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()