def setUp(self): self.data = data.fromfile(testfile('testdata10.txt')) self.neteval = evaluator.NetworkEvaluator( self.data, network.fromdata(self.data), prior.UniformPrior(self.data.variables.size)) self.neteval.network.edges.add_many([(1,0),(2,0),(3,0)])
def setUp(self): a,b,c,d,e = 0,1,2,3,4 self.data = data.fromfile(testfile('testdata9.txt')) self.net = network.fromdata(self.data) self.net.edges.add_many([(a,c), (b,c), (c,d), (c,e)]) self.neteval1 = self.neteval_type(self.data, self.net, max_iterations="10*n**2")
def setUp(self): a, b, c, d, e = 0, 1, 2, 3, 4 self.data = data.fromfile(testfile('testdata9.txt')) self.net = network.fromdata(self.data) self.net.edges.add_many([(a, c), (b, c), (c, d), (c, e)]) self.neteval1 = self.neteval_type(self.data, self.net, max_iterations="10*n**2")
def test_gibbs_scoring(self): # score two nets (correct one and bad one) with missing values. # ensure that correct one scores better. (can't check for exact score) # score network: {a,b}->c->{d,e} score1 = self.neteval1.score_network() # score network: {a,b}->{d,e} c a, b, c, d, e = 0, 1, 2, 3, 4 net2 = network.fromdata(self.data) net2.edges.add_many([(a, d), (a, e), (b, d), (b, e)]) neteval2 = self.neteval_type(self.data, net2) score2 = neteval2.score_network() # score1 should be better than score2 print score1, score2 assert score1 > score2, "Gibbs sampling can find goodhidden node."
def test_gibbs_scoring(self): # score two nets (correct one and bad one) with missing values. # ensure that correct one scores better. (can't check for exact score) # score network: {a,b}->c->{d,e} score1 = self.neteval1.score_network() # score network: {a,b}->{d,e} c a,b,c,d,e = 0,1,2,3,4 net2 = network.fromdata(self.data) net2.edges.add_many([(a,d), (a,e), (b,d), (b,e)]) neteval2 = self.neteval_type(self.data, net2) score2 = neteval2.score_network() # score1 should be better than score2 print score1, score2 assert score1 > score2, "Gibbs sampling can find goodhidden node."
def fromconfig(data_=None, network_=None, prior_=None): """Create an evaluator based on configuration parameters. This function will return the correct evaluator based on the relevant configuration parameters. """ data_ = data_ or data.fromconfig() network_ = network_ or network.fromdata(data_) prior_ = prior_ or prior.fromconfig() if data_.missing.any(): e = _missingdata_evaluators[config.get('evaluator.missingdata_evaluator')] return e(data_, network_, prior_) else: return SmartNetworkEvaluator(data_, network_, prior_)
def fromconfig(data_=None, network_=None, prior_=None): """Create an evaluator based on configuration parameters. This function will return the correct evaluator based on the relevant configuration parameters. """ data_ = data_ or data.fromconfig() network_ = network_ or network.fromdata(data_) prior_ = prior_ or prior.fromconfig() if data_.missing.any(): e = _missingdata_evaluators[config.get( 'evaluator.missingdata_evaluator')] return e(data_, network_, prior_) else: return SmartNetworkEvaluator(data_, network_, prior_)
def setUp(self): self.data = data.fromfile(testfile('testdata10.txt')) self.ne = evaluator.SmartNetworkEvaluator(self.data, network.fromdata(self.data))
def setUp(self): self.data = data.fromfile(testfile('testdata10.txt')) self.neteval = evaluator.NetworkEvaluator( self.data, network.fromdata(self.data), prior.UniformPrior(self.data.variables.size)) self.neteval.network.edges.add_many([(1, 0), (2, 0), (3, 0)])
def setUp(self): self.data = data.fromfile(testfile('testdata10.txt')) self.neteval = evaluator.NetworkEvaluator(self.data, network.fromdata(self.data)) self.neteval.network.edges.add_many([(1, 0), (2, 0), (3, 0)]) # {1,2,3} --> 0
def setUp(self): self.data = data.fromfile(testfile('testdata10.txt')) self.ne = evaluator.SmartNetworkEvaluator( self.data, network.fromdata(self.data))
def setUp(self): self.data = data.fromfile(testfile('testdata10.txt')) self.neteval = evaluator.NetworkEvaluator(self.data, network.fromdata(self.data)) self.neteval.network.edges.add_many([(1,0), (2,0), (3,0)]) # {1,2,3} --> 0