def cp_all(self):
if len(self.c_history) < 2:
return 0.0
estimator = SpanSeigelEstimator(len(self.c_history))
for c in self.c_history:
if c is not None:
estimator.add(c)
return estimator.value()
def cp_all(self):
if len(self.c_history) < 2:
return 0.0
estimator = SpanSeigelEstimator(len(self.c_history))
for c in self.c_history:
if c is not None:
estimator.add(c)
return estimator.value()
def pei_all(self):
if len(self.pe_history) < 2:
return 0.0
estimator = SpanSeigelEstimator(len(self.pe_history))
for pe in self.pe_history:
if pe is not None:
estimator.add(pe)
return estimator.value()
def pei_all(self):
if len(self.pe_history) < 2:
return 0.0
estimator = SpanSeigelEstimator(len(self.pe_history))
for pe in self.pe_history:
if pe is not None:
estimator.add(pe)
return estimator.value()
def __init__(self, bounds, graph, uid, cfg, depth=0, w=None):
""""""
Cell.__init__(self, bounds, graph, uid, depth=depth, w=w, cfg=cfg)
self.goals = []
self.c_history = [] # C : Competence
self.cp_history = [0.0] # CP : Competence Progress
self.estimator_cp = SpanSeigelEstimator(cfg.cell.max_cp)
def __init__(self, bounds, graph, uid, cfg, depth=0, w=None):
""""""
Cell.__init__(self, bounds, graph, uid, depth=depth, w=w, cfg=cfg)
self.predictions = []
self.pe_history = [] # PE : Prediction Error
self.pei_history = [0.0] # PEI: PE Improvement
self.estimator_pei = SpanSeigelEstimator(cfg.cell.max_pei)
def __init__(self, bounds, graph, uid, cfg, depth = 0, w = None):
""""""
Cell.__init__(self, bounds, graph, uid, depth = depth, w = w, cfg = cfg)
self.goals = []
self.c_history = [] # C : Competence
self.cp_history = [0.0] # CP : Competence Progress
self.estimator_cp = SpanSeigelEstimator(cfg.cell.max_cp)
def __init__(self, bounds, graph, uid, cfg, depth = 0, w = None):
""""""
Cell.__init__(self, bounds, graph, uid, depth = depth, w = w, cfg = cfg)
self.predictions = []
self.pe_history = [] # PE : Prediction Error
self.pei_history = [0.0] # PEI: PE Improvement
self.estimator_pei = SpanSeigelEstimator(cfg.cell.max_pei)
class GoalCell(Cell):
def __init__(self, bounds, graph, uid, cfg, depth=0, w=None):
""""""
Cell.__init__(self, bounds, graph, uid, depth=depth, w=w, cfg=cfg)
self.goals = []
self.c_history = [] # C : Competence
self.cp_history = [0.0] # CP : Competence Progress
self.estimator_cp = SpanSeigelEstimator(cfg.cell.max_cp)
def add(self, goal, effect, competence=None):
if goal is None or not self.belongs(goal):
return
self.goals.append(goal)
self.effects.append(effect)
if competence is None:
competence = cmpce.competence(goal, effect, cfg=self.cfg)
self.c_history.append(competence)
self.estimator_cp.add(competence)
if len(self.c_history) >= self.cfg.cell.min_cp:
self.cp_history.append(self.estimator_cp.value())
else:
self.cp_history.append(0.0)
def split(self, low_bounds, high_bounds):
return self.graph.goal_split(self, low_bounds, high_bounds)
@property
def competence(self):
return self.c_history[-1]
@property
def cp_all(self):
if len(self.c_history) < 2:
return 0.0
estimator = SpanSeigelEstimator(len(self.c_history))
for c in self.c_history:
if c is not None:
estimator.add(c)
return estimator.value()
@property
def cp(self):
return self.cp_history[-1]
def avg_competence(self, span=None):
if len(self.c_history) == 0:
return None
if span is not None and span > 0:
return sum(self.c_history[:-span]) / span
else:
return sum(self.c_history) / len(self.c_history)
def __len__(self):
return len(self.goals)
class EffectCell(Cell):
def __init__(self, bounds, graph, uid, cfg, depth=0, w=None):
""""""
Cell.__init__(self, bounds, graph, uid, depth=depth, w=w, cfg=cfg)
self.predictions = []
self.pe_history = [] # PE : Prediction Error
self.pei_history = [0.0] # PEI: PE Improvement
self.estimator_pei = SpanSeigelEstimator(cfg.cell.max_pei)
def add(self, effect, prediction=None, pred_error=None):
if not self.belongs(effect):
return False
self.effects.append(effect)
if prediction is not None or pred_error is not None:
self.predictions.append(prediction)
if pred_error is None:
pred_error = cmpce.competence(prediction, effect, cfg=self.cfg)
self.pe_history.append(pred_error)
self.estimator_pei.add(pred_error)
if len(self.pe_history) >= self.cfg.cell.min_pei:
self.pei_history.append(self.estimator_pei.value())
else:
self.pei_history.append(0.0)
return True
def split(self, low_bounds, high_bounds):
return self.graph.effect_split(self, low_bounds, high_bounds)
@property
def pe(self):
return self.pe_history[-1]
@property
def pei(self):
return self.pei_history[-1]
@property
def pei_all(self):
if len(self.pe_history) < 2:
return 0.0
estimator = SpanSeigelEstimator(len(self.pe_history))
for pe in self.pe_history:
if pe is not None:
estimator.add(pe)
return estimator.value()
def avg_pred_error(self, span=None):
if len(self.pe_history) == 0:
return None
if span is not None and span > 0:
return sum(self.pe_history[:-span]) / span
else:
return sum(self.pe_history) / len(self.pe_history)
def __len__(self):
return len(self.effects)
class GoalCell(Cell):
def __init__(self, bounds, graph, uid, cfg, depth = 0, w = None):
""""""
Cell.__init__(self, bounds, graph, uid, depth = depth, w = w, cfg = cfg)
self.goals = []
self.c_history = [] # C : Competence
self.cp_history = [0.0] # CP : Competence Progress
self.estimator_cp = SpanSeigelEstimator(cfg.cell.max_cp)
def add(self, goal, effect, competence = None):
if goal is None or not self.belongs(goal):
return
self.goals.append(goal)
self.effects.append(effect)
if competence is None:
competence = cmpce.competence(goal, effect, cfg = self.cfg)
self.c_history.append(competence)
self.estimator_cp.add(competence)
if len(self.c_history) >= self.cfg.cell.min_cp:
self.cp_history.append(self.estimator_cp.value())
else:
self.cp_history.append(0.0)
def split(self, low_bounds, high_bounds):
return self.graph.goal_split(self, low_bounds, high_bounds)
@property
def competence(self):
return self.c_history[-1]
@property
def cp_all(self):
if len(self.c_history) < 2:
return 0.0
estimator = SpanSeigelEstimator(len(self.c_history))
for c in self.c_history:
if c is not None:
estimator.add(c)
return estimator.value()
@property
def cp(self):
return self.cp_history[-1]
def avg_competence(self, span = None):
if len(self.c_history) == 0:
return None
if span is not None and span > 0:
return sum(self.c_history[:-span])/span
else:
return sum(self.c_history)/len(self.c_history)
def __len__(self):
return len(self.goals)
class EffectCell(Cell):
def __init__(self, bounds, graph, uid, cfg, depth = 0, w = None):
""""""
Cell.__init__(self, bounds, graph, uid, depth = depth, w = w, cfg = cfg)
self.predictions = []
self.pe_history = [] # PE : Prediction Error
self.pei_history = [0.0] # PEI: PE Improvement
self.estimator_pei = SpanSeigelEstimator(cfg.cell.max_pei)
def add(self, effect, prediction = None, pred_error = None):
if not self.belongs(effect):
return False
self.effects.append(effect)
if prediction is not None or pred_error is not None:
self.predictions.append(prediction)
if pred_error is None:
pred_error = cmpce.competence(prediction, effect, cfg = self.cfg)
self.pe_history.append(pred_error)
self.estimator_pei.add(pred_error)
if len(self.pe_history) >= self.cfg.cell.min_pei:
self.pei_history.append(self.estimator_pei.value())
else:
self.pei_history.append(0.0)
return True
def split(self, low_bounds, high_bounds):
return self.graph.effect_split(self, low_bounds, high_bounds)
@property
def pe(self):
return self.pe_history[-1]
@property
def pei(self):
return self.pei_history[-1]
@property
def pei_all(self):
if len(self.pe_history) < 2:
return 0.0
estimator = SpanSeigelEstimator(len(self.pe_history))
for pe in self.pe_history:
if pe is not None:
estimator.add(pe)
return estimator.value()
def avg_pred_error(self, span = None):
if len(self.pe_history) == 0:
return None
if span is not None and span > 0:
return sum(self.pe_history[:-span])/span
else:
return sum(self.pe_history)/len(self.pe_history)
def __len__(self):
return len(self.effects)