|
|
|
@ -108,7 +108,7 @@ fn check_compatibility_between_dataset_and_network<T: StructureLearningAlgorithm |
|
|
|
|
} |
|
|
|
|
} |
|
|
|
|
|
|
|
|
|
let data = trajectory_generator(&net, 100, 20.0, Some(6347747169756259)); |
|
|
|
|
let data = trajectory_generator(&net, 100, 30.0, Some(6347747169756259)); |
|
|
|
|
|
|
|
|
|
let mut net = CtbnNetwork::new(); |
|
|
|
|
let _n1 = net |
|
|
|
@ -466,7 +466,7 @@ pub fn chi_square_call() { |
|
|
|
|
let N3: usize = 2; |
|
|
|
|
let N2: usize = 1; |
|
|
|
|
let N1: usize = 0; |
|
|
|
|
let separation_set = BTreeSet::new(); |
|
|
|
|
let mut separation_set = BTreeSet::new(); |
|
|
|
|
let parameter_learning = BayesianApproach { alpha: 1, tau:1.0 }; |
|
|
|
|
let mut cache = Cache::new(parameter_learning, data); |
|
|
|
|
let chi_sq = ChiSquare::new(0.0001); |
|
|
|
@ -474,5 +474,6 @@ pub fn chi_square_call() { |
|
|
|
|
assert!(chi_sq.call(&net, N1, N3, &separation_set, &mut cache)); |
|
|
|
|
assert!(!chi_sq.call(&net, N3, N1, &separation_set, &mut cache)); |
|
|
|
|
assert!(!chi_sq.call(&net, N3, N2, &separation_set, &mut cache)); |
|
|
|
|
separation_set.insert(N1); |
|
|
|
|
assert!(chi_sq.call(&net, N2, N3, &separation_set, &mut cache)); |
|
|
|
|
} |
|
|
|
|