CODEWITHSUNDEEP
pymcpymc3
charles.fox
charles.fox

Reputation: 493

pymc3 how to code multi-state discrete Bayes net CPT?

I'm trying to build a simple Bayesian network, where rain and sprinkler are the parents of wetgrass, but rain and sprinkler each have three (fuzzy-logic type rather rather than the usual two boolean) states, and wetgrass has two states (true/false). I can't find anywhere in the pymc3 docs what syntax to use to describe the CPTs for this -- I'm trying the following based on 2-state examples but it's not generalizing to three states the way I thought it would. Can anyone show the correct way to do this? (And also for the more general case where wetgrass has three states too.)

rain = mc.Categorical('rain', p = np.array([0.5, 0. ,0.5]))   

sprinker = mc.Categorical('sprinkler', p=np.array([0.33,0.33,0.34]))  

wetgrass = mc.Categorical('wetgrass',
    mc.math.switch(rain,
        mc.math.switch(sprinker, 10, 1, -4),
        mc.math.switch(sprinker, -20, 1, 3),
        mc.math.switch(sprinker, -5, 1, -0.5)))

[gives error at wetgrass definition: Wrong number of inputs for Switch.make_node (got 4((, , , )), expected 3) ]

As I understand it - switch is a theano function similar to (b?a:b) in a C program; which is only doing a two way comparison. It's maybe possible to set up the CPT using a whole load of binary switches like this, but I really want to just give a 3D matrix CPT as the input as in BNT and other bayes net libraries. Is this currently possible ?

Upvotes: 0

Views: 288

Answers (1)

aseyboldt
aseyboldt

Reputation: 1090

You can code a three-way switch using two individual switches:

tt.switch(sprinker == 0,
    10
    tt.switch(sprinker == 1, 1, -4))

But in general it is probably better to index into a table:

table = tt.constant(np.array([[...], [...]]))
value = table[rain, sprinker]

Upvotes: 1

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