Removed `node.rs`

src/ctbn.rs

@@ -1,6 +1,5 @@
 use ndarray::prelude::*;
-use crate::node;
+use crate::params::{StateType, Params, ParamsTrait};
-use crate::params::{StateType, ParamsTrait};
 use crate::network;
 use std::collections::BTreeSet;
 
@@ -19,7 +18,6 @@ use std::collections::BTreeSet;
 ///    
 /// use std::collections::BTreeSet;
 /// use reCTBN::network::Network;
-/// use reCTBN::node;
 /// use reCTBN::params;
 /// use reCTBN::ctbn::*;
 ///
@@ -29,16 +27,16 @@ use std::collections::BTreeSet;
 /// domain.insert(String::from("B"));
 ///
 /// //Create the parameters for a discrete node using the domain
-/// let param = params::DiscreteStatesContinousTimeParams::new(domain); 
+/// let param = params::DiscreteStatesContinousTimeParams::new("X1".to_string(), domain); 
 ///
 /// //Create the node using the parameters
-/// let X1 = node::Node::new(params::Params::DiscreteStatesContinousTime(param),String::from("X1"));
+/// let X1 = params::Params::DiscreteStatesContinousTime(param);
 ///
 /// let mut domain = BTreeSet::new();
 /// domain.insert(String::from("A"));
 /// domain.insert(String::from("B"));
-/// let param = params::DiscreteStatesContinousTimeParams::new(domain);
+/// let param = params::DiscreteStatesContinousTimeParams::new("X2".to_string(), domain);
-/// let X2 = node::Node::new(params::Params::DiscreteStatesContinousTime(param), String::from("X2"));
+/// let X2 = params::Params::DiscreteStatesContinousTime(param);
 /// 
 /// //Initialize a ctbn
 /// let mut net = CtbnNetwork::new();
@@ -56,7 +54,7 @@ use std::collections::BTreeSet;
 /// ```
 pub struct CtbnNetwork {
     adj_matrix: Option<Array2<u16>>,
-    nodes: Vec<node::Node>
+    nodes: Vec<Params>
 }
 
 
@@ -75,8 +73,8 @@ impl network::Network for CtbnNetwork {
 
     }
 
-    fn add_node(&mut self, mut n:  node::Node) -> Result<usize, network::NetworkError> {
+    fn add_node(&mut self, mut n:  Params) -> Result<usize, network::NetworkError> {
-        n.params.reset_params();
+        n.reset_params();
         self.adj_matrix = Option::None;
         self.nodes.push(n);
         Ok(self.nodes.len() -1)        
@@ -89,7 +87,7 @@ impl network::Network for CtbnNetwork {
 
         if let Some(network) = &mut self.adj_matrix {
             network[[parent, child]] = 1;
-            self.nodes[child].params.reset_params();
+            self.nodes[child].reset_params();
         }
     }
 
@@ -101,12 +99,12 @@ impl network::Network for CtbnNetwork {
         self.nodes.len()
     }
 
-    fn get_node(&self, node_idx: usize) -> &node::Node{
+    fn get_node(&self, node_idx: usize) -> &Params{
         &self.nodes[node_idx]
     }
 
 
-    fn get_node_mut(&mut self, node_idx: usize) -> &mut node::Node{
+    fn get_node_mut(&mut self, node_idx: usize) -> &mut Params{
         &mut self.nodes[node_idx]
     }
 
@@ -114,8 +112,8 @@ impl network::Network for CtbnNetwork {
     fn get_param_index_network(&self, node: usize, current_state: &Vec<StateType>) -> usize{
         self.adj_matrix.as_ref().unwrap().column(node).iter().enumerate().fold((0, 1), |mut acc, x| {
             if x.1 > &0 {
-                acc.0 += self.nodes[x.0].params.state_to_index(&current_state[x.0]) * acc.1;
+                acc.0 += self.nodes[x.0].state_to_index(&current_state[x.0]) * acc.1;
-                acc.1 *= self.nodes[x.0].params.get_reserved_space_as_parent();
+                acc.1 *= self.nodes[x.0].get_reserved_space_as_parent();
             }
             acc
         }).0
@@ -124,8 +122,8 @@ impl network::Network for CtbnNetwork {
 
     fn get_param_index_from_custom_parent_set(&self, current_state: &Vec<StateType>, parent_set: &BTreeSet<usize>) -> usize {
         parent_set.iter().fold((0, 1), |mut acc, x| {
-            acc.0 += self.nodes[*x].params.state_to_index(&current_state[*x]) * acc.1;
+            acc.0 += self.nodes[*x].state_to_index(&current_state[*x]) * acc.1;
-            acc.1 *= self.nodes[*x].params.get_reserved_space_as_parent();
+            acc.1 *= self.nodes[*x].get_reserved_space_as_parent();
             acc
         }).0
     }

src/lib.rs

@@ -2,7 +2,6 @@
 #[macro_use]
 extern crate approx;
 
-pub mod node;
 pub mod params;
 pub mod network;
 pub mod ctbn;

src/network.rs

@@ -1,6 +1,5 @@
 use thiserror::Error;
 use crate::params;
-use crate::node;
 use std::collections::BTreeSet;
 
 /// Error types for trait Network
@@ -15,14 +14,14 @@ pub enum NetworkError {
 ///The Network trait define the required methods for a structure used as pgm (such as ctbn).
 pub trait Network {
     fn initialize_adj_matrix(&mut self);
-    fn add_node(&mut self, n:  node::Node) -> Result<usize, NetworkError>;
+    fn add_node(&mut self, n:  params::Params) -> Result<usize, NetworkError>;
     fn add_edge(&mut self, parent: usize, child: usize);
 
     ///Get all the indices of the nodes contained inside the network
     fn get_node_indices(&self) -> std::ops::Range<usize>;
     fn get_number_of_nodes(&self) -> usize;
-    fn get_node(&self, node_idx: usize) -> &node::Node;
+    fn get_node(&self, node_idx: usize) -> &params::Params;
-    fn get_node_mut(&mut self, node_idx: usize) -> &mut node::Node;
+    fn get_node_mut(&mut self, node_idx: usize) -> &mut params::Params;
 
     ///Compute the index that must be used to access the parameters of a node given a specific
     ///configuration of the network. Usually, the only values really used in *current_state* are

src/node.rs

@@ -1,25 +0,0 @@
-use crate::params::*;
-
-
-pub struct Node {
-    pub params:  Params,
-    pub label: String
-}
-
-impl Node {
-    pub fn new(params: Params, label: String) -> Node {
-        Node{
-            params: params,
-            label:label
-        }
-    }
-
-}
-
-impl PartialEq for Node {
-    fn eq(&self, other: &Node) -> bool{
-        self.label == other.label
-    }
-}
-
-

src/parameter_learning.rs

@@ -24,7 +24,6 @@ pub fn sufficient_statistics<T:network::Network>(
     //Get the number of values assumable by the node
     let node_domain = net
         .get_node(node.clone())
-        .params
         .get_reserved_space_as_parent();
 
     //Get the number of values assumable by each parent of the node
@@ -32,7 +31,6 @@ pub fn sufficient_statistics<T:network::Network>(
         .iter()
         .map(|x| {
             net.get_node(x.clone())
-                .params
                 .get_reserved_space_as_parent()
         })
         .collect();

src/params.rs

@@ -49,6 +49,9 @@ pub trait ParamsTrait {
 
     /// Validate parameters against domain
     fn validate_params(&self) -> Result<(), ParamsError>;
+    
+    /// Return a reference to the associated label
+    fn get_label(&self) -> &String;
 }
 
 /// The Params enum is the core element for building different types of nodes. The goal is to
@@ -70,6 +73,7 @@ pub enum Params {
 ///     - **residence_time**: permanence time in each possible states given a specific
 ///     realization of the parent set
 pub struct DiscreteStatesContinousTimeParams {
+    label: String,
     domain: BTreeSet<String>,
     cim: Option<Array3<f64>>,
     transitions: Option<Array3<u64>>,
@@ -77,8 +81,9 @@ pub struct DiscreteStatesContinousTimeParams {
 }
 
 impl DiscreteStatesContinousTimeParams {
-    pub fn new(domain: BTreeSet<String>) -> DiscreteStatesContinousTimeParams {
+    pub fn new(label: String, domain: BTreeSet<String>) -> DiscreteStatesContinousTimeParams {
         DiscreteStatesContinousTimeParams {
+            label,
             domain,
             cim: Option::None,
             transitions: Option::None,
@@ -244,4 +249,9 @@ impl ParamsTrait for DiscreteStatesContinousTimeParams {
 
         return Ok(());
     }
+
+    fn get_label(&self) ->  &String {
+        &self.label
+    }
+
 }

src/structure_learning/score_function.rs

@@ -44,7 +44,7 @@ impl LogLikelihood {
         T: network::Network,
     {   
         //Identify the type of node used
-        match &net.get_node(node).params {
+        match &net.get_node(node){
             params::Params::DiscreteStatesContinousTime(_params) => {
                 //Compute the sufficient statistics M (number of transistions) and T (residence
                 //time)

src/tools.rs

@@ -1,5 +1,4 @@
 use crate::network;
-use crate::node;
 use crate::params;
 use crate::params::ParamsTrait;
 use ndarray::prelude::*;
@@ -80,7 +79,7 @@ pub fn trajectory_generator<T: network::Network>(
         //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))
+            .map(|x| net.get_node(x).get_random_state_uniform(&mut rng))
             .collect();
         //History of all the configurations of the process variables. 
         let mut events: Vec<Array1<usize>> = Vec::new();
@@ -106,9 +105,8 @@ pub fn trajectory_generator<T: network::Network>(
                 if let None = val {
                     *val = Some(
                         net.get_node(idx)
-                            .params
                             .get_random_residence_time(
-                                net.get_node(idx).params.state_to_index(&current_state[idx]),
+                                net.get_node(idx).state_to_index(&current_state[idx]),
                                 net.get_param_index_network(idx, &current_state),
                                 &mut rng,
                             )
@@ -137,10 +135,8 @@ pub fn trajectory_generator<T: network::Network>(
             //Compute the new state of the transitioning variable.
             current_state[next_node_transition] = net
                 .get_node(next_node_transition)
-                .params
                 .get_random_state(
                     net.get_node(next_node_transition)
-                        .params
                         .state_to_index(&current_state[next_node_transition]),
                     net.get_param_index_network(next_node_transition, &current_state),
                     &mut rng,

tests/ctbn.rs

@@ -1,10 +1,9 @@
 mod utils;
-use utils::generate_discrete_time_continous_node;
+use reCTBN::ctbn::*;
 use reCTBN::network::Network;
-use reCTBN::node;
+use reCTBN::params::{self, ParamsTrait};
-use reCTBN::params;
 use std::collections::BTreeSet;
-use reCTBN::ctbn::*;
+use utils::generate_discrete_time_continous_node;
 
 #[test]
 fn define_simpe_ctbn() {
@@ -15,15 +14,21 @@ fn define_simpe_ctbn() {
 #[test]
 fn add_node_to_ctbn() {
     let mut net = CtbnNetwork::new();
-    let n1 = net.add_node(generate_discrete_time_continous_node(String::from("n1"),2)).unwrap();
+    let n1 = net
-    assert_eq!(String::from("n1"), net.get_node(n1).label);
+        .add_node(generate_discrete_time_continous_node(String::from("n1"), 2))
+        .unwrap();
+    assert_eq!(&String::from("n1"), net.get_node(n1).get_label());
 }
 
 #[test]
 fn add_edge_to_ctbn() {
     let mut net = CtbnNetwork::new();
-    let n1 = net.add_node(generate_discrete_time_continous_node(String::from("n1"),2)).unwrap();
+    let n1 = net
-    let n2 = net.add_node(generate_discrete_time_continous_node(String::from("n2"),2)).unwrap();
+        .add_node(generate_discrete_time_continous_node(String::from("n1"), 2))
+        .unwrap();
+    let n2 = net
+        .add_node(generate_discrete_time_continous_node(String::from("n2"), 2))
+        .unwrap();
     net.add_edge(n1, n2);
     let cs = net.get_children_set(n1);
     assert_eq!(&n2, cs.iter().next().unwrap());
@@ -32,8 +37,12 @@ fn add_edge_to_ctbn() {
 #[test]
 fn children_and_parents() {
     let mut net = CtbnNetwork::new();
-    let n1 = net.add_node(generate_discrete_time_continous_node(String::from("n1"),2)).unwrap();
+    let n1 = net
-    let n2 = net.add_node(generate_discrete_time_continous_node(String::from("n2"),2)).unwrap();
+        .add_node(generate_discrete_time_continous_node(String::from("n1"), 2))
+        .unwrap();
+    let n2 = net
+        .add_node(generate_discrete_time_continous_node(String::from("n2"), 2))
+        .unwrap();
     net.add_edge(n1, n2);
     let cs = net.get_children_set(n1);
     assert_eq!(&n2, cs.iter().next().unwrap());
@@ -41,59 +50,81 @@ fn children_and_parents() {
     assert_eq!(&n1, ps.iter().next().unwrap());
 }
 
-
 #[test]
 fn compute_index_ctbn() {
     let mut net = CtbnNetwork::new();
-    let n1 = net.add_node(generate_discrete_time_continous_node(String::from("n1"),2)).unwrap();
+    let n1 = net
-    let n2 = net.add_node(generate_discrete_time_continous_node(String::from("n2"),2)).unwrap();
+        .add_node(generate_discrete_time_continous_node(String::from("n1"), 2))
-    let n3 = net.add_node(generate_discrete_time_continous_node(String::from("n3"),2)).unwrap();
+        .unwrap();
+    let n2 = net
+        .add_node(generate_discrete_time_continous_node(String::from("n2"), 2))
+        .unwrap();
+    let n3 = net
+        .add_node(generate_discrete_time_continous_node(String::from("n3"), 2))
+        .unwrap();
     net.add_edge(n1, n2);
     net.add_edge(n3, n2);
-    let idx = net.get_param_index_network(n2, &vec![
+    let idx = net.get_param_index_network(
+        n2,
+        &vec![
             params::StateType::Discrete(1),
             params::StateType::Discrete(1),
-                                          params::StateType::Discrete(1)]);
+            params::StateType::Discrete(1),
+        ],
+    );
     assert_eq!(3, idx);
 
-
+    let idx = net.get_param_index_network(
-    let idx = net.get_param_index_network(n2, &vec![
+        n2,
+        &vec![
             params::StateType::Discrete(0),
             params::StateType::Discrete(1),
-                                          params::StateType::Discrete(1)]);
+            params::StateType::Discrete(1),
+        ],
+    );
     assert_eq!(2, idx);
 
-
+    let idx = net.get_param_index_network(
-    let idx = net.get_param_index_network(n2, &vec![
+        n2,
+        &vec![
             params::StateType::Discrete(1),
             params::StateType::Discrete(1),
-                                          params::StateType::Discrete(0)]);
+            params::StateType::Discrete(0),
+        ],
+    );
     assert_eq!(1, idx);
-
 }
 
-
-
 #[test]
 fn compute_index_from_custom_parent_set() {
     let mut net = CtbnNetwork::new();
-    let _n1 = net.add_node(generate_discrete_time_continous_node(String::from("n1"),2)).unwrap();
+    let _n1 = net
-    let _n2 = net.add_node(generate_discrete_time_continous_node(String::from("n2"),2)).unwrap();
+        .add_node(generate_discrete_time_continous_node(String::from("n1"), 2))
-    let _n3 = net.add_node(generate_discrete_time_continous_node(String::from("n3"),2)).unwrap();
+        .unwrap();
-
+    let _n2 = net
-
+        .add_node(generate_discrete_time_continous_node(String::from("n2"), 2))
-    let idx = net.get_param_index_from_custom_parent_set(&vec![
+        .unwrap();
+    let _n3 = net
+        .add_node(generate_discrete_time_continous_node(String::from("n3"), 2))
+        .unwrap();
+
+    let idx = net.get_param_index_from_custom_parent_set(
+        &vec![
             params::StateType::Discrete(0),
             params::StateType::Discrete(0),
-                                          params::StateType::Discrete(1)],
+            params::StateType::Discrete(1),
-                                          &BTreeSet::from([1]));
+        ],
+        &BTreeSet::from([1]),
+    );
     assert_eq!(0, idx);
 
-
+    let idx = net.get_param_index_from_custom_parent_set(
-    let idx = net.get_param_index_from_custom_parent_set(&vec![
+        &vec![
             params::StateType::Discrete(0),
             params::StateType::Discrete(0),
-                                          params::StateType::Discrete(1)],
+            params::StateType::Discrete(1),
-                                          &BTreeSet::from([1,2]));
+        ],
+        &BTreeSet::from([1, 2]),
+    );
     assert_eq!(2, idx);
 }

tests/parameter_learning.rs

@@ -1,20 +1,16 @@
 mod utils;
 use utils::*;
 
-use reCTBN::parameter_learning::*;
+use ndarray::arr3;
 use reCTBN::ctbn::*;
 use reCTBN::network::Network;
-use reCTBN::node;
+use reCTBN::parameter_learning::*;
-use reCTBN::params;
+use reCTBN::{params, tools::*};
-use reCTBN::tools::*;
-use ndarray::arr3;
 use std::collections::BTreeSet;
 
-
 #[macro_use]
 extern crate approx;
 
-
 fn learn_binary_cim<T: ParameterLearning>(pl: T) {
     let mut net = CtbnNetwork::new();
     let n1 = net
@@ -25,29 +21,32 @@ fn learn_binary_cim<T: ParameterLearning> (pl: T) {
         .unwrap();
     net.add_edge(n1, n2);
 
-    match &mut net.get_node_mut(n1).params {
+    match &mut net.get_node_mut(n1) {
         params::Params::DiscreteStatesContinousTime(param) => {
             assert_eq!(Ok(()), param.set_cim(arr3(&[[[-3.0, 3.0], [2.0, -2.0]]])));
         }
     }
 
-    match &mut net.get_node_mut(n2).params {
+    match &mut net.get_node_mut(n2) {
         params::Params::DiscreteStatesContinousTime(param) => {
-            assert_eq!(Ok(()), param.set_cim(arr3(&[
+            assert_eq!(
+                Ok(()),
+                param.set_cim(arr3(&[
                     [[-1.0, 1.0], [4.0, -4.0]],
                     [[-6.0, 6.0], [2.0, -2.0]],
-            ])));
+                ]))
+            );
         }
     }
 
-    let data = trajectory_generator(&net, 100, 100.0, Some(6347747169756259),);
+    let data = trajectory_generator(&net, 100, 100.0, Some(6347747169756259));
     let (CIM, M, T) = pl.fit(&net, &data, 1, None);
     print!("CIM: {:?}\nM: {:?}\nT: {:?}\n", CIM, M, T);
     assert_eq!(CIM.shape(), [2, 2, 2]);
-    assert!(CIM.abs_diff_eq(&arr3(&[
+    assert!(CIM.abs_diff_eq(
-                [[-1.0, 1.0], [4.0, -4.0]],
+        &arr3(&[[[-1.0, 1.0], [4.0, -4.0]], [[-6.0, 6.0], [2.0, -2.0]],]),
-                [[-6.0, 6.0], [2.0, -2.0]],
+        0.1
-            ]), 0.1));
+    ));
 }
 
 #[test]
@@ -56,12 +55,9 @@ fn learn_binary_cim_MLE() {
     learn_binary_cim(mle);
 }
 
-
 #[test]
 fn learn_binary_cim_BA() {
-    let ba = BayesianApproach{
+    let ba = BayesianApproach { alpha: 1, tau: 1.0 };
-        alpha: 1,
-        tau: 1.0};
     learn_binary_cim(ba);
 }
 
@@ -75,48 +71,55 @@ fn learn_ternary_cim<T: ParameterLearning> (pl: T) {
         .unwrap();
     net.add_edge(n1, n2);
 
-    match &mut net.get_node_mut(n1).params {
+    match &mut net.get_node_mut(n1) {
         params::Params::DiscreteStatesContinousTime(param) => {
-            assert_eq!(Ok(()), param.set_cim(arr3(&[[[-3.0, 2.0, 1.0], 
+            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]]])));
+                    [0.4, 0.6, -1.0]
+                ]]))
+            );
         }
     }
 
-    match &mut net.get_node_mut(n2).params {
+    match &mut net.get_node_mut(n2) {
         params::Params::DiscreteStatesContinousTime(param) => {
-            assert_eq!(Ok(()), param.set_cim(arr3(&[
+            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, 200.0, Some(6347747169756259),);
+    let data = trajectory_generator(&net, 100, 200.0, Some(6347747169756259));
     let (CIM, M, T) = pl.fit(&net, &data, 1, None);
     print!("CIM: {:?}\nM: {:?}\nT: {:?}\n", CIM, M, T);
     assert_eq!(CIM.shape(), [3, 3, 3]);
-    assert!(CIM.abs_diff_eq(&arr3(&[
+    assert!(CIM.abs_diff_eq(
+        &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]],
-            ]), 0.1));
+        ]),
+        0.1
+    ));
 }
 
-
 #[test]
 fn learn_ternary_cim_MLE() {
     let mle = MLE {};
     learn_ternary_cim(mle);
 }
 
-
 #[test]
 fn learn_ternary_cim_BA() {
-    let ba = BayesianApproach{
+    let ba = BayesianApproach { alpha: 1, tau: 1.0 };
-        alpha: 1,
-        tau: 1.0};
     learn_ternary_cim(ba);
 }
 
@@ -130,50 +133,54 @@ fn learn_ternary_cim_no_parents<T: ParameterLearning> (pl: T) {
         .unwrap();
     net.add_edge(n1, n2);
 
-    match &mut net.get_node_mut(n1).params {
+    match &mut net.get_node_mut(n1) {
         params::Params::DiscreteStatesContinousTime(param) => {
-            assert_eq!(Ok(()), param.set_cim(arr3(&[[[-3.0, 2.0, 1.0], 
+            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]]])));
+                    [0.4, 0.6, -1.0]
+                ]]))
+            );
         }
     }
 
-    match &mut net.get_node_mut(n2).params {
+    match &mut net.get_node_mut(n2) {
         params::Params::DiscreteStatesContinousTime(param) => {
-            assert_eq!(Ok(()), param.set_cim(arr3(&[
+            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, 200.0, Some(6347747169756259),);
+    let data = trajectory_generator(&net, 100, 200.0, Some(6347747169756259));
     let (CIM, M, T) = pl.fit(&net, &data, 0, None);
     print!("CIM: {:?}\nM: {:?}\nT: {:?}\n", CIM, M, T);
     assert_eq!(CIM.shape(), [1, 3, 3]);
-    assert!(CIM.abs_diff_eq(&arr3(&[[[-3.0, 2.0, 1.0], 
+    assert!(CIM.abs_diff_eq(
-                                  [1.5, -2.0, 0.5],
+        &arr3(&[[[-3.0, 2.0, 1.0], [1.5, -2.0, 0.5], [0.4, 0.6, -1.0]]]),
-                                  [0.4, 0.6, -1.0]]]), 0.1));
+        0.1
+    ));
 }
 
-
 #[test]
 fn learn_ternary_cim_no_parents_MLE() {
     let mle = MLE {};
     learn_ternary_cim_no_parents(mle);
 }
 
-
 #[test]
 fn learn_ternary_cim_no_parents_BA() {
-    let ba = BayesianApproach{
+    let ba = BayesianApproach { alpha: 1, tau: 1.0 };
-        alpha: 1,
-        tau: 1.0};
     learn_ternary_cim_no_parents(ba);
 }
 
-
 fn learn_mixed_discrete_cim<T: ParameterLearning>(pl: T) {
     let mut net = CtbnNetwork::new();
     let n1 = net
@@ -190,61 +197,159 @@ fn learn_mixed_discrete_cim<T: ParameterLearning> (pl: T) {
     net.add_edge(n1, n3);
     net.add_edge(n2, n3);
 
-    match &mut net.get_node_mut(n1).params {
+    match &mut net.get_node_mut(n1) {
         params::Params::DiscreteStatesContinousTime(param) => {
-            assert_eq!(Ok(()), param.set_cim(arr3(&[[[-3.0, 2.0, 1.0], 
+            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]]])));
+                    [0.4, 0.6, -1.0]
+                ]]))
+            );
         }
     }
 
-    match &mut net.get_node_mut(n2).params {
+    match &mut net.get_node_mut(n2) {
         params::Params::DiscreteStatesContinousTime(param) => {
-            assert_eq!(Ok(()), param.set_cim(arr3(&[
+            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) {
-    match &mut net.get_node_mut(n3).params {
         params::Params::DiscreteStatesContinousTime(param) => {
-            assert_eq!(Ok(()), param.set_cim(arr3(&[
+            assert_eq!(
-                [[-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]],
+                Ok(()),
-                [[-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]],
+                param.set_cim(arr3(&[
-                [[-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]],
+                    [
-
+                        [-1.0, 0.5, 0.3, 0.2],
-                [[-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]],
+                        [0.5, -4.0, 2.5, 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]],
+                        [2.5, 0.5, -4.0, 1.0],
-                [[-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]],
+                        [0.7, 0.2, 0.1, -1.0]
-
+                    ],
-                [[-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]],
+                        [-6.0, 2.0, 3.0, 1.0],
-                [[-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]],
+                        [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, 300.0, Some(6347747169756259));
-    let data = trajectory_generator(&net, 300, 300.0, Some(6347747169756259),);
     let (CIM, M, T) = pl.fit(&net, &data, 2, None);
     print!("CIM: {:?}\nM: {:?}\nT: {:?}\n", CIM, M, T);
     assert_eq!(CIM.shape(), [9, 4, 4]);
-    assert!(CIM.abs_diff_eq(&arr3(&[
+    assert!(CIM.abs_diff_eq(
-                [[-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]],
+        &arr3(&[
-                [[-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]],
+                [-1.0, 0.5, 0.3, 0.2],
-
+                [0.5, -4.0, 2.5, 1.0],
-                [[-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]],
+                [2.5, 0.5, -4.0, 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]],
+                [0.7, 0.2, 0.1, -1.0]
-                [[-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]],
+                [-6.0, 2.0, 3.0, 1.0],
-                [[-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]],
+                [1.5, -3.0, 0.5, 1.0],
-                [[-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]],
+                [2.0, 1.3, -5.0, 1.7],
-            ]), 0.1));
+                [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]
+            ],
+        ]),
+        0.1
+    ));
 }
 
 #[test]
@@ -253,11 +358,8 @@ fn learn_mixed_discrete_cim_MLE() {
     learn_mixed_discrete_cim(mle);
 }
 
-
 #[test]
 fn learn_mixed_discrete_cim_BA() {
-    let ba = BayesianApproach{
+    let ba = BayesianApproach { alpha: 1, tau: 1.0 };
-        alpha: 1,
-        tau: 1.0};
     learn_mixed_discrete_cim(ba);
 }

tests/params.rs

@@ -1,16 +1,15 @@
 use ndarray::prelude::*;
-use reCTBN::params::*;
+use rand_chacha::{rand_core::SeedableRng, ChaCha8Rng};
-use std::collections::BTreeSet;
+use reCTBN::params::{ParamsTrait, *};
-use rand_chacha::ChaCha8Rng;
-use rand_chacha::rand_core::SeedableRng;
 
 mod utils;
 
 #[macro_use]
 extern crate approx;
 
+
 fn create_ternary_discrete_time_continous_param() -> DiscreteStatesContinousTimeParams {
-    let mut params = utils::generate_discrete_time_continous_param(3);
+    let mut params = utils::generate_discrete_time_continous_params("A".to_string(), 3);
 
     let cim = array![[[-3.0, 2.0, 1.0], [1.0, -5.0, 4.0], [2.3, 1.7, -4.0]]];
 
@@ -18,6 +17,12 @@ fn create_ternary_discrete_time_continous_param() -> DiscreteStatesContinousTime
     params
 }
 
+#[test]
+fn test_get_label() {
+    let param = create_ternary_discrete_time_continous_param();
+    assert_eq!(&String::from("A"), param.get_label())
+}
+
 #[test]
 fn test_uniform_generation() {
     let param = create_ternary_discrete_time_continous_param();
@@ -79,15 +84,19 @@ fn test_validate_params_valid_cim() {
 
 #[test]
 fn test_validate_params_valid_cim_with_huge_values() {
-    let mut param = utils::generate_discrete_time_continous_param(3);
+    let mut param = utils::generate_discrete_time_continous_params("A".to_string(), 3);
-    let cim = array![[[-2e10, 1e10, 1e10], [1.5e10, -3e10, 1.5e10], [1e10, 1e10, -2e10]]];
+    let cim = array![[
+        [-2e10, 1e10, 1e10],
+        [1.5e10, -3e10, 1.5e10],
+        [1e10, 1e10, -2e10]
+    ]];
     let result = param.set_cim(cim);
     assert_eq!(Ok(()), result);
 }
 
 #[test]
 fn test_validate_params_cim_not_initialized() {
-    let param = utils::generate_discrete_time_continous_param(3);
+    let param = utils::generate_discrete_time_continous_params("A".to_string(), 3);
     assert_eq!(
         Err(ParamsError::ParametersNotInitialized(String::from(
             "CIM not initialized",
@@ -98,7 +107,7 @@ fn test_validate_params_cim_not_initialized() {
 
 #[test]
 fn test_validate_params_wrong_shape() {
-    let mut param = utils::generate_discrete_time_continous_param(4);
+    let mut param = utils::generate_discrete_time_continous_params("A".to_string(), 4);
     let cim = array![[[-3.0, 2.0, 1.0], [1.0, -5.0, 4.0], [2.3, 1.7, -4.0]]];
     let result = param.set_cim(cim);
     assert_eq!(
@@ -111,7 +120,7 @@ fn test_validate_params_wrong_shape() {
 
 #[test]
 fn test_validate_params_positive_diag() {
-    let mut param = utils::generate_discrete_time_continous_param(3);
+    let mut param = utils::generate_discrete_time_continous_params("A".to_string(), 3);
     let cim = array![[[2.0, -3.0, 1.0], [1.0, -5.0, 4.0], [2.3, 1.7, -4.0]]];
     let result = param.set_cim(cim);
     assert_eq!(
@@ -124,7 +133,7 @@ fn test_validate_params_positive_diag() {
 
 #[test]
 fn test_validate_params_row_not_sum_to_zero() {
-    let mut param = utils::generate_discrete_time_continous_param(3);
+    let mut param = utils::generate_discrete_time_continous_params("A".to_string(), 3);
     let cim = array![[[-3.0, 2.0, 1.0], [1.0, -5.0, 4.0], [2.3, 1.701, -4.0]]];
     let result = param.set_cim(cim);
     assert_eq!(

tests/structure_learning.rs

@@ -1,17 +1,14 @@
-
 mod utils;
 use utils::*;
 
+use ndarray::{arr1, arr2, arr3};
 use reCTBN::ctbn::*;
 use reCTBN::network::Network;
-use reCTBN::tools::*;
+use reCTBN::params;
 use reCTBN::structure_learning::score_function::*;
-use reCTBN::structure_learning::score_based_algorithm::*;
+use reCTBN::structure_learning::{score_based_algorithm::*, StructureLearningAlgorithm};
-use reCTBN::structure_learning::StructureLearningAlgorithm;
+use reCTBN::tools::*;
-use ndarray::{arr1, arr2, arr3};
 use std::collections::BTreeSet;
-use reCTBN::params;
-
 
 #[macro_use]
 extern crate approx;
@@ -23,19 +20,19 @@ fn simple_score_test() {
         .add_node(generate_discrete_time_continous_node(String::from("n1"), 2))
         .unwrap();
 
-    let trj = Trajectory::new(
+    let trj = Trajectory::new(arr1(&[0.0, 0.1, 0.3]), arr2(&[[0], [1], [1]]));
-        arr1(&[0.0,0.1,0.3]),
-        arr2(&[[0],[1],[1]]));
 
     let dataset = Dataset::new(vec![trj]);
 
     let ll = LogLikelihood::new(1, 1.0);
 
-    assert_abs_diff_eq!(0.04257, ll.call(&net, n1, &BTreeSet::new(), &dataset), epsilon=1e-3);
+    assert_abs_diff_eq!(
-
+        0.04257,
+        ll.call(&net, n1, &BTreeSet::new(), &dataset),
+        epsilon = 1e-3
+    );
 }
 
-
 #[test]
 fn simple_bic() {
     let mut net = CtbnNetwork::new();
@@ -43,19 +40,18 @@ fn simple_bic() {
         .add_node(generate_discrete_time_continous_node(String::from("n1"), 2))
         .unwrap();
 
-    let trj = Trajectory::new(
+    let trj = Trajectory::new(arr1(&[0.0, 0.1, 0.3]), arr2(&[[0], [1], [1]]));
-        arr1(&[0.0,0.1,0.3]),
-        arr2(&[[0],[1],[1]]));
 
     let dataset = Dataset::new(vec![trj]);
     let bic = BIC::new(1, 1.0);
 
-    assert_abs_diff_eq!(-0.65058, bic.call(&net, n1, &BTreeSet::new(), &dataset), epsilon=1e-3);
+    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::new();
     let n1 = net
@@ -66,25 +62,33 @@ fn check_compatibility_between_dataset_and_network<T: StructureLearningAlgorithm
         .unwrap();
     net.add_edge(n1, n2);
 
-    match &mut net.get_node_mut(n1).params {
+    match &mut net.get_node_mut(n1) {
         params::Params::DiscreteStatesContinousTime(param) => {
-            assert_eq!(Ok(()), param.set_cim(arr3(&[[[-3.0, 2.0, 1.0], 
+            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]]])));
+                    [0.4, 0.6, -1.0]
+                ]]))
+            );
         }
     }
 
-    match &mut net.get_node_mut(n2).params {
+    match &mut net.get_node_mut(n2) {
         params::Params::DiscreteStatesContinousTime(param) => {
-            assert_eq!(Ok(()), param.set_cim(arr3(&[
+            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 data = trajectory_generator(&net, 100, 20.0, Some(6347747169756259));
 
     let mut net = CtbnNetwork::new();
     let _n1 = net
@@ -93,7 +97,6 @@ fn check_compatibility_between_dataset_and_network<T: StructureLearningAlgorithm
     let net = sl.fit_transform(net, &data);
 }
 
-
 #[test]
 #[should_panic]
 pub fn check_compatibility_between_dataset_and_network_hill_climbing() {
@@ -112,32 +115,39 @@ fn learn_ternary_net_2_nodes<T: StructureLearningAlgorithm> (sl: T) {
         .unwrap();
     net.add_edge(n1, n2);
 
-    match &mut net.get_node_mut(n1).params {
+    match &mut net.get_node_mut(n1) {
         params::Params::DiscreteStatesContinousTime(param) => {
-            assert_eq!(Ok(()), param.set_cim(arr3(&[[[-3.0, 2.0, 1.0], 
+            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]]])));
+                    [0.4, 0.6, -1.0]
+                ]]))
+            );
         }
     }
 
-    match &mut net.get_node_mut(n2).params {
+    match &mut net.get_node_mut(n2) {
         params::Params::DiscreteStatesContinousTime(param) => {
-            assert_eq!(Ok(()), param.set_cim(arr3(&[
+            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 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::new(1, 1.0);
@@ -152,7 +162,6 @@ pub fn learn_ternary_net_2_nodes_hill_climbing_bic() {
     learn_ternary_net_2_nodes(hl);
 }
 
-
 fn get_mixed_discrete_net_3_nodes_with_data() -> (CtbnNetwork, Dataset) {
     let mut net = CtbnNetwork::new();
     let n1 = net
@@ -169,45 +178,97 @@ fn get_mixed_discrete_net_3_nodes_with_data() -> (CtbnNetwork, Dataset) {
     net.add_edge(n1, n3);
     net.add_edge(n2, n3);
 
-    match &mut net.get_node_mut(n1).params {
+    match &mut net.get_node_mut(n1) {
         params::Params::DiscreteStatesContinousTime(param) => {
-            assert_eq!(Ok(()), param.set_cim(arr3(&[[[-3.0, 2.0, 1.0], 
+            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]]])));
+                    [0.4, 0.6, -1.0]
+                ]]))
+            );
         }
     }
 
-    match &mut net.get_node_mut(n2).params {
+    match &mut net.get_node_mut(n2) {
         params::Params::DiscreteStatesContinousTime(param) => {
-            assert_eq!(Ok(()), param.set_cim(arr3(&[
+            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) {
-    match &mut net.get_node_mut(n3).params {
         params::Params::DiscreteStatesContinousTime(param) => {
-            assert_eq!(Ok(()), param.set_cim(arr3(&[
+            assert_eq!(
-                [[-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]],
+                Ok(()),
-                [[-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]],
+                param.set_cim(arr3(&[
-                [[-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]],
+                    [
-
+                        [-1.0, 0.5, 0.3, 0.2],
-                [[-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]],
+                        [0.5, -4.0, 2.5, 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]],
+                        [2.5, 0.5, -4.0, 1.0],
-                [[-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]],
+                        [0.7, 0.2, 0.1, -1.0]
-
+                    ],
-                [[-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]],
+                        [-6.0, 2.0, 3.0, 1.0],
-                [[-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]],
+                        [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));
-    let data = trajectory_generator(&net, 300, 30.0, Some(6347747169756259),);
     return (net, data);
 }
 
@@ -219,7 +280,6 @@ fn learn_mixed_discrete_net_3_nodes<T: StructureLearningAlgorithm> (sl: T) {
     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::new(1, 1.0);
@@ -234,8 +294,6 @@ pub fn learn_mixed_discrete_net_3_nodes_hill_climbing_bic() {
     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);
@@ -244,7 +302,6 @@ fn learn_mixed_discrete_net_3_nodes_1_parent_constraint<T: StructureLearningAlgo
     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::new(1, 1.0);

tests/tools.rs

@@ -1,13 +1,9 @@
-
+use ndarray::{arr1, arr2, arr3};
-use reCTBN::tools::*;
-use reCTBN::network::Network;
 use reCTBN::ctbn::*;
-use reCTBN::node;
+use reCTBN::network::Network;
 use reCTBN::params;
+use reCTBN::tools::*;
 use std::collections::BTreeSet;
-use ndarray::{arr1, arr2, arr3};
-
-
 
 #[macro_use]
 extern crate approx;
@@ -17,32 +13,44 @@ mod utils;
 #[test]
 fn run_sampling() {
     let mut net = CtbnNetwork::new();
-    let n1 = net.add_node(utils::generate_discrete_time_continous_node(String::from("n1"),2)).unwrap();
+    let n1 = net
-    let n2 = net.add_node(utils::generate_discrete_time_continous_node(String::from("n2"),2)).unwrap();
+        .add_node(utils::generate_discrete_time_continous_node(
+            String::from("n1"),
+            2,
+        ))
+        .unwrap();
+    let n2 = net
+        .add_node(utils::generate_discrete_time_continous_node(
+            String::from("n2"),
+            2,
+        ))
+        .unwrap();
     net.add_edge(n1, n2);
 
-    match &mut net.get_node_mut(n1).params {
+    match &mut net.get_node_mut(n1) {
         params::Params::DiscreteStatesContinousTime(param) => {
             param.set_cim(arr3(&[[[-3.0, 3.0], [2.0, -2.0]]]));
         }
     }
 
-
+    match &mut net.get_node_mut(n2) {
-    match &mut net.get_node_mut(n2).params {
         params::Params::DiscreteStatesContinousTime(param) => {
             param.set_cim(arr3(&[
                 [[-1.0, 1.0], [4.0, -4.0]],
-                                     [[-6.0,6.0],[2.0,-2.0]]]));
+                [[-6.0, 6.0], [2.0, -2.0]],
+            ]));
         }
     }
 
-    let data = trajectory_generator(&net, 4, 1.0, Some(6347747169756259),);
+    let data = trajectory_generator(&net, 4, 1.0, Some(6347747169756259));
 
     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]);
+    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() {
@@ -51,7 +59,6 @@ fn run_sampling() {
     Trajectory::new(time, events);
 }
 
-
 #[test]
 #[should_panic]
 fn dataset_wrong_shape() {
@@ -59,7 +66,6 @@ fn dataset_wrong_shape() {
     let events = arr2(&[[0, 3], [1, 2]]);
     let t1 = Trajectory::new(time, events);
 
-
     let time = arr1(&[0.0, 0.2]);
     let events = arr2(&[[0, 3, 3], [1, 2, 3]]);
     let t2 = Trajectory::new(time, events);

tests/utils.rs

@@ -1,16 +1,17 @@
 use reCTBN::params;
-use reCTBN::node;
 use std::collections::BTreeSet;
 
-pub fn generate_discrete_time_continous_node(name: String, cardinality: usize) -> node::Node {
+pub fn generate_discrete_time_continous_node(label: String, cardinality: usize) -> params::Params {
-    node::Node::new(params::Params::DiscreteStatesContinousTime(generate_discrete_time_continous_param(cardinality)), name)
+    params::Params::DiscreteStatesContinousTime(generate_discrete_time_continous_params(label, cardinality))
 }
 
 
-pub fn generate_discrete_time_continous_param(cardinality: usize) -> params::DiscreteStatesContinousTimeParams{
+pub fn generate_discrete_time_continous_params(label: String, cardinality: usize) -> params::DiscreteStatesContinousTimeParams{
     let domain: BTreeSet<String> = (0..cardinality).map(|x| x.to_string()).collect();
-    params::DiscreteStatesContinousTimeParams::new(domain)
+    params::DiscreteStatesContinousTimeParams::new(label, domain)
 }
 
 
 
+
+