Added doc to: params, process, parameter_learning, ctmp, ctbn

reCTBN/src/parameter_learning.rs

@@ -7,7 +7,17 @@ use ndarray::prelude::*;
 use crate::params::*;
 use crate::{process, tools::Dataset};
 
+/// It defines the required methods for learn the `Parameters` from data.
 pub trait ParameterLearning: Sync {
+    /// Fit the parameter of the `node` over a `dataset` given a `parent_set`
+    ///
+    /// # Arguments
+    ///
+    /// * `net`: a `NetworkProcess` instance
+    /// * `dataset`: a dataset compatible with `net` used for computing the sufficient statistics
+    /// * `node`: the node index for which we want to compute the sufficient statistics
+    /// * `parent_set`: an `Option` containing the parent set used for computing the parameters of
+    /// `node`. If `None`, the parent set defined in `net` will be used.
     fn fit<T: process::NetworkProcess>(
         &self,
         net: &T,
@@ -17,6 +27,19 @@ pub trait ParameterLearning: Sync {
     ) -> Params;
 }
 
+/// Compute the sufficient statistics of a parameters computed from a dataset
+///
+/// # Arguments
+///
+/// * `net`: a `NetworkProcess` instance
+/// * `dataset`: a dataset compatible with `net` used for computing the sufficient statistics
+/// * `node`: the node index for which we want to compute the sufficient statistics
+/// * `parent_set`: the set of nodes (identified by indices) we want to use as parents of `node`
+///
+/// # Return
+///
+///  * A tuple containing the number of transitions (`Array3<usize>`) and the residence time
+///  (`Array2<f64>`).
 pub fn sufficient_statistics<T: process::NetworkProcess>(
     net: &T,
     dataset: &Dataset,
@@ -70,6 +93,8 @@ pub fn sufficient_statistics<T: process::NetworkProcess>(
     return (M, T);
 }
 
+
+/// Maximum Likelihood Estimation method for learning the parameters given a dataset.
 pub struct MLE {}
 
 impl ParameterLearning for MLE {
@@ -114,6 +139,13 @@ impl ParameterLearning for MLE {
     }
 }
 
+
+/// Bayesian Approach for learning the parameters given a dataset.
+///
+/// # Arguments 
+///
+/// `alpha`: hyperparameter for the priori over the number of transitions.
+/// `tau`: hyperparameter for the priori over the residence time.
 pub struct BayesianApproach {
     pub alpha: usize,
     pub tau: f64,

reCTBN/src/params.rs

@@ -3,7 +3,7 @@
 use std::collections::BTreeSet;
 
 use enum_dispatch::enum_dispatch;
-use log::{debug, error, info, trace, warn};
+use log::{debug, error, trace, warn};
 use ndarray::prelude::*;
 use rand::Rng;
 use rand_chacha::ChaCha8Rng;
@@ -262,8 +262,8 @@ impl ParamsTrait for DiscreteStatesContinousTimeParams {
             Option::None => {
                 warn!("Cim not initialized for node {}", self.get_label());
                 Err(ParamsError::ParametersNotInitialized(String::from(
-                "CIM not initialized",
-            )))
+                    "CIM not initialized",
+                )))
             }
         }
     }
@@ -305,7 +305,10 @@ impl ParamsTrait for DiscreteStatesContinousTimeParams {
             .axis_iter(Axis(0))
             .any(|x| x.diag().iter().any(|x| x >= &0.0))
         {
-            warn!("The diagonal of each cim for node {} must be non-positive", self.get_label());
+            warn!(
+                "The diagonal of each cim for node {} must be non-positive",
+                self.get_label()
+            );
             return Err(ParamsError::InvalidCIM(String::from(
                 "The diagonal of each cim must be non-positive",
             )));
@@ -317,7 +320,10 @@ impl ParamsTrait for DiscreteStatesContinousTimeParams {
             .iter()
             .any(|x| f64::abs(x.clone()) > f64::EPSILON.sqrt())
         {
-            warn!("The sum of each row of the cim for node {} must be 0", self.get_label());
+            warn!(
+                "The sum of each row of the cim for node {} must be 0",
+                self.get_label()
+            );
             return Err(ParamsError::InvalidCIM(String::from(
                 "The sum of each row must be 0",
             )));

reCTBN/src/process.rs

@@ -23,8 +23,20 @@ pub type NetworkProcessState = Vec<params::StateType>;
 /// as a CTBN).
 pub trait NetworkProcess: Sync {
     fn initialize_adj_matrix(&mut self);
+
+    /// Add a **node** to the network
+    ///
+    ///  # Arguments
+    ///
+    ///  * `n` - instantiation of the `enum params::Params` describing a node
+    ///
+    ///  # Return
+    ///
+    ///  * A `Result` containing the `node_idx` automatically assigned if everything is fine, 
+    ///    or a `NetworkError` if something went wrong.
     fn add_node(&mut self, n: params::Params) -> Result<usize, NetworkError>;
-    /// Add an **directed edge** between a two nodes of the network.
+
+    /// Add a **directed edge** between a two nodes of the network.
     ///
     /// # Arguments
     ///

reCTBN/src/process/ctbn.rs

@@ -2,6 +2,7 @@
 
 use std::collections::BTreeSet;
 
+use log::info;
 use ndarray::prelude::*;
 
 use crate::params::{DiscreteStatesContinousTimeParams, Params, ParamsTrait, StateType};
@@ -77,6 +78,7 @@ impl CtbnNetwork {
     ///
     /// * The equivalent *CtmpProcess* computed from the current CtbnNetwork
     pub fn amalgamation(&self) -> CtmpProcess {
+        info!("Network Amalgamation Started");
         let variables_domain =
             Array1::from_iter(self.nodes.iter().map(|x| x.get_reserved_space_as_parent()));
 
@@ -141,14 +143,12 @@ impl CtbnNetwork {
 }
 
 impl process::NetworkProcess for CtbnNetwork {
-    /// Initialize an Adjacency matrix.
     fn initialize_adj_matrix(&mut self) {
         self.adj_matrix = Some(Array2::<u16>::zeros(
             (self.nodes.len(), self.nodes.len()).f(),
         ));
     }
 
-    /// Add a new node.
     fn add_node(&mut self, mut n: Params) -> Result<usize, process::NetworkError> {
         n.reset_params();
         self.adj_matrix = Option::None;
@@ -156,7 +156,6 @@ impl process::NetworkProcess for CtbnNetwork {
         Ok(self.nodes.len() - 1)
     }
 
-    /// Connect two nodes with a new edge.
     fn add_edge(&mut self, parent: usize, child: usize) {
         if let None = self.adj_matrix {
             self.initialize_adj_matrix();
@@ -172,7 +171,6 @@ impl process::NetworkProcess for CtbnNetwork {
         0..self.nodes.len()
     }
 
-    /// Get the number of nodes of the network.
     fn get_number_of_nodes(&self) -> usize {
         self.nodes.len()
     }
@@ -217,7 +215,6 @@ impl process::NetworkProcess for CtbnNetwork {
             .0
     }
 
-    /// Get all the parents of the given node.
     fn get_parent_set(&self, node: usize) -> BTreeSet<usize> {
         self.adj_matrix
             .as_ref()
@@ -229,7 +226,6 @@ impl process::NetworkProcess for CtbnNetwork {
             .collect()
     }
 
-    /// Get all the children of the given node.
     fn get_children_set(&self, node: usize) -> BTreeSet<usize> {
         self.adj_matrix
             .as_ref()

reCTBN/src/process/ctmp.rs

@@ -6,7 +6,7 @@ use crate::{
 };
 
 use super::{NetworkProcess, NetworkProcessState};
-use log::{debug, error, info, trace, warn};
+use log::warn;
 
 /// This structure represents a Continuous Time Markov process
 ///
@@ -100,8 +100,9 @@ impl NetworkProcess for CtmpProcess {
             Some(_) => {
                 warn!("A CTMP do not support more than one node");
                 Err(process::NetworkError::NodeInsertionError(
-                "CtmpProcess has only one node".to_string(),
-            ))}
+                    "CtmpProcess has only one node".to_string(),
+                ))
+            }
         }
     }