Added doctest for parameter_learning

reCTBN/src/parameter_learning.rs

@@ -95,6 +95,87 @@ pub fn sufficient_statistics<T: process::NetworkProcess>(
 
 
 /// Maximum Likelihood Estimation method for learning the parameters given a dataset.
+///
+/// # Example
+/// ```rust
+///
+/// use std::collections::BTreeSet;
+/// use reCTBN::process::NetworkProcess;
+/// use reCTBN::params;
+/// use reCTBN::process::ctbn::*;
+/// use ndarray::arr3;
+/// use reCTBN::tools::*;
+/// use reCTBN::parameter_learning::*;
+/// use approx::AbsDiffEq;
+///
+/// //Create the domain for a discrete node
+/// let mut domain = BTreeSet::new();
+/// domain.insert(String::from("A"));
+/// domain.insert(String::from("B"));
+///
+/// //Create the parameters for a discrete node using the domain
+/// let param = params::DiscreteStatesContinousTimeParams::new("X1".to_string(), domain);
+///
+/// //Create the node using the parameters
+/// let X1 = params::Params::DiscreteStatesContinousTime(param);
+///
+/// let mut domain = BTreeSet::new();
+/// domain.insert(String::from("A"));
+/// domain.insert(String::from("B"));
+/// let param = params::DiscreteStatesContinousTimeParams::new("X2".to_string(), domain);
+/// let X2 = params::Params::DiscreteStatesContinousTime(param);
+///
+/// //Initialize a ctbn
+/// let mut net = CtbnNetwork::new();
+///
+/// //Add nodes
+/// let X1 = net.add_node(X1).unwrap();
+/// let X2 = net.add_node(X2).unwrap();
+///
+/// //Add an edge
+/// net.add_edge(X1, X2);
+///
+/// //Add the CIMs for each node
+/// match &mut net.get_node_mut(X1) {
+///     params::Params::DiscreteStatesContinousTime(param) => {
+///         assert_eq!(Ok(()), param.set_cim(arr3(&[[[-0.1, 0.1], [1.0, -1.0]]])));
+///     }
+/// }
+/// match &mut net.get_node_mut(X2) {
+///     params::Params::DiscreteStatesContinousTime(param) => {
+///         assert_eq!(
+///             Ok(()),
+///             param.set_cim(arr3(&[
+///                 [[-0.01, 0.01], [5.0, -5.0]],
+///                 [[-5.0, 5.0], [0.01, -0.01]]
+///             ]))
+///         );
+///     }
+/// }
+///
+/// //Generate a synthetic dataset from net
+///  let data = trajectory_generator(&net, 100, 100.0, Some(6347747169756259));
+/// 
+/// //Initialize the `struct MLE`
+///  let pl = MLE{};
+///
+///  // Fit the parameters for X2
+///  let p = match pl.fit(&net, &data, X2, None) {
+///      params::Params::DiscreteStatesContinousTime(p) => p,
+///  };
+///
+///  // Check the shape of the CIM
+///  assert_eq!(p.get_cim().as_ref().unwrap().shape(), [2, 2, 2]);
+///
+///  // Check if the learned parameters are close enough to the real ones.
+///  assert!(p.get_cim().as_ref().unwrap().abs_diff_eq(
+///      &arr3(&[
+///                 [[-0.01, 0.01], [5.0, -5.0]],
+///                 [[-5.0, 5.0], [0.01, -0.01]]
+///      ]),
+///      0.1
+///  ));
+/// ```
 pub struct MLE {}
 
 impl ParameterLearning for MLE {
@@ -146,6 +227,88 @@ impl ParameterLearning for MLE {
 ///
 /// `alpha`: hyperparameter for the priori over the number of transitions.
 /// `tau`: hyperparameter for the priori over the residence time.
+///
+/// # Example
+///
+/// ```rust
+///
+/// use std::collections::BTreeSet;
+/// use reCTBN::process::NetworkProcess;
+/// use reCTBN::params;
+/// use reCTBN::process::ctbn::*;
+/// use ndarray::arr3;
+/// use reCTBN::tools::*;
+/// use reCTBN::parameter_learning::*;
+/// use approx::AbsDiffEq;
+///
+/// //Create the domain for a discrete node
+/// let mut domain = BTreeSet::new();
+/// domain.insert(String::from("A"));
+/// domain.insert(String::from("B"));
+///
+/// //Create the parameters for a discrete node using the domain
+/// let param = params::DiscreteStatesContinousTimeParams::new("X1".to_string(), domain);
+///
+/// //Create the node using the parameters
+/// let X1 = params::Params::DiscreteStatesContinousTime(param);
+///
+/// let mut domain = BTreeSet::new();
+/// domain.insert(String::from("A"));
+/// domain.insert(String::from("B"));
+/// let param = params::DiscreteStatesContinousTimeParams::new("X2".to_string(), domain);
+/// let X2 = params::Params::DiscreteStatesContinousTime(param);
+///
+/// //Initialize a ctbn
+/// let mut net = CtbnNetwork::new();
+///
+/// //Add nodes
+/// let X1 = net.add_node(X1).unwrap();
+/// let X2 = net.add_node(X2).unwrap();
+///
+/// //Add an edge
+/// net.add_edge(X1, X2);
+///
+/// //Add the CIMs for each node
+/// match &mut net.get_node_mut(X1) {
+///     params::Params::DiscreteStatesContinousTime(param) => {
+///         assert_eq!(Ok(()), param.set_cim(arr3(&[[[-0.1, 0.1], [1.0, -1.0]]])));
+///     }
+/// }
+/// match &mut net.get_node_mut(X2) {
+///     params::Params::DiscreteStatesContinousTime(param) => {
+///         assert_eq!(
+///             Ok(()),
+///             param.set_cim(arr3(&[
+///                 [[-0.01, 0.01], [5.0, -5.0]],
+///                 [[-5.0, 5.0], [0.01, -0.01]]
+///             ]))
+///         );
+///     }
+/// }
+///
+/// //Generate a synthetic dataset from net
+///  let data = trajectory_generator(&net, 100, 100.0, Some(6347747169756259));
+/// 
+/// //Initialize the `struct BayesianApproach`
+///  let pl = BayesianApproach{alpha: 1, tau: 1.0};
+///
+///  // Fit the parameters for X2
+///  let p = match pl.fit(&net, &data, X2, None) {
+///      params::Params::DiscreteStatesContinousTime(p) => p,
+///  };
+///
+///  // Check the shape of the CIM
+///  assert_eq!(p.get_cim().as_ref().unwrap().shape(), [2, 2, 2]);
+///
+///  // Check if the learned parameters are close enough to the real ones.
+///  assert!(p.get_cim().as_ref().unwrap().abs_diff_eq(
+///      &arr3(&[
+///                 [[-0.01, 0.01], [5.0, -5.0]],
+///                 [[-5.0, 5.0], [0.01, -0.01]]
+///      ]),
+///      0.1
+///  ));
+/// ```
 pub struct BayesianApproach {
     pub alpha: usize,
     pub tau: f64,

reCTBN/src/process/ctmp.rs

@@ -107,7 +107,7 @@ impl NetworkProcess for CtmpProcess {
     }
 
     fn add_edge(&mut self, _parent: usize, _child: usize) {
-        warn!("A CTMP cannot have endges");
+        warn!("A CTMP cannot have edges");
         unimplemented!("CtmpProcess has only one node")
     }