model.value_iteration(0.1)
if time % 500 == 1 and time > split_step:
if split_strategy == "always":
model.reset_decision_tree()
model.chain_split()
elif split_strategy == "start_chain":
model.chain_split()
else:
model.value_iteration(0.1)
if time > training_steps:
total_reward += reward
if PRINT and time > training_steps:
print scenario.get_incoming_load(), scenario.get_current_capacity(), \
100 * meas[PC_READ_LOAD], 100 * meas[IO_PER_SEC]
total_splits = 0
good_splits = 0
marg_splits = 0
splits = model.get_splits_per_parameter()
for par, s in splits.iteritems():
total_splits += s
if par in scenario.get_relevant_params():
good_splits += s
if par in scenario.get_marginal_params():
marg_splits += s
if not PRINT:
print "Reward:", total_reward, "Total splits:", total_splits, "Good splits:", good_splits, "Marginal splits:", marg_splits
sys.stdout.flush()
model.value_iteration(0.1)
if time % 500 == 1 and time > split_step:
if split_strategy == "always":
model.reset_decision_tree()
model.chain_split()
elif split_strategy == "start_chain":
model.chain_split()
else:
model.value_iteration(0.1)
if time > training_steps:
total_reward += reward
if PRINT and time > training_steps:
print(scenario.get_incoming_load(),
scenario.get_current_capacity(), 100 * meas[PC_READ_LOAD],
100 * meas[IO_PER_SEC])
total_splits = 0
good_splits = 0
marg_splits = 0
splits = model.get_splits_per_parameter()
for par, s in splits.items():
total_splits += s
if par in scenario.get_relevant_params():
good_splits += s
if par in scenario.get_marginal_params():
marg_splits += s
if not PRINT: