The singular value decomposition (SVD) provides another way to factorize a matrix, into singular vectors and singular values. The SVD allows us to discover some of the same kind of information as the eigendecomposition. However, the SVD is more generally applicable. — Pages 44-45, Deep Learni...
library(greta)#>#> Attaching package: 'greta'#> The following objects are masked from 'package:stats':#>#> binomial, cov2cor, poisson#> The following objects are masked from 'package:base':#>#> %*%, apply, backsolve, beta, chol2inv, colMeans, colSums, diag,#> eigen, forwardsolve, ...
print(A) # calculate the mean of each column M = mean(A.T, axis=1) print(M) # center columns by subtracting column means C = A - M print(C) # calculate covariance matrix of centered matrix V = cov(C.T) print(V) # eigendecomposition of covariance matrix values, vectors = eig(...
bool obbDisjointAndLowerBoundDistance(const Matrix3f& B, const Vec3f& T, const Vec3f& a_, const Vec3f& b_, const CollisionRequest& request, FCL_REAL& squaredLowerBoundDistance) { assert(request.security_margin > -2 * (std::min)(a_.minCoeff(), b_.minCoeff()) - 10 * Eigen::NumTr...
The singular value decomposition (SVD) provides another way to factorize a matrix, into singular vectors and singular values. The SVD allows us to discover some of the same kind of information as the eigendecomposition. However, the SVD is more generally applicable. — Pages 44-45, Deep ...