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@ -10,6 +10,7 @@ use reCTBN::parameter_learning::BayesianApproach; |
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use reCTBN::parameter_learning::Cache; |
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use reCTBN::params; |
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use reCTBN::structure_learning::hypothesis_test::*; |
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use reCTBN::structure_learning::constraint_based_algorithm::*; |
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use reCTBN::structure_learning::score_based_algorithm::*; |
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use reCTBN::structure_learning::score_function::*; |
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use reCTBN::structure_learning::StructureLearningAlgorithm; |
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@ -497,3 +498,81 @@ pub fn f_call() { |
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separation_set.insert(N1); |
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assert!(f.call(&net, N2, N3, &separation_set, &mut cache)); |
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} |
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#[test] |
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pub fn learn_ternary_net_2_nodes_ctpc() { |
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let mut net = CtbnNetwork::new(); |
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let n1 = net |
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.add_node(generate_discrete_time_continous_node(String::from("n1"), 3)) |
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.unwrap(); |
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let n2 = net |
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.add_node(generate_discrete_time_continous_node(String::from("n2"), 3)) |
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.unwrap(); |
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net.add_edge(n1, n2); |
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match &mut net.get_node_mut(n1) { |
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params::Params::DiscreteStatesContinousTime(param) => { |
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assert_eq!( |
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Ok(()), |
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param.set_cim(arr3(&[ |
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[ |
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[-3.0, 2.0, 1.0], |
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[1.5, -2.0, 0.5], |
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[0.4, 0.6, -1.0] |
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], |
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])) |
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); |
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} |
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} |
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match &mut net.get_node_mut(n2) { |
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params::Params::DiscreteStatesContinousTime(param) => { |
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assert_eq!( |
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Ok(()), |
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param.set_cim(arr3(&[ |
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[ |
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[-1.0, 0.5, 0.5], |
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[3.0, -4.0, 1.0], |
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[0.9, 0.1, -1.0] |
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], |
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[ |
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[-6.0, 2.0, 4.0], |
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[1.5, -2.0, 0.5], |
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[3.0, 1.0, -4.0] |
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], |
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[ |
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[-1.0, 0.1, 0.9], |
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[2.0, -2.5, 0.5], |
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[0.9, 0.1, -1.0] |
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], |
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])) |
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); |
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} |
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} |
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let data = trajectory_generator(&net, 100, 20.0, Some(6347747169756259)); |
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let f = F::new(0.000001); |
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let chi_sq = ChiSquare::new(0.0001); |
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let parameter_learning = BayesianApproach { alpha: 1, tau:1.0 }; |
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let cache = Cache::new(parameter_learning, data.clone()); |
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let mut ctpc = CTPC::new(f, chi_sq, cache); |
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let net = ctpc.fit_transform(net, &data); |
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assert_eq!(BTreeSet::from_iter(vec![n1]), net.get_parent_set(n2)); |
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assert_eq!(BTreeSet::new(), net.get_parent_set(n1)); |
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} |
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#[test] |
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fn learn_mixed_discrete_net_3_nodes_ctpc() { |
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let (_, data) = get_mixed_discrete_net_3_nodes_with_data(); |
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let f = F::new(1e-24); |
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let chi_sq = ChiSquare::new(1e-24); |
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let parameter_learning = BayesianApproach { alpha: 1, tau:1.0 }; |
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let cache = Cache::new(parameter_learning, data); |
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let ctpc = CTPC::new(f, chi_sq, cache); |
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learn_mixed_discrete_net_3_nodes(ctpc); |
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} |
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