.github/workflows/build.yml

@@ -16,12 +16,20 @@ jobs:
 
     steps:
     - uses: actions/checkout@v3
-    - name: Setup (rust)
+    - name: Setup Rust stable (default)
       uses: actions-rs/toolchain@v1
       with:
         profile: minimal
         toolchain: stable
+        default: true
         components: clippy, rustfmt
+    - name: Setup Rust nightly
+      uses: actions-rs/toolchain@v1
+      with:
+        profile: minimal
+        toolchain: nightly
+        default: false
+        components: rustfmt
     - name: Linting (clippy)
       uses: actions-rs/clippy-check@v1
       with:
@@ -30,6 +38,7 @@ jobs:
     - name: Formatting (rustfmt)
       uses: actions-rs/cargo@v1
       with:
+        toolchain: nightly
         command: fmt
         args: --all -- --check --verbose
     - name: Tests (test)

rustfmt.toml

@@ -33,4 +33,7 @@ newline_style = "Unix"
 #error_on_unformatted = true
 
 # Files to ignore like third party code which is formatted upstream.
-#ignore = []
+# Ignoring tests is a temporary measure due some issues regarding rank-3 tensors
+ignore = [
+    "tests/"
+]

src/ctbn.rs

@@ -1,10 +1,9 @@
-use ndarray::prelude::*;
-use crate::params::{StateType, Params, ParamsTrait};
-use crate::network;
 use std::collections::BTreeSet;
 
+use ndarray::prelude::*;
 
-
+use crate::network;
+use crate::params::{Params, ParamsTrait, StateType};
 
 ///CTBN network. It represents both the structure and the parameters of a CTBN. CtbnNetwork is
 ///composed by the following elements:
@@ -54,30 +53,30 @@ use std::collections::BTreeSet;
 /// ```
 pub struct CtbnNetwork {
     adj_matrix: Option<Array2<u16>>,
-    nodes: Vec<Params>
+    nodes: Vec<Params>,
 }
 
-
 impl CtbnNetwork {
     pub fn new() -> CtbnNetwork {
         CtbnNetwork {
             adj_matrix: None,
-            nodes: Vec::new()
+            nodes: Vec::new(),
         }
     }
 }
 
 impl network::Network for CtbnNetwork {
     fn initialize_adj_matrix(&mut self) {
-        self.adj_matrix = Some(Array2::<u16>::zeros((self.nodes.len(), self.nodes.len()).f()));
-
+        self.adj_matrix = Some(Array2::<u16>::zeros(
+            (self.nodes.len(), self.nodes.len()).f(),
+        ));
     }
 
     fn add_node(&mut self, mut n: Params) -> Result<usize, network::NetworkError> {
         n.reset_params();
         self.adj_matrix = Option::None;
         self.nodes.push(n);
-        Ok(self.nodes.len() -1)        
+        Ok(self.nodes.len() - 1)
     }
 
     fn add_edge(&mut self, parent: usize, child: usize) {
@@ -91,7 +90,7 @@ impl network::Network for CtbnNetwork {
         }
     }
 
-    fn get_node_indices(&self) -> std::ops::Range<usize>{
+    fn get_node_indices(&self) -> std::ops::Range<usize> {
         0..self.nodes.len()
     }
 
@@ -99,64 +98,65 @@ impl network::Network for CtbnNetwork {
         self.nodes.len()
     }
 
-    fn get_node(&self, node_idx: usize) -> &Params{
+    fn get_node(&self, node_idx: usize) -> &Params {
         &self.nodes[node_idx]
     }
 
-
-    fn get_node_mut(&mut self, node_idx: usize) -> &mut Params{
+    fn get_node_mut(&mut self, node_idx: usize) -> &mut Params {
         &mut self.nodes[node_idx]
     }
 
-
-    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| {
+    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].state_to_index(&current_state[x.0]) * acc.1;
                     acc.1 *= self.nodes[x.0].get_reserved_space_as_parent();
                 }
                 acc
-        }).0
+            })
+            .0
     }
 
-
-    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| {
+    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].state_to_index(&current_state[*x]) * acc.1;
                 acc.1 *= self.nodes[*x].get_reserved_space_as_parent();
                 acc
-        }).0
+            })
+            .0
     }
 
     fn get_parent_set(&self, node: usize) -> BTreeSet<usize> {
-        self.adj_matrix.as_ref()
+        self.adj_matrix
+            .as_ref()
             .unwrap()
             .column(node)
             .iter()
             .enumerate()
-            .filter_map(|(idx, x)| {
-                if x > &0 {
-                    Some(idx)
-                } else {
-                    None
-                }
-            }).collect()
+            .filter_map(|(idx, x)| if x > &0 { Some(idx) } else { None })
+            .collect()
     }
 
-    fn get_children_set(&self, node: usize) -> BTreeSet<usize>{
-        self.adj_matrix.as_ref()
+    fn get_children_set(&self, node: usize) -> BTreeSet<usize> {
+        self.adj_matrix
+            .as_ref()
             .unwrap()
             .row(node)
             .iter()
             .enumerate()
-            .filter_map(|(idx, x)| {
-                if x > &0 {
-                    Some(idx)
-                } else {
-                    None
-                }
-            }).collect()
+            .filter_map(|(idx, x)| if x > &0 { Some(idx) } else { None })
+            .collect()
     }
-
 }
-

src/lib.rs

@@ -2,10 +2,9 @@
 #[cfg(test)]
 extern crate approx;
 
-pub mod params;
-pub mod network;
 pub mod ctbn;
-pub mod tools;
+pub mod network;
 pub mod parameter_learning;
+pub mod params;
 pub mod structure_learning;
-
+pub mod tools;

src/network.rs

@@ -1,15 +1,16 @@
+use std::collections::BTreeSet;
+
 use thiserror::Error;
+
 use crate::params;
-use std::collections::BTreeSet;
 
 /// Error types for trait Network
 #[derive(Error, Debug)]
 pub enum NetworkError {
     #[error("Error during node insertion")]
-    NodeInsertionError(String)
+    NodeInsertionError(String),
 }
 
-
 ///Network
 ///The Network trait define the required methods for a structure used as pgm (such as ctbn).
 pub trait Network {
@@ -26,13 +27,17 @@ pub trait Network {
     ///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
     ///the ones in the parent set of the *node*.
-    fn get_param_index_network(&self, node: usize, current_state: &Vec<params::StateType>) -> usize;
-
+    fn get_param_index_network(&self, node: usize, current_state: &Vec<params::StateType>)
+        -> usize;
 
     ///Compute the index that must be used to access the parameters of a node given a specific
     ///configuration of the network and a generic parent_set. Usually, the only values really used
     ///in *current_state* are the ones in the parent set of the *node*.
-    fn get_param_index_from_custom_parent_set(&self, current_state: &Vec<params::StateType>, parent_set: &BTreeSet<usize>) -> usize;
+    fn get_param_index_from_custom_parent_set(
+        &self,
+        current_state: &Vec<params::StateType>,
+        parent_set: &BTreeSet<usize>,
+    ) -> usize;
     fn get_parent_set(&self, node: usize) -> BTreeSet<usize>;
     fn get_children_set(&self, node: usize) -> BTreeSet<usize>;
 }

src/parameter_learning.rs

@@ -1,11 +1,12 @@
-use crate::network;
-use crate::params::*;
-use crate::tools;
-use ndarray::prelude::*;
 use std::collections::BTreeSet;
 
-pub trait ParameterLearning{
-    fn fit<T:network::Network>(
+use ndarray::prelude::*;
+
+use crate::params::*;
+use crate::{network, tools};
+
+pub trait ParameterLearning {
+    fn fit<T: network::Network>(
         &self,
         net: &T,
         dataset: &tools::Dataset,
@@ -14,24 +15,19 @@ pub trait ParameterLearning{
     ) -> Params;
 }
 
-pub fn sufficient_statistics<T:network::Network>(
+pub fn sufficient_statistics<T: network::Network>(
     net: &T,
     dataset: &tools::Dataset,
     node: usize,
-    parent_set: &BTreeSet<usize>
-    ) -> (Array3<usize>, Array2<f64>) {
+    parent_set: &BTreeSet<usize>,
+) -> (Array3<usize>, Array2<f64>) {
     //Get the number of values assumable by the node
-    let node_domain = net
-        .get_node(node.clone())
-        .get_reserved_space_as_parent();
+    let node_domain = net.get_node(node.clone()).get_reserved_space_as_parent();
 
     //Get the number of values assumable by each parent of the node
     let parentset_domain: Vec<usize> = parent_set
         .iter()
-        .map(|x| {
-            net.get_node(x.clone())
-                .get_reserved_space_as_parent()
-        })
+        .map(|x| net.get_node(x.clone()).get_reserved_space_as_parent())
         .collect();
 
     //Vector used to convert a specific configuration of the parent_set to the corresponding index
@@ -70,13 +66,11 @@ pub fn sufficient_statistics<T:network::Network>(
     }
 
     return (M, T);
-
 }
 
 pub struct MLE {}
 
 impl ParameterLearning for MLE {
-
     fn fit<T: network::Network>(
         &self,
         net: &T,
@@ -84,7 +78,6 @@ impl ParameterLearning for MLE {
         node: usize,
         parent_set: Option<BTreeSet<usize>>,
     ) -> Params {
-
         //Use parent_set from parameter if present. Otherwise use parent_set from network.
         let parent_set = match parent_set {
             Some(p) => p,
@@ -96,7 +89,7 @@ impl ParameterLearning for MLE {
         let mut CIM: Array3<f64> = Array::zeros((M.shape()[0], M.shape()[1], M.shape()[2]));
         CIM.axis_iter_mut(Axis(2))
             .zip(M.mapv(|x| x as f64).axis_iter(Axis(2)))
-            .for_each(|(mut C, m)| C.assign(&(&m/&T)));
+            .for_each(|(mut C, m)| C.assign(&(&m / &T)));
 
         //Set the diagonal of the inner matrices to the the row sum multiplied by -1
         let tmp_diag_sum: Array2<f64> = CIM.sum_axis(Axis(2)).mapv(|x| x * -1.0);
@@ -106,8 +99,6 @@ impl ParameterLearning for MLE {
                 C.diag_mut().assign(&diag);
             });
 
-
-
         let mut n: Params = net.get_node(node).clone();
 
         match n {
@@ -115,8 +106,6 @@ impl ParameterLearning for MLE {
                 dsct.set_cim_unchecked(CIM);
                 dsct.set_transitions(M);
                 dsct.set_residence_time(T);
-
-
             }
         };
         return n;
@@ -125,7 +114,7 @@ impl ParameterLearning for MLE {
 
 pub struct BayesianApproach {
     pub alpha: usize,
-    pub tau: f64
+    pub tau: f64,
 }
 
 impl ParameterLearning for BayesianApproach {
@@ -151,7 +140,7 @@ impl ParameterLearning for BayesianApproach {
         let mut CIM: Array3<f64> = Array::zeros((M.shape()[0], M.shape()[1], M.shape()[2]));
         CIM.axis_iter_mut(Axis(2))
             .zip(M.mapv(|x| x as f64).axis_iter(Axis(2)))
-            .for_each(|(mut C, m)| C.assign(&(&m.mapv(|y| y + alpha)/&T.mapv(|y| y + tau))));
+            .for_each(|(mut C, m)| C.assign(&(&m.mapv(|y| y + alpha) / &T.mapv(|y| y + tau))));
 
         //Set the diagonal of the inner matrices to the the row sum multiplied by -1
         let tmp_diag_sum: Array2<f64> = CIM.sum_axis(Axis(2)).mapv(|x| x * -1.0);
@@ -161,8 +150,6 @@ impl ParameterLearning for BayesianApproach {
                 C.diag_mut().assign(&diag);
             });
 
-
-
         let mut n: Params = net.get_node(node).clone();
 
         match n {
@@ -170,27 +157,25 @@ impl ParameterLearning for BayesianApproach {
                 dsct.set_cim_unchecked(CIM);
                 dsct.set_transitions(M);
                 dsct.set_residence_time(T);
-
-
             }
         };
         return n;
     }
 }
 
-
 pub struct Cache<P: ParameterLearning> {
     parameter_learning: P,
     dataset: tools::Dataset,
 }
 
 impl<P: ParameterLearning> Cache<P> {
-    pub fn fit<T:network::Network>(
+    pub fn fit<T: network::Network>(
         &mut self,
         net: &T,
         node: usize,
         parent_set: Option<BTreeSet<usize>>,
     ) -> Params {
-        self.parameter_learning.fit(net, &self.dataset, node, parent_set)
+        self.parameter_learning
+            .fit(net, &self.dataset, node, parent_set)
     }
 }

src/params.rs

@@ -1,9 +1,10 @@
+use std::collections::BTreeSet;
+
 use enum_dispatch::enum_dispatch;
 use ndarray::prelude::*;
 use rand::Rng;
-use std::collections::{BTreeSet};
-use thiserror::Error;
 use rand_chacha::ChaCha8Rng;
+use thiserror::Error;
 
 /// Error types for trait Params
 #[derive(Error, Debug, PartialEq)]
@@ -35,11 +36,21 @@ pub trait ParamsTrait {
 
     /// Randomly generate a residence time for the given node taking into account the node state
     /// and its parent set.
-    fn get_random_residence_time(&self, state: usize, u: usize, rng: &mut ChaCha8Rng) -> Result<f64, ParamsError>;
+    fn get_random_residence_time(
+        &self,
+        state: usize,
+        u: usize,
+        rng: &mut ChaCha8Rng,
+    ) -> Result<f64, ParamsError>;
 
     /// Randomly generate a possible state for the given node taking into account the node state
     /// and its parent set.
-    fn get_random_state(&self, state: usize, u: usize, rng: &mut ChaCha8Rng) -> Result<StateType, ParamsError>;
+    fn get_random_state(
+        &self,
+        state: usize,
+        u: usize,
+        rng: &mut ChaCha8Rng,
+    ) -> Result<StateType, ParamsError>;
 
     /// Used by childern of the node described by this parameters to reserve spaces in their CIMs.
     fn get_reserved_space_as_parent(&self) -> usize;
@@ -102,7 +113,7 @@ impl DiscreteStatesContinousTimeParams {
     ///This function check if the cim is valid using the validate_params method.
     ///- **Valid cim inserted**: it substitute the CIM in self.cim and return Ok(())
     ///- **Invalid cim inserted**: it replace the self.cim value with None and it retu  ParamsError
-    pub fn set_cim(&mut self, cim: Array3<f64>) -> Result<(), ParamsError>{
+    pub fn set_cim(&mut self, cim: Array3<f64>) -> Result<(), ParamsError> {
         self.cim = Some(cim);
         match self.validate_params() {
             Ok(()) => Ok(()),
@@ -113,7 +124,6 @@ impl DiscreteStatesContinousTimeParams {
         }
     }
 
-
     ///Unchecked version of the setter function for CIM.
     pub fn set_cim_unchecked(&mut self, cim: Array3<f64>) {
         self.cim = Some(cim);
@@ -124,7 +134,6 @@ impl DiscreteStatesContinousTimeParams {
         &self.transitions
     }
 
-
     ///Setter function for transitions
     pub fn set_transitions(&mut self, transitions: Array3<usize>) {
         self.transitions = Some(transitions);
@@ -135,12 +144,10 @@ impl DiscreteStatesContinousTimeParams {
         &self.residence_time
     }
 
-
     ///Setter function for residence_time
     pub fn set_residence_time(&mut self, residence_time: Array2<f64>) {
         self.residence_time = Some(residence_time);
     }
-
 }
 
 impl ParamsTrait for DiscreteStatesContinousTimeParams {
@@ -154,7 +161,12 @@ impl ParamsTrait for DiscreteStatesContinousTimeParams {
         StateType::Discrete(rng.gen_range(0..(self.domain.len())))
     }
 
-    fn get_random_residence_time(&self, state: usize, u: usize, rng: &mut ChaCha8Rng) -> Result<f64, ParamsError> {
+    fn get_random_residence_time(
+        &self,
+        state: usize,
+        u: usize,
+        rng: &mut ChaCha8Rng,
+    ) -> Result<f64, ParamsError> {
         // Generate a random residence time given the current state of the node and its parent set.
         // The method used is described in:
         // https://en.wikipedia.org/wiki/Exponential_distribution#Generating_exponential_variates
@@ -170,7 +182,12 @@ impl ParamsTrait for DiscreteStatesContinousTimeParams {
         }
     }
 
-    fn get_random_state(&self, state: usize, u: usize, rng: &mut ChaCha8Rng) -> Result<StateType, ParamsError> {
+    fn get_random_state(
+        &self,
+        state: usize,
+        u: usize,
+        rng: &mut ChaCha8Rng,
+    ) -> Result<StateType, ParamsError> {
         // Generate a random transition given the current state of the node and its parent set.
         // The method used is described in:
         // https://en.wikipedia.org/wiki/Multinomial_distribution#Sampling_from_a_multinomial_distribution
@@ -246,7 +263,9 @@ impl ParamsTrait for DiscreteStatesContinousTimeParams {
         }
 
         // Check if each row sum up to 0
-        if cim.sum_axis(Axis(2)).iter()
+        if cim
+            .sum_axis(Axis(2))
+            .iter()
             .any(|x| f64::abs(x.clone()) > f64::EPSILON * 3.0)
         {
             return Err(ParamsError::InvalidCIM(String::from(
@@ -260,5 +279,4 @@ impl ParamsTrait for DiscreteStatesContinousTimeParams {
     fn get_label(&self) -> &String {
         &self.label
     }
-
 }

src/structure_learning.rs

@@ -1,12 +1,11 @@
-pub mod score_function;
-pub mod score_based_algorithm;
 pub mod constraint_based_algorithm;
 pub mod hypothesis_test;
-use crate::network;
-use crate::tools;
+pub mod score_based_algorithm;
+pub mod score_function;
+use crate::{network, tools};
 
 pub trait StructureLearningAlgorithm {
-    fn fit_transform<T, >(&self, net: T, dataset: &tools::Dataset) -> T
+    fn fit_transform<T>(&self, net: T, dataset: &tools::Dataset) -> T
     where
         T: network::Network;
 }

src/structure_learning/constraint_based_algorithm.rs

@@ -1,5 +1,3 @@
-
 //pub struct CTPC {
 //
 //}
-

src/structure_learning/hypothesis_test.rs

@@ -1,11 +1,10 @@
-use ndarray::Array3;
-use ndarray::Axis;
+use std::collections::BTreeSet;
+
+use ndarray::{Array3, Axis};
 use statrs::distribution::{ChiSquared, ContinuousCDF};
 
-use crate::network;
-use crate::parameter_learning;
 use crate::params::*;
-use std::collections::BTreeSet;
+use crate::{network, parameter_learning};
 
 pub trait HypothesisTest {
     fn call<T, P>(
@@ -110,15 +109,15 @@ impl HypothesisTest for ChiSquare {
         // Prendo dalla cache l'apprendimento dei parametri, che sarebbe una CIM
         // di dimensione nxn
         //  (CIM, M, T)
-        let P_small = match cache.fit(net, child_node, Some(separation_set.clone())){
-            Params::DiscreteStatesContinousTime(node) => node
+        let P_small = match cache.fit(net, child_node, Some(separation_set.clone())) {
+            Params::DiscreteStatesContinousTime(node) => node,
         };
         //
         let mut extended_separation_set = separation_set.clone();
         extended_separation_set.insert(parent_node);
 
-        let P_big = match cache.fit(net, child_node, Some(extended_separation_set.clone())){
-            Params::DiscreteStatesContinousTime(node) => node
+        let P_big = match cache.fit(net, child_node, Some(extended_separation_set.clone())) {
+            Params::DiscreteStatesContinousTime(node) => node,
         };
         // Commentare qui
         let partial_cardinality_product: usize = extended_separation_set
@@ -132,7 +131,12 @@ impl HypothesisTest for ChiSquare {
                     / (partial_cardinality_product
                         * net.get_node(parent_node).get_reserved_space_as_parent()))
                     * partial_cardinality_product;
-            if !self.compare_matrices(idx_M_small, P_small.get_transitions().as_ref().unwrap(), idx_M_big, P_big.get_transitions().as_ref().unwrap()) {
+            if !self.compare_matrices(
+                idx_M_small,
+                P_small.get_transitions().as_ref().unwrap(),
+                idx_M_big,
+                P_big.get_transitions().as_ref().unwrap(),
+            ) {
                 return false;
             }
         }

src/structure_learning/score_based_algorithm.rs

@@ -1,8 +1,8 @@
-use crate::network;
+use std::collections::BTreeSet;
+
 use crate::structure_learning::score_function::ScoreFunction;
 use crate::structure_learning::StructureLearningAlgorithm;
-use crate::tools;
-use std::collections::BTreeSet;
+use crate::{network, tools};
 
 pub struct HillClimbing<S: ScoreFunction> {
     score_function: S,

src/structure_learning/score_function.rs

@@ -1,10 +1,9 @@
-use crate::network;
-use crate::parameter_learning;
-use crate::params;
-use crate::tools;
+use std::collections::BTreeSet;
+
 use ndarray::prelude::*;
 use statrs::function::gamma;
-use std::collections::BTreeSet;
+
+use crate::{network, parameter_learning, params, tools};
 
 pub trait ScoreFunction {
     fn call<T>(
@@ -25,7 +24,6 @@ pub struct LogLikelihood {
 
 impl LogLikelihood {
     pub fn new(alpha: usize, tau: f64) -> LogLikelihood {
-
         //Tau must be >=0.0
         if tau < 0.0 {
             panic!("tau must be >=0.0");
@@ -44,7 +42,7 @@ impl LogLikelihood {
         T: network::Network,
     {
         //Identify the type of node used
-        match &net.get_node(node){
+        match &net.get_node(node) {
             params::Params::DiscreteStatesContinousTime(_params) => {
                 //Compute the sufficient statistics M (number of transistions) and T (residence
                 //time)
@@ -57,33 +55,38 @@ impl LogLikelihood {
                 let tau = self.tau / M.shape()[0] as f64;
 
                 //Compute the log likelihood for q
-                let log_ll_q:f64 = M
+                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 + *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| 
+                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();
+                                                - gamma::ln_gamma(alpha)
+                                        })
+                                        .sum::<f64>()
+                            })
+                            .sum::<f64>()
+                    })
+                    .sum();
                 (log_ll_theta + log_ll_q, M)
             }
         }
     }
-
-
-
 }
 
 impl ScoreFunction for LogLikelihood {
@@ -102,13 +105,13 @@ impl ScoreFunction for LogLikelihood {
 }
 
 pub struct BIC {
-    ll: LogLikelihood
+    ll: LogLikelihood,
 }
 
 impl BIC {
     pub fn new(alpha: usize, tau: f64) -> BIC {
         BIC {
-            ll: LogLikelihood::new(alpha, tau)
+            ll: LogLikelihood::new(alpha, tau),
         }
     }
 }
@@ -122,14 +125,19 @@ impl ScoreFunction for BIC {
         dataset: &tools::Dataset,
     ) -> f64
     where
-        T: network::Network {
+        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();
+        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

@@ -1,10 +1,10 @@
-use crate::network;
-use crate::params;
-use crate::params::ParamsTrait;
 use ndarray::prelude::*;
 use rand_chacha::rand_core::SeedableRng;
 use rand_chacha::ChaCha8Rng;
 
+use crate::params::ParamsTrait;
+use crate::{network, params};
+
 pub struct Trajectory {
     time: Array1<f64>,
     events: Array2<usize>,
@@ -35,7 +35,6 @@ pub struct Dataset {
 
 impl Dataset {
     pub fn new(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
@@ -58,7 +57,6 @@ pub fn trajectory_generator<T: network::Network>(
     t_end: f64,
     seed: Option<u64>,
 ) -> Dataset {
-    
     //Tmp growing vector containing generated trajectories.
     let mut trajectories: Vec<Trajectory> = Vec::new();
 
@@ -67,7 +65,7 @@ pub fn trajectory_generator<T: network::Network>(
         //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()
+        None => SeedableRng::from_entropy(),
     };
 
     //Each iteration generate one trajectory
@@ -78,7 +76,8 @@ pub fn trajectory_generator<T: network::Network>(
         let mut time: Vec<f64> = Vec::new();
         //Configuration of the process variables at time t initialized with an uniform
         //distribution.
-        let mut current_state: Vec<params::StateType> = net.get_node_indices()
+        let mut current_state: Vec<params::StateType> = net
+            .get_node_indices()
             .map(|x| net.get_node(x).get_random_state_uniform(&mut rng))
             .collect();
         //History of all the configurations of the process variables.

tests/ctbn.rs

@@ -1,8 +1,9 @@
 mod utils;
+use std::collections::BTreeSet;
+
 use reCTBN::ctbn::*;
 use reCTBN::network::Network;
 use reCTBN::params::{self, ParamsTrait};
-use std::collections::BTreeSet;
 use utils::generate_discrete_time_continous_node;
 
 #[test]

tests/parameter_learning.rs

@@ -1,13 +1,13 @@
 #![allow(non_snake_case)]
 
 mod utils;
-use utils::*;
-
 use ndarray::arr3;
 use reCTBN::ctbn::*;
 use reCTBN::network::Network;
 use reCTBN::parameter_learning::*;
-use reCTBN::{params, tools::*};
+use reCTBN::params;
+use reCTBN::tools::*;
+use utils::*;
 
 extern crate approx;
 
@@ -32,8 +32,14 @@ fn learn_binary_cim<T: ParameterLearning>(pl: T) {
             assert_eq!(
                 Ok(()),
                 param.set_cim(arr3(&[
-                    [[-1.0, 1.0], [4.0, -4.0]],
-                    [[-6.0, 6.0], [2.0, -2.0]],
+                    [
+                        [-1.0, 1.0],
+                        [4.0, -4.0]
+                    ],
+                    [
+                        [-6.0, 6.0],
+                        [2.0, -2.0]
+                    ],
                 ]))
             );
         }
@@ -41,11 +47,20 @@ fn learn_binary_cim<T: ParameterLearning>(pl: T) {
 
     let data = trajectory_generator(&net, 100, 100.0, Some(6347747169756259));
     let p = match pl.fit(&net, &data, 1, None) {
-        params::Params::DiscreteStatesContinousTime(p) => p
+        params::Params::DiscreteStatesContinousTime(p) => p,
     };
     assert_eq!(p.get_cim().as_ref().unwrap().shape(), [2, 2, 2]);
     assert!(p.get_cim().as_ref().unwrap().abs_diff_eq(
-        &arr3(&[[[-1.0, 1.0], [4.0, -4.0]], [[-6.0, 6.0], [2.0, -2.0]],]),
+        &arr3(&[
+            [
+                [-1.0, 1.0],
+                [4.0, -4.0]
+            ],
+            [
+                [-6.0, 6.0],
+                [2.0, -2.0]
+            ],
+        ]),
         0.1
     ));
 }
@@ -76,11 +91,13 @@ fn learn_ternary_cim<T: ParameterLearning>(pl: T) {
         params::Params::DiscreteStatesContinousTime(param) => {
             assert_eq!(
                 Ok(()),
-                param.set_cim(arr3(&[[
+                param.set_cim(arr3(&[
+                    [
                         [-3.0, 2.0, 1.0],
                         [1.5, -2.0, 0.5],
                         [0.4, 0.6, -1.0]
-                ]]))
+                    ],
+                ]))
             );
         }
     }
@@ -90,24 +107,48 @@ fn learn_ternary_cim<T: ParameterLearning>(pl: T) {
             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]],
+                    [
+                        [-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 p = match pl.fit(&net, &data, 1, None){
-        params::Params::DiscreteStatesContinousTime(p) => p
+    let p = match pl.fit(&net, &data, 1, None) {
+        params::Params::DiscreteStatesContinousTime(p) => p,
     };
     assert_eq!(p.get_cim().as_ref().unwrap().shape(), [3, 3, 3]);
     assert!(p.get_cim().as_ref().unwrap().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]],
+            [
+                [-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
     ));
@@ -139,11 +180,13 @@ fn learn_ternary_cim_no_parents<T: ParameterLearning>(pl: T) {
         params::Params::DiscreteStatesContinousTime(param) => {
             assert_eq!(
                 Ok(()),
-                param.set_cim(arr3(&[[
+                param.set_cim(arr3(&[
+                    [
                         [-3.0, 2.0, 1.0],
                         [1.5, -2.0, 0.5],
                         [0.4, 0.6, -1.0]
-                ]]))
+                    ]
+                ]))
             );
         }
     }
@@ -153,21 +196,39 @@ fn learn_ternary_cim_no_parents<T: ParameterLearning>(pl: T) {
             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]],
+                    [
+                        [-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 p = match pl.fit(&net, &data, 0, None){
-        params::Params::DiscreteStatesContinousTime(p) => p
+    let p = match pl.fit(&net, &data, 0, None) {
+        params::Params::DiscreteStatesContinousTime(p) => p,
     };
     assert_eq!(p.get_cim().as_ref().unwrap().shape(), [1, 3, 3]);
     assert!(p.get_cim().as_ref().unwrap().abs_diff_eq(
-        &arr3(&[[[-3.0, 2.0, 1.0], [1.5, -2.0, 0.5], [0.4, 0.6, -1.0]]]),
+        &arr3(&[
+            [
+                [-3.0, 2.0, 1.0],
+                [1.5, -2.0, 0.5],
+                [0.4, 0.6, -1.0]
+            ],
+        ]),
         0.1
     ));
 }
@@ -204,11 +265,13 @@ fn learn_mixed_discrete_cim<T: ParameterLearning>(pl: T) {
         params::Params::DiscreteStatesContinousTime(param) => {
             assert_eq!(
                 Ok(()),
-                param.set_cim(arr3(&[[
+                param.set_cim(arr3(&[
+                    [
                         [-3.0, 2.0, 1.0],
                         [1.5, -2.0, 0.5],
                         [0.4, 0.6, -1.0]
-                ]]))
+                    ],
+                ]))
             );
         }
     }
@@ -218,9 +281,21 @@ fn learn_mixed_discrete_cim<T: ParameterLearning>(pl: T) {
             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]],
+                    [
+                        [-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]
+                    ],
                 ]))
             );
         }
@@ -291,8 +366,8 @@ fn learn_mixed_discrete_cim<T: ParameterLearning>(pl: T) {
     }
 
     let data = trajectory_generator(&net, 300, 300.0, Some(6347747169756259));
-    let p = match pl.fit(&net, &data, 2, None){
-        params::Params::DiscreteStatesContinousTime(p) => p
+    let p = match pl.fit(&net, &data, 2, None) {
+        params::Params::DiscreteStatesContinousTime(p) => p,
     };
     assert_eq!(p.get_cim().as_ref().unwrap().shape(), [9, 4, 4]);
     assert!(p.get_cim().as_ref().unwrap().abs_diff_eq(

tests/params.rs

@@ -1,5 +1,6 @@
 use ndarray::prelude::*;
-use rand_chacha::{rand_core::SeedableRng, ChaCha8Rng};
+use rand_chacha::rand_core::SeedableRng;
+use rand_chacha::ChaCha8Rng;
 use reCTBN::params::{ParamsTrait, *};
 
 mod utils;

tests/structure_learning.rs

@@ -1,17 +1,18 @@
 #![allow(non_snake_case)]
 
 mod utils;
-use utils::*;
+use std::collections::BTreeSet;
 
 use ndarray::{arr1, arr2, arr3};
 use reCTBN::ctbn::*;
 use reCTBN::network::Network;
 use reCTBN::params;
-use reCTBN::structure_learning::score_function::*;
-use reCTBN::structure_learning::{score_based_algorithm::*, StructureLearningAlgorithm};
 use reCTBN::structure_learning::hypothesis_test::*;
+use reCTBN::structure_learning::score_based_algorithm::*;
+use reCTBN::structure_learning::score_function::*;
+use reCTBN::structure_learning::StructureLearningAlgorithm;
 use reCTBN::tools::*;
-use std::collections::BTreeSet;
+use utils::*;
 
 #[macro_use]
 extern crate approx;
@@ -69,11 +70,13 @@ fn check_compatibility_between_dataset_and_network<T: StructureLearningAlgorithm
         params::Params::DiscreteStatesContinousTime(param) => {
             assert_eq!(
                 Ok(()),
-                param.set_cim(arr3(&[[
+                param.set_cim(arr3(&[
+                    [
                         [-3.0, 2.0, 1.0],
                         [1.5, -2.0, 0.5],
                         [0.4, 0.6, -1.0]
-                ]]))
+                    ],
+                ]))
             );
         }
     }
@@ -83,9 +86,21 @@ fn check_compatibility_between_dataset_and_network<T: StructureLearningAlgorithm
             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]],
+                    [
+                        [-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]
+                    ],
                 ]))
             );
         }
@@ -122,11 +137,13 @@ fn learn_ternary_net_2_nodes<T: StructureLearningAlgorithm>(sl: T) {
         params::Params::DiscreteStatesContinousTime(param) => {
             assert_eq!(
                 Ok(()),
-                param.set_cim(arr3(&[[
+                param.set_cim(arr3(&[
+                    [
                         [-3.0, 2.0, 1.0],
                         [1.5, -2.0, 0.5],
                         [0.4, 0.6, -1.0]
-                ]]))
+                    ],
+                ]))
             );
         }
     }
@@ -136,9 +153,21 @@ fn learn_ternary_net_2_nodes<T: StructureLearningAlgorithm>(sl: T) {
             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]],
+                    [
+                        [-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]
+                    ],
                 ]))
             );
         }
@@ -185,11 +214,13 @@ fn get_mixed_discrete_net_3_nodes_with_data() -> (CtbnNetwork, Dataset) {
         params::Params::DiscreteStatesContinousTime(param) => {
             assert_eq!(
                 Ok(()),
-                param.set_cim(arr3(&[[
+                param.set_cim(arr3(&[
+                    [
                         [-3.0, 2.0, 1.0],
                         [1.5, -2.0, 0.5],
                         [0.4, 0.6, -1.0]
-                ]]))
+                    ],
+                ]))
             );
         }
     }
@@ -199,9 +230,21 @@ fn get_mixed_discrete_net_3_nodes_with_data() -> (CtbnNetwork, Dataset) {
             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]],
+                    [
+                        [-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]
+                    ],
                 ]))
             );
         }
@@ -320,42 +363,56 @@ pub fn learn_mixed_discrete_net_3_nodes_hill_climbing_bic_1_parent_constraint()
 }
 
 #[test]
-pub fn chi_square_compare_matrices () {
+pub fn chi_square_compare_matrices() {
     let i: usize = 1;
     let M1 = arr3(&[
-       [[ 0,  2,  3],
+        [
+            [ 0,  2,  3],
             [ 4,  0,  6],
-        [ 7,  8,  0]],
-       [[0, 12,  90],
+            [ 7,  8,  0]
+        ],
+        [
+            [0, 12,  90],
             [ 3, 0,  40],
-        [ 6, 40,  0]],
-       [[ 0,  2,  3],
+            [ 6, 40,  0]
+        ],
+        [
+            [ 0,  2,  3],
             [ 4,  0,  6],
-        [ 44, 66, 0]]
+            [ 44, 66, 0]
+        ],
     ]);
     let j: usize = 0;
     let M2 = arr3(&[
-       [[ 0,  200,  300],
+        [
+            [ 0,  200,  300],
             [ 400,  0,  600],
-        [  700, 800,  0]]
+            [  700, 800,  0]
+        ],
     ]);
     let chi_sq = ChiSquare::new(0.1);
-    assert!(!chi_sq.compare_matrices( i, &M1, j, &M2));
+    assert!(!chi_sq.compare_matrices(i, &M1, j, &M2));
 }
 
 #[test]
-pub fn chi_square_compare_matrices_2 () {
+pub fn chi_square_compare_matrices_2() {
     let i: usize = 1;
     let M1 = arr3(&[
-       [[ 0,  2,  3],
+        [
+            [ 0,  2,  3],
             [ 4,  0,  6],
-        [ 7,  8,  0]],
-       [[0, 20,  30],
+            [ 7,  8,  0]
+        ],
+        [
+            [0, 20,  30],
             [ 40, 0,  60],
-        [ 70, 80,  0]],
-       [[ 0,  2,  3],
+            [ 70, 80,  0]
+        ],
+        [
+            [ 0,  2,  3],
             [ 4,  0,  6],
-        [ 44, 66, 0]]
+            [ 44, 66, 0]
+        ],
     ]);
     let j: usize = 0;
     let M2 = arr3(&[
@@ -364,29 +421,37 @@ pub fn chi_square_compare_matrices_2 () {
         [  700, 800,  0]]
     ]);
     let chi_sq = ChiSquare::new(0.1);
-    assert!(chi_sq.compare_matrices( i, &M1, j, &M2));
+    assert!(chi_sq.compare_matrices(i, &M1, j, &M2));
 }
 
 #[test]
-pub fn chi_square_compare_matrices_3 () {
+pub fn chi_square_compare_matrices_3() {
     let i: usize = 1;
     let M1 = arr3(&[
-       [[ 0,  2,  3],
+        [
+            [ 0,  2,  3],
             [ 4,  0,  6],
-        [ 7,  8,  0]],
-       [[0, 21,  31],
+            [ 7,  8,  0]
+        ],
+        [
+            [0, 21,  31],
             [ 41, 0,  59],
-        [ 71, 79,  0]],
-       [[ 0,  2,  3],
+            [ 71, 79,  0]
+        ],
+        [
+            [ 0,  2,  3],
             [ 4,  0,  6],
-        [ 44, 66, 0]]
+            [ 44, 66, 0]
+        ],
     ]);
     let j: usize = 0;
     let M2 = arr3(&[
-       [[ 0,  200,  300],
+        [
+            [ 0,  200,  300],
             [ 400,  0,  600],
-        [  700, 800,  0]]
+            [  700, 800,  0]
+        ],
     ]);
     let chi_sq = ChiSquare::new(0.1);
-    assert!(chi_sq.compare_matrices( i, &M1, j, &M2));
+    assert!(chi_sq.compare_matrices(i, &M1, j, &M2));
 }

tests/tools.rs

@@ -29,15 +29,26 @@ fn run_sampling() {
 
     match &mut net.get_node_mut(n1) {
         params::Params::DiscreteStatesContinousTime(param) => {
-            param.set_cim(arr3(&[[[-3.0, 3.0], [2.0, -2.0]]]));
+            param.set_cim(arr3(&[
+                [
+                    [-3.0, 3.0],
+                    [2.0, -2.0]
+                ],
+            ]));
         }
     }
 
     match &mut net.get_node_mut(n2) {
         params::Params::DiscreteStatesContinousTime(param) => {
             param.set_cim(arr3(&[
-                [[-1.0, 1.0], [4.0, -4.0]],
-                [[-6.0, 6.0], [2.0, -2.0]],
+                [
+                    [-1.0, 1.0],
+                    [4.0, -4.0]
+                ],
+                [
+                    [-6.0, 6.0],
+                    [2.0, -2.0]
+                ],
             ]));
         }
     }

tests/utils.rs

@@ -1,12 +1,19 @@
-use reCTBN::params;
 use std::collections::BTreeSet;
 
+use reCTBN::params;
+
 #[allow(dead_code)]
 pub fn generate_discrete_time_continous_node(label: String, cardinality: usize) -> params::Params {
-    params::Params::DiscreteStatesContinousTime(generate_discrete_time_continous_params(label, cardinality))
+    params::Params::DiscreteStatesContinousTime(generate_discrete_time_continous_params(
+        label,
+        cardinality,
+    ))
 }
 
-pub fn generate_discrete_time_continous_params(label: String, 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(label, domain)
 }