improve generalised filtering nodes

Author: LegrandNico

docs/source/notebooks/0.3-Generalised_filtering.ipynb

@@ -308,7 +308,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": null,
    "id": "1798765e-3d65-4bfd-964b-7f9b6b0902be",
    "metadata": {},
    "outputs": [
@@ -358,7 +358,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": null,
    "id": "2d921e51-a940-42b2-88f2-e25bd7ab5a01",
    "metadata": {
     "editable": true,

examples/exponential.rs

@@ -7,7 +7,7 @@ fn main() {
 
     // create a network with two exponential family state nodes
     network.add_nodes(
-        "exponential-state",
+        "ef-state",
         None,
         None,
         None,

src/lib.rs

@@ -1,4 +1,4 @@
 pub mod model;
 pub mod utils;
-pub mod math;
+pub mod maths;
 pub mod updates;

src/math.rs

@@ -0,0 +1 @@
+pub mod sufficient_statistics;

src/maths/mod.rs

@@ -0,0 +1,15 @@
+pub fn normal(x: &f64) -> Vec<f64> {
+    vec![*x, x.powf(2.0)]
+}
+
+pub fn multivariate_normal(x: &Vec<f64>) -> Vec<f64> {
+    vec![*x, x.powf(2.0)]
+}
+
+pub fn get_sufficient_statistics_fn(distribution: String) {
+    if distribution == "normal" {
+        normal;
+    } else if distribution == "multivariate_normal" {
+        multivariate_normal;
+    }
+}

src/maths/sufficient_statistics.rs

@@ -73,8 +73,21 @@ impl Network {
     /// * `value_children` - The indexes of the node's value children.
     /// * `volatility_children` - The indexes of the node's volatility children.
     /// * `volatility_parents` - The indexes of the node's volatility parents.
-    #[pyo3(signature = (kind="continuous-state", value_parents=None, value_children=None, volatility_parents=None, volatility_children=None,))]
-    pub fn add_nodes(&mut self, kind: &str, value_parents: Option<Vec<usize>>, 
+    #[pyo3(signature = (
+        kind="continuous-state", 
+        value_parents=None, 
+        value_children=None, 
+        volatility_parents=None, 
+        volatility_children=None,
+        ef_dimension=None,
+        ef_distribution=None,
+        ef_learning=None,
+    )
+    )]
+    pub fn add_nodes(
+        &mut self, 
+        kind: &str, 
+        value_parents: Option<Vec<usize>>, 
         value_children: Option<Vec<usize>>,
         volatility_parents: Option<Vec<usize>>, volatility_children: Option<Vec<usize>>, )  {
 
@@ -86,6 +99,7 @@ impl Network {
             self.inputs.push(node_id);
         }
 
+        // Update the edges variable
         let edges = AdjacencyLists{
             node_type: String::from(kind),
             value_parents: value_parents,
@@ -94,6 +108,11 @@ impl Network {
             volatility_children: volatility_children,
         };
 
+        // Add emtpy adjacency lists in the new node
+        self.edges.insert(node_id, edges);
+
+        // TODO: Update the edges of parents and children accordingly
+
         // add edges and attributes
         if kind == "continuous-state" {
 
@@ -107,7 +126,6 @@ impl Network {
                 (String::from("autoconnection_strength"), 1.0)].into_iter().collect();
 
             self.attributes.floats.insert(node_id, attributes);
-            self.edges.insert(node_id, edges);
 
         } else if kind == "ef-state" {
 
@@ -123,6 +141,7 @@ impl Network {
 
         }
     }
+}
 
     pub fn set_update_sequence(&mut self) {
         self.update_sequence = set_update_sequence(self);

src/model.rs

@@ -3,7 +3,7 @@ use crate::model::Network;
 /// Prediction from a continuous state node
 /// 
 /// # Arguments
-/// * `network` - The main network containing the node.
+/// * `network` - The main network structure.
 /// * `node_idx` - The node index.
 /// 
 /// # Returns

src/updates/prediction/continuous.rs

@@ -1,15 +1,15 @@
 use crate::model::Network;
-use crate::math::sufficient_statistics;
 
 /// Updating an exponential family state node
 /// 
 /// # Arguments
 /// * `network` - The main network containing the node.
 /// * `node_idx` - The node index.
+/// * `sufficient_statistics` - A function computing the sufficient statistics of an exponential family distribution.
 /// 
 /// # Returns
 /// * `network` - The network after message passing.
-pub fn prediction_error_exponential_state_node(network: &mut Network, node_idx: usize) {
+pub fn prediction_error_exponential_state_node(network: &mut Network, node_idx: usize, sufficient_statistics: fn(&f64) -> Vec<f64>) {
 
     let floats_attributes = network.attributes.floats.get_mut(&node_idx).expect("No floats attributes");
     let vectors_attributes = network.attributes.vectors.get_mut(&node_idx).expect("No vector attributes");

src/updates/prediction_error/exponential.rs

@@ -1,5 +1,6 @@
 use crate::{model::{AdjacencyLists, Network, UpdateSequence}, updates::{posterior::continuous::posterior_update_continuous_state_node, prediction::continuous::prediction_continuous_state_node, prediction_error::{continuous::prediction_error_continuous_state_node, exponential::prediction_error_exponential_state_node}}};
 use crate::utils::function_pointer::FnType;
+use crate::maths::sufficient_statistics::get_sufficient_statistics_fn;
 
 pub fn set_update_sequence(network: &Network) -> UpdateSequence {
     let predictions = get_predictions_sequence(network);