def main():
num_of_comps = 1
tau_mag = .5
tau_degs = 35
rho_degs = 45
if num_of_comps == 1:
size1 = 3
size2 = 4
max_n_sum = 5
pa1 = BayesNode(0, "parent1", size=size1)
pa2 = BayesNode(1, "parent2", size=size2)
pa1.state_names = [str(k) for k in range(size1)]
pa2.state_names = [str(k) for k in range(size2)]
bs = BeamSplitter(2, "a_bs", pa1, pa2,
tau_mag, tau_degs, rho_degs, num_of_comps, max_n_sum)
print("pa1 state names: ", pa1.state_names)
print("pa2 state names: ", pa2.state_names)
print("bs state names: ", bs.state_names)
print(bs.potential)
print("full dict of total probs: ",
bs.potential.get_total_probs())
print("brief dict of total probs: ",
bs.potential.get_total_probs(brief=True))
elif num_of_comps == 2:
size1 = 6
size2 = 8
max_n_sum = 7
pa1 = BayesNode(0, "parent1", size=size1)
pa2 = BayesNode(1, "parent2", size=size2)
pa1.set_state_names_to_product(
[range(2), range(3)], trim=False)
pa2.set_state_names_to_product(
[range(2), range(4)], trim=False)
bs = BeamSplitter(2, "a_bs", pa1, pa2,
tau_mag, tau_degs, rho_degs, num_of_comps, max_n_sum)
print("pa1 state names: ", pa1.state_names)
print("pa2 state names: ", pa2.state_names)
print("bs state names: ", bs.state_names)
print(bs.potential)
print("full dict of total probs: ",
bs.potential.get_total_probs())
print("brief dict of total probs: ",
bs.potential.get_total_probs(brief=True))
def main():
pa_nd = BayesNode(0, "pa_nd", size=24)
# has_commas = True
has_commas = False
pa_nd.set_state_names_to_product(
[range(2), range(3), range(4)], trim=not has_commas)
marg = Marginalizer(1,
"marg",
False,
pa_nd,
projected_axis=1,
has_commas=has_commas)
print("pa state names: ", pa_nd.state_names)
print("marg state names: ", marg.state_names)
print(marg.potential)
print(marg.potential.get_total_probs())
def main():
theta_degs = 35
max_n_sum = 3
in_nd = BayesNode(0, "parent1", size=6)
in_nd.set_state_names_to_product([range(2), range(3)], trim=False)
pr = PolarizationRot(1, "pol_rot", in_nd, theta_degs, max_n_sum)
print("in_nd state names: ", in_nd.state_names)
print("pol rot state names: ", pr.state_names)
print(pr.potential)
print("full dict of total probs: ", pr.potential.get_total_probs())
print("brief dict of total probs: ",
pr.potential.get_total_probs(brief=True))
BayesNode.__init__(self, id_num, name, size=size)
self.add_parent(pa_nd)
self.state_names = non_rep_name_list
pot = DiscreteCondPot(is_quantum, [pa_nd, self], bias=0)
self.potential = pot
for k, name in enumerate(self.state_names):
for r, pa_name in enumerate(rep_name_list):
if name == pa_name:
# remember, focus node is last topo_index
self.potential[r, k] = 1
if __name__ == "__main__":
pa_nd = BayesNode(0, "pa_nd", size=24)
# has_commas = True
has_commas = False
pa_nd.set_state_names_to_product(
[range(2), range(3), range(4)], trim=not has_commas)
marg = Marginalizer(1, "marg",
False, pa_nd, projected_axis=1, has_commas=has_commas)
print("pa state names: ", pa_nd.state_names)
print("marg state names: ", marg.state_names)
print(marg.potential)
print(marg.potential.get_total_probs())
mx : list[int]
my : list[int]
Returns
-------
int
"""
return self.fill_trans_mat_and_st_names_of_nd(
mx, my, dry_run=True)
if __name__ == "__main__":
theta_degs = 35
max_n_sum = 3
in_nd = BayesNode(0, "parent1", size=6)
in_nd.set_state_names_to_product(
[range(2), range(3)], trim=False)
pr = PolarizationRot(1, "pol_rot", in_nd, theta_degs, max_n_sum)
print("in_nd state names: ", in_nd.state_names)
print("pol rot state names: ", pr.state_names)
print(pr.potential)
print("full dict of total probs: ",
pr.potential.get_total_probs())
print("brief dict of total probs: ",
pr.potential.get_total_probs(brief=True))
mx : list[int]
my : list[int]
Returns
-------
int
"""
return self.fill_trans_mat_and_st_names_of_nd(
mx, my, dry_run=True)
if __name__ == "__main__":
theta_degs = 35
max_n_sum = 3
in_nd = BayesNode(0, "parent1", size=6)
in_nd.set_state_names_to_product(
[range(2), range(3)], trim=False)
pr = PolarizationRot(1, "pol_rot", in_nd, theta_degs, max_n_sum)
print("in_nd state names: ", in_nd.state_names)
print("pol rot state names: ", pr.state_names)
print(pr.potential)
print("full dict of total probs: ",
pr.potential.get_total_probs())
print("brief dict of total probs: ",
pr.potential.get_total_probs(brief=True))
print("pa1 state names: ", pa1.state_names)
print("pa2 state names: ", pa2.state_names)
print("bs state names: ", bs.state_names)
print(bs.potential)
print("full dict of total probs: ",
bs.potential.get_total_probs())
print("brief dict of total probs: ",
bs.potential.get_total_probs(brief=True))
elif num_of_comps == 2:
size1 = 6
size2 = 8
max_n_sum = 7
pa1 = BayesNode(0, "parent1", size=size1)
pa2 = BayesNode(1, "parent2", size=size2)
pa1.set_state_names_to_product(
[range(2), range(3)], trim=False)
pa2.set_state_names_to_product(
[range(2), range(4)], trim=False)
bs = BeamSplitter(2, "a_bs", pa1, pa2,
tau_mag, tau_degs, rho_degs, num_of_comps, max_n_sum)
print("pa1 state names: ", pa1.state_names)
print("pa2 state names: ", pa2.state_names)
print("bs state names: ", bs.state_names)
print(bs.potential)
print("full dict of total probs: ",
bs.potential.get_total_probs())
print("brief dict of total probs: ",
bs.potential.get_total_probs(brief=True))
