Added ctmp

reCTBN/src/lib.rs

@@ -3,10 +3,9 @@
 #[cfg(test)]
 extern crate approx;
 
-pub mod ctbn;
-pub mod network;
 pub mod parameter_learning;
 pub mod params;
 pub mod sampling;
 pub mod structure_learning;
 pub mod tools;
+pub mod process;

reCTBN/src/parameter_learning.rs

@@ -5,10 +5,10 @@ use std::collections::BTreeSet;
 use ndarray::prelude::*;
 
 use crate::params::*;
-use crate::{network, tools};
+use crate::{process, tools};
 
 pub trait ParameterLearning {
-    fn fit<T: network::Network>(
+    fn fit<T: process::NetworkProcess>(
         &self,
         net: &T,
         dataset: &tools::Dataset,
@@ -17,7 +17,7 @@ pub trait ParameterLearning {
     ) -> Params;
 }
 
-pub fn sufficient_statistics<T: network::Network>(
+pub fn sufficient_statistics<T: process::NetworkProcess>(
     net: &T,
     dataset: &tools::Dataset,
     node: usize,
@@ -73,7 +73,7 @@ pub fn sufficient_statistics<T: network::Network>(
 pub struct MLE {}
 
 impl ParameterLearning for MLE {
-    fn fit<T: network::Network>(
+    fn fit<T: process::NetworkProcess>(
         &self,
         net: &T,
         dataset: &tools::Dataset,
@@ -120,7 +120,7 @@ pub struct BayesianApproach {
 }
 
 impl ParameterLearning for BayesianApproach {
-    fn fit<T: network::Network>(
+    fn fit<T: process::NetworkProcess>(
         &self,
         net: &T,
         dataset: &tools::Dataset,
@@ -177,7 +177,7 @@ impl<P: ParameterLearning> Cache<P> {
             dataset,
         }
     }
-    pub fn fit<T: network::Network>(
+    pub fn fit<T: process::NetworkProcess>(
         &mut self,
         net: &T,
         node: usize,

reCTBN/src/process.rs

@@ -1,5 +1,8 @@
 //! Defines methods for dealing with Probabilistic Graphical Models like the CTBNs
 
+pub mod ctbn;
+pub mod ctmp;
+
 use std::collections::BTreeSet;
 
 use thiserror::Error;
@@ -15,7 +18,7 @@ pub enum NetworkError {
 
 /// It defines the required methods for a structure used as a Probabilistic Graphical Models (such
 /// as a CTBN).
-pub trait Network {
+pub trait NetworkProcess {
     fn initialize_adj_matrix(&mut self);
     fn add_node(&mut self, n: params::Params) -> Result<usize, NetworkError>;
     /// Add an **directed edge** between a two nodes of the network.

reCTBN/src/process/ctbn.rs

@@ -4,7 +4,7 @@ use std::collections::BTreeSet;
 
 use ndarray::prelude::*;
 
-use crate::network;
+use crate::process;
 use crate::params::{Params, ParamsTrait, StateType};
 
 /// It represents both the structure and the parameters of a CTBN.
@@ -20,9 +20,9 @@ use crate::params::{Params, ParamsTrait, StateType};
 ///
 /// ```rust
 /// use std::collections::BTreeSet;
-/// use reCTBN::network::Network;
+/// use reCTBN::process::NetworkProcess;
 /// use reCTBN::params;
-/// use reCTBN::ctbn::*;
+/// use reCTBN::process::ctbn::*;
 ///
 /// //Create the domain for a discrete node
 /// let mut domain = BTreeSet::new();
@@ -69,7 +69,7 @@ impl CtbnNetwork {
     }
 }
 
-impl network::Network for CtbnNetwork {
+impl process::NetworkProcess for CtbnNetwork {
     /// Initialize an Adjacency matrix.
     fn initialize_adj_matrix(&mut self) {
         self.adj_matrix = Some(Array2::<u16>::zeros(
@@ -78,7 +78,7 @@ impl network::Network for CtbnNetwork {
     }
 
     /// Add a new node.
-    fn add_node(&mut self, mut n: Params) -> Result<usize, network::NetworkError> {
+    fn add_node(&mut self, mut n: Params) -> Result<usize, process::NetworkError> {
         n.reset_params();
         self.adj_matrix = Option::None;
         self.nodes.push(n);

reCTBN/src/process/ctmp.rs

@@ -0,0 +1,106 @@
+use std::collections::BTreeSet;
+
+use crate::{process, params::{Params, StateType}};
+
+use super::NetworkProcess;
+
+pub struct CtmpProcess {
+    param: Option<Params>
+}
+
+impl CtmpProcess {
+    pub fn new() -> CtmpProcess {
+        CtmpProcess { param: None }
+    }
+}
+
+impl NetworkProcess for CtmpProcess {
+    fn initialize_adj_matrix(&mut self) {
+        unimplemented!("CtmpProcess has only one node")
+    }
+
+    fn add_node(&mut self, n: crate::params::Params) -> Result<usize, process::NetworkError> {
+        match self.param {
+            None => {
+                self.param = Some(n);
+                Ok(0)
+            },
+            Some(_) => Err(process::NetworkError::NodeInsertionError("CtmpProcess has only one node".to_string()))
+        }
+    }
+
+    fn add_edge(&mut self, parent: usize, child: usize) {
+        unimplemented!("CtmpProcess has only one node")
+    }
+
+    fn get_node_indices(&self) -> std::ops::Range<usize> {
+        match self.param {
+            None => 0..0,
+            Some(_) => 0..1
+        }
+    }
+
+    fn get_number_of_nodes(&self) -> usize {
+        match self.param {
+            None => 0,
+            Some(_) => 1
+        }
+    }
+
+    fn get_node(&self, node_idx: usize) -> &crate::params::Params {
+        if node_idx == 0 {
+            self.param.as_ref().unwrap()
+        } else {
+            unimplemented!("CtmpProcess has only one node")
+        }
+    }
+
+    fn get_node_mut(&mut self, node_idx: usize) -> &mut crate::params::Params {
+        if node_idx == 0 {
+            self.param.as_mut().unwrap()
+        } else {
+            unimplemented!("CtmpProcess has only one node")
+        }
+    }
+
+    fn get_param_index_network(&self, node: usize, current_state: &Vec<crate::params::StateType>)
+        -> usize {
+        if node == 0 {
+            match current_state[0] {
+                StateType::Discrete(x) => x
+            }
+        } else {
+            unimplemented!("CtmpProcess has only one node")
+        }
+    }
+
+    fn get_param_index_from_custom_parent_set(
+        &self,
+        current_state: &Vec<crate::params::StateType>,
+        parent_set: &std::collections::BTreeSet<usize>,
+    ) -> usize {
+        unimplemented!("CtmpProcess has only one node")
+    }
+
+    fn get_parent_set(&self, node: usize) -> std::collections::BTreeSet<usize> {
+        match self.param {
+            Some(_) => if node == 0 {
+                BTreeSet::new()
+            } else {
+                unimplemented!("CtmpProcess has only one node")
+            },
+            None => panic!("Uninitialized CtmpProcess")
+        }
+    }
+
+    fn get_children_set(&self, node: usize) -> std::collections::BTreeSet<usize> {
+        match self.param {
+            Some(_) => if node == 0 {
+                BTreeSet::new()
+            } else {
+                unimplemented!("CtmpProcess has only one node")
+            },
+            None => panic!("Uninitialized CtmpProcess")
+        }
+    }
+}

reCTBN/src/sampling.rs

@@ -1,7 +1,7 @@
 //! Module containing methods for the sampling.
 
 use crate::{
-    network::Network,
+    process::NetworkProcess,
     params::{self, ParamsTrait},
 };
 use rand::SeedableRng;
@@ -13,7 +13,7 @@ pub trait Sampler: Iterator {
 
 pub struct ForwardSampler<'a, T>
 where
-    T: Network,
+    T: NetworkProcess,
 {
     net: &'a T,
     rng: ChaCha8Rng,
@@ -22,7 +22,7 @@ where
     next_transitions: Vec<Option<f64>>,
 }
 
-impl<'a, T: Network> ForwardSampler<'a, T> {
+impl<'a, T: NetworkProcess> ForwardSampler<'a, T> {
     pub fn new(net: &'a T, seed: Option<u64>) -> ForwardSampler<'a, T> {
         let rng: ChaCha8Rng = match seed {
             //If a seed is present use it to initialize the random generator.
@@ -42,7 +42,7 @@ impl<'a, T: Network> ForwardSampler<'a, T> {
     }
 }
 
-impl<'a, T: Network> Iterator for ForwardSampler<'a, T> {
+impl<'a, T: NetworkProcess> Iterator for ForwardSampler<'a, T> {
     type Item = (f64, Vec<params::StateType>);
 
     fn next(&mut self) -> Option<Self::Item> {
@@ -100,7 +100,7 @@ impl<'a, T: Network> Iterator for ForwardSampler<'a, T> {
     }
 }
 
-impl<'a, T: Network> Sampler for ForwardSampler<'a, T> {
+impl<'a, T: NetworkProcess> Sampler for ForwardSampler<'a, T> {
     fn reset(&mut self) {
         self.current_time = 0.0;
         self.current_state = self

reCTBN/src/structure_learning.rs

@@ -4,10 +4,10 @@ pub mod constraint_based_algorithm;
 pub mod hypothesis_test;
 pub mod score_based_algorithm;
 pub mod score_function;
-use crate::{network, tools};
+use crate::{process, tools};
 
 pub trait StructureLearningAlgorithm {
     fn fit_transform<T>(&self, net: T, dataset: &tools::Dataset) -> T
     where
-        T: network::Network;
+        T: process::NetworkProcess;
 }

reCTBN/src/structure_learning/hypothesis_test.rs

@@ -6,7 +6,7 @@ use ndarray::{Array3, Axis};
 use statrs::distribution::{ChiSquared, ContinuousCDF};
 
 use crate::params::*;
-use crate::{network, parameter_learning};
+use crate::{process, parameter_learning};
 
 pub trait HypothesisTest {
     fn call<T, P>(
@@ -18,7 +18,7 @@ pub trait HypothesisTest {
         cache: &mut parameter_learning::Cache<P>,
     ) -> bool
     where
-        T: network::Network,
+        T: process::NetworkProcess,
         P: parameter_learning::ParameterLearning;
 }
 
@@ -135,7 +135,7 @@ impl HypothesisTest for ChiSquare {
         cache: &mut parameter_learning::Cache<P>,
     ) -> bool
     where
-        T: network::Network,
+        T: process::NetworkProcess,
         P: parameter_learning::ParameterLearning,
     {
         // Prendo dalla cache l'apprendimento dei parametri, che sarebbe una CIM

reCTBN/src/structure_learning/score_based_algorithm.rs

@@ -4,7 +4,7 @@ use std::collections::BTreeSet;
 
 use crate::structure_learning::score_function::ScoreFunction;
 use crate::structure_learning::StructureLearningAlgorithm;
-use crate::{network, tools};
+use crate::{process, tools};
 
 pub struct HillClimbing<S: ScoreFunction> {
     score_function: S,
@@ -23,7 +23,7 @@ impl<S: ScoreFunction> HillClimbing<S> {
 impl<S: ScoreFunction> StructureLearningAlgorithm for HillClimbing<S> {
     fn fit_transform<T>(&self, net: T, dataset: &tools::Dataset) -> T
     where
-        T: network::Network,
+        T: process::NetworkProcess,
     {
         //Check the coherence between dataset and network
         if net.get_number_of_nodes() != dataset.get_trajectories()[0].get_events().shape()[1] {

reCTBN/src/structure_learning/score_function.rs

@@ -5,7 +5,7 @@ use std::collections::BTreeSet;
 use ndarray::prelude::*;
 use statrs::function::gamma;
 
-use crate::{network, parameter_learning, params, tools};
+use crate::{process, parameter_learning, params, tools};
 
 pub trait ScoreFunction {
     fn call<T>(
@@ -16,7 +16,7 @@ pub trait ScoreFunction {
         dataset: &tools::Dataset,
     ) -> f64
     where
-        T: network::Network;
+        T: process::NetworkProcess;
 }
 
 pub struct LogLikelihood {
@@ -41,7 +41,7 @@ impl LogLikelihood {
         dataset: &tools::Dataset,
     ) -> (f64, Array3<usize>)
     where
-        T: network::Network,
+        T: process::NetworkProcess,
     {
         //Identify the type of node used
         match &net.get_node(node) {
@@ -100,7 +100,7 @@ impl ScoreFunction for LogLikelihood {
         dataset: &tools::Dataset,
     ) -> f64
     where
-        T: network::Network,
+        T: process::NetworkProcess,
     {
         self.compute_score(net, node, parent_set, dataset).0
     }
@@ -127,7 +127,7 @@ impl ScoreFunction for BIC {
         dataset: &tools::Dataset,
     ) -> f64
     where
-        T: network::Network,
+        T: process::NetworkProcess,
     {
         //Compute the log-likelihood
         let (ll, M) = self.ll.compute_score(net, node, parent_set, dataset);

reCTBN/src/tools.rs

@@ -3,7 +3,7 @@
 use ndarray::prelude::*;
 
 use crate::sampling::{ForwardSampler, Sampler};
-use crate::{network, params};
+use crate::{process, params};
 
 pub struct Trajectory {
     time: Array1<f64>,
@@ -51,7 +51,7 @@ impl Dataset {
     }
 }
 
-pub fn trajectory_generator<T: network::Network>(
+pub fn trajectory_generator<T: process::NetworkProcess>(
     net: &T,
     n_trajectories: u64,
     t_end: f64,

reCTBN/tests/ctbn.rs

@@ -1,8 +1,8 @@
 mod utils;
 use std::collections::BTreeSet;
 
-use reCTBN::ctbn::*;
-use reCTBN::network::Network;
+use reCTBN::process::ctbn::*;
+use reCTBN::process::NetworkProcess;
 use reCTBN::params::{self, ParamsTrait};
 use utils::generate_discrete_time_continous_node;
 

reCTBN/tests/ctmp.rs

@@ -0,0 +1,127 @@
+mod utils;
+
+use std::collections::BTreeSet;
+
+use reCTBN::{
+    params,
+    params::ParamsTrait,
+    process::{ctmp::*, NetworkProcess},
+};
+use utils::generate_discrete_time_continous_node;
+
+#[test]
+fn define_simple_ctmp() {
+    let _ = CtmpProcess::new();
+    assert!(true);
+}
+
+#[test]
+fn add_node_to_ctmp() {
+    let mut net = CtmpProcess::new();
+    let n1 = net
+        .add_node(generate_discrete_time_continous_node(String::from("n1"), 2))
+        .unwrap();
+    assert_eq!(&String::from("n1"), net.get_node(n1).get_label());
+}
+
+#[test]
+fn add_two_nodes_to_ctmp() {
+    let mut net = CtmpProcess::new();
+    let _n1 = net
+        .add_node(generate_discrete_time_continous_node(String::from("n1"), 2))
+        .unwrap();
+    let n2 = net.add_node(generate_discrete_time_continous_node(String::from("n1"), 2));
+
+    match n2 {
+        Ok(_) => assert!(false),
+        Err(_) => assert!(true),
+    };
+}
+
+#[test]
+#[should_panic]
+fn add_edge_to_ctmp() {
+    let mut net = CtmpProcess::new();
+    let _n1 = net
+        .add_node(generate_discrete_time_continous_node(String::from("n1"), 2))
+        .unwrap();
+    let n2 = net.add_node(generate_discrete_time_continous_node(String::from("n1"), 2));
+
+    net.add_edge(0, 1)
+}
+
+#[test]
+fn childen_and_parents() {
+    let mut net = CtmpProcess::new();
+    let _n1 = net
+        .add_node(generate_discrete_time_continous_node(String::from("n1"), 2))
+        .unwrap();
+
+    assert_eq!(0, net.get_parent_set(0).len());
+    assert_eq!(0, net.get_children_set(0).len());
+}
+
+#[test]
+#[should_panic]
+fn get_childen_panic() {
+    let mut net = CtmpProcess::new();
+    net.get_children_set(0);
+}
+
+#[test]
+#[should_panic]
+fn get_childen_panic2() {
+    let mut net = CtmpProcess::new();
+    let _n1 = net
+        .add_node(generate_discrete_time_continous_node(String::from("n1"), 2))
+        .unwrap();
+    net.get_children_set(1);
+}
+
+#[test]
+#[should_panic]
+fn get_parent_panic() {
+    let mut net = CtmpProcess::new();
+    net.get_parent_set(0);
+}
+
+#[test]
+#[should_panic]
+fn get_parent_panic2() {
+    let mut net = CtmpProcess::new();
+    let _n1 = net
+        .add_node(generate_discrete_time_continous_node(String::from("n1"), 2))
+        .unwrap();
+    net.get_parent_set(1);
+}
+
+#[test]
+fn compute_index_ctmp() {
+    let mut net = CtmpProcess::new();
+    let n1 = net
+        .add_node(generate_discrete_time_continous_node(
+            String::from("n1"),
+            10,
+        ))
+        .unwrap();
+
+    let idx = net.get_param_index_network(n1, &vec![params::StateType::Discrete(6)]);
+    assert_eq!(6, idx);
+}
+
+#[test]
+#[should_panic]
+fn compute_index_from_custom_parent_set_ctmp() {
+    let mut net = CtmpProcess::new();
+    let _n1 = net
+        .add_node(generate_discrete_time_continous_node(
+            String::from("n1"),
+            10,
+        ))
+        .unwrap();
+
+    let _idx = net.get_param_index_from_custom_parent_set(
+        &vec![params::StateType::Discrete(6)],
+        &BTreeSet::from([0])
+    );
+}

reCTBN/tests/parameter_learning.rs

@@ -2,8 +2,8 @@
 
 mod utils;
 use ndarray::arr3;
-use reCTBN::ctbn::*;
-use reCTBN::network::Network;
+use reCTBN::process::ctbn::*;
+use reCTBN::process::NetworkProcess;
 use reCTBN::parameter_learning::*;
 use reCTBN::params;
 use reCTBN::tools::*;

reCTBN/tests/structure_learning.rs

@@ -4,8 +4,8 @@ mod utils;
 use std::collections::BTreeSet;
 
 use ndarray::{arr1, arr2, arr3};
-use reCTBN::ctbn::*;
-use reCTBN::network::Network;
+use reCTBN::process::ctbn::*;
+use reCTBN::process::NetworkProcess;
 use reCTBN::parameter_learning::BayesianApproach;
 use reCTBN::parameter_learning::Cache;
 use reCTBN::params;

reCTBN/tests/tools.rs

@@ -1,6 +1,6 @@
 use ndarray::{arr1, arr2, arr3};
-use reCTBN::ctbn::*;
-use reCTBN::network::Network;
+use reCTBN::process::ctbn::*;
+use reCTBN::process::NetworkProcess;
 use reCTBN::params;
 use reCTBN::tools::*;