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@ -85,7 +85,7 @@ fn learn_ternary_net_2_nodes<T: StructureLearningAlgorithm> (sl: T) { |
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} |
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} |
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let data = trajectory_generator(&net, 100, 200.0, Some(6347747169756259),); |
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let data = trajectory_generator(&net, 100, 20.0, Some(6347747169756259),); |
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let net = sl.call(net, &data); |
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assert_eq!(BTreeSet::from_iter(vec![n1]), net.get_parent_set(n2)); |
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@ -106,3 +106,82 @@ pub fn learn_ternary_net_2_nodes_hill_climbing_bic() { |
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let hl = HillClimbing::init(bic); |
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learn_ternary_net_2_nodes(hl); |
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} |
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fn learn_mixed_discrete_net_3_nodes<T: StructureLearningAlgorithm> (sl: T) { |
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let mut net = CtbnNetwork::init(); |
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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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let n3 = net |
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.add_node(generate_discrete_time_continous_node(String::from("n3"),4)) |
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.unwrap(); |
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net.add_edge(n1, n2); |
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net.add_edge(n1, n3); |
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net.add_edge(n2, n3); |
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match &mut net.get_node_mut(n1).params { |
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params::Params::DiscreteStatesContinousTime(param) => { |
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assert_eq!(Ok(()), param.set_cim(arr3(&[[[-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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match &mut net.get_node_mut(n2).params { |
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params::Params::DiscreteStatesContinousTime(param) => { |
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assert_eq!(Ok(()), param.set_cim(arr3(&[ |
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[[-1.0, 0.5, 0.5], [3.0, -4.0, 1.0], [0.9, 0.1, -1.0]], |
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[[-6.0, 2.0, 4.0], [1.5, -2.0, 0.5], [3.0, 1.0, -4.0]], |
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[[-1.0, 0.1, 0.9], [2.0, -2.5, 0.5], [0.9, 0.1, -1.0]], |
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]))); |
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} |
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} |
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match &mut net.get_node_mut(n3).params { |
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params::Params::DiscreteStatesContinousTime(param) => { |
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assert_eq!(Ok(()), param.set_cim(arr3(&[ |
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[[-1.0, 0.5, 0.3, 0.2], [0.5, -4.0, 2.5, 1.0], [2.5, 0.5, -4.0, 1.0], [0.7, 0.2, 0.1, -1.0]], |
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[[-6.0, 2.0, 3.0, 1.0], [1.5, -3.0, 0.5, 1.0], [2.0, 1.3, -5.0 ,1.7], [2.5, 0.5, 1.0, -4.0]], |
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[[-1.3, 0.3, 0.1, 0.9], [1.4, -4.0, 0.5, 2.1], [1.0, 1.5, -3.0, 0.5], [0.4, 0.3, 0.1, -0.8]], |
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[[-2.0, 1.0, 0.7, 0.3], [1.3, -5.9, 2.7, 1.9], [2.0, 1.5, -4.0, 0.5], [0.2, 0.7, 0.1, -1.0]], |
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[[-6.0, 1.0, 2.0, 3.0], [0.5, -3.0, 1.0, 1.5], [1.4, 2.1, -4.3, 0.8], [0.5, 1.0, 2.5, -4.0]], |
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[[-1.3, 0.9, 0.3, 0.1], [0.1, -1.3, 0.2, 1.0], [0.5, 1.0, -3.0, 1.5], [0.1, 0.4, 0.3, -0.8]], |
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[[-2.0, 1.0, 0.6, 0.4], [2.6, -7.1, 1.4, 3.1], [5.0, 1.0, -8.0, 2.0], [1.4, 0.4, 0.2, -2.0]], |
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[[-3.0, 1.0, 1.5, 0.5], [3.0, -6.0, 1.0, 2.0], [0.3, 0.5, -1.9, 1.1], [5.0, 1.0, 2.0, -8.0]], |
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[[-2.6, 0.6, 0.2, 1.8], [2.0, -6.0, 3.0, 1.0], [0.1, 0.5, -1.3, 0.7], [0.8, 0.6, 0.2, -1.6]], |
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]))); |
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} |
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} |
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let data = trajectory_generator(&net, 300, 30.0, Some(6347747169756259),); |
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let net = sl.call(net, &data); |
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assert_eq!(BTreeSet::new(), net.get_parent_set(n1)); |
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assert_eq!(BTreeSet::from_iter(vec![n1]), net.get_parent_set(n2)); |
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assert_eq!(BTreeSet::from_iter(vec![n1, n2]), net.get_parent_set(n3)); |
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} |
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#[test] |
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pub fn learn_mixed_discrete_net_3_nodes_hill_climbing_ll() { |
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let ll = LogLikelihood::init(1, 1.0); |
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let hl = HillClimbing::init(ll); |
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learn_mixed_discrete_net_3_nodes(hl); |
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} |
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#[test] |
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pub fn learn_mixed_discrete_net_3_nodes_hill_climbing_bic() { |
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let bic = BIC::init(1, 1.0); |
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let hl = HillClimbing::init(bic); |
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learn_mixed_discrete_net_3_nodes(hl); |
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} |
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