def eager_mvn(loc, scale_tril, value):
assert len(loc.shape) == 1
assert len(scale_tril.shape) == 2
assert value.output == loc.output
if not is_affine(loc) or not is_affine(value):
return None # lazy
info_vec = scale_tril.data.new_zeros(scale_tril.data.shape[:-1])
precision = ops.cholesky_inverse(scale_tril.data)
scale_diag = Tensor(scale_tril.data.diagonal(dim1=-1, dim2=-2), scale_tril.inputs)
log_prob = -0.5 * scale_diag.shape[0] * math.log(2 * math.pi) - scale_diag.log().sum()
inputs = scale_tril.inputs.copy()
var = gensym('value')
inputs[var] = reals(scale_diag.shape[0])
gaussian = log_prob + Gaussian(info_vec, precision, inputs)
return gaussian(**{var: value - loc})
def test_transform_exp(shape):
point = Tensor(ops.abs(randn(shape)))
x = Variable('x', reals(*shape))
actual = Delta('y', point)(y=ops.exp(x))
expected = Delta('x', point.log(), point.log().sum())
assert_close(actual, expected)
