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
