def run(self):
"""Run the learner.
Returns a LearnerResult instance. Also sets self.result to that
instance.
"""
# max_time and max_iterations are mutually exclusive stopping critera
if 'max_time' not in self.options:
_stop = self._stop_after_iterations
else:
_stop = self._stop_after_time
self.stats = GreedyLearnerStatistics()
self.result = result.LearnerResult(self)
self.evaluator = evaluator.fromconfig(self.data, self.seed, self.prior)
self.evaluator.score_network(self.seed.copy())
first = True
self.result.start_run()
while not _stop():
self._run_without_restarts(_stop, self._restart,
randomize_net=(not first))
first = False
self.result.stop_run()
return self.result
def setUp(self):
nodes = [data.Variable(x) for x in range(5)]
nets = ("0,1", "1,0" , "1,2;2,3", "4,1;1,2", "4,2;4,3")
scores = (-10.5, -11, -8.5, -12, -13)
self.result1 = result.LearnerResult(size=0)
self.result1.nodes = nodes
self.result1.start_run()
for n,s in zip(nets, scores):
self.result1.add_network(network.Network(nodes, n), s)
self.result2 = result.LearnerResult(size=0)
self.result2.nodes = nodes
self.result2.start_run()
self.result2.add_network(network.Network(nodes, "1,2;2,3;3,4"), -6)
self.result2.add_network(network.Network(nodes, "1,2;2,3;3,4;0,4;0,2"), -5.5)
self.result2.add_network(network.Network(nodes, "0,1"), -10.5)
def run(self):
self.result = result.LearnerResult(self)
self.evaluator = evaluator.fromconfig(self.data, prior_=self.prior)
self.result.start_run()
for net in self.networks:
self.result.add_network(net, self.evaluator.score_network(net))
self.result.stop_run()
return self.result
def setUp(self):
nodes = [data.Variable(x) for x in range(5)]
self.result = result.LearnerResult(size=self.size)
self.result.nodes = nodes
self.result.start_run()
nets = ("0,1", "1,0" , "1,2;2,3", "4,1;1,2", "4,2;4,3")
scores = (-10.5, -11, -8.5, -12, -13)
for n,s in zip(nets, scores):
self.result.add_network(network.Network(nodes, n), s)
def run(self):
self.result = result.LearnerResult(self)
self.evaluator = evaluator.fromconfig(self.data, prior_=self.prior)
self.result.start_run()
# algorithm here!
print 'Yay! Nothing to see here...'
# get prior!
# complete datasets!
# self.evaluator.score_network()
self.result.stop_run()
return self.result
def run(self):
"""Run the learner."""
self.stats = SALearnerStatistics(self.start_temp, self.delta_temp,
self.max_iters_at_temp)
self.result = result.LearnerResult(self)
self.evaluator = evaluator.fromconfig(self.data, self.seed, self.prior)
self.evaluator.score_network(self.seed.copy())
self.result.start_run()
curscore = self.evaluator.score_network()
# temperature decays exponentially, so we'll never get to 0.
# So, we continue until temp < 1
while self.stats.temp >= 1:
try:
newscore = self._alter_network_randomly_and_score()
except CannotAlterNetworkException:
return
self.result.add_network(self.evaluator.network, newscore)
if self._accept(newscore):
# set current score
self.stats.current_score = newscore
if self.stats.current_score > self.stats.best_score:
self.stats.best_score = self.stats.current_score
else:
# undo network alteration
self.evaluator.restore_network()
# temp not updated EVERY iteration. just whenever criteria met.
self.stats.update()
self.result.stop_run()
return self.result