def individual_nudge(old_X: dit.Distribution,
eps: float = 0.01,
rvs_other=None) -> dit.Distribution:
mask = old_X._mask
base = old_X.get_base()
if old_X.outcome_length() == 1:
return global_nudge(old_X, eps)
outcomes = old_X.outcomes
rv_names = old_X.get_rv_names()
if rvs_other == None:
rvs = old_X.get_rv_names()
rvs_other = np.random.choice(rvs, len(rvs) - 1, replace=False)
X_other, Xi_given_Xother = old_X.condition_on(rvs_other)
nudge_size = len(Xi_given_Xother[0])
if base == 'linear':
nudge = generate_nudge(nudge_size, eps / len(Xi_given_Xother))
for Xi in Xi_given_Xother:
perform_nudge(Xi, nudge)
else:
nudge, sign = generate_log_nudge(nudge_size, eps)
for Xi in Xi_given_Xother:
perform_log_nudge(Xi, nudge, sign)
new_X = dit.joint_from_factors(X_other, Xi_given_Xother).copy(base)
#add back any missing outcomes
dct = {o: new_X[o] if o in new_X.outcomes else 0.0 for o in outcomes}
#print(outcomes, dct)
new_X = dit.Distribution(dct)
new_X.set_rv_names(rv_names)
new_X.make_dense()
new_X._mask = mask
return new_X
def max_individual(input: dit.Distribution,
conditional: np.ndarray,
eps: float = 0.01,
minimal_entropy_idx=None):
rvs = input.get_rv_names()
conditional = conditional / conditional.sum()
states = len(input.alphabet[0])
if not minimal_entropy_idx == 0 and not minimal_entropy_idx:
minimal_entropy_idx = np.argmin([
entropy(input.marginal([rv], rv_mode='indices').pmf)
for rv in range(len(rvs))
])
non_minimal_rvs = rvs[:minimal_entropy_idx] + rvs[minimal_entropy_idx + 1:]
non_minimal_marginal, minimal_conditional = input.condition_on(
non_minimal_rvs)
[d.make_dense() for d in minimal_conditional]
# minimal_conditional = np.stack([d.pmf for d in minimal_conditional])
# print("minimal_conditional:",minimal_conditional)
indiv_shape = (len(minimal_conditional), len(minimal_conditional[0]))
# minimal_conditional = minimal_conditional.flatten()
nudge_vector = np.zeros(indiv_shape)
rotated_conditional = R(conditional, minimal_entropy_idx, len(rvs), states)
total_max_impact = 0
# print(len(rvs), (eps / 2)/len(minimal_conditional))
for i, mc_dist in enumerate(minimal_conditional):
rows = rotated_conditional[i * states:(i + 1) * states, :]
max_impact = 0
for allignment in itertools.product(
[-1, 1], repeat=rotated_conditional.shape[1]):
allignment = np.array(allignment)
if np.all(allignment == 1) or np.all(allignment == -1):
continue
scores = np.sum(allignment * rows, axis=1)
# Add rotation of scores so that scores are well aligned.
# Weigh scores using the non_minimal_marginal
vector, impact = find_max_impact(scores, mc_dist.pmf, (eps / 2) /
len(minimal_conditional))
if impact > max_impact:
nudge_vector[i, :] = vector
max_impact = impact
total_max_impact += max_impact
return nudge_vector, total_max_impact, minimal_entropy_idx
def get_marginals(
d: dit.Distribution) -> (dit.Distribution, List[dit.Distribution]):
rvs = d.get_rv_names()[:-1] #everything except the output
return d.condition_on(rvs)
