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Proposed fixes to control.py and utils.py #56

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Proposed fixes to control.py and utils.py #56

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4d6b2ec
Two proposed fixes: 1) control.py's action_marginals should sum only …
Dec 15, 2021
95d4f03
Two proposed small fixes to control.py and utils.py: action_marginals…
conorheins Nov 25, 2021
b4bd691
Merge branch 'master' of https://github.com/infer-actively/pymdp
Dec 15, 2021
615e676
Merge branch 'master' of https://github.com/spetey/pymdp
Dec 15, 2021
d4b9cff
Proposed fixes to control.py and utils.py: action_marginals should on…
Dec 15, 2021
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6 changes: 3 additions & 3 deletions pymdp/control.py
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Original file line number Diff line number Diff line change
Expand Up @@ -492,9 +492,9 @@ def sample_action(q_pi, policies, num_controls, action_selection="deterministic"

# weight each action according to its integrated posterior probability over policies and timesteps
for pol_idx, policy in enumerate(policies):
for t in range(policy.shape[0]):
for factor_i, action_i in enumerate(policy[t, :]):
action_marginals[factor_i][action_i] += q_pi[pol_idx]
for factor_i, action_i in enumerate(policy[0, :]):
# to get the marginals we just want to add up the actions at time 0
action_marginals[factor_i][action_i] += q_pi[pol_idx]

selected_policy = np.zeros(num_factors)
for factor_i in range(num_factors):
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2 changes: 1 addition & 1 deletion pymdp/utils.py
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Expand Up @@ -13,7 +13,7 @@
import itertools

def sample(probabilities):
sample_onehot = np.random.multinomial(1, probabilities.squeeze())
sample_onehot = np.random.multinomial(1, probabilities)
return np.where(sample_onehot == 1)[0][0]

def sample_obj_array(arr):
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