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108 lines
3.0 KiB
108 lines
3.0 KiB
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mod utils;
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use utils::*;
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use rustyCTBN::ctbn::*;
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use rustyCTBN::network::Network;
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use rustyCTBN::tools::*;
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use rustyCTBN::structure_learning::score_function::*;
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use rustyCTBN::structure_learning::score_based_algorithm::*;
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use rustyCTBN::structure_learning::StructureLearningAlgorithm;
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use ndarray::{arr1, arr2, arr3};
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use std::collections::BTreeSet;
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use rustyCTBN::params;
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#[macro_use]
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extern crate approx;
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#[test]
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fn simple_log_likelihood() {
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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"),2))
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.unwrap();
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let trj = Trajectory{
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time: arr1(&[0.0,0.1,0.3]),
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events: arr2(&[[0],[1],[1]])};
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let dataset = Dataset{
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trajectories: vec![trj]};
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let ll = LogLikelihood::init(1, 1.0);
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assert_abs_diff_eq!(0.04257, ll.call(&net, n1, &BTreeSet::new(), &dataset), epsilon=1e-3);
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}
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#[test]
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fn simple_bic() {
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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"),2))
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.unwrap();
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let trj = Trajectory{
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time: arr1(&[0.0,0.1,0.3]),
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events: arr2(&[[0],[1],[1]])};
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let dataset = Dataset{
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trajectories: vec![trj]};
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let ll = BIC::init(1, 1.0);
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assert_abs_diff_eq!(-0.65058, ll.call(&net, n1, &BTreeSet::new(), &dataset), epsilon=1e-3);
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}
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fn learn_ternary_net_2_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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net.add_edge(n1, n2);
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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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let data = trajectory_generator(&net, 100, 200.0, 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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assert_eq!(BTreeSet::new(), net.get_parent_set(n1));
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}
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#[test]
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pub fn learn_ternary_net_2_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_ternary_net_2_nodes(hl);
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}
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#[test]
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pub fn learn_ternary_net_2_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_ternary_net_2_nodes(hl);
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}
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