本文整理汇总了Python中quex.engine.state_machine.core.StateMachine.states[new_state_index]方法的典型用法代码示例。如果您正苦于以下问题：Python StateMachine.states[new_state_index]方法的具体用法？Python StateMachine.states[new_state_index]怎么用？Python StateMachine.states[new_state_index]使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类quex.engine.state_machine.core.StateMachine的用法示例。
在下文中一共展示了StateMachine.states[new_state_index]方法的1个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: create_state_machine
# 需要导入模块: from quex.engine.state_machine.core import StateMachine [as 别名]
# 或者: from quex.engine.state_machine.core.StateMachine import states[new_state_index] [as 别名]
def create_state_machine(SM, Result, Class_StateMachine, Class_State):
# If all states are of size one, this means, that there were no states that
# could have been combined. In this case a simple copy of the original
# state machine will do.
if len(filter(lambda state_set: len(state_set) != 1, Result.state_set_list)) == 0:
return SM.clone()
# Define a mapping from the state set to a new target state index
#
# map: state_set_index ---> index of the state that represents it
#
map_new_state_index = dict([(i, state_machine_index.get()) for i in xrange(len(Result.state_set_list))])
# The state set that contains the initial state becomes the initial state of
# the new state machine.
state_set_containing_initial_state_i = Result.map[SM.init_state_index]
new_init_state_index = map_new_state_index[state_set_containing_initial_state_i]
result = StateMachine(new_init_state_index)
# Ensure that each target state index has a state inside the state machine
# Build up the state machine out of the state sets
for state_set_idx, state_set in enumerate(Result.state_set_list):
new_state_index = map_new_state_index[state_set_idx]
# Merge all core information of the states inside the state set.
# If one state set contains an acceptance state, then the result is 'acceptance'.
# (Note: The initial split separates acceptance states from those that are not
# acceptance states. There can be no state set containing acceptance and
# non-acceptance states)
# (Note, that the prototype's info has not been included yet, consider whole set)
result.states[new_state_index] = Class_State.new_merged_core_state(SM.states[i] for i in state_set)
for state_set_idx, state_set in enumerate(Result.state_set_list):
# The prototype: States in one set behave all equivalent with respect to target state sets
# thus only one state from the start set has to be considered.
prototype = SM.states[state_set[0]]
representative = result.states[map_new_state_index[state_set_idx]]
# The representative must have all transitions that the prototype has
for target_state_index, trigger_set in prototype.target_map.get_map().iteritems():
target_state_set_index = Result.map[target_state_index]
target_index = map_new_state_index[target_state_set_index]
representative.add_transition(trigger_set, target_index)
return result