Sale!

# COMP3702/7702 ASSIGNMENT 3:Learning Bayesian Network solved

\$30.00

Category:

## Description

Part’I'[50’points]:’Learning’Conditional’Probability’Tables.
The! training! data! sets! for! this! part! are! the! .txt! files! whose! names! start! with!
CPTNoMissingData.!The!format!of!each!data!file!is:
• The! first!line!contains!two!numbers!separated!by!a!white!space.!The! first!number!is!
2
the!number!of!nodes!in!the!Bayesian!Network.!The!second!number!is!the!number!of!
data!in!the!file.!Let’s!denote!the!number!of!nodes!as!K!and!the!number!of!data!as!N.
• Each!line!at!line!2!to!K+1!represents!a!node!of!the!Bayesian!Network!and!its!parents.!
Each! line! contains! one! or! more! words,! separated! by! a! white! space.! The! first! word
represents!the!name!of! the!node,!while! the!rest!represents! the!names!of!the!node’s
parents.!For!example,!A!B!C!means!Node!B!and!C!are!the!parents!of!node!A.
• Each!line!at!line!K+2!to!K+N+1!represents!the!data,!in!the!same!order!the!nodes!are!
written.! For! instance,! in! CPTNoMissingData8d1.txt,! each! line! of! data! represents! the!
value!of!A!B!C.
TaskS1′ [25′ points]. Given! a! set! of! training! data! together! with! the! structure! of! the!
Bayesian! Network! using! Maximum! Likelihood! Estimate! as! discussed! in! class. Please!
output!the!CPTs!to!a!file!named!cpt8[NameOfTrainingData].txt!in!the!following!format:
• The!output! file!contains!2K!lines,!representing!K!blocks!of!CPTs!(K!is!the!number!of!
nodes). Each!CPT!block!consists!of!two!lines,!where:
• The!first!line!contains!one!or!more!words,!separated!by!a!white!space.!The!first!
word!represents!the!name!of! the!node,!while! the!rest!represents! the!names!of!
the!node’s!parents.,!i.e.,!
Node!Parent81!Parent82!…!Parent8L!
where!L!is!the!number!of!parents!of!node!“Name”.
• The! second! line! represent! the! CPT! of! the! node. It! contains! 2L! numbers.! Each
number!represents!the!conditional!probability!of!the!Node!being!True!given!the!
value! of! the! parents,! sorted! in! ascending! order.! For! example,! suppose! the!
parents!of!Node!A!are!B!and!C,!and!P(A!|!B,!C)!=!0.3,!P(A!|!B,!~C)!=!0.5,!P(A!|!~B,!
C)!=!0.4,!P(A!|!~B,!~C)!=!0.7.!Then!the!output!format!for!the!CPT!of!A!will!be:!0.7!
0.4!0.5!0.3
• Line!2K+1!is!the!log8likelihood!of!the!data!given!the!Bayesian!Network!model.
likelihood!of!the!training!data!set!given!the!!CPTs.
set.
• Please! explain! how! the! likelihood! and! log8likelihood! measure! of! the! Bayesian!
Network!differs!as!the!number!of!training!data!set!increases.
• Please! explain! how! the! likelihood! and! log8likelihood! measure! of! the! Bayesian!
Network!differs!as!the!number!of!variables!(nodes)!increases.
• Please! write! a! short! discussion! on!how!the! likelihood! and! log8likelihood!measure!
will!differ!when!the!possible!values!of!each!variable!increases.
Part’II'[50’points]:’Learning’Structure’and’Conditional’Probability’Tables.
The! training! data! sets! for! this! part! are the! .txt! files! whose! names! start! with!
noMissingData.!The!format!of!each!data!file!is!the!same!as!the!input!format!for!Part!I,!but!
without!the!parents!information,!i.e.:
• The! first!line!contains!two!numbers!separated!by!a!white!space.!The! first!number!is!
the!number!of!nodes!in!the!Bayesian!Network.!The!second!number!is!the!number!of!
3
data!in!the!file.!Let’s!denote!the!number!of!nodes!as!K!and!the!number!of!data!as!N.
• The!second!line!represents!the!names!of!the!nodes,!separated!by!a!white!space.
• Each!line!at!line!3!to!N+2!represents!the!data.!
a!Bayesian!network!given!a!set!of!training!data.!You!can!use!a!greedy!search!method!and!
the! scoring! function! as! discussed! in! class. Please! use! at! most! 3! minutes! search! time.!
format:
• Each!line!at!line!1! to!K! represents!a! node! of! the!Bayesian!Network!and!its! parents.!
Each! line! contains! one! or! more! words,! separated! by! a! white! space.! The! first! word!
represents!the!name!of! the!node,!while! the!rest!represents! the!names!of! the!node’s!
parents.!For!example,!A!B!C!means!Node!B!and!C!are!the!parents!of!node!A.
• Line! K+1! to! N+K! represents! blocks! of! CPTs!(K! is! the! number! of! nodes).! Each! CPT!
block!consists!of!two!lines,!where:!
• The!first!line!contains!one!or!more!words,!separated!by!a!white!space.!The!first!
word!represents!the!name!of! the!node,!while! the!rest!represents! the!names!of!
the!node’s!parents.,!i.e.,!
Node!Parent81!Parent82!…!Parent8L!
where!L!is!the!number!of!parents!of!Node.
• The! second!line! represents! the! CPT! of! the! node.! It! contains! 2L! numbers.! Each!
number!represents! the!conditional!probability! that! the!Node!is!True!given! the!
value! of! the! parents,! sorted! in! ascending! order.! For! example,! suppose! the!
parents!of!Node!A!are!B!and!C,!and!P(A!|!B,!C)!=!0.3,!P(A!|!B,!~C)!=!0.5,!P(A!|!~B,!
C)!=!0.4,!P(A!|!~B,!~C)!=!0.7.!Then!the!output!format!for!the!CPT!of!A!will!be:!0.7!
0.4!0.5!0.3
• Line!N+K+1!consists!of!two!number!separated!by!a!white!space.!The!first!number!is!
the!log8likelihood!of!the!data!given!the!Bayesian!Network!model.!The!second!number!
is!the!score!of!the!Bayesian!Network.
TaskS5′ [7′ points].’ Please! experiment! with! the! scoring! function! by! changing! the!
constant! parameter.! For! each! parameter,! please! run! the! program! you’ve! written! for!
change!as!the!parameter!increases/decreases.!
TaskS6 [8′ points]. Please! implement! “no! edge”! and! “random! chain”! to! initialize! the!
initialization! methods! on! each! data! set! and! compare! the! final! Bayesian! Network! (in!
terms!of the!scoring!function!and!structural!complexity)!after!3!minutes!searching!time.!
and!compare!the!final!Bayesian!Networks!results!with!the!Bayesian!Networks!generated!
Part’ III’ –’ Bonus’ [15′ points]:’ Learning’ Structure’ and’ Conditional’ Probability’
Tables.
4
The!training!data!sets!for!this!part!are!files!that!start!with!someMissingData.!The!format!
of!each!data!file!is!the!same!as!the!input!format!for!Part!II,!but!each!data!input!may!have!
a!value!of!0,!1,!or!H1/H2/…/HM,!where!Hi!means!missing!data8i!and!M!is!the!number!of
missing!data.
TaskS8 [15′ points].’ Please! write! a! program! to! generate! the! structure! and! CPT! of! a!
Bayesian! network! given! a! set! of! training! data! where! some! of! these! data! are! missing.!
learn!the!CPTs.!
file! should! start! with! the! same! output! format! as! in! Part! II,! but! appended! with! the!
probabilities!!of!each!missing!data!having!the!value!`1’.!To!be!more!precise,!the!format!is:
• Each!line!at!line! 1! to!K! represents!a!node! of! the!Bayesian!Network!and!its!parents.!
Each! line! contains! one! or! more! words,! separated! by! a! white! space.! The! first! word!
represents! the!name!of! the!node,!while! the!rest!represents! the!names!of! the!node’s!
parents.!For!example,!A!B!C!means!Node!B!and!C!are!the!parents!of!node!A.
• Line! K+1! to! N+K! represents! blocks! of! CPTs! (K! is! the! number! of! nodes).! Each! CPT!
block!consists!of!two!lines,!where:!
• The!first!line!contains!one!or!more!words,!separated!by!a!white!space.!The!first!
word!represents! the!name!of! the!node,!while! the!rest!represents! the!names!of!
the!node’s!parents.,!i.e.,!
Node!Parent81!Parent82!…!Parent8L!
where!L!is!the!number!of!parents!of!Node.
• The! second!line! represents! the! CPT! of! the! node.! It! contains! 2L! numbers.! Each!
number!represents! the!conditional!probability! that! the!Node!is!True!given! the!
value! of! the! parents,! sorted! in! ascending! order.! For! example,! suppose! the!
parents!of!Node!A!are!B!and!C,!and!P(A!|!B,!C)!=!0.3,!P(A!|!B,!~C)!=!0.5,!P(A!|!~B,!
C)!=!0.4,!P(A!|!~B,!~C)!=!0.7.!Then!the!output!format!for!the!CPT!of!A!will!be:!0.7!
0.4!0.5!0.3
• Line!N+K+1!consists!of!two!number!separated!by!a!white!space.!The!first!number!is!
the!log8likelihood!of!the!data!given!the!Bayesian!Network!model.!The!second!number!
is!the!score!of!the!Bayesian!Network.
• Line! N+K+2! to! line! M+N+K+2! consists! of! two! words! separated! by! a! white! space,!
where!M!is!the!number!of!missing!data.!The!first!word!is!the!name!of!the!missing!data!
(e.g.,!H1,!H2,!etc.).!The!second!word!is!the!probability!that!the!missing!data!has!value!
`1’.! For! example,!if! P(H1! |! Data,!Model)! =! 0.6,! then! the! output! will! be:! “H1! 0.6”! (of!
course!without!the!quotation!mark).