MHOD: only solve the optimization problem if p[current_state][state_n] != 0
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@ -200,10 +200,11 @@ def mhod(state_config, otf, window_sizes, bruteforce_step, learning_steps,
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log.debug('MHOD current_state:' + str(current_state))
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log.debug('MHOD p[current_state]:' + str(p[current_state]))
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state_n = len(state_config)
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tmp = set(p[current_state])
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#tmp = set(p[current_state])
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#if len(tmp) != 1 or 0 not in tmp:
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if p[current_state][state_n] != 0 and not (len(tmp) == 1 and 0 in tmp):
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#if p[current_state][state_n] != 0 and not (len(tmp) == 1 and 0 in tmp):
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state_n = len(state_config)
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if p[current_state][state_n] != 0:
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policy = bruteforce.optimize(
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bruteforce_step, 1.0, otf, (migration_time / time_step),
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ls, p, state_vector, time_in_states, time_in_state_n)
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