Cargo.toml

@@ -12,6 +12,7 @@ thiserror = "*"
 rand = "*"
 bimap = "*"
 enum_dispatch = "*"
+statrs = "*"
 rand_chacha = "*"
 
 [dev-dependencies]

src/lib.rs

@@ -8,4 +8,5 @@ pub mod network;
 pub mod ctbn;
 pub mod tools;
 pub mod parameter_learning;
+pub mod structure_learning;
 

src/parameter_learning.rs

@@ -57,12 +57,12 @@ pub fn sufficient_statistics<T:network::Network>(
     let mut T: Array2<f64> = Array::zeros((parentset_domain.iter().product(), node_domain));
 
     //Compute the sufficient statistics
-    for trj in dataset.trajectories.iter() {
-        for idx in 0..(trj.time.len() - 1) {
-            let t1 = trj.time[idx];
-            let t2 = trj.time[idx + 1];
-            let ev1 = trj.events.row(idx);
-            let ev2 = trj.events.row(idx + 1);
+    for trj in dataset.get_trajectories().iter() {
+        for idx in 0..(trj.get_time().len() - 1) {
+            let t1 = trj.get_time()[idx];
+            let t2 = trj.get_time()[idx + 1];
+            let ev1 = trj.get_events().row(idx);
+            let ev2 = trj.get_events().row(idx + 1);
             let idx1 = vector_to_idx.dot(&ev1);
 
             T[[idx1, ev1[node]]] += t2 - t1;

src/structure_learning.rs

@@ -0,0 +1,10 @@
+pub mod score_function;
+pub mod score_based_algorithm;
+use crate::network;
+use crate::tools;
+
+pub trait StructureLearningAlgorithm {
+    fn fit_transform<T, >(&self, net: T, dataset: &tools::Dataset) -> T
+    where
+        T: network::Network;
+}

src/structure_learning/score_based_algorithm.rs

@@ -0,0 +1,83 @@
+use crate::network;
+use crate::structure_learning::score_function::ScoreFunction;
+use crate::structure_learning::StructureLearningAlgorithm;
+use crate::tools;
+use std::collections::BTreeSet;
+
+pub struct HillClimbing<S: ScoreFunction> {
+    score_function: S,
+    max_parent_set: Option<usize>,
+}
+
+impl<S: ScoreFunction> HillClimbing<S> {
+    pub fn init(score_function: S, max_parent_set: Option<usize>) -> HillClimbing<S> {
+        HillClimbing {
+            score_function,
+            max_parent_set,
+        }
+    }
+}
+
+impl<S: ScoreFunction> StructureLearningAlgorithm for HillClimbing<S> {
+    fn fit_transform<T>(&self, net: T, dataset: &tools::Dataset) -> T
+    where
+        T: network::Network,
+    {
+        //Check the coherence between dataset and network
+        if net.get_number_of_nodes() != dataset.get_trajectories()[0].get_events().shape()[1] {
+            panic!("Dataset and Network must have the same number of variables.")
+        }
+
+        //Make the network mutable.
+        let mut net = net;
+        //Check if the max_parent_set constraint is present.
+        let max_parent_set = self.max_parent_set.unwrap_or(net.get_number_of_nodes());
+        //Reset the adj matrix
+        net.initialize_adj_matrix();
+        //Iterate over each node to learn their parent set.
+        for node in net.get_node_indices() {
+            //Initialize an empty parent set.
+            let mut parent_set: BTreeSet<usize> = BTreeSet::new();
+            //Compute the score for the empty parent set
+            let mut current_score = self.score_function.call(&net, node, &parent_set, dataset);
+            //Set the old score to -\infty.
+            let mut old_score = f64::NEG_INFINITY;
+            //Iterate until convergence
+            while current_score > old_score {
+                //Save the current_score.
+                old_score = current_score;
+                //Iterate over each node.
+                for parent in net.get_node_indices() {
+                    //Continue if the parent and the node are the same.
+                    if parent == node {
+                        continue;
+                    }
+                    //Try to remove parent from the parent_set.
+                    let is_removed = parent_set.remove(&parent);
+                    //If parent was not in the parent_set add it.
+                    if !is_removed && parent_set.len() < max_parent_set {
+                        parent_set.insert(parent);
+                    }
+                    //Compute the score with the modified parent_set.
+                    let tmp_score = self.score_function.call(&net, node, &parent_set, dataset);
+                    //If tmp_score is worst than current_score revert the change to the parent set
+                    if tmp_score < current_score {
+                        if is_removed {
+                            parent_set.insert(parent);
+                        } else {
+                            parent_set.remove(&parent);
+                        }
+                    }
+                    //Otherwise save the computed score as current_score
+                    else {
+                        current_score = tmp_score;
+                    }
+                }
+            }
+            //Apply the learned parent_set to the network struct.
+            parent_set.iter().for_each(|p| net.add_edge(*p, node));
+        }
+
+        return net;
+    }
+}

src/structure_learning/score_function.rs

@@ -0,0 +1,136 @@
+use crate::network;
+use crate::parameter_learning;
+use crate::params;
+use crate::tools;
+use ndarray::prelude::*;
+use statrs::function::gamma;
+use std::collections::BTreeSet;
+
+pub trait ScoreFunction {
+    fn call<T>(
+        &self,
+        net: &T,
+        node: usize,
+        parent_set: &BTreeSet<usize>,
+        dataset: &tools::Dataset,
+    ) -> f64
+    where
+        T: network::Network;
+}
+
+pub struct LogLikelihood {
+    alpha: usize,
+    tau: f64,
+}
+
+impl LogLikelihood {
+    pub fn init(alpha: usize, tau: f64) -> LogLikelihood {
+
+        //Tau must be >=0.0
+        if tau < 0.0 {
+            panic!("tau must be >=0.0");
+        }
+        LogLikelihood { alpha, tau }
+    }
+
+    fn compute_score<T>(
+        &self,
+        net: &T,
+        node: usize,
+        parent_set: &BTreeSet<usize>,
+        dataset: &tools::Dataset,
+    ) -> (f64, Array3<usize>)
+    where
+        T: network::Network,
+    {   
+        //Identify the type of node used
+        match &net.get_node(node).params {
+            params::Params::DiscreteStatesContinousTime(_params) => {
+                //Compute the sufficient statistics M (number of transistions) and T (residence
+                //time)
+                let (M, T) =
+                    parameter_learning::sufficient_statistics(net, dataset, node, parent_set);
+
+                //Scale alpha accordingly to the size of the parent set
+                let alpha = self.alpha as f64 / M.shape()[0] as f64;
+                //Scale tau accordingly to the size of the parent set
+                let tau = self.tau / M.shape()[0] as f64;
+                
+                //Compute the log likelihood for q
+                let log_ll_q:f64 = M
+                    .sum_axis(Axis(2))
+                    .iter()
+                    .zip(T.iter())
+                    .map(|(m, t)| {
+                        gamma::ln_gamma(alpha + *m as f64 + 1.0)
+                            + (alpha + 1.0) * f64::ln(tau)
+                            - gamma::ln_gamma(alpha + 1.0)
+                            - (alpha + *m as f64 + 1.0) * f64::ln(tau + t)
+                    })
+                    .sum();
+                
+                //Compute the log likelihood for theta
+                let log_ll_theta: f64 = M.outer_iter()
+                    .map(|x| x.outer_iter()
+                         .map(|y| gamma::ln_gamma(alpha) 
+                              - gamma::ln_gamma(alpha + y.sum() as f64)
+                              + y.iter().map(|z| 
+                                             gamma::ln_gamma(alpha + *z as f64) 
+                                             - gamma::ln_gamma(alpha)).sum::<f64>()).sum::<f64>()).sum();
+                (log_ll_theta + log_ll_q, M)
+            }
+        }
+    }
+
+
+
+}
+
+impl ScoreFunction for LogLikelihood {
+    fn call<T>(
+        &self,
+        net: &T,
+        node: usize,
+        parent_set: &BTreeSet<usize>,
+        dataset: &tools::Dataset,
+    ) -> f64
+    where
+        T: network::Network,
+    {
+        self.compute_score(net, node, parent_set, dataset).0
+    }
+}
+
+pub struct BIC {
+    ll: LogLikelihood
+}
+
+impl BIC {
+    pub fn init(alpha: usize, tau: f64) -> BIC {
+        BIC {
+            ll: LogLikelihood::init(alpha, tau)
+        }
+    }
+}
+
+impl ScoreFunction for BIC {
+    fn call<T>(
+        &self,
+        net: &T,
+        node: usize,
+        parent_set: &BTreeSet<usize>,
+        dataset: &tools::Dataset,
+    ) -> f64
+    where
+        T: network::Network {
+        //Compute the log-likelihood
+        let (ll, M) = self.ll.compute_score(net, node, parent_set, dataset);
+        //Compute the number of parameters
+        let n_parameters = M.shape()[0] * M.shape()[1] * (M.shape()[2] - 1);
+        //TODO: Optimize this
+        //Compute the sample size
+        let sample_size: usize = dataset.get_trajectories().iter().map(|x| x.get_time().len() - 1).sum();
+        //Compute BIC
+        ll - f64::ln(sample_size as f64) / 2.0 * n_parameters as f64
+    }
+}

src/tools.rs

@@ -3,16 +3,54 @@ use crate::node;
 use crate::params;
 use crate::params::ParamsTrait;
 use ndarray::prelude::*;
-use rand_chacha::ChaCha8Rng;
 use rand_chacha::rand_core::SeedableRng;
+use rand_chacha::ChaCha8Rng;
 
 pub struct Trajectory {
-    pub time: Array1<f64>,
-    pub events: Array2<usize>,
+    time: Array1<f64>,
+    events: Array2<usize>,
+}
+
+impl Trajectory {
+    pub fn init(time: Array1<f64>, events: Array2<usize>) -> Trajectory {
+        //Events and time are two part of the same trajectory. For this reason they must have the
+        //same number of sample.
+        if time.shape()[0] != events.shape()[0] {
+            panic!("time.shape[0] must be equal to events.shape[0]");
+        }
+        Trajectory { time, events }
+    }
+    
+    pub fn get_time(&self) -> &Array1<f64> {
+        &self.time
+    }
+
+    pub fn get_events(&self) -> &Array2<usize> {
+        &self.events
+    }
 }
 
 pub struct Dataset {
-    pub trajectories: Vec<Trajectory>,
+    trajectories: Vec<Trajectory>,
+}
+
+impl Dataset {
+    pub fn init(trajectories: Vec<Trajectory>) -> Dataset {
+
+        //All the trajectories in the same dataset must represent the same process. For this reason
+        //each trajectory must represent the same number of variables.
+        if trajectories
+            .iter()
+            .any(|x| trajectories[0].get_events().shape()[1] != x.get_events().shape()[1])
+        {
+            panic!("All the trajectories mus represents the same number of variables");
+        }
+        Dataset { trajectories }
+    }
+
+    pub fn get_trajectories(&self) -> &Vec<Trajectory> {
+        &self.trajectories
+    }
 }
 
 pub fn trajectory_generator<T: network::Network>(
@@ -21,25 +59,38 @@ pub fn trajectory_generator<T: network::Network>(
     t_end: f64,
     seed: Option<u64>,
 ) -> Dataset {
-    let mut dataset = Dataset {
-        trajectories: Vec::new(),
-    };
     
-    let seed = seed.unwrap_or_else(rand::random);
+    //Tmp growing vector containing generated trajectories.
+    let mut trajectories: Vec<Trajectory> = Vec::new();
     
-    let mut rng = ChaCha8Rng::seed_from_u64(seed);
+    //Random Generator object
+    let mut rng: ChaCha8Rng = match seed {
+        //If a seed is present use it to initialize the random generator.
+        Some(seed) => SeedableRng::seed_from_u64(seed),
+        //Otherwise create a new random generator using the method `from_entropy`
+        None => SeedableRng::from_entropy()
+    };
     
-    let node_idx: Vec<_> = net.get_node_indices().collect();
+    //Each iteration generate one trajectory
     for _ in 0..n_trajectories {
+        //Current time of the sampling process
         let mut t = 0.0;
+        //History of all the moments in which something changed
         let mut time: Vec<f64> = Vec::new();
-        let mut events: Vec<Array1<usize>> = Vec::new();
-        let mut current_state: Vec<params::StateType> = node_idx
-            .iter()
-            .map(|x| net.get_node(*x).params.get_random_state_uniform(&mut rng))
+        //Configuration of the process variables at time t initialized with an uniform
+        //distribution.
+        let mut current_state: Vec<params::StateType> = net.get_node_indices()
+            .map(|x| net.get_node(x).params.get_random_state_uniform(&mut rng))
             .collect();
+        //History of all the configurations of the process variables. 
+        let mut events: Vec<Array1<usize>> = Vec::new();
+        //Vector containing to time to the next transition for each variable.
         let mut next_transitions: Vec<Option<f64>> =
-            (0..node_idx.len()).map(|_| Option::None).collect();
+            net.get_node_indices().map(|_| Option::None).collect();
+        
+        //Add the starting time for the trajectory.
+        time.push(t.clone());
+        //Add the starting configuration of the trajectory.
         events.push(
             current_state
                 .iter()
@@ -48,8 +99,9 @@ pub fn trajectory_generator<T: network::Network>(
                 })
                 .collect(),
         );
-        time.push(t.clone());
+        //Generate new samples until ending time is reached.
         while t < t_end {
+            //Generate the next transition time for each uninitialized variable.
             for (idx, val) in next_transitions.iter_mut().enumerate() {
                 if let None = val {
                     *val = Some(
@@ -66,18 +118,23 @@ pub fn trajectory_generator<T: network::Network>(
                 }
             }
             
+            //Get the variable with the smallest transition time.
             let next_node_transition = next_transitions
                 .iter()
                 .enumerate()
                 .min_by(|x, y| x.1.unwrap().partial_cmp(&y.1.unwrap()).unwrap())
                 .unwrap()
                 .0;
+            //Check if the next transition take place after the ending time.
             if next_transitions[next_node_transition].unwrap() > t_end {
                 break;
             }
+            //Get the time in which the next transition occurs.
             t = next_transitions[next_node_transition].unwrap().clone();
+            //Add the transition time to next
             time.push(t.clone());
             
+            //Compute the new state of the transitioning variable.
             current_state[next_node_transition] = net
                 .get_node(next_node_transition)
                 .params
@@ -90,6 +147,7 @@ pub fn trajectory_generator<T: network::Network>(
                 )
                 .unwrap();
             
+            //Add the new state to events
             events.push(Array::from_vec(
                 current_state
                     .iter()
@@ -98,13 +156,16 @@ pub fn trajectory_generator<T: network::Network>(
                     })
                     .collect(),
             ));
+            //Reset the next transition time for the transitioning node.
             next_transitions[next_node_transition] = None;
 
+            //Reset the next transition time for each child of the transitioning node.
             for child in net.get_children_set(next_node_transition) {
                 next_transitions[child] = None
             }
         }
         
+        //Add current_state as last state.
         events.push(
             current_state
                 .iter()
@@ -113,16 +174,19 @@ pub fn trajectory_generator<T: network::Network>(
                 })
                 .collect(),
         );
+        //Add t_end as last time.
         time.push(t_end.clone());
         
-        dataset.trajectories.push(Trajectory {
-            time: Array::from_vec(time),
-            events: Array2::from_shape_vec(
+        //Add the sampled trajectory to trajectories.
+        trajectories.push(Trajectory::init(
+            Array::from_vec(time),
+            Array2::from_shape_vec(
                 (events.len(), current_state.len()),
                 events.iter().flatten().cloned().collect(),
             )
             .unwrap(),
-        });
+        ));
     }
-    dataset
+    //Return a dataset object with the sampled trajectories.
+    Dataset::init(trajectories)
 }

tests/structure_learning.rs

@@ -0,0 +1,260 @@
+
+mod utils;
+use utils::*;
+
+use rustyCTBN::ctbn::*;
+use rustyCTBN::network::Network;
+use rustyCTBN::tools::*;
+use rustyCTBN::structure_learning::score_function::*;
+use rustyCTBN::structure_learning::score_based_algorithm::*;
+use rustyCTBN::structure_learning::StructureLearningAlgorithm;
+use ndarray::{arr1, arr2, arr3};
+use std::collections::BTreeSet;
+use rustyCTBN::params;
+
+
+#[macro_use]
+extern crate approx;
+
+#[test]
+fn simple_score_test() {
+    let mut net = CtbnNetwork::init();
+    let n1 = net
+        .add_node(generate_discrete_time_continous_node(String::from("n1"),2))
+        .unwrap();
+
+    let trj = Trajectory::init(
+        arr1(&[0.0,0.1,0.3]),
+        arr2(&[[0],[1],[1]]));
+
+    let dataset = Dataset::init(vec![trj]);
+
+    let ll = LogLikelihood::init(1, 1.0);
+
+    assert_abs_diff_eq!(0.04257, ll.call(&net, n1, &BTreeSet::new(), &dataset), epsilon=1e-3);
+
+}
+
+
+#[test]
+fn simple_bic() {
+    let mut net = CtbnNetwork::init();
+    let n1 = net
+        .add_node(generate_discrete_time_continous_node(String::from("n1"),2))
+        .unwrap();
+
+    let trj = Trajectory::init(
+        arr1(&[0.0,0.1,0.3]),
+        arr2(&[[0],[1],[1]]));
+
+    let dataset = Dataset::init(vec![trj]);
+    let bic = BIC::init(1, 1.0);
+
+    assert_abs_diff_eq!(-0.65058, bic.call(&net, n1, &BTreeSet::new(), &dataset), epsilon=1e-3);
+
+}
+
+
+
+fn check_compatibility_between_dataset_and_network<T: StructureLearningAlgorithm> (sl: T) {
+    let mut net = CtbnNetwork::init();
+    let n1 = net
+        .add_node(generate_discrete_time_continous_node(String::from("n1"),3))
+        .unwrap();
+    let n2 = net
+        .add_node(generate_discrete_time_continous_node(String::from("n2"),3))
+        .unwrap();
+    net.add_edge(n1, n2);
+
+    match &mut net.get_node_mut(n1).params {
+        params::Params::DiscreteStatesContinousTime(param) => {
+            assert_eq!(Ok(()), param.set_cim(arr3(&[[[-3.0, 2.0, 1.0], 
+                                  [1.5, -2.0, 0.5],
+                                  [0.4, 0.6, -1.0]]])));
+        }
+    }
+
+    match &mut net.get_node_mut(n2).params {
+        params::Params::DiscreteStatesContinousTime(param) => {
+            assert_eq!(Ok(()), param.set_cim(arr3(&[
+                [[-1.0, 0.5, 0.5], [3.0, -4.0, 1.0], [0.9, 0.1, -1.0]],
+                [[-6.0, 2.0, 4.0], [1.5, -2.0, 0.5], [3.0, 1.0, -4.0]],
+                [[-1.0, 0.1, 0.9], [2.0, -2.5, 0.5], [0.9, 0.1, -1.0]],
+            ])));
+        }
+    }
+
+    let data = trajectory_generator(&net, 100, 20.0, Some(6347747169756259),);
+
+    let mut net = CtbnNetwork::init();
+    let _n1 = net
+        .add_node(generate_discrete_time_continous_node(String::from("n1"),3))
+        .unwrap();
+    let net = sl.fit_transform(net, &data);
+}
+
+
+#[test]
+#[should_panic]
+pub fn check_compatibility_between_dataset_and_network_hill_climbing() {
+    let ll = LogLikelihood::init(1, 1.0);
+    let hl = HillClimbing::init(ll, None);
+    check_compatibility_between_dataset_and_network(hl);
+}
+
+fn learn_ternary_net_2_nodes<T: StructureLearningAlgorithm> (sl: T) {
+    let mut net = CtbnNetwork::init();
+    let n1 = net
+        .add_node(generate_discrete_time_continous_node(String::from("n1"),3))
+        .unwrap();
+    let n2 = net
+        .add_node(generate_discrete_time_continous_node(String::from("n2"),3))
+        .unwrap();
+    net.add_edge(n1, n2);
+
+    match &mut net.get_node_mut(n1).params {
+        params::Params::DiscreteStatesContinousTime(param) => {
+            assert_eq!(Ok(()), param.set_cim(arr3(&[[[-3.0, 2.0, 1.0], 
+                                  [1.5, -2.0, 0.5],
+                                  [0.4, 0.6, -1.0]]])));
+        }
+    }
+
+    match &mut net.get_node_mut(n2).params {
+        params::Params::DiscreteStatesContinousTime(param) => {
+            assert_eq!(Ok(()), param.set_cim(arr3(&[
+                [[-1.0, 0.5, 0.5], [3.0, -4.0, 1.0], [0.9, 0.1, -1.0]],
+                [[-6.0, 2.0, 4.0], [1.5, -2.0, 0.5], [3.0, 1.0, -4.0]],
+                [[-1.0, 0.1, 0.9], [2.0, -2.5, 0.5], [0.9, 0.1, -1.0]],
+            ])));
+        }
+    }
+
+    let data = trajectory_generator(&net, 100, 20.0, Some(6347747169756259),);
+
+    let net = sl.fit_transform(net, &data);
+    assert_eq!(BTreeSet::from_iter(vec![n1]), net.get_parent_set(n2));
+    assert_eq!(BTreeSet::new(), net.get_parent_set(n1));
+}
+
+
+#[test]
+pub fn learn_ternary_net_2_nodes_hill_climbing_ll() {
+    let ll = LogLikelihood::init(1, 1.0);
+    let hl = HillClimbing::init(ll, None);
+    learn_ternary_net_2_nodes(hl);
+}
+
+#[test]
+pub fn learn_ternary_net_2_nodes_hill_climbing_bic() {
+    let bic = BIC::init(1, 1.0);
+    let hl = HillClimbing::init(bic, None);
+    learn_ternary_net_2_nodes(hl);
+}
+
+
+fn get_mixed_discrete_net_3_nodes_with_data() -> (CtbnNetwork, Dataset) {
+    let mut net = CtbnNetwork::init();
+    let n1 = net
+        .add_node(generate_discrete_time_continous_node(String::from("n1"),3))
+        .unwrap();
+    let n2 = net
+        .add_node(generate_discrete_time_continous_node(String::from("n2"),3))
+        .unwrap();
+
+    let n3 = net
+        .add_node(generate_discrete_time_continous_node(String::from("n3"),4))
+        .unwrap();
+    net.add_edge(n1, n2);
+    net.add_edge(n1, n3);
+    net.add_edge(n2, n3);
+
+    match &mut net.get_node_mut(n1).params {
+        params::Params::DiscreteStatesContinousTime(param) => {
+            assert_eq!(Ok(()), param.set_cim(arr3(&[[[-3.0, 2.0, 1.0], 
+                                  [1.5, -2.0, 0.5],
+                                  [0.4, 0.6, -1.0]]])));
+        }
+    }
+
+    match &mut net.get_node_mut(n2).params {
+        params::Params::DiscreteStatesContinousTime(param) => {
+            assert_eq!(Ok(()), param.set_cim(arr3(&[
+                [[-1.0, 0.5, 0.5], [3.0, -4.0, 1.0], [0.9, 0.1, -1.0]],
+                [[-6.0, 2.0, 4.0], [1.5, -2.0, 0.5], [3.0, 1.0, -4.0]],
+                [[-1.0, 0.1, 0.9], [2.0, -2.5, 0.5], [0.9, 0.1, -1.0]],
+            ])));
+        }
+    }
+
+
+    match &mut net.get_node_mut(n3).params {
+        params::Params::DiscreteStatesContinousTime(param) => {
+            assert_eq!(Ok(()), param.set_cim(arr3(&[
+                [[-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]],
+                [[-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]],
+                [[-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]],
+
+                [[-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]],
+                [[-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]],
+                [[-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]],
+
+                [[-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]],
+                [[-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]],
+                [[-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]],
+            ])));
+        }
+    }
+
+
+    let data = trajectory_generator(&net, 300, 30.0, Some(6347747169756259),);
+    return (net, data);
+}
+
+fn learn_mixed_discrete_net_3_nodes<T: StructureLearningAlgorithm> (sl: T) {
+    let (net, data) = get_mixed_discrete_net_3_nodes_with_data();
+    let net = sl.fit_transform(net, &data);
+    assert_eq!(BTreeSet::new(), net.get_parent_set(0));
+    assert_eq!(BTreeSet::from_iter(vec![0]), net.get_parent_set(1));
+    assert_eq!(BTreeSet::from_iter(vec![0, 1]), net.get_parent_set(2));
+}
+
+
+#[test]
+pub fn learn_mixed_discrete_net_3_nodes_hill_climbing_ll() {
+    let ll = LogLikelihood::init(1, 1.0);
+    let hl = HillClimbing::init(ll, None);
+    learn_mixed_discrete_net_3_nodes(hl);
+}
+
+#[test]
+pub fn learn_mixed_discrete_net_3_nodes_hill_climbing_bic() {
+    let bic = BIC::init(1, 1.0);
+    let hl = HillClimbing::init(bic, None);
+    learn_mixed_discrete_net_3_nodes(hl);
+}
+
+
+
+fn learn_mixed_discrete_net_3_nodes_1_parent_constraint<T: StructureLearningAlgorithm> (sl: T) {
+    let (net, data) = get_mixed_discrete_net_3_nodes_with_data();
+    let net = sl.fit_transform(net, &data);
+    assert_eq!(BTreeSet::new(), net.get_parent_set(0));
+    assert_eq!(BTreeSet::from_iter(vec![0]), net.get_parent_set(1));
+    assert_eq!(BTreeSet::from_iter(vec![0]), net.get_parent_set(2));
+}
+
+
+#[test]
+pub fn learn_mixed_discrete_net_3_nodes_hill_climbing_ll_1_parent_constraint() {
+    let ll = LogLikelihood::init(1, 1.0);
+    let hl = HillClimbing::init(ll, Some(1));
+    learn_mixed_discrete_net_3_nodes_1_parent_constraint(hl);
+}
+
+#[test]
+pub fn learn_mixed_discrete_net_3_nodes_hill_climbing_bic_1_parent_constraint() {
+    let bic = BIC::init(1, 1.0);
+    let hl = HillClimbing::init(bic, Some(1));
+    learn_mixed_discrete_net_3_nodes_1_parent_constraint(hl);
+}

tests/tools.rs

@@ -5,7 +5,7 @@ use rustyCTBN::ctbn::*;
 use rustyCTBN::node;
 use rustyCTBN::params;
 use std::collections::BTreeSet;
-use ndarray::arr3;
+use ndarray::{arr1, arr2, arr3};
 
 
 
@@ -38,8 +38,30 @@ fn run_sampling() {
 
     let data = trajectory_generator(&net, 4, 1.0, Some(6347747169756259),);
 
-    assert_eq!(4, data.trajectories.len());
-    assert_relative_eq!(1.0, data.trajectories[0].time[data.trajectories[0].time.len()-1]);
+    assert_eq!(4, data.get_trajectories().len());
+    assert_relative_eq!(1.0, data.get_trajectories()[0].get_time()[data.get_trajectories()[0].get_time().len()-1]);
 }
 
 
+#[test]
+#[should_panic]
+    fn trajectory_wrong_shape() {
+    let time = arr1(&[0.0, 0.2]);
+    let events = arr2(&[[0,3]]);
+    Trajectory::init(time, events);
+}
+
+
+#[test]
+#[should_panic]
+fn dataset_wrong_shape() {
+    let time = arr1(&[0.0, 0.2]);
+    let events = arr2(&[[0,3], [1,2]]);
+    let t1 = Trajectory::init(time, events);
+
+
+    let time = arr1(&[0.0, 0.2]);
+    let events = arr2(&[[0,3,3], [1,2,3]]);
+    let t2 = Trajectory::init(time, events);
+    Dataset::init(vec![t1, t2]);
+}