def EigenSolver(self): L = self.D - self.W R = self.D lam, y = eig(L, R) index = np.argsort(lam) top2 = lam[index[:2]].real smallest_2 = y[:, index[1]] print('dense eigenvector') return smallest_2.real
def EigenSolver(self): L = self.D - self.W R = self.D lam, y = eig(L, R) index = np.argsort(lam) top2 = lam[index[:2]].real smallest_2 = y[:, index[1]] print('dense eigenvector: {} with shape of {}'.format( smallest_2, smallest_2.shape)) return smallest_2.real
def eigval_decomp(sym_array): """ Returns ------- W: array of eigenvectors eigva: list of eigenvalues k: largest eigenvector """ #check if symmetric, do not include shock period eigva, W = decomp.eig(sym_array, left=True, right=False) k = np.argmax(eigva) return W, eigva, k
def eigval_decomp(sym_array): """ Returns ------- W: array of eigenvectors eigva: list of eigenvalues k: largest eigenvector """ #check if symmetric, do not include shock period eigva, W = decomp.eig(sym_array, left=True, right=False) k = np.argmax(eigva) return W, eigva, k