def test_ddp_to_sa_and_to_product():
n, m = 3, 2
R = np.array([[0, 1], [1, 0], [-np.inf, 1]])
Q = np.empty((n, m, n))
Q[:] = 1/n
Q[0, 0, 0] = 0
Q[0, 0, 1] = 2/n
beta = 0.95
sparse_R = np.array([0, 1, 1, 0, 1])
_Q = np.full((5, 3), 1/3)
_Q[0, 0] = 0
_Q[0, 1] = 2/n
sparse_Q = sparse.coo_matrix(_Q)
ddp = DiscreteDP(R, Q, beta)
ddp_sa = ddp.to_sa_pair_form()
ddp_sa2 = ddp_sa.to_sa_pair_form()
ddp_sa3 = ddp.to_sa_pair_form(sparse=False)
ddp2 = ddp_sa.to_product_form()
ddp3 = ddp_sa2.to_product_form()
ddp4 = ddp.to_product_form()
# make sure conversion worked
for ddp_s in [ddp_sa, ddp_sa2, ddp_sa3]:
assert_allclose(ddp_s.R, sparse_R)
# allcose doesn't work on sparse
np.max(np.abs((sparse_Q - ddp_s.Q))) < 1e-15
assert_allclose(ddp_s.beta, beta)
# these two will have probability 0 in state 2, action 0 b/c
# of the infeasiability in R
funky_Q = np.empty((n, m, n))
funky_Q[:] = 1/n
funky_Q[0, 0, 0] = 0
funky_Q[0, 0, 1] = 2/n
funky_Q[2, 0, :] = 0
for ddp_f in [ddp2, ddp3]:
assert_allclose(ddp_f.R, ddp.R)
assert_allclose(ddp_f.Q, funky_Q)
assert_allclose(ddp_f.beta, ddp.beta)
# this one is just the original one.
assert_allclose(ddp4.R, ddp.R)
assert_allclose(ddp4.Q, ddp.Q)
assert_allclose(ddp4.beta, ddp.beta)
for method in ["pi", "vi", "mpi"]:
sol1 = ddp.solve(method=method)
for ddp_other in [ddp_sa, ddp_sa2, ddp_sa3, ddp2, ddp3, ddp4]:
sol2 = ddp_other.solve(method=method)
for k in ["v", "sigma", "num_iter"]:
assert_allclose(sol1[k], sol2[k])
def test_ddp_to_sa_and_to_product():
n, m = 3, 2
R = np.array([[0, 1], [1, 0], [-np.inf, 1]])
Q = np.empty((n, m, n))
Q[:] = 1 / n
Q[0, 0, 0] = 0
Q[0, 0, 1] = 2 / n
beta = 0.95
sparse_R = np.array([0, 1, 1, 0, 1])
_Q = np.full((5, 3), 1 / 3)
_Q[0, 0] = 0
_Q[0, 1] = 2 / n
sparse_Q = sparse.coo_matrix(_Q)
ddp = DiscreteDP(R, Q, beta)
ddp_sa = ddp.to_sa_pair_form()
ddp_sa2 = ddp_sa.to_sa_pair_form()
ddp_sa3 = ddp.to_sa_pair_form(sparse=False)
ddp2 = ddp_sa.to_product_form()
ddp3 = ddp_sa2.to_product_form()
ddp4 = ddp.to_product_form()
# make sure conversion worked
for ddp_s in [ddp_sa, ddp_sa2, ddp_sa3]:
assert_allclose(ddp_s.R, sparse_R)
# allcose doesn't work on sparse
np.max(np.abs((sparse_Q - ddp_s.Q))) < 1e-15
assert_allclose(ddp_s.beta, beta)
# these two will have probability 0 in state 2, action 0 b/c
# of the infeasiability in R
funky_Q = np.empty((n, m, n))
funky_Q[:] = 1 / n
funky_Q[0, 0, 0] = 0
funky_Q[0, 0, 1] = 2 / n
funky_Q[2, 0, :] = 0
for ddp_f in [ddp2, ddp3]:
assert_allclose(ddp_f.R, ddp.R)
assert_allclose(ddp_f.Q, funky_Q)
assert_allclose(ddp_f.beta, ddp.beta)
# this one is just the original one.
assert_allclose(ddp4.R, ddp.R)
assert_allclose(ddp4.Q, ddp.Q)
assert_allclose(ddp4.beta, ddp.beta)
for method in ["pi", "vi", "mpi"]:
sol1 = ddp.solve(method=method)
for ddp_other in [ddp_sa, ddp_sa2, ddp_sa3, ddp2, ddp3, ddp4]:
sol2 = ddp_other.solve(method=method)
for k in ["v", "sigma", "num_iter"]:
assert_allclose(sol1[k], sol2[k])
