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263 lines
8.9 KiB
263 lines
8.9 KiB
mod utils;
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use utils::*;
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use rustyCTBN::parameter_learning::*;
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use rustyCTBN::ctbn::*;
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use rustyCTBN::network::Network;
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use rustyCTBN::node;
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use rustyCTBN::params;
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use rustyCTBN::tools::*;
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use ndarray::arr3;
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use std::collections::BTreeSet;
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#[macro_use]
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extern crate approx;
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fn learn_binary_cim<T: ParameterLearning> (pl: 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"),2))
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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"),2))
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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, 3.0], [2.0, -2.0]]])));
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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, 1.0], [4.0, -4.0]],
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[[-6.0, 6.0], [2.0, -2.0]],
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])));
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}
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}
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let data = trajectory_generator(&net, 100, 100.0);
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let (CIM, M, T) = pl.fit(&net, &data, 1, None);
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print!("CIM: {:?}\nM: {:?}\nT: {:?}\n", CIM, M, T);
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assert_eq!(CIM.shape(), [2, 2, 2]);
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assert!(CIM.abs_diff_eq(&arr3(&[
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[[-1.0, 1.0], [4.0, -4.0]],
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[[-6.0, 6.0], [2.0, -2.0]],
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]), 0.2));
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}
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#[test]
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fn learn_binary_cim_MLE() {
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let mle = MLE{};
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learn_binary_cim(mle);
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}
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#[test]
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fn learn_binary_cim_BA() {
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let ba = BayesianApproach{
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alpha: 1,
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tau: 1.0};
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learn_binary_cim(ba);
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}
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fn learn_ternary_cim<T: ParameterLearning> (pl: 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);
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let (CIM, M, T) = pl.fit(&net, &data, 1, None);
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print!("CIM: {:?}\nM: {:?}\nT: {:?}\n", CIM, M, T);
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assert_eq!(CIM.shape(), [3, 3, 3]);
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assert!(CIM.abs_diff_eq(&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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]), 0.2));
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}
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#[test]
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fn learn_ternary_cim_MLE() {
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let mle = MLE{};
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learn_ternary_cim(mle);
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}
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#[test]
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fn learn_ternary_cim_BA() {
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let ba = BayesianApproach{
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alpha: 1,
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tau: 1.0};
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learn_ternary_cim(ba);
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}
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fn learn_ternary_cim_no_parents<T: ParameterLearning> (pl: 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);
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let (CIM, M, T) = pl.fit(&net, &data, 0, None);
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print!("CIM: {:?}\nM: {:?}\nT: {:?}\n", CIM, M, T);
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assert_eq!(CIM.shape(), [1, 3, 3]);
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assert!(CIM.abs_diff_eq(&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]]]), 0.2));
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}
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#[test]
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fn learn_ternary_cim_no_parents_MLE() {
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let mle = MLE{};
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learn_ternary_cim_no_parents(mle);
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}
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#[test]
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fn learn_ternary_cim_no_parents_BA() {
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let ba = BayesianApproach{
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alpha: 1,
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tau: 1.0};
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learn_ternary_cim_no_parents(ba);
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}
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fn learn_mixed_discrete_cim<T: ParameterLearning> (pl: 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, 300.0);
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let (CIM, M, T) = pl.fit(&net, &data, 2, None);
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print!("CIM: {:?}\nM: {:?}\nT: {:?}\n", CIM, M, T);
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assert_eq!(CIM.shape(), [9, 4, 4]);
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assert!(CIM.abs_diff_eq(&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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]), 0.2));
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}
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#[test]
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fn learn_mixed_discrete_cim_MLE() {
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let mle = MLE{};
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learn_mixed_discrete_cim(mle);
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}
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#[test]
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fn learn_mixed_discrete_cim_BA() {
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let ba = BayesianApproach{
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alpha: 1,
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tau: 1.0};
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learn_mixed_discrete_cim(ba);
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}
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