def test_gamma():
dvec = np.array([0, 30, 2, 1])
p = dvec.shape[0] - 1
alpha = randint(1, 10, (p, p + 1)) * .1
dvec[0] = 1000 - dvec[1:].sum()
man = ComplexFlag(dvec, alpha=alpha)
X = man.rand()
eta = man.randvec(X)
g2 = man.christoffel_gamma(X, eta, eta)
egrad = man._rand_ambient()
ehess = man._rand_ambient()
ehess_val = man.base_inner_ambient(ehess, eta)
print(man.rhess02_alt(X, eta, eta, egrad, ehess_val))
print(man.rhess02(X, eta, eta, egrad, ehess))
print(ehess_val - man.base_inner_ambient(g2, egrad))
print(man.exp(X, eta))
def test_rhess_02():
np.random.seed(0)
dvec = np.array([10, 3, 2, 3])
p = dvec.shape[0] - 1
alpha = randint(1, 10, (p, p + 1)) * .1
man = ComplexFlag(dvec, alpha=alpha)
n = man.n
d = man.d
Y = man.rand()
UU = {}
p = alpha.shape[0]
VV = {}
gidx = man._g_idx
for rr in range(p):
UU[rr] = make_sym_pos(n)
VV[rr] = crandn(n, dvec[rr + 1])
def f(Y):
ss = 0
for rr in range(p):
br, er = gidx[rr + 1]
wr = Y[:, br:er]
ss += trace(UU[rr] @ wr @ wr.T.conjugate()).real
return ss
def df(W):
ret = np.zeros_like(W)
for rr in range(p):
br, er = gidx[rr + 1]
wr = W[:, br:er]
ret[:, br:er] += 2 * UU[rr] @ wr
return ret
def ehess_form(W, xi, eta):
ss = 0
for rr in range(p):
br, er = gidx[rr + 1]
ss += 2 * trace(
UU[rr] @ xi[:, br:er] @ eta[:, br:er].T.conjugate()).real
return ss
def ehess_vec(W, xi):
ret = np.zeros_like(W)
for rr in range(p):
br, er = gidx[rr + 1]
ret[:, br:er] += 2 * UU[rr] @ xi[:, br:er]
return ret
xxi = crandn(n, d)
dlt = 1e-8
Ynew = Y + dlt * xxi
d1 = (f(Ynew) - f(Y)) / dlt
d2 = df(Y)
print(d1 - trace(d2 @ xxi.T.conjugate()).real)
eeta = crandn(n, d)
d1 = trace((df(Ynew) - df(Y)) @ eeta.T.conjugate()).real / dlt
ehess_val = ehess_form(Y, xxi, eeta)
# ehess_val2 = ehess_form(Y, eeta, xxi)
dv2 = ehess_vec(Y, xxi)
print(trace(dv2 @ eeta.T.conjugate()).real)
print(d1, ehess_val, d1 - ehess_val)
# now check the formula: ehess = xi (eta_func(f)) - <D_xi eta, df(Y)>
# promote eta to a vector field.
m1 = crandn(n, n)
m2 = crandn(d, d)
def eta_field(Yin):
return m1 @ (Yin - Y) @ m2 + eeta
# xietaf: should go to ehess(xi, eta) + df(Y) @ etafield)
xietaf = trace(
df(Ynew) @ eta_field(Ynew).T.conjugate() -
df(Y) @ eta_field(Y).T.conjugate()).real / dlt
# appy eta_func to f: should go to tr(m1 @ xxi @ m2 @ df(Y).T.conjugate())
Dxietaf = trace(
(eta_field(Ynew) - eta_field(Y)) @ df(Y).T.conjugate()).real / dlt
# this is ehess. should be same as d1 or ehess_val
print(xietaf - Dxietaf)
print(xietaf - Dxietaf - ehess_val)
# now check: rhess. Need to make sure xi, eta in the tangent space.
# first compare this with numerical differentiation
xi1 = man.proj(Y, xxi)
eta1 = man.proj(Y, eeta)
egvec = df(Y)
ehvec = ehess_vec(Y, xi1)
rhessvec = man.ehess2rhess(Y, egvec, ehvec, xi1)
# check it numerically:
def rgrad_func(Y):
return man.proj_g_inv(Y, df(Y))
val2, _, _ = calc_covar_numeric(man, Y, xi1, rgrad_func)
val2_p = man.proj(Y, val2)
# print(rhessvec)
# print(val2_p)
print(check_zero(rhessvec - val2_p))
rhessval = man.inner(Y, rhessvec, eta1)
print(man.inner(Y, val2, eta1))
print(rhessval)
# check symmetric:
ehvec_e = ehess_vec(Y, eta1)
ehess_valp = ehess_form(Y, xi1, eta1)
rhessvec_e = man.ehess2rhess(Y, egvec, ehvec_e, eta1)
rhessval_e = man.inner(Y, rhessvec_e, xi1)
rhessval_e1 = man.rhess02(Y, xi1, eta1, egvec, ehess_valp)
# rhessval_e2 = man.rhess02_alt(Y, xi1, eta1, egvec,
# trace([email protected]()).real)
# print(rhessval_e, rhessval_e1, rhessval_e2)
print(rhessval_e, rhessval_e1, rhessval_e - rhessval_e1)
print('rhessval_e %f ' % rhessval_e)
# the above computed inner_prod(Nabla_xi Pi * df, eta)
# in the following check. Extend eta1 to eta_proj
# (Pi Nabla_hat Pi g_inv df, g eta)
# = D_xi (Pi g_inv df, g eta) - (Pi g_inv df g Pi Nabla_hat eta)
def eta_proj(Y):
return man.proj(Y, eta_field(Y))
print(check_zero(eta1 - eta_proj(Y)))
e1 = man.inner(Y, man.proj_g_inv(Y, df(Y)), eta_proj(Y))
e1a = trace(df(Y) @ eta_proj(Y).T.conjugate()).real
print(e1, e1a, e1 - e1a)
Ynew = Y + xi1 * dlt
e2 = man.inner(Ynew, man.proj_g_inv(Ynew, df(Ynew)), eta_proj(Ynew))
e2a = trace(df(Ynew) @ eta_proj(Ynew).T.conjugate()).real
print(e2, e2a, e2 - e2a)
first = (e2 - e1) / dlt
first1 = trace(
df(Ynew) @ eta_proj(Ynew).T.conjugate() -
df(Y) @ eta_proj(Y).T.conjugate()).real / dlt
print(first - first1)
val3, _, _ = calc_covar_numeric(man, Y, xi1, eta_proj)
second = man.inner(Y, man.proj_g_inv(Y, df(Y)), man.proj(Y, val3))
second2 = man.inner(Y, man.proj_g_inv(Y, df(Y)), val3)
print(second, second2, second - second2)
print('same as rhess_val %f' % (first - second))