perf

src/model.rs

@@ -1,6 +1,7 @@
 use std::collections::HashMap;
-use crate::{updates::observations::observation_update, utils::function_pointer::FnType};
+use crate::utils::function_pointer::FnType;
 use crate::utils::set_sequence::set_update_sequence;
+use crate::utils::beliefs_propagation::belief_propagation;
 use crate::utils::function_pointer::get_func_map;
 use pyo3::types::PyTuple;
 use pyo3::{prelude::*, types::{PyList, PyDict}};
@@ -129,33 +130,6 @@ impl Network {
         self.update_sequence = set_update_sequence(self);
     }
 
-    /// Single time slice belief propagation.
-    /// 
-    /// # Arguments
-    /// * `observations` - A vector of values, each value is one new observation associated
-    /// with one node.
-    pub fn belief_propagation(&mut self, observations_set: Vec<f64>) {
-
-        let predictions = self.update_sequence.predictions.clone();
-        let updates = self.update_sequence.updates.clone();
-
-        // 1. prediction steps
-        for (idx, step) in predictions.iter() {
-            step(self, *idx);
-        }
-
-        // 2. observation steps
-        for (i, observations) in observations_set.iter().enumerate() {
-            let idx = self.inputs[i];
-            observation_update(self, idx, *observations);
-        } 
-
-        // 3. update steps
-        for (idx, step) in updates.iter() {
-            step(self, *idx);
-        }
-    }
-
     /// Add a sequence of observations.
     /// 
     /// # Arguments
@@ -164,6 +138,8 @@ impl Network {
     pub fn input_data(&mut self, input_data: Vec<f64>) {
 
         let n_time = input_data.len();
+        let predictions = self.update_sequence.predictions.clone();
+        let updates = self.update_sequence.updates.clone();
 
         // initialize the belief trajectories result struture
         let mut node_trajectories = NodeTrajectories {floats: HashMap::new(), vectors: HashMap::new()};
@@ -193,7 +169,7 @@ impl Network {
         for observation in input_data {
 
             // 1. belief propagation for one time slice
-            self.belief_propagation(vec![observation]);
+            belief_propagation(self, vec![observation], &predictions, &updates);
 
             // 2. append the new beliefs in the trajectories structure
             // iterate over the float hashmap

src/utils/beliefs_propagation.rs

@@ -0,0 +1,25 @@
+use crate::{utils::function_pointer::FnType, model::Network, updates::observations::observation_update};
+
+/// Single time slice belief propagation.
+/// 
+/// # Arguments
+/// * `observations` - A vector of values, each value is one new observation associated
+/// with one node.
+pub fn belief_propagation(network: &mut Network, observations_set: Vec<f64>, predictions: & Vec<(usize, FnType)>, updates: & Vec<(usize, FnType)>) {
+
+    // 1. prediction steps
+    for (idx, step) in predictions.iter() {
+        step(network, *idx);
+    }
+
+    // 2. observation steps
+    for (i, observations) in observations_set.iter().enumerate() {
+        let idx = network.inputs[i];
+        observation_update(network, idx, *observations);
+    } 
+
+    // 3. update steps
+    for (idx, step) in updates.iter() {
+        step(network, *idx);
+    }
+}

src/utils/mod.rs

@@ -1,2 +1,3 @@
 pub mod set_sequence;
-pub mod function_pointer;
+pub mod function_pointer;
+pub mod beliefs_propagation;