def test_tt(self): sample, updates = rejection_sample([self.fair_coin,], tensor.eq(tensor.sum(tensor.eq(self.coin, self.data)), 5)) sampler = theano.function([], sample, updates=updates) # TODO: this is super-slow, how can bher do this fast? for i in range(100): print sampler()
def test_tt(self): sample, updates = rejection_sample([ self.fair_coin, ], tensor.eq(tensor.sum(tensor.eq(self.coin, self.data)), 5)) sampler = theano.function([], sample, updates=updates) # TODO: this is super-slow, how can bher do this fast? for i in range(100): print sampler()
def test_rejection_sampler_no_cond(self): sample, updates = rejection_sample([self.A, self.B, self.C]) # create a runnable function sampler = theano.function(inputs=[], outputs = sample, updates = updates) # generate some data data = [] for i in range(100): data.append(sampler()) # plot histogram pylab.hist(numpy.asarray(data)) pylab.show()
def test_rejection_sampler_no_cond(self): sample, updates = rejection_sample([self.A, self.B, self.C]) # create a runnable function sampler = theano.function(inputs=[], outputs=sample, updates=updates) # generate some data data = [] for i in range(100): data.append(sampler()) # plot histogram pylab.hist(numpy.asarray(data)) pylab.show()