def test_vec_solve():
mu_12 = Tensor('mu_12', rank=0)
p_1, p_2 = map(lambda x: Tensor(x, rank=1), ['p_1', 'p_2'])
A = Tensor('A', rank=2)
print solve_vec_eqn(
-mu_12 * (T(p_1) * A * p_2) - mu_12 * (T(p_2) * A * p_1) +
(T(p_2) * A * p_1), mu_12)
def testInverseMul():
ans = 5 * I
A = Tensor('A', rank=2, has_inv=True)
A_I = Inverse(A)
e = 5 * A
assert (A_I * e == ans)
assert (e * A_I == ans)
e = 5 * A_I
assert (A * e == ans)
assert (e * A == ans)
ans = 5 * one
p = Tensor('p', rank=1)
pAp = T(p) * A * p
pAp_I = Inverse(pAp)
e = 5 * pAp
assert (e * pAp_I == ans)
assert (pAp_I * e == ans)
e = 5 * pAp_I
assert (e * pAp == ans)
assert (pAp * e == ans)
e = 5 * pAp * A
assert (A_I * e == 5 * pAp * I)
assert (e * pAp_I == 5 * A)
def test_chronos_cg():
skip("Test takes too long")
delta_1, omega_2, pi_1, pi_2, mu_12 = map(lambda x: Tensor(x, rank=0), \
['delta_1', 'omega_2', 'pi_1', 'pi_2', 'mu_12'])
r_1, r_2, q_1, q_2, p_1, p_2, x_1, x_2 = map(lambda x: Tensor(x, rank=1), \
['r_1', 'r_2', 'q_1', 'q_2', 'p_1', 'p_2', 'x_1', 'x_2'])
A, R_0, P_0 = map(lambda x: Tensor(x, rank=2), ['A', 'R_0', 'P_0'])
# Specify which variables are known
knowns = [pi_1, p_1, r_1, q_1, x_1, A, R_0, P_0]
# Now try the chronos variant and repeat.
chronos_eqns = [
r_2 - r_1 - delta_1 * q_1,
q_2 - A * p_2,
p_2 - r_2 + p_1 * mu_12,
q_2 - A * r_2 + q_1 * mu_12,
x_2 - x_1 - delta_1 * p_1,
omega_2 - T(r_2) * r_2,
pi_2 - T(p_2) * A * p_2,
T(R_0) * r_2,
T(r_1) * r_2,
T(P_0) * A * p_2,
T(p_1) * A * p_2,
T(p_2) * A * p_2 - T(r_2) * A * r_2 + T(mu_12) * pi_1 * mu_12,
]
run_cg_algorithms(chronos_eqns, knowns)
def test_5 ():
q, r, s = map(lambda x: Tensor(x, rank=1), 'qrs')
s_t = Transpose(s)
delta = Tensor('delta', rank=0)
eqn1 = r - s - q * delta
eqn2 = s_t * r
print assump_solve([eqn1, eqn2], [r, q])
def test_forward_solve():
q, r, s = map(lambda x: Tensor(x, rank=1), 'qrs')
delta = Tensor('delta', rank=0)
eqn1 = s + q
eqn2 = delta * r - q
sol_dict = forward_solve([eqn1, eqn2], [delta, r])
print "sol_dict should be: {q: delta*r, s: -q }"
print " actual:", sol_dict
def test_mul_inverse():
delta_11 = Tensor('delta_11', 0)
r_1, p_1 = map(lambda x: Tensor(x, 1), ['r_1', 'p_1'])
A = Tensor('A', 2)
a = Tensor('a', 0)
expr = (-(T(p_1) * A * p_1) * (T(p_1) * A * r_1) * a + (T(p_1) * A * r_1))
expr = expr.subs(a, (T(p_1) * A * p_1)**-1)
assert (simplify(expr) == S(0))
def test_3 ():
q, r, s = map(lambda x: Tensor(x, rank=1), 'qrs')
s_t = Transpose(s)
delta = Tensor('delta', rank=0)
eqn1 = r - s - q * delta
eqn2 = s_t * r
print forward_solve([eqn1, eqn2], [r, s, q])
print forward_solve([eqn1, eqn2], [delta, s, q])
print forward_solve([eqn2, eqn1], [delta, s, q])
def test_0 ():
A = Tensor('A', rank=2, has_inv=True)
x = Tensor('x', rank=1)
y = Tensor('y', rank=1)
alpha = Tensor('alpha', rank=0)
beta = Tensor('beta', rank=0)
eqn = alpha * A * x + beta * y
print eqn, "rank:", expr_rank(eqn)
print "solution for y", solve_vec_eqn(eqn, y)
print "solution for x", solve_vec_eqn(eqn, x)
def test_backward_sub():
q, r, s = map(lambda x: Tensor(x, rank=1), 'qrs')
s_t = Transpose(s)
delta = Tensor('delta', rank=0)
eqn1 = r - s - q * delta
eqn2 = s_t * r
sol_dict, failed_var = backward_sub([eqn1, eqn2], [q, s], [r, delta])
print sol_dict, failed_var
sol_dict, failed_var = backward_sub([eqn1, eqn2], [q, s], [delta, r])
print sol_dict, failed_var
def testUpdate():
k = Symbol('k')
A = Tensor('A', 2)
A_TL = A.update(l_ind='TL')
A_TL_2 = A_TL.update(u_ind='2', rank=2)
A_01 = A_TL.update(l_ind='01', shape=(k, k))
a_02 = T(A_TL.update(l_ind='02', shape=(1, k), rank=1))
assert(A_TL == Tensor('A_TL', 2))
assert(A_TL_2 == Tensor('A_TL^2', 2))
assert(A_01 == Tensor('A_01', 2, shape=(k, k)))
assert(a_02 == T(Tensor('a_02', 1, shape=(1, k))))
def test_overdetermined_forward_solve():
pi_1, pi_2, mu_12 = \
map(lambda x: Tensor(x, rank=0),
['pi_1', 'pi_2', 'mu_12'])
r_2, p_1, p_2 = \
map(lambda x: Tensor(x, rank=1),
['r_2', 'p_1', 'p_2'])
A = Tensor('A', rank=2)
eqns = [ pi_2 - T(r_2) * A * r_2 + T(mu_12) * pi_1 * mu_12,
p_2 - r_2 + p_1 * mu_12,
pi_2 - T(p_2) * A * p_2,
]
return forward_solve(eqns, [r_2, p_1, mu_12, A, pi_1], True)
def test_algebra():
delta_1, omega_2, pi_1, pi_2, gamma_2, mu_12 = \
map(lambda x: Tensor(x, rank=0),
['delta_1', 'omega_2', 'pi_1', 'pi_2', 'gamma_2', 'mu_12'])
r_1, r_2, q_1, q_2, p_1, p_2, s_1, s_2 = \
map(lambda x: Tensor(x, rank=1),
['r_1', 'r_2', 'q_1', 'q_2', 'p_1', 'p_2', 's_1', 's_2'])
A, R_0, P_0 = map(lambda x: Tensor(x, rank=2), ['A', 'R_0', 'P_0'])
eqn = -2 * (T(p_1) * A * r_2) / (T(p_1) * A * p_1) * (T(p_1) * A * r_2)
eqn -= -2 * (T(p_1) * A * r_2) * (T(p_1) * A * r_2) / (T(p_1) * A * p_1)
assert(eqn == S(0))
assert(T(p_1) * A * r_2 == T(r_2) * A * p_1)
def testOne():
A = Tensor('A', 2)
a = Tensor('a', 1)
alpha = Tensor('alpha', 0)
ONE = Tensor('1', 2)
one = Tensor('1', 0)
assert (A * ONE == A)
assert (ONE * A == A)
assert (A * one == A)
assert (one * A == A)
assert (a * one == a)
assert (one * a == a)
assert (alpha * one == alpha)
assert (one * alpha == alpha)
def test_reg_cg():
delta_1, mu_12 = map(lambda x: Tensor(x, rank=0), ['delta_1', 'mu_12'])
r_1, r_2, p_1, p_2, x_1, x_2 = map(lambda x: Tensor(x, rank=1), \
['r_1', 'r_2', 'p_1', 'p_2', 'x_1', 'x_2'])
A = Tensor('A', rank=2)
# Specify which variables are known
knowns = [p_1, r_1, x_1, A]
# Specify the CG eqns (coming from a 4x4 PME)
cg_eqns = [delta_1 * A * p_1 - r_1 + r_2,
p_2 - r_2 + p_1 * mu_12,
x_2 - x_1 - delta_1 * p_1,
T(r_1) * r_2,
T(p_1) * A * p_2,
]
run_cg_algorithms(cg_eqns, knowns)
def testZero():
A = Tensor('A', 2)
a = Tensor('a', 1)
alpha = Tensor('alpha', 0)
Z = Tensor('0', 2)
z = Tensor('0', 1)
z_0 = Tensor('0', 0)
assert (A + Z == A)
assert (Z + A == A)
assert (A * Z == Z)
assert (Z * A == Z)
assert (a + z == a)
assert (z + a == a)
assert (A * z == z)
assert (T(a) * z == z_0)
assert (T(z) * a == z_0)
assert (z * T(a) == Z)
assert (a * T(z) == Z)
assert (alpha + z_0 == alpha)
assert (alpha * z_0 == z_0)
assert (alpha * z == z)
assert (alpha * Z == Z)
assert (z_0 * A == Z)
assert (z_0 * a == z)
assert (A + z_0 == A)
assert (z_0 + A == A)
def testUpdate():
k = Symbol('k')
A = Tensor('A', 2)
A_TL = A.update(l_ind='TL')
A_TL_2 = A_TL.update(u_ind='2', rank=2)
A_01 = A_TL.update(l_ind='01', shape=(k, k))
a_02 = T(A_TL.update(l_ind='02', shape=(1, k), rank=1))
assert (A_TL == Tensor('A_TL', 2))
assert (A_TL_2 == Tensor('A_TL^2', 2))
assert (A_01 == Tensor('A_01', 2, shape=(k, k)))
assert (a_02 == T(Tensor('a_02', 1, shape=(1, k))))
def get_chronos_eqns_3 ():
delta_1, omega_2, pi_1, pi_2, gamma_2, mu_12 = \
map(lambda x: Tensor(x, rank=0),
['delta_1', 'omega_2', 'pi_1', 'pi_2', 'gamma_2', 'mu_12'])
r_1, r_2, q_1, q_2, p_1, p_2, s_1, s_2 = \
map(lambda x: Tensor(x, rank=1),
['r_1', 'r_2', 'q_1', 'q_2', 'p_1', 'p_2', 's_1', 's_2'])
A, R_0, P_0 = map(lambda x: Tensor(x, rank=2), ['A', 'R_0', 'P_0'])
eqns = [r_2 - r_1 - delta_1 * q_1,
q_2 - A * p_2,
p_2 - r_2 + p_1 * mu_12,
q_2 - A * r_2 + q_1 * mu_12,
omega_2 - T(r_2) * r_2,
pi_2 - T(p_2) * A * p_2,
pi_2 - T(r_2) * A * r_2 + T(mu_12) * pi_1 * mu_12,
T(R_0) * r_2,
T(r_1) * r_2,
T(P_0) * A * p_2,
T(p_1) * A * p_2,
]
knowns = [p_1, r_1, q_1, pi_1, A, R_0, P_0]
return eqns, knowns
def test_sol_without_recomputes ():
a, b, c, d = map(lambda x: Tensor(x, rank=0), 'abcd')
sol_dict = {a: b + c*d, b: c*d}
ord_unk = [a, b]
assert(sol_without_recomputes((sol_dict, ord_unk)) == (sol_dict, ord_unk))
sol_dict = {a: b + c*d, b: c*d}
ord_unk = [b, a]
assert(sol_without_recomputes((sol_dict, ord_unk)) is None)
sol_dict = {a: set([b + c*d]), b: set([c*d])}
ord_unk = [b, a]
assert(sol_without_recomputes((sol_dict, ord_unk)) is None)
sol_dict = {a: set([b + c*d, b*d]), b: set([c*d])}
ord_unk = [b, a]
assert(sol_without_recomputes((sol_dict, ord_unk)) == \
({a: set([b*d]), b: set([c*d])}, ord_unk))
sol_dict = {a: set([b + c*d, b*d]), b: set([c*d])}
ord_unk = [a, b]
assert(sol_without_recomputes((sol_dict, ord_unk)) == (sol_dict, ord_unk))
def test_2 ():
from sympy import expand, S
A = Tensor('A', rank=2)
A_t = Transpose(A)
B = Tensor('B', rank=2)
x = Tensor('x', rank=1)
y = Tensor('y', rank=1)
x_t = Transpose(x)
y_t = Transpose(y)
alpha = Tensor('alpha', rank=0)
beta = Tensor('beta', rank=0)
teqn = Transpose((A + B) * x)
print "teqn.expand: ", teqn.expand(), "should be x'*A' + x'*B'"
print expand(S(2) * beta * (alpha * A + beta * B) * x), "should be 2*beta**2*B*x + 2*alpha*beta*A*x"
print expr_shape(expand(S(2) * A * x)), "should be (n, 1)"
def iterative_arg(name, rank, *args, **kws):
ten = Tensor(name, rank)
arg_src = kws.get("arg_src", PObj.ARG_SRC["Input"])
if type(arg_src) is str:
arg_src = PObj.ARG_SRC[arg_src]
part_suffix = kws.get("part_suffix", None)
part_fun = kws.get("part_fun", None)
repart_fun = kws.get("repart_fun", None)
fuse_fun = kws.get("fuse_fun", None)
if part_suffix is None:
part_suffix = PART_SUFFIX_DEFAULT
if part_fun is not None:
part_suffix = get_part_suffix(part_fun.__name__)
elif repart_fun is not None:
part_suffix = get_part_suffix(repart_fun.__name__)
elif fuse_fun is not None:
part_suffix = get_part_suffix(fuse_fun.__name__)
if part_fun is None:
try:
part_fun = getattr(iterative_prules, "Part_" + part_suffix)()
except AttributeError:
raise ValueError("Unable to find partition function for argument.")
if repart_fun is None:
try:
repart_fun = getattr(iterative_prules, "Repart_" + part_suffix)()
except AttributeError:
raise ValueError(
"Unable to find repartition function for argument.")
if fuse_fun is None:
try:
fuse_fun = getattr(iterative_prules, "Fuse_" + part_suffix)()
except AttributeError:
raise ValueError("Unable to find fuse function for argument.")
return PObj(ten,
part_fun=part_fun,
repart_fun=repart_fun,
fuse_fun=fuse_fun,
arg_src=arg_src)
from ignition.flame.tensors import (numpy_print, latex_print, T, Tensor)
delta_1, omega_2, pi_1, pi_2, gamma_2, mu_12 = \
map(lambda x: Tensor(x, rank=0),
['delta_1', 'omega_2', 'pi_1', 'pi_2', 'gamma_2', 'mu_12'])
r_1, r_2, q_1, q_2, p_1, p_2, s_1, s_2, x_1 = \
map(lambda x: Tensor(x, rank=1),
['r_1', 'r_2', 'q_1', 'q_2', 'p_1', 'p_2', 's_1', 's_2', 'x_1'])
A, R_0, P_0 = map(lambda x: Tensor(x, rank=2), ['A', 'R_0', 'P_0'])
def test_numpy_print():
assert (numpy_print(-mu_12 * p_1 + r_2) == "-dot(mu_12, p_1) + r_2")
assert (numpy_print(delta_1 * A * p_1 +
r_1) == "dot(delta_1, dot(A, p_1)) + r_1")
assert (numpy_print(delta_1 * p_1 + x_1) == "dot(delta_1, p_1) + x_1")
assert(numpy_print((T(r_1) * r_1) / (T(r_1) * A * p_1)) == \
"dot(dot((r_1).transpose(), r_1), 1.0 / (dot(dot((p_1).transpose(), A), r_1)))")
assert(numpy_print((T(p_1) * A * r_2) / (T(p_1) * A * p_1)) == \
"dot(dot(dot((p_1).transpose(), A), r_2), 1.0 / (dot(dot((p_1).transpose(), A), p_1)))")
def test_latex_print():
assert (latex_print(-mu_12 * p_1 + r_2) == "-\mu_{12} p_1 + r_2")
assert (latex_print(delta_1 * A * p_1 + r_1) == "\delta_1 A p_1 + r_1")
assert (latex_print(delta_1 * p_1 + x_1) == "\delta_1 p_1 + x_1")
assert(latex_print((T(r_1) * r_1) / (T(r_1) * A * p_1)) == \
"{r_1}^t r_1 1.0 / ({p_1}^t A r_1)")
assert(latex_print((T(p_1) * A * r_2) / (T(p_1) * A * p_1)) == \
"{p_1}^t A r_2 1.0 / ({p_1}^t A p_1)")
def testZeroOne():
ZERO = Tensor('0', 2)
One = Tensor('1', 1)
k = Tensor('k', 1)
assert (k - ZERO * One == k)
def test_numerator ():
a, b, c = map(lambda x: Tensor(x, rank=0), 'abc')
expr = (a + b) / c
sol = solve_vec_eqn(expr, a)
assert(sol == -b)
def test_cyclic_solve ():
a, b = map(lambda x: Tensor(x, rank=0), 'ab')
assert(backward_sub([a + b], [], [a, b]) == (None, b))
