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Small fixed to spiking TE demo (sent from David)

This commit is contained in:
Joseph Lizier 2023-07-26 14:52:03 +10:00
parent d920b1e9a9
commit 107fabad32
1 changed files with 8 additions and 8 deletions
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16
demos/python/EffectiveNetworkInference/spiking/net_inf.py
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@ -49,23 +49,23 @@ LOG_FILE_NAME = "logs/" + net_type_name + "_" + num_spikes_string + "_" + repeat
log = open(LOG_FILE_NAME, "w")
sys.stdout = log
def prepare_conditional_trains(calc_object, cond_set):
def prepare_conditional_trains(calc_object, cond_set, spikes):
cond_trains = []
calc_object.clearConditionalIntervals()
if len(cond_set) > 0:
for key in cond_set.keys():
cond_trains.append(spikes[key])
teCalc.appendConditionalIntervals(JArray(JInt, 1)(cond_set[key]))
calc_object.appendConditionalIntervals(JArray(JInt, 1)(cond_set[key]))
return cond_trains
def set_target_embeddings(embedding_list):
def set_target_embeddings(embedding_list, calc_object):
if len(embedding_list) > 0:
embedding_string = str(embedding_list[0])
for i in range(2, len(embedding_list)):
embedding_string += "," + str(embedding_list[i])
teCalc.setProperty("DEST_PAST_INTERVALS", embedding_string)
calc_object.setProperty("DEST_PAST_INTERVALS", embedding_string)
else:
teCalc.setProperty("DEST_PAST_INTERVALS", "")
calc_object.setProperty("DEST_PAST_INTERVALS", "")
target_index = int(target_index_string)
@ -100,7 +100,7 @@ next_target_interval = 2
still_significant = True
print("**** Determining target embedding set ****\n")
while still_significant:
set_target_embeddings(target_embedding_set)
set_target_embeddings(target_embedding_set, teCalc)
teCalc.setProperty("SOURCE_PAST_INTERVALS", str(next_target_interval))
teCalc.startAddObservations()
teCalc.addObservations(JArray(JDouble, 1)(spikes[target_index]), JArray(JDouble, 1)(spikes[target_index]))
@ -136,7 +136,7 @@ while still_significant:
for key in cond_set.keys():
print("source", key, "intervals", cond_set[key])
print("\nEstimating TE on candidate sources")
cond_trains = prepare_conditional_trains(teCalc, cond_set)
cond_trains = prepare_conditional_trains(teCalc, cond_set, spikes)
TE_vals = np.zeros(next_interval_for_each_candidate.shape[0])
debiased_TE_vals = -1 * np.ones(next_interval_for_each_candidate.shape[0])
surrogate_vals = -1 * np.ones((next_interval_for_each_candidate.shape[0], NUM_SURROGATES_PER_TE_VAL))
@ -218,7 +218,7 @@ while not everything_significant:
else:
cond_set_minus_candidate.pop(candidate_source)
teCalc.setProperty("SOURCE_PAST_INTERVALS", str(cond_set[candidate_source][-1]))
cond_trains = prepare_conditional_trains(teCalc, cond_set_minus_candidate)
cond_trains = prepare_conditional_trains(teCalc, cond_set_minus_candidate, spikes)
teCalc.startAddObservations()
if len(cond_set_minus_candidate) > 0:
teCalc.addObservations(JArray(JDouble, 1)(spikes[candidate_source]), JArray(JDouble, 1)(spikes[target_index]), JArray(JDouble, 2)(cond_trains))