# Boolean operations support for theano.tensor
**URL:** https://discourse.pymc.io/t/boolean-operations-support-for-theano-tensor/159
**Category:** Questions
**Tags:** theano
**Created:** [July 12, 2017, 11:28pm UTC](https://discourse.pymc.io/t/boolean-operations-support-for-theano-tensor/159 "2017-07-12T23:28:29Z")
**Posts on this page:** 2
**Page:** 1
### Author:  [@zahybnaya](https://discourse.pymc.io/u/zahybnaya)
#### Post date: [July 12, 2017, 11:28pm UTC](https://discourse.pymc.io/t/boolean-operations-support-for-theano-tensor/159/1 "2017-07-12T23:28:29Z")
Hello,
My model consists of two simple distributions (a=DiscreteUniform and b=Uniform) and an additional extension of `Discrete` with my own implementation of logp.
My custom logp implementation is pretty complicated (running a tree search mechanism with the values of a and b as parameters). The values of a and b are used in various ways. For example to determine an order in a priority queue.
I keep getting “TypeError: Variables do not support boolean operations.”. I guess this is because \<=,==,\> operations are not supported by theano.tensor. (I’m using numpy 1.13).
However, this seems like a trivial modeling issue. What is the right way to tackle this? what is a good workaround?
I’ve tried to call eval(), cast to floats/ints… nothing works.
Thanks.
---
### Author:  [@aseyboldt](https://discourse.pymc.io/u/aseyboldt)
#### Post date: [July 13, 2017, 6:33am UTC](https://discourse.pymc.io/t/boolean-operations-support-for-theano-tensor/159/2 "2017-07-13T06:33:01Z")
It is hard to be sure without any code, but from what I understand the problem is probably that your custom logp implementation doesn’t work with theano variables. Comparisons are in principle supported by theano, but the results can’t be used in `if`, `for` or `while` statements. Since you seem to have only two parameters, you probably don’t really need gradients. Metropolis should still work here. So you could wrap your custom logp function with `as_op` (see the [docs](https://pymc-devs.github.io/pymc3/notebooks/getting_started.html#Arbitrary-deterministics) for an example). If you want to get an overview about how theano works with pymc3, you can have a look an introduction (still wip): [https://github.com/pymc-devs/pymc3/blob/1db42f786cdadaa53214fb89eedc6829c6b1d018/docs/source/theano.rst](https://github.com/pymc-devs/pymc3/blob/1db42f786cdadaa53214fb89eedc6829c6b1d018/docs/source/theano.rst)