reCTBN/Cargo.toml

@@ -14,6 +14,7 @@ enum_dispatch = "~0.3"
 statrs = "~0.16"
 rand_chacha = "~0.3"
 itertools = "~0.10"
+rayon = "~1.6"
 
 [dev-dependencies]
 approx = { package = "approx", version = "~0.5" }

reCTBN/src/parameter_learning.rs

@@ -8,7 +8,7 @@ use ndarray::prelude::*;
 use crate::params::*;
 use crate::{process, tools::Dataset};
 
-pub trait ParameterLearning {
+pub trait ParameterLearning: Sync {
     fn fit<T: process::NetworkProcess>(
         &self,
         net: &T,

reCTBN/src/process.rs

@@ -21,7 +21,7 @@ pub type NetworkProcessState = Vec<params::StateType>;
 
 /// It defines the required methods for a structure used as a Probabilistic Graphical Models (such
 /// as a CTBN).
-pub trait NetworkProcess {
+pub trait NetworkProcess: Sync {
     fn initialize_adj_matrix(&mut self);
     fn add_node(&mut self, n: params::Params) -> Result<usize, NetworkError>;
     /// Add an **directed edge** between a two nodes of the network.

reCTBN/src/structure_learning/constraint_based_algorithm.rs

@@ -1,6 +1,8 @@
 //! Module containing constraint based algorithms like CTPC and Hiton.
 
 use itertools::Itertools;
+use rayon::iter::{IntoParallelIterator, ParallelIterator};
+use rayon::prelude::ParallelExtend;
 use std::collections::BTreeSet;
 use std::usize;
 
@@ -41,7 +43,8 @@ impl<P: ParameterLearning> StructureLearningAlgorithm for CTPC<P> {
 
         net.initialize_adj_matrix();
 
-        for child_node in net.get_node_indices() {
+        let mut learned_parent_sets: Vec<(usize, BTreeSet<usize>)> = vec![];
+        learned_parent_sets.par_extend(net.get_node_indices().into_par_iter().map(|child_node| {
             let mut cache = Cache::new(&self.parameter_learning);
             let mut candidate_parent_set: BTreeSet<usize> = net
                 .get_node_indices()
@@ -82,6 +85,9 @@ impl<P: ParameterLearning> StructureLearningAlgorithm for CTPC<P> {
                 candidate_parent_set = candidate_parent_set_TMP;
                 separation_set_size += 1;
             }
+            (child_node, candidate_parent_set)
+        }));
+        for (child_node, candidate_parent_set) in learned_parent_sets {
             for parent_node in candidate_parent_set.iter() {
                 net.add_edge(*parent_node, child_node);
             }