pub fn new(shape: Vec<usize>, cpts: Vec<CPT>) -> BayesianNetwork {
        // sort the CPTs in topological order
        let mut sorted : Vec<CPT> = Vec::new();
        let mut added : HashSet<usize> = HashSet::new();
        fn add(sorted: &mut Vec<CPT>, added: &mut HashSet<BnVar>, cpts: &Vec<CPT>, cpt: &CPT) -> () {
            if added.contains(&cpt.var) { return () };
            // add the parents
            for parent in cpt.parents.iter() {
                let par = cpts.iter().find(|x| x.var == *parent).unwrap();
                add(sorted, added, cpts, par);
            }
            assert!(!added.contains(&cpt.var), "Cycle detected in Bayesian network CPT structure");
            added.insert(cpt.var);
            sorted.push(cpt.clone())
        }
        for cpt in cpts.iter() {
            add(&mut sorted, &mut added, &cpts, cpt);
        }
        return BayesianNetwork { shape: shape, cpts: sorted }
    }
/// Generate a new Bayesian network
/// The `shape` dictates how many variables there are and their domain.
/// shape[i] says how many values variable i can take.
Generate a new Bayesian network
The `shape` dictates how many variables there are and their domain.
shape[i] says how many values variable i can take.
Generate a new Bayesian network The ` shape ` dictates how many variables there are and their domain . shape [ i ] says how many values variable i can take .
pub fn new(shape: Vec<usize>, cpts: Vec<CPT>) -> BayesianNetwork {
        // sort the CPTs in topological order
        let mut sorted : Vec<CPT> = Vec::new();
        let mut added : HashSet<usize> = HashSet::new();
        fn add(sorted: &mut Vec<CPT>, added: &mut HashSet<BnVar>, cpts: &Vec<CPT>, cpt: &CPT) -> () {
            if added.contains(&cpt.var) { return () };
            // add the parents
            for parent in cpt.parents.iter() {
                let par = cpts.iter().find(|x| x.var == *parent).unwrap();
                add(sorted, added, cpts, par);
            }
            assert!(!added.contains(&cpt.var), "Cycle detected in Bayesian network CPT structure");
            added.insert(cpt.var);
            sorted.push(cpt.clone())
        }
        for cpt in cpts.iter() {
            add(&mut sorted, &mut added, &cpts, cpt);
        }
        return BayesianNetwork { shape: shape, cpts: sorted }
    }
pub fn new ( shape : Vec < usize > , cpts : Vec < CPT > ) -> BayesianNetwork { // sort the CPTs in topological order let mut sorted : Vec < CPT > = Vec :: new ( ) ; let mut added : HashSet < usize > = HashSet :: new ( ) ; fn add ( sorted : & mut Vec < CPT > , added : & mut HashSet < BnVar > , cpts : & Vec < CPT > , cpt : & CPT ) -> ( ) { if added . contains ( & cpt . var ) { return ( ) } ; // add the parents for parent in cpt . parents . iter ( ) { let par = cpts . iter ( ) . find ( | x | x . var == * parent ) . unwrap ( ) ; add ( sorted , added , cpts , par ) ; } assert ! ( added contains ( cpt var ) " " ) ; added . insert ( cpt . var ) ; sorted . push ( cpt . clone ( ) ) } for cpt in cpts . iter ( ) { add ( & mut sorted , & mut added , & cpts , cpt ) ; } return BayesianNetwork { shape : shape , cpts : sorted } }
Generate a new Bayesian network
The `shape` dictates how many variables there are and their domain.
Generate a new Bayesian network The ` shape ` dictates how many variables there are and their domain .
shape
Vec<usize>
cpts
Vec<CPT>
shape
Vec<usize>
cpts
Vec<CPT>