Refactored `src/` and `tests/` files to be compliant to `rustfmt`

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:
@@ -54,23 +53,23 @@ 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> {
@@ -103,60 +102,61 @@ impl network::Network for CtbnNetwork {
         &self.nodes[node_idx]
     }
 
-
     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| {
+        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()
+        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

@@ -1,9 +1,10 @@
-use crate::network;
-use crate::params::*;
-use crate::tools;
-use ndarray::prelude::*;
 use std::collections::BTreeSet;
 
+use ndarray::prelude::*;
+
+use crate::params::*;
+use crate::{network, tools};
+
 pub trait ParameterLearning {
     fn fit<T: network::Network>(
         &self,
@@ -18,20 +19,15 @@ pub fn sufficient_statistics<T:network::Network>(
     net: &T,
     dataset: &tools::Dataset,
     node: usize,
-    parent_set: &BTreeSet<usize>
+    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,
@@ -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 {
@@ -161,8 +150,6 @@ impl ParameterLearning for BayesianApproach {
                 C.diag_mut().assign(&diag);
             });
 
-
-
         let mut n: Params = net.get_node(node).clone();
 
         match n {
@@ -170,15 +157,12 @@ 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,
@@ -191,6 +175,7 @@ impl<P: ParameterLearning> Cache<P> {
         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;
@@ -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>(
@@ -111,14 +110,14 @@ impl HypothesisTest for ChiSquare {
         // di dimensione nxn
         //  (CIM, M, T)
         let P_small = match cache.fit(net, child_node, Some(separation_set.clone())) {
-            Params::DiscreteStatesContinousTime(node) => node
+            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
+            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");
@@ -62,28 +60,33 @@ impl LogLikelihood {
                     .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;
 
@@ -41,7 +41,7 @@ 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(
@@ -100,7 +100,7 @@ fn learn_ternary_cim<T: ParameterLearning>(pl: T) {
 
     let data = trajectory_generator(&net, 100, 200.0, 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(), [3, 3, 3]);
     assert!(p.get_cim().as_ref().unwrap().abs_diff_eq(
@@ -163,7 +163,7 @@ fn learn_ternary_cim_no_parents<T: ParameterLearning>(pl: T) {
 
     let data = trajectory_generator(&net, 100, 200.0, Some(6347747169756259));
     let p = match pl.fit(&net, &data, 0, None) {
-        params::Params::DiscreteStatesContinousTime(p) => p
+        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(
@@ -292,7 +292,7 @@ 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
+        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;
@@ -323,22 +324,12 @@ pub fn learn_mixed_discrete_net_3_nodes_hill_climbing_bic_1_parent_constraint()
 pub fn chi_square_compare_matrices() {
     let i: usize = 1;
     let M1 = arr3(&[
-       [[ 0,  2,  3],
-        [ 4,  0,  6],
-        [ 7,  8,  0]],
-       [[0, 12,  90],
-        [ 3, 0,  40],
-        [ 6, 40,  0]],
-       [[ 0,  2,  3],
-        [ 4,  0,  6],
-        [ 44, 66, 0]]
+        [[0, 2, 3], [4, 0, 6], [7, 8, 0]],
+        [[0, 12, 90], [3, 0, 40], [6, 40, 0]],
+        [[0, 2, 3], [4, 0, 6], [44, 66, 0]],
     ]);
     let j: usize = 0;
-    let M2 = arr3(&[
-       [[ 0,  200,  300],
-        [ 400,  0,  600],
-        [  700, 800,  0]]
-    ]);
+    let M2 = arr3(&[[[0, 200, 300], [400, 0, 600], [700, 800, 0]]]);
     let chi_sq = ChiSquare::new(0.1);
     assert!(!chi_sq.compare_matrices(i, &M1, j, &M2));
 }
@@ -347,22 +338,12 @@ pub fn chi_square_compare_matrices () {
 pub fn chi_square_compare_matrices_2() {
     let i: usize = 1;
     let M1 = arr3(&[
-       [[ 0,  2,  3],
-        [ 4,  0,  6],
-        [ 7,  8,  0]],
-       [[0, 20,  30],
-        [ 40, 0,  60],
-        [ 70, 80,  0]],
-       [[ 0,  2,  3],
-        [ 4,  0,  6],
-        [ 44, 66, 0]]
+        [[0, 2, 3], [4, 0, 6], [7, 8, 0]],
+        [[0, 20, 30], [40, 0, 60], [70, 80, 0]],
+        [[0, 2, 3], [4, 0, 6], [44, 66, 0]],
     ]);
     let j: usize = 0;
-    let M2 = arr3(&[
-       [[ 0,  200,  300],
-        [ 400,  0,  600],
-        [  700, 800,  0]]
-    ]);
+    let M2 = arr3(&[[[0, 200, 300], [400, 0, 600], [700, 800, 0]]]);
     let chi_sq = ChiSquare::new(0.1);
     assert!(chi_sq.compare_matrices(i, &M1, j, &M2));
 }
@@ -371,22 +352,12 @@ pub fn chi_square_compare_matrices_2 () {
 pub fn chi_square_compare_matrices_3() {
     let i: usize = 1;
     let M1 = arr3(&[
-       [[ 0,  2,  3],
-        [ 4,  0,  6],
-        [ 7,  8,  0]],
-       [[0, 21,  31],
-        [ 41, 0,  59],
-        [ 71, 79,  0]],
-       [[ 0,  2,  3],
-        [ 4,  0,  6],
-        [ 44, 66, 0]]
+        [[0, 2, 3], [4, 0, 6], [7, 8, 0]],
+        [[0, 21, 31], [41, 0, 59], [71, 79, 0]],
+        [[0, 2, 3], [4, 0, 6], [44, 66, 0]],
     ]);
     let j: usize = 0;
-    let M2 = arr3(&[
-       [[ 0,  200,  300],
-        [ 400,  0,  600],
-        [  700, 800,  0]]
-    ]);
+    let M2 = arr3(&[[[0, 200, 300], [400, 0, 600], [700, 800, 0]]]);
     let chi_sq = ChiSquare::new(0.1);
     assert!(chi_sq.compare_matrices(i, &M1, j, &M2));
 }

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)
 }