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