Mixture models and breaking class symmetry

Questions
5

Ah, I posted old code that used to work in slightly different circumstances. Lesson for next time: Always execute code before posting…

This should work (tested on master):

class Ordered(pm.distributions.transforms.ElemwiseTransform):
    name = "ordered"

    def forward(self, x):
        out = tt.zeros(x.shape)
        out = tt.inc_subtensor(out[0], x[0])
        out = tt.inc_subtensor(out[1:], tt.log(x[1:] - x[:-1]))
        return out
    
    def forward_val(self, x, point=None):
        x, = pm.distributions.distribution.draw_values([x], point=point)
        return self.forward(x)

    def backward(self, y):
        out = tt.zeros(y.shape)
        out = tt.inc_subtensor(out[0], y[0])
        out = tt.inc_subtensor(out[1:], tt.exp(y[1:]))
        return tt.cumsum(out)

    def jacobian_det(self, y):
        return tt.sum(y[1:])


class Composed(pm.distributions.transforms.Transform):    
    def __init__(self, trafo1, trafo2):
        self._trafo1 = trafo1
        self._trafo2 = trafo2
        self.name = '_'.join([trafo1.name, trafo2.name])

    def forward(self, x):
        return self._trafo2.forward(self._trafo1.forward(x))
    
    def forward_val(self, x, point=None):
        return self.forward(x)

    def backward(self, y):
        return self._trafo1.backward(self._trafo2.backward(y))

    def jacobian_det(self, y):
        y2 = self._trafo2.backward(y)
        det1 = self._trafo1.jacobian_det(y2)
        det2 = self._trafo2.jacobian_det(y)
        return det1 + det2


with pm.Model() as model:
    trafo = Composed(pm.distributions.transforms.LogOdds(), Ordered())
    rates = pm.Beta('rates', 2, 2, shape=3, transform=trafo,
                    testval=[0.3, 0.4, 0.5])
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