Merge branch '35-refactor-constructor' into 'dev'

src/ctbn.rs

@@ -29,19 +29,19 @@ use std::collections::BTreeSet;
 /// domain.insert(String::from("B"));
 ///
 /// //Create the parameters for a discrete node using the domain
-/// let param = params::DiscreteStatesContinousTimeParams::init(domain); 
+/// let param = params::DiscreteStatesContinousTimeParams::new(domain); 
 ///
 /// //Create the node using the parameters
-/// let X1 = node::Node::init(params::Params::DiscreteStatesContinousTime(param),String::from("X1"));
+/// let X1 = node::Node::new(params::Params::DiscreteStatesContinousTime(param),String::from("X1"));
 ///
 /// let mut domain = BTreeSet::new();
 /// domain.insert(String::from("A"));
 /// domain.insert(String::from("B"));
-/// let param = params::DiscreteStatesContinousTimeParams::init(domain);
+/// let param = params::DiscreteStatesContinousTimeParams::new(domain);
-/// let X2 = node::Node::init(params::Params::DiscreteStatesContinousTime(param), String::from("X2"));
+/// let X2 = node::Node::new(params::Params::DiscreteStatesContinousTime(param), String::from("X2"));
 /// 
 /// //Initialize a ctbn
-/// let mut net = CtbnNetwork::init();
+/// let mut net = CtbnNetwork::new();
 ///
 /// //Add nodes
 /// let X1 = net.add_node(X1).unwrap();
@@ -61,7 +61,7 @@ pub struct CtbnNetwork {
 
 
 impl CtbnNetwork {
-    pub fn init() -> CtbnNetwork {
+    pub fn new() -> CtbnNetwork {
         CtbnNetwork {
             adj_matrix: None,
             nodes: Vec::new()

src/node.rs

@@ -7,7 +7,7 @@ pub struct Node {
 }
 
 impl Node {
-    pub fn init(params: Params, label: String) -> Node {
+    pub fn new(params: Params, label: String) -> Node {
         Node{
             params: params,
             label:label

src/params.rs

@@ -77,7 +77,7 @@ pub struct DiscreteStatesContinousTimeParams {
 }
 
 impl DiscreteStatesContinousTimeParams {
-    pub fn init(domain: BTreeSet<String>) -> DiscreteStatesContinousTimeParams {
+    pub fn new(domain: BTreeSet<String>) -> DiscreteStatesContinousTimeParams {
         DiscreteStatesContinousTimeParams {
             domain,
             cim: Option::None,

src/structure_learning/score_based_algorithm.rs

@@ -10,7 +10,7 @@ pub struct HillClimbing<S: ScoreFunction> {
 }
 
 impl<S: ScoreFunction> HillClimbing<S> {
-    pub fn init(score_function: S, max_parent_set: Option<usize>) -> HillClimbing<S> {
+    pub fn new(score_function: S, max_parent_set: Option<usize>) -> HillClimbing<S> {
         HillClimbing {
             score_function,
             max_parent_set,

src/structure_learning/score_function.rs

@@ -24,7 +24,7 @@ pub struct LogLikelihood {
 }
 
 impl LogLikelihood {
-    pub fn init(alpha: usize, tau: f64) -> LogLikelihood {
+    pub fn new(alpha: usize, tau: f64) -> LogLikelihood {
 
         //Tau must be >=0.0
         if tau < 0.0 {
@@ -106,9 +106,9 @@ pub struct BIC {
 }
 
 impl BIC {
-    pub fn init(alpha: usize, tau: f64) -> BIC {
+    pub fn new(alpha: usize, tau: f64) -> BIC {
         BIC {
-            ll: LogLikelihood::init(alpha, tau)
+            ll: LogLikelihood::new(alpha, tau)
         }
     }
 }

src/tools.rs

@@ -12,7 +12,7 @@ pub struct Trajectory {
 }
 
 impl Trajectory {
-    pub fn init(time: Array1<f64>, events: Array2<usize>) -> Trajectory {
+    pub fn new(time: Array1<f64>, events: Array2<usize>) -> Trajectory {
         //Events and time are two part of the same trajectory. For this reason they must have the
         //same number of sample.
         if time.shape()[0] != events.shape()[0] {
@@ -35,7 +35,7 @@ pub struct Dataset {
 }
 
 impl Dataset {
-    pub fn init(trajectories: Vec<Trajectory>) -> 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.
@@ -178,7 +178,7 @@ pub fn trajectory_generator<T: network::Network>(
         time.push(t_end.clone());
         
         //Add the sampled trajectory to trajectories.
-        trajectories.push(Trajectory::init(
+        trajectories.push(Trajectory::new(
             Array::from_vec(time),
             Array2::from_shape_vec(
                 (events.len(), current_state.len()),
@@ -188,5 +188,5 @@ pub fn trajectory_generator<T: network::Network>(
         ));
     }
     //Return a dataset object with the sampled trajectories.
-    Dataset::init(trajectories)
+    Dataset::new(trajectories)
 }

tests/ctbn.rs

@@ -8,20 +8,20 @@ use rustyCTBN::ctbn::*;
 
 #[test]
 fn define_simpe_ctbn() {
-    let _ = CtbnNetwork::init();
+    let _ = CtbnNetwork::new();
     assert!(true);
 }
 
 #[test]
 fn add_node_to_ctbn() {
-    let mut net = CtbnNetwork::init();
+    let mut net = CtbnNetwork::new();
     let n1 = net.add_node(generate_discrete_time_continous_node(String::from("n1"),2)).unwrap();
     assert_eq!(String::from("n1"), net.get_node(n1).label);
 }
 
 #[test]
 fn add_edge_to_ctbn() {
-    let mut net = CtbnNetwork::init();
+    let mut net = CtbnNetwork::new();
     let n1 = net.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);
@@ -31,7 +31,7 @@ fn add_edge_to_ctbn() {
 
 #[test]
 fn children_and_parents() {
-    let mut net = CtbnNetwork::init();
+    let mut net = CtbnNetwork::new();
     let n1 = net.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);
@@ -44,7 +44,7 @@ fn children_and_parents() {
 
 #[test]
 fn compute_index_ctbn() {
-    let mut net = CtbnNetwork::init();
+    let mut net = CtbnNetwork::new();
     let n1 = net.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();
     let n3 = net.add_node(generate_discrete_time_continous_node(String::from("n3"),2)).unwrap();
@@ -76,7 +76,7 @@ fn compute_index_ctbn() {
 
 #[test]
 fn compute_index_from_custom_parent_set() {
-    let mut net = CtbnNetwork::init();
+    let mut net = CtbnNetwork::new();
     let _n1 = net.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();
     let _n3 = net.add_node(generate_discrete_time_continous_node(String::from("n3"),2)).unwrap();

tests/parameter_learning.rs

@@ -16,7 +16,7 @@ extern crate approx;
 
 
 fn learn_binary_cim<T: ParameterLearning> (pl: T) {
-    let mut net = CtbnNetwork::init();
+    let mut net = CtbnNetwork::new();
     let n1 = net
         .add_node(generate_discrete_time_continous_node(String::from("n1"),2))
         .unwrap();
@@ -66,7 +66,7 @@ fn learn_binary_cim_BA() {
 }
 
 fn learn_ternary_cim<T: ParameterLearning> (pl: T) {
-    let mut net = CtbnNetwork::init();
+    let mut net = CtbnNetwork::new();
     let n1 = net
         .add_node(generate_discrete_time_continous_node(String::from("n1"),3))
         .unwrap();
@@ -121,7 +121,7 @@ fn learn_ternary_cim_BA() {
 }
 
 fn learn_ternary_cim_no_parents<T: ParameterLearning> (pl: T) {
-    let mut net = CtbnNetwork::init();
+    let mut net = CtbnNetwork::new();
     let n1 = net
         .add_node(generate_discrete_time_continous_node(String::from("n1"),3))
         .unwrap();
@@ -175,7 +175,7 @@ fn learn_ternary_cim_no_parents_BA() {
 
 
 fn learn_mixed_discrete_cim<T: ParameterLearning> (pl: T) {
-    let mut net = CtbnNetwork::init();
+    let mut net = CtbnNetwork::new();
     let n1 = net
         .add_node(generate_discrete_time_continous_node(String::from("n1"),3))
         .unwrap();

tests/structure_learning.rs

@@ -18,18 +18,18 @@ extern crate approx;
 
 #[test]
 fn simple_score_test() {
-    let mut net = CtbnNetwork::init();
+    let mut net = CtbnNetwork::new();
     let n1 = net
         .add_node(generate_discrete_time_continous_node(String::from("n1"),2))
         .unwrap();
 
-    let trj = Trajectory::init(
+    let trj = Trajectory::new(
         arr1(&[0.0,0.1,0.3]),
         arr2(&[[0],[1],[1]]));
 
-    let dataset = Dataset::init(vec![trj]);
+    let dataset = Dataset::new(vec![trj]);
 
-    let ll = LogLikelihood::init(1, 1.0);
+    let ll = LogLikelihood::new(1, 1.0);
 
     assert_abs_diff_eq!(0.04257, ll.call(&net, n1, &BTreeSet::new(), &dataset), epsilon=1e-3);
 
@@ -38,17 +38,17 @@ fn simple_score_test() {
 
 #[test]
 fn simple_bic() {
-    let mut net = CtbnNetwork::init();
+    let mut net = CtbnNetwork::new();
     let n1 = net
         .add_node(generate_discrete_time_continous_node(String::from("n1"),2))
         .unwrap();
 
-    let trj = Trajectory::init(
+    let trj = Trajectory::new(
         arr1(&[0.0,0.1,0.3]),
         arr2(&[[0],[1],[1]]));
 
-    let dataset = Dataset::init(vec![trj]);
+    let dataset = Dataset::new(vec![trj]);
-    let bic = BIC::init(1, 1.0);
+    let bic = BIC::new(1, 1.0);
 
     assert_abs_diff_eq!(-0.65058, bic.call(&net, n1, &BTreeSet::new(), &dataset), epsilon=1e-3);
 
@@ -57,7 +57,7 @@ fn simple_bic() {
 
 
 fn check_compatibility_between_dataset_and_network<T: StructureLearningAlgorithm> (sl: T) {
-    let mut net = CtbnNetwork::init();
+    let mut net = CtbnNetwork::new();
     let n1 = net
         .add_node(generate_discrete_time_continous_node(String::from("n1"),3))
         .unwrap();
@@ -86,7 +86,7 @@ fn check_compatibility_between_dataset_and_network<T: StructureLearningAlgorithm
 
     let data = trajectory_generator(&net, 100, 20.0, Some(6347747169756259),);
 
-    let mut net = CtbnNetwork::init();
+    let mut net = CtbnNetwork::new();
     let _n1 = net
         .add_node(generate_discrete_time_continous_node(String::from("n1"),3))
         .unwrap();
@@ -97,13 +97,13 @@ fn check_compatibility_between_dataset_and_network<T: StructureLearningAlgorithm
 #[test]
 #[should_panic]
 pub fn check_compatibility_between_dataset_and_network_hill_climbing() {
-    let ll = LogLikelihood::init(1, 1.0);
+    let ll = LogLikelihood::new(1, 1.0);
-    let hl = HillClimbing::init(ll, None);
+    let hl = HillClimbing::new(ll, None);
     check_compatibility_between_dataset_and_network(hl);
 }
 
 fn learn_ternary_net_2_nodes<T: StructureLearningAlgorithm> (sl: T) {
-    let mut net = CtbnNetwork::init();
+    let mut net = CtbnNetwork::new();
     let n1 = net
         .add_node(generate_discrete_time_continous_node(String::from("n1"),3))
         .unwrap();
@@ -140,21 +140,21 @@ fn learn_ternary_net_2_nodes<T: StructureLearningAlgorithm> (sl: T) {
 
 #[test]
 pub fn learn_ternary_net_2_nodes_hill_climbing_ll() {
-    let ll = LogLikelihood::init(1, 1.0);
+    let ll = LogLikelihood::new(1, 1.0);
-    let hl = HillClimbing::init(ll, None);
+    let hl = HillClimbing::new(ll, None);
     learn_ternary_net_2_nodes(hl);
 }
 
 #[test]
 pub fn learn_ternary_net_2_nodes_hill_climbing_bic() {
-    let bic = BIC::init(1, 1.0);
+    let bic = BIC::new(1, 1.0);
-    let hl = HillClimbing::init(bic, None);
+    let hl = HillClimbing::new(bic, None);
     learn_ternary_net_2_nodes(hl);
 }
 
 
 fn get_mixed_discrete_net_3_nodes_with_data() -> (CtbnNetwork, Dataset) {
-    let mut net = CtbnNetwork::init();
+    let mut net = CtbnNetwork::new();
     let n1 = net
         .add_node(generate_discrete_time_continous_node(String::from("n1"),3))
         .unwrap();
@@ -222,15 +222,15 @@ fn learn_mixed_discrete_net_3_nodes<T: StructureLearningAlgorithm> (sl: T) {
 
 #[test]
 pub fn learn_mixed_discrete_net_3_nodes_hill_climbing_ll() {
-    let ll = LogLikelihood::init(1, 1.0);
+    let ll = LogLikelihood::new(1, 1.0);
-    let hl = HillClimbing::init(ll, None);
+    let hl = HillClimbing::new(ll, None);
     learn_mixed_discrete_net_3_nodes(hl);
 }
 
 #[test]
 pub fn learn_mixed_discrete_net_3_nodes_hill_climbing_bic() {
-    let bic = BIC::init(1, 1.0);
+    let bic = BIC::new(1, 1.0);
-    let hl = HillClimbing::init(bic, None);
+    let hl = HillClimbing::new(bic, None);
     learn_mixed_discrete_net_3_nodes(hl);
 }
 
@@ -247,14 +247,14 @@ fn learn_mixed_discrete_net_3_nodes_1_parent_constraint<T: StructureLearningAlgo
 
 #[test]
 pub fn learn_mixed_discrete_net_3_nodes_hill_climbing_ll_1_parent_constraint() {
-    let ll = LogLikelihood::init(1, 1.0);
+    let ll = LogLikelihood::new(1, 1.0);
-    let hl = HillClimbing::init(ll, Some(1));
+    let hl = HillClimbing::new(ll, Some(1));
     learn_mixed_discrete_net_3_nodes_1_parent_constraint(hl);
 }
 
 #[test]
 pub fn learn_mixed_discrete_net_3_nodes_hill_climbing_bic_1_parent_constraint() {
-    let bic = BIC::init(1, 1.0);
+    let bic = BIC::new(1, 1.0);
-    let hl = HillClimbing::init(bic, Some(1));
+    let hl = HillClimbing::new(bic, Some(1));
     learn_mixed_discrete_net_3_nodes_1_parent_constraint(hl);
 }

tests/tools.rs

@@ -16,7 +16,7 @@ mod utils;
 
 #[test]
 fn run_sampling() {
-    let mut net = CtbnNetwork::init();
+    let mut net = CtbnNetwork::new();
     let n1 = net.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);
@@ -48,7 +48,7 @@ fn run_sampling() {
     fn trajectory_wrong_shape() {
     let time = arr1(&[0.0, 0.2]);
     let events = arr2(&[[0,3]]);
-    Trajectory::init(time, events);
+    Trajectory::new(time, events);
 }
 
 
@@ -57,11 +57,11 @@ fn run_sampling() {
 fn dataset_wrong_shape() {
     let time = arr1(&[0.0, 0.2]);
     let events = arr2(&[[0,3], [1,2]]);
-    let t1 = Trajectory::init(time, events);
+    let t1 = Trajectory::new(time, events);
 
 
     let time = arr1(&[0.0, 0.2]);
     let events = arr2(&[[0,3,3], [1,2,3]]);
-    let t2 = Trajectory::init(time, events);
+    let t2 = Trajectory::new(time, events);
-    Dataset::init(vec![t1, t2]);
+    Dataset::new(vec![t1, t2]);
 }

tests/utils.rs

@@ -3,13 +3,13 @@ use rustyCTBN::node;
 use std::collections::BTreeSet;
 
 pub fn generate_discrete_time_continous_node(name: String, cardinality: usize) -> node::Node {
-    node::Node::init(params::Params::DiscreteStatesContinousTime(generate_discrete_time_continous_param(cardinality)), name)
+    node::Node::new(params::Params::DiscreteStatesContinousTime(generate_discrete_time_continous_param(cardinality)), name)
 }
 
 
 pub fn generate_discrete_time_continous_param(cardinality: usize) -> params::DiscreteStatesContinousTimeParams{
     let domain: BTreeSet<String> = (0..cardinality).map(|x| x.to_string()).collect();
-    params::DiscreteStatesContinousTimeParams::init(domain)
+    params::DiscreteStatesContinousTimeParams::new(domain)
 }