diff options
Diffstat (limited to 'lib')
| -rwxr-xr-x | lib/automata.py | 111 | ||||
| -rwxr-xr-x | lib/dfatool.py | 1 |
2 files changed, 100 insertions, 12 deletions
diff --git a/lib/automata.py b/lib/automata.py index 0e6cc28..c61b065 100755 --- a/lib/automata.py +++ b/lib/automata.py @@ -1,44 +1,133 @@ class Transition: - def __init__(self, orig_state, dest_state, name, arguments, arg_param_map): + def __init__(self, orig_state, dest_state, name, + energy = 0, energy_function = None, + duration = 0, duration_function = None, + timeout = 0, timeout_function = None, + is_interrupt = False, + arguments = [], param_update_function = None): self.name = name self.origin = orig_state self.destination = dest_state - self.arguments = list(arguments) + self.energy = energy + self.energy_function = energy_function + self.duration = duration + self.duration_function = duration_function + self.timeout = timeout + self.timeout_function = timeout_function + self.is_interrupt = is_interrupt + self.arguments = arguments.copy() + self.param_update_function = param_update_function + + def get_duration(self, parameters = [], args = []): + if self.duration_function: + return self.duration_function(parameters, args) + return self.duration + + def get_energy(self, parameters = [], args = []): + if self.energy_function: + return self.energy_function(parameters, args) + return self.energy + + def get_timeout(self, parameters = []): + if self.timeout_function: + return self.timeout_function(parameters) + return self.timeout + + def get_params_after_transition(self, parameters, args = []): + if self.param_update_function: + return self.param_update_function(parameters, args) + return parameters class State: - def __init__(self, name): + def __init__(self, name, power = 0, power_function = None): self.name = name - self.outgoing_transitions = [] + self.power = power + self.power_function = power_function + self.outgoing_transitions = {} def add_outgoing_transition(self, new_transition): - self.outgoing_transitions.append(new_transition) + self.outgoing_transitions[new_transition.name] = new_transition + + def get_energy(self, duration, parameters = []): + if self.power_function: + return self.power_function(parameters) * duration + return self.power * duration + + def get_transition(self, transition_name): + return self.outgoing_transitions[transition_name] + + def has_interrupt_transitions(self): + for trans in self.outgoing_transitions.values(): + if trans.is_interrupt: + return True + return False + + def get_next_interrupt(self, parameters): + interrupts = filter(lambda x: x.is_interrupt, self.outgoing_transitions.values()) + interrupts = sorted(interrupts, key = lambda x: x.get_timeout(parameters)) + return interrupts[0] def dfs(self, depth): if depth == 0: - for trans in self.outgoing_transitions: + for trans in self.outgoing_transitions.values(): yield [trans.name] else: - for trans in self.outgoing_transitions: + for trans in self.outgoing_transitions.values(): for suffix in trans.destination.dfs(depth - 1): new_suffix = [trans.name] new_suffix.extend(suffix) yield new_suffix class PTA: - def __init__(self, state_names, parameters): + def __init__(self, state_names = [], parameters = [], initial_param_values = None): self.states = dict([[state_name, State(state_name)] for state_name in state_names]) - self.parameters = list(parameters) + self.parameters = parameters.copy() + if initial_param_values: + self.initial_param_values = initial_param_values.copy() + else: + self.initial_param_values = [None for x in self.parameters] self.transitions = [] if not 'UNINITIALIZED' in state_names: self.states['UNINITIALIZED'] = State('UNINITIALIZED') - def add_transition(self, orig_state, dest_state, function_name, arguments, arg_param_map): + def add_state(self, state_name, **kwargs): + self.states[state_name] = State(state_name, **kwargs) + + def add_transition(self, orig_state, dest_state, function_name, **kwargs): orig_state = self.states[orig_state] dest_state = self.states[dest_state] - new_transition = Transition(orig_state, dest_state, function_name, arguments, arg_param_map) + new_transition = Transition(orig_state, dest_state, function_name, **kwargs) self.transitions.append(new_transition) orig_state.add_outgoing_transition(new_transition) def dfs(self, depth = 10, orig_state = 'UNINITIALIZED'): return self.states[orig_state].dfs(depth) + + def simulate(self, trace, orig_state = 'UNINITIALIZED'): + total_duration = 0. + total_energy = 0. + state = self.states[orig_state] + parameters = self.initial_param_values + for function in trace: + function_name = function[0] + function_args = function[1 : ] + if function_name == 'sleep': + duration = function_args[0] + total_energy += state.get_energy(duration, parameters) + total_duration += duration + else: + transition = state.get_transition(function_name) + total_duration += transition.get_duration(parameters, function_args) + total_energy += transition.get_energy(parameters, function_args) + parameters = transition.get_params_after_transition(parameters, function_args) + state = transition.destination + while (state.has_interrupt_transitions()): + transition = state.get_next_interrupt(parameters) + duration = transition.get_timeout(parameters) + total_duration += duration + total_energy += state.get_energy(duration, parameters) + parameters = transition.get_params_after_transition(parameters) + state = transition.destination + + return total_energy, total_duration, state, parameters diff --git a/lib/dfatool.py b/lib/dfatool.py index 5b8f69f..6e0e829 100755 --- a/lib/dfatool.py +++ b/lib/dfatool.py @@ -8,7 +8,6 @@ import numpy as np import os import re from scipy import optimize -from gplearn.genetic import SymbolicRegressor from sklearn.metrics import r2_score import struct import sys |