK. LWORK >= max(1,3*N), and if JOBVL = 'V' or JOBVR = 'V', LWORK >= 4*N....

Use numpy.linalg.eigh or scipy.linalg.eigh. These functions are designed for symmetric (or Hermitian) matrices, and with a real symmetric matrix, they should always return real eigenvalues and eigenvectors. For example, In [62]: from numpy.linalg import eigh In [63]: a Out[63]: array([[ 2., 1., 0., 0.], [ 1., 2., 0., 0.], [...

Just play with small numbers to debug your problem. Start with A=np.random.randn(3,2) instead of your much larger matrix with size (50,20) In my random case, I find that v1 = array([[-0.33872745, 0.94088454], [-0.94088454, -0.33872745]]) and for v2: v2 = array([[ 0.33872745, -0.94088454], [ 0.94088454, 0.33872745]]) they only differ for a...

machine-learning,cluster-analysis,pca,eigenvalue,eigenvector

As far as I can tell, you have mixed and shuffled aa number of approaches. No wonder it doesn't work... you could simply use jaccard distance (a simple inversion of jaccard similarity) + hierachical clustering you could do MDS to project you data, then k-means (probably what you are trying...