def crossover_pivot(cls, rules_set_1, rules_set_2):
"""
1. Select pivot locus <i> in rule sets A and B
2. All rules up until <i> remain in original set
3. Rules A[i] and B[i] in each set is crossed-over:
a. Its parameters (target, change, left context, right context, optionality) are crossed-over with uniform probability
4. Rules after <i> are switched between the two sets
"""
num_rules_1 = len(rules_set_1.rules)
num_rules_2 = len(rules_set_2.rules)
max_rules = max(num_rules_1, num_rules_2)
if max_rules == 0:
return rules_set_1, rules_set_2
rules_1 = deepcopy(rules_set_1.rules)
rules_2 = deepcopy(rules_set_2.rules)
# pad the rule-set that has fewer rules
rules_1 += [None] * (max_rules - num_rules_1)
rules_2 += [None] * (max_rules - num_rules_2)
locus = randrange(max_rules)
print(locus)
offspring_1_rules = []
offspring_2_rules = []
for i in range(max_rules):
rule_1 = rules_1[i]
rule_2 = rules_2[i]
if i < locus:
rule_for_offspring_1 = rule_1
rule_for_offspring_2 = rule_2
if i == locus:
rule_for_offspring_1, rule_for_offspring_2 = cls._crossover_rule_pair(
rule_1, rule_2)
if i > locus:
rule_for_offspring_1 = rule_2
rule_for_offspring_2 = rule_1
if rule_for_offspring_1 is not None:
offspring_1_rules.append(rule_for_offspring_1)
if rule_for_offspring_2 is not None:
offspring_2_rules.append(rule_for_offspring_2)
offspring_1_rules = deepcopy(offspring_1_rules)
offspring_2_rules = deepcopy(offspring_2_rules)
return RuleSet(offspring_1_rules), RuleSet(offspring_2_rules)
def crossover(self, h1, h2):
if GeneticAlgorithm._is_incest(h1, h2):
return h1, h2,
crossover_rules = False
crossover_hmm = False
if ga_config.CROSSOVER_BOTH_HMM_AND_RULES:
if configurations["EVOLVE_RULES"]:
crossover_rules = True
if configurations["EVOLVE_HMM"]:
crossover_hmm = True
else:
loci = []
if configurations["EVOLVE_RULES"]:
loci.append('rules')
if configurations["EVOLVE_HMM"]:
loci.append('hmm')
locus = random.choice(loci)
if locus == 'rules':
crossover_rules = True
elif locus == 'hmm':
crossover_hmm = True
offspring_1 = deepcopy(h1)
offspring_2 = deepcopy(h2)
if crossover_rules:
offspring_1_rule_set, offspring_2_rule_set = RuleSet.crossover(
offspring_1.grammar.rule_set, offspring_2.grammar.rule_set)
else:
offspring_1_rule_set, offspring_2_rule_set = offspring_1.grammar.rule_set, offspring_2.grammar.rule_set
if crossover_hmm:
offspring_1_hmm, offspring_2_hmm = HMM.crossover(
offspring_1.grammar.hmm, offspring_2.grammar.hmm)
else:
offspring_1_hmm, offspring_2_hmm = offspring_1.grammar.hmm, offspring_2.grammar.hmm
offspring_1.grammar = Grammar(offspring_1_hmm, offspring_1_rule_set)
offspring_2.grammar = Grammar(offspring_2_hmm, offspring_2_rule_set)
# Invalidate mutated offspring fitness value (i.e. mark for re-evaluation)
offspring_1.invalidate_fitness()
offspring_1.invalidate_energy()
offspring_2.invalidate_fitness()
offspring_2.invalidate_energy()
return offspring_1, offspring_2,
def mutate(hypothesis):
new_hypothesis = deepcopy(hypothesis)
success = new_hypothesis.grammar.make_mutation()
if success:
# Invalidate mutated offspring energy and fitness value (i.e. mark for re-evaluation)
new_hypothesis.invalidate_energy()
new_hypothesis.invalidate_fitness()
return new_hypothesis,
return hypothesis,
def crossover_unilateral(cls, rule_set_1, rule_set_2):
""" Uniformly select rules from rule set 2, add them to rule set 1 at their original index in set 2"""
num_rules_in_source = len(rule_set_2.rules)
if num_rules_in_source == 0 or len(
rule_set_1.rules) == configurations["MAX_NUMBER_OF_RULES"]:
return rule_set_1, rule_set_2
offspring_1 = deepcopy(rule_set_1)
offspring_2 = deepcopy(rule_set_2)
transition_probab = 1 / max(1, log(num_rules_in_source, 2))
for source_rule_idx, source_rule in enumerate(rule_set_2.rules):
if len(offspring_1.rules) == configurations["MAX_NUMBER_OF_RULES"]:
break
if random() < transition_probab:
offspring_1.rules.insert(source_rule_idx, source_rule)
return offspring_1, offspring_2
def init_target_hypothesis(self):
target_tuple = self.simulation.target_tuple
target_rule_set = RuleSet.load_form_flat_list(target_tuple[1])
target_hypothesis = Hypothesis.create_hypothesis(
HMM(deepcopy(target_tuple[0])), target_rule_set)
target_energy = target_hypothesis.get_energy()
self.logger.info('Target hypothesis:')
log_hypothesis(target_hypothesis, self.logger.info)
self.logger.info('Target energy: {}'.format(target_energy))
self.logger.info('Target hypothesis energy signature: {}'.format(
target_hypothesis.get_recent_energy_signature()))
return target_hypothesis, target_energy
def crossover_old(cls, rules_set_1, rules_set_2):
rules_1 = deepcopy(rules_set_1.rules)
rules_2 = deepcopy(rules_set_2.rules)
rule_idx_1, crossover_rule_1, is_null_1 = cls._select_rule_for_crossover(
rules_1)
rule_idx_2, crossover_rule_2, is_null_2 = cls._select_rule_for_crossover(
rules_2)
if is_null_1 or is_null_2:
offspring_1_rule_set = RuleSet(rules_2)
offspring_2_rule_set = RuleSet(rules_1)
else:
# TODO Randomize crossover locus
rule_offspring_1 = Rule(
target=crossover_rule_1.target_feature_bundle_list,
change=crossover_rule_1.change_feature_bundle_list,
left_context=crossover_rule_2.left_context_feature_bundle_list,
right_context=crossover_rule_2.
right_context_feature_bundle_list,
obligatory=crossover_rule_2.obligatory)
rule_offspring_2 = Rule(
target=crossover_rule_2.target_feature_bundle_list,
change=crossover_rule_2.change_feature_bundle_list,
left_context=crossover_rule_1.left_context_feature_bundle_list,
right_context=crossover_rule_1.
right_context_feature_bundle_list,
obligatory=crossover_rule_1.obligatory)
offspring_1_rule_set = RuleSet(rules_1[:rule_idx_1] +
[rule_offspring_1] +
rules_2[rule_idx_2 + 1:])
offspring_2_rule_set = RuleSet(rules_2[:rule_idx_2] +
[rule_offspring_2] +
rules_1[rule_idx_1 + 1:])
return offspring_1_rule_set, offspring_2_rule_set
def crossover_uniform(cls, rules_set_1, rules_set_2):
"""
For each rule pair r1, r2 in parent rule-sets A, B, select whether to switch between r1, r2.
If one rule-set has more rules than the other, rules can be moved instead of switching.
"""
num_rules_1 = len(rules_set_1.rules)
num_rules_2 = len(rules_set_2.rules)
max_rules = max(num_rules_1, num_rules_2)
if max_rules == 0:
return rules_set_1, rules_set_2
rules_1 = deepcopy(rules_set_1.rules)
rules_2 = deepcopy(rules_set_2.rules)
rule_idx_should_cross_over = [randint(0, 1) for _ in range(max_rules)]
# pad the rule-set that has fewer rules
rules_1 += [None] * (max_rules - num_rules_1)
rules_2 += [None] * (max_rules - num_rules_2)
rules = [rules_1, rules_2]
offspring_1_rules = []
offspring_2_rules = []
for i in range(max_rules):
rule_for_offspring_1 = rules[rule_idx_should_cross_over[i]][i]
rule_for_offspring_2 = rules[rule_idx_should_cross_over[i] - 1][i]
if rule_for_offspring_1 is not None:
offspring_1_rules.append(rule_for_offspring_1)
if rule_for_offspring_2 is not None:
offspring_2_rules.append(rule_for_offspring_2)
return RuleSet(offspring_1_rules), RuleSet(offspring_2_rules)
def run(self):
if self.getTime() < 2.0:
walk_request.noWalk()
kick_request.setNoKick()
commands.setStiffness(cfgstiff.One, 0.3)
return
st = self.state
if st.inState(st.stop):
st.transition(st.checkarms)
if st.inState(st.checkarms):
shoulderCutoff = core.DEG_T_RAD * -90
lpitch = core.joint_values[core.LShoulderPitch]
rpitch = core.joint_values[core.RShoulderPitch]
if lpitch > shoulderCutoff and rpitch > shoulderCutoff:
st.transition(st.sit)
else:
st.transition(st.movearms)
elif st.inState(st.movearms):
pose = util.deepcopy(cfgpose.sittingPoseV3)
for joint, val in cfgpose.armSidePose.items():
pose[joint] = val
st.transition(st.sit)
return ToPoseMoveHead(tilt=0.0, pose=pose)
elif st.inState(st.sit):
self.skippedState = False
st.transition(st.relaxknee)
return ToPoseMoveHead(pose=cfgpose.sittingPoseV3, time=1.0)
elif st.inState(st.relaxknee):
self.lower_time = self.getTime()
commands.setStiffness(cfgstiff.ZeroKneeAnklePitch, 0.3)
st.transition(st.relaxbody)
elif st.inState(st.relaxbody) and st.timeSinceTransition() > 0.7:
commands.setStiffness(cfgstiff.Zero, 0.3)
st.transition(st.finish)
elif st.inState(st.finish):
self.finish()
def run(self):
if self.getTime() < 2.0:
core.walk_request.noWalk()
core.kick_request.setNoKick()
commands.setStiffness(cfgstiff.One, 0.3)
return
st = self.state
if st.inState(st.stop):
st.transition(st.checkarms)
if st.inState(st.checkarms):
shoulderCutoff = core.DEG_T_RAD * -90
if percepts.joint_angles[core.LShoulderPitch] > shoulderCutoff and percepts.joint_angles[core.RShoulderPitch] > shoulderCutoff:
st.transition(st.sit)
else:
st.transition(st.movearms)
elif st.inState(st.movearms):
pose = util.deepcopy(cfgpose.sittingPoseV3)
for joint, val in cfgpose.armSidePose.items():
pose[joint] = val
self.subtask = skills.ToPoseMoveHead(tilt = 0.0, pose = pose)
self.subtask.start()
st.transition(st.sit)
elif st.inState(st.sit) and (not self.subtask or self.subtask.finished()):
self.skippedState = False
self.subtask = skills.ToPoseMoveHead(pose = cfgpose.sittingPoseV3, time = 1.0)
self.subtask.start()
st.transition(st.relaxknee)
elif st.inState(st.relaxknee) and self.subtask.finished():
self.lower_time = self.getTime()
commands.setStiffness(cfgstiff.ZeroKneeAnklePitch, 0.3)
st.transition(st.relaxbody)
elif st.inState(st.relaxbody) and st.timeSinceTransition() > 0.7:
commands.setStiffness(cfgstiff.Zero, 0.3)
st.transition(st.finish)
elif st.inState(st.finish):
self.finish()
def run(self):
if self.getTime() < 2.0:
walk_request.noWalk()
kick_request.setNoKick()
commands.setStiffness(cfgstiff.One, 0.3)
return
st = self.state
if st.inState(st.stop):
st.transition(st.checkarms)
if st.inState(st.checkarms):
shoulderCutoff = core.DEG_T_RAD * -90
lpitch = core.joint_values[core.LShoulderPitch]
rpitch = core.joint_values[core.RShoulderPitch]
if lpitch > shoulderCutoff and rpitch > shoulderCutoff:
st.transition(st.sit)
else:
st.transition(st.movearms)
elif st.inState(st.movearms):
pose = util.deepcopy(cfgpose.sittingPoseV3)
for joint, val in cfgpose.armSidePose.items():
pose[joint] = val
st.transition(st.sit)
return ToPoseMoveHead(tilt = 0.0, pose = pose)
elif st.inState(st.sit):
self.skippedState = False
st.transition(st.relaxknee)
return ToPoseMoveHead(pose = cfgpose.sittingPoseV3)
elif st.inState(st.relaxknee):
self.lower_time = self.getTime()
commands.setStiffness(cfgstiff.ZeroKneeAnklePitch, 0.3)
st.transition(st.relaxbody)
elif st.inState(st.relaxbody) and st.timeSinceTransition() > 0.7:
commands.setStiffness(cfgstiff.Zero, 0.3)
st.transition(st.finish)
elif st.inState(st.finish):
self.finish()
def __init__(self,
blocks,
total_cells,
netlists,
raw_netlist,
board,
board_pos,
is_legal=None,
fold_reg=True,
seed=0):
"""Please notice that netlists has to be prepared already, i.e., replace
the remote partition with a pseudo block.
Also assumes that available_pos is the same size as blocks. If not,
you have to shrink it the available_pos.
The board can be an empty board.
"""
self.blocks = blocks
# TODO:
# switch every thing into dictionary based
available_pos = total_cells["p"]
self.total_cells = total_cells
self.available_pos = available_pos
self.netlists = netlists
self.blk_pos = board_pos
self.board = board
if fold_reg:
assert (len(blocks) >= len(available_pos))
else:
assert (len(blocks) == len(available_pos))
if is_legal is None:
if fold_reg:
self.is_legal = self.__is_legal_fold
else:
self.is_legal = lambda pos, blk_id, s: True
else:
self.is_legal = is_legal
self.fold_reg = fold_reg
# figure out which position on which regs cannot be on the top
self.reg_no_pos = {}
if fold_reg:
linked_nets, _, _ = group_reg_nets(raw_netlist)
for net_id in netlists:
net = netlists[net_id]
if net_id in linked_nets:
for reg_net_id in linked_nets[net_id]:
if reg_net_id in netlists:
net += netlists[reg_net_id]
for blk in net:
# we only care about the wire it's driving
if blk[0] == "r" and blk in self.blocks:
if blk not in self.reg_no_pos:
self.reg_no_pos[blk] = set()
for bb in net:
if bb == blk:
continue
if bb in self.blocks:
self.reg_no_pos[blk].add(bb)
rand = random.Random()
rand.seed(seed)
state = self.__init_placement(rand)
Annealer.__init__(self, initial_state=state, rand=rand)
# schedule
self.steps = len(blocks) * 125
self.num_nets = len(netlists)
# fast calculation
self.moves = set()
self.blk_index = self.__index_netlists(netlists, blocks)
self.pre_state = deepcopy(state)
self.pre_energy = self.init_energy()
def __init_placement(self, rand):
# filling in PE tiles first
pos = list(self.available_pos)
num_pos = len(pos)
state = {}
pe_blocks = [b for b in self.blocks if b[0] == "p"]
pe_blocks.sort(key=lambda x: int(x[1:]))
reg_blocks = [b for b in self.blocks if b[0] == "r"]
reg_blocks.sort(key=lambda x: int(x[1:]))
special_blocks = [
b for b in self.blocks
if b not in pe_blocks and b not in reg_blocks
]
# make sure there is enough space
assert (max(len(pe_blocks), len(reg_blocks) < len(pos)))
total_blocks = pe_blocks + reg_blocks
board = {}
pos_index = 0
index = 0
while index < len(total_blocks):
blk_id = total_blocks[index]
new_pos = pos[pos_index % num_pos]
pos_index += 1
if new_pos not in board:
board[new_pos] = []
if len(board[new_pos]) > 1:
continue
if blk_id[0] == "p":
if len(board[new_pos]) > 0 and board[new_pos][0][0] == "p":
continue
# make sure we're not putting it in the reg net
elif len(board[new_pos]) > 0 and board[new_pos][0][0] == "r":
reg = board[new_pos][0]
if reg in self.reg_no_pos and \
blk_id in self.reg_no_pos[reg]:
continue
board[new_pos].append(blk_id)
state[blk_id] = new_pos
index += 1
else:
if len(board[new_pos]) > 0 and board[new_pos][0][0] == "r":
continue
# make sure we're not putting it in the reg net
elif len(board[new_pos]) > 0 and board[new_pos][0][0] == "p":
p_block = board[new_pos][0]
if blk_id in self.reg_no_pos and \
p_block in self.reg_no_pos[blk_id]:
continue
board[new_pos].append(blk_id)
state[blk_id] = new_pos
index += 1
# place special blocks
special_cells = deepcopy(self.total_cells)
for blk_type in special_cells:
blks = [b for b in special_blocks if b[0] == blk_type]
if len(blks) == 0:
continue
# random pick up some blocks
available_cells = special_cells[blk_type]
cells = rand.sample(available_cells, len(blks))
for i in range(len(blks)):
state[blks[i]] = cells[i]
return state
def copy_resource(routing_resource):
res = deepcopy(routing_resource)
return res
