Safe Haskell | Safe-Infered |
---|

Test of learning

In this example, two networks are used : `simple`

which is the reference and `wrong`

which is a wrong start.
The goal is to use test patterns to learn the right `simple`

network from `wrong`

. Only the values are learnt.
The topology of both networks is the same.

simple :: ([`TDV`

Bool],`SBN`

`CPT`

) simple =`runBN`

$ do a <-`variable`

"a" (`t`

:: Bool) b <-`variable`

"b" (`t`

:: Bool) --`proba`

a`~~`

[0.4,0.6]`cpt`

b [a]`~~`

[0.8,0.2,0.2,0.8] -- return [a,b]

and `wrong`

where the probability for a is wrong.

wrong :: ([`TDV`

Bool],`SBN`

`CPT`

) wrong =`runBN`

$ do a <-`variable`

"a" (`t`

:: Bool) b <-`variable`

"b" (`t`

:: Bool) --`proba`

a`~~`

[0.2,0.8]`cpt`

b [a]`~~`

[0.8,0.2,0.2,0.8] -- return [a,b]

So, the first thing to do is generate test patterns. We are using the `discreteAncestralSampler`

for this. This function is
generating a sequence of graphs. We are just interested in the values. So, we get the values with `allVertexValues`

.

generatePatterns :: IO [[DVI]] generatePatterns = do let (vars@[a,b],exampleG) = simple r <-`runSampling`

5000 0 (`discreteAncestralSampler`

exampleG) return (map`allVertexValues`

r)

Once we have the data, we can try to learn the network:

emTest = do samples <- generatePatterns let (_,simpleG) = simple (_,wrongG) = wrong print simpleG`printGraphValues`

simpleG`printGraphValues`

wrongG --`printGraphValues`

(`learnEM`

samples wrongG)

First, we display the topology of the graph and the values for the reference graph and the wrong one.
Then, we use the `learnEM`

function to learn a new network from the samples. And, we print the new values to check.