def O_Task(self):
# Get a new knowledgeability table
knowledgeability = Task.NKnowledgeability_Task(self.hypo_table,
self.num_hypo,
self.num_feature,
self.num_label,
self.knowledge)
# knowledgeability = [self.delta_g_h]
for n in range(len(knowledgeability)):
print(knowledgeability[n])
p, s = Task.Probability_Task(self.hypo_table, self.num_hypo,
self.num_feature, self.num_label,
copy.deepcopy(knowledgeability[n]),
1000)
print(p, s, sep="\n")
Report.Plot_P(Task.Average_Hypo(p, self.num_hypo),
self.num_feature, n)
# If reaches the identitly matrix, the loop will be ended
if numpy.array_equal(knowledgeability[n],
numpy.eye(self.num_hypo)):
break
mtp.legend()
mtp.show()
return
