Fix mixture random sampling for multidimensional sizes

[AustinRochford]

cc @fonnesbeck

[twiecki]

_________________ TestMatchesScipy.test_zeroinflatedbinomial __________________
self = <pymc3.tests.test_distributions.TestMatchesScipy object at 0x7fa0117fd690>
    def test_zeroinflatedbinomial(self):
        self.checkd(ZeroInflatedBinomial, Nat,
>                   {'psi': Unit, 'n': NatSmall, 'p': Unit})
pymc3/tests/test_distributions.py:596: 
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
pymc3/tests/test_distributions.py:415: in checkd
    check(m, m.named_vars['value'], valuedomain, vardomains)
_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 
self = <pymc3.tests.test_distributions.TestMatchesScipy object at 0x7fa0117fd690>
model = <pymc3.model.Model object at 0x7fa0117fdfd0>, value = value
domain = <pymc3.tests.test_distributions.Domain object at 0x7fa09803a450>
paramdomains = {'n': <pymc3.tests.test_distributions.Domain object at 0x7fa09803a5d0>, 'p': <pymc3.tests.test_distributions.Domain object at 0x7fa09803a250>, 'psi': <pymc3.tests.test_distributions.Domain object at 0x7fa09803a250>}
    def check_int_to_1(self, model, value, domain, paramdomains):
        pdf = model.fastfn(exp(model.logpt))
        for pt in product(paramdomains, n_samples=100):
            pt = Point(pt, value=value.tag.test_value, model=model)
            bij = DictToVarBijection(value, (), pt)
            pdfx = bij.mapf(pdf)
            area = integrate_nd(pdfx, domain, value.dshape, value.dtype)
>           assert_almost_equal(area, 1, err_msg=str(pt))
E           AssertionError: 
E           Arrays are not almost equal to 7 decimals
E           {'p': array(0.001), 'psi': array(0.001), 'value': array(0), 'n': array(3)}
E            ACTUAL: nan
E            DESIRED: 1
pymc3/tests/test_distributions.py:380: AssertionError

[twiecki]

Closing because of age. Feel free to reopen if you want to fix this up.