Hill Climbing + Simple test

src/structure_learning/mod.rs

@@ -0,0 +1,10 @@
+pub mod score_function;
+pub mod score_based_algorithm;
+use crate::network;
+use crate::tools;
+
+pub trait StructureLearningAlgorithm {
+    fn call<T, >(&self, net: T, dataset: &tools::Dataset) -> T
+    where
+        T: network::Network;
+}

src/structure_learning/score_based_algorithm.rs

@@ -0,0 +1,61 @@
+use crate::params;
+use crate::structure_learning::score_function::ScoreFunction;
+use crate::structure_learning::StructureLearningAlgorithm;
+use crate::tools;
+use crate::{network, parameter_learning};
+use ndarray::prelude::*;
+use rand::prelude::*;
+use rand_chacha::ChaCha8Rng;
+use std::collections::BTreeSet;
+
+pub struct HillClimbing<S: ScoreFunction> {
+    score_function: S,
+}
+
+impl<S: ScoreFunction> HillClimbing<S> {
+    pub fn init(score_function: S) -> HillClimbing<S> {
+        HillClimbing { score_function }
+    }
+}
+
+impl<S: ScoreFunction> StructureLearningAlgorithm for HillClimbing<S> {
+    fn call<T>(&self, net: T, dataset: &tools::Dataset) -> T
+    where
+        T: network::Network,
+    {
+        let mut net = net;
+        net.initialize_adj_matrix();
+        for node in net.get_node_indices() {
+            let mut parent_set: BTreeSet<usize> = BTreeSet::new();
+            let mut current_ll = self.score_function.call(&net, node, &parent_set, dataset);
+            let mut old_ll = f64::NEG_INFINITY;
+            while current_ll > old_ll {
+                old_ll = current_ll;
+                for parent in net.get_node_indices() {
+                    if parent == node {
+                        continue;
+                    }
+                    let is_removed = parent_set.remove(&parent);
+                    if !is_removed {
+                        parent_set.insert(parent);
+                    }
+
+                    let tmp_ll = self.score_function.call(&net, node, &parent_set, dataset);
+
+                    if tmp_ll < current_ll {
+                        if is_removed {
+                            parent_set.insert(parent);
+                        } else {
+                            parent_set.remove(&parent);
+                        }
+                    } else {
+                        current_ll = tmp_ll;
+                    }
+                }
+            }
+            parent_set.iter().for_each(|p| net.add_edge(*p, node));
+        }
+
+        return net;
+    }
+}

src/structure_learning/score_function.rs

@@ -6,12 +6,6 @@ use ndarray::prelude::*;
 use statrs::function::gamma;
 use std::collections::BTreeSet;
 
-pub trait StructureLearning {
-    fn fit<T>(&self, net: T, dataset: &tools::Dataset) -> T
-    where
-        T: network::Network;
-}
-
 pub trait ScoreFunction {
     fn call<T>(
         &self,

tests/structure_learning.rs

@@ -5,9 +5,12 @@ use utils::*;
 use rustyCTBN::ctbn::*;
 use rustyCTBN::network::Network;
 use rustyCTBN::tools::*;
-use rustyCTBN::structure_learning::*;
+use rustyCTBN::structure_learning::score_function::*;
-use ndarray::{arr1, arr2};
+use rustyCTBN::structure_learning::score_based_algorithm::*;
+use rustyCTBN::structure_learning::StructureLearningAlgorithm;
+use ndarray::{arr1, arr2, arr3};
 use std::collections::BTreeSet;
+use rustyCTBN::params;
 
 
 #[macro_use]
@@ -53,3 +56,45 @@ fn simple_bic() {
     assert_abs_diff_eq!(-0.65058, ll.call(&net, n1, &BTreeSet::new(), &dataset), epsilon=1e-3);
 
 }
+
+fn learn_ternary_net_2_nodes<T: StructureLearningAlgorithm> (sl: T) {
+    let mut net = CtbnNetwork::init();
+    let n1 = net
+        .add_node(generate_discrete_time_continous_node(String::from("n1"),3))
+        .unwrap();
+    let n2 = net
+        .add_node(generate_discrete_time_continous_node(String::from("n2"),3))
+        .unwrap();
+    net.add_edge(n1, n2);
+
+    match &mut net.get_node_mut(n1).params {
+        params::Params::DiscreteStatesContinousTime(param) => {
+            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]]])));
+        }
+    }
+
+    match &mut net.get_node_mut(n2).params {
+        params::Params::DiscreteStatesContinousTime(param) => {
+            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 net = sl.call(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() {
+    let bic = BIC::init(1, 1.0);
+    let hl = HillClimbing::init(bic);
+    learn_ternary_net_2_nodes(hl);
+}