def grad(theta, i):
# gradient of the ith eigenvalue of K with respect to theta
K = np.zeros((n, n))
_ratematrix.build_ratemat(np.exp(theta), n, None, K)
w, V = scipy.linalg.eig(K)
order = np.argsort(np.real(w))
V = np.real(np.ascontiguousarray(V[:, order]))
U = np.ascontiguousarray(scipy.linalg.inv(V).T)
g = np.zeros(len(theta))
for u in range(len(theta)):
dKu = np.zeros((n, n))
_ratematrix.dK_dtheta_A(np.exp(theta), n, u, None, None, dKu)
out = np.zeros(n)
temp = np.zeros(n)
_ratematrix.dw_du(dKu, U, V, n, temp, out)
g[u] = out[i]
return g
def grad(theta, i):
# gradient of the ith eigenvalue of K with respect to theta
K = np.zeros((n, n))
_ratematrix.build_ratemat(theta, n, K)
w, V = scipy.linalg.eig(K)
order = np.argsort(np.real(w))
V = np.real(np.ascontiguousarray(V[:, order]))
U = np.ascontiguousarray(scipy.linalg.inv(V).T)
g = np.zeros(len(theta))
for u in range(len(theta)):
dKu = np.zeros((n, n))
_ratematrix.dK_dtheta_ij(theta, n, u, None, dKu)
out = np.zeros(n)
temp = np.zeros(n)
_ratematrix.dw_du(dKu, U, V, n, temp, out)
g[u] = out[i]
return g