class Fitting1D(unittest.TestCase):
def setUp(self):
self.obs = tensor.as_tensor_variable(
numpy.asarray([0.0, 1.01, 0.7, 0.65, 0.3]))
self.rstream = RandomStreams(234)
self.n = self.rstream.normal()
self.u = self.rstream.uniform()
def test_normal_ml(self):
up = self.rstream.ml(self.n, self.obs)
p = self.rstream.params(self.n)
f = theano.function([], [up[p[0]], up[p[1]]])
m,v = f()
assert numpy.allclose([m,v], [.532, 0.34856276335])
def test_uniform_ml(self):
up = self.rstream.ml(self.u, self.obs)
p = self.rstream.params(self.u)
f = theano.function([], [up[p[0]], up[p[1]]])
l,h = f()
assert numpy.allclose([l,h], [0.0, 1.01])
class Fitting1D(unittest.TestCase):
def setUp(self):
self.obs = tensor.as_tensor_variable(
numpy.asarray([0.0, 1.01, 0.7, 0.65, 0.3]))
self.rstream = RandomStreams(234)
self.n = self.rstream.normal()
self.u = self.rstream.uniform()
def test_normal_ml(self):
up = self.rstream.ml(self.n, self.obs)
p = self.rstream.params(self.n)
f = theano.function([], [up[p[0]], up[p[1]]])
m, v = f()
assert numpy.allclose([m, v], [.532, 0.34856276335])
def test_uniform_ml(self):
up = self.rstream.ml(self.u, self.obs)
p = self.rstream.params(self.u)
f = theano.function([], [up[p[0]], up[p[1]]])
l, h = f()
assert numpy.allclose([l, h], [0.0, 1.01])
