reCTBN/src/lib.rs

@@ -6,6 +6,7 @@ extern crate approx;
 pub mod parameter_learning;
 pub mod params;
 pub mod process;
+pub mod reward_function;
 pub mod sampling;
 pub mod structure_learning;
 pub mod tools;

reCTBN/src/process.rs

@@ -16,6 +16,9 @@ pub enum NetworkError {
     NodeInsertionError(String),
 }
 
+/// This type is used to represent a specific realization of a generic NetworkProcess
+pub type NetworkProcessState = Vec<params::StateType>;
+
 /// It defines the required methods for a structure used as a Probabilistic Graphical Models (such
 /// as a CTBN).
 pub trait NetworkProcess {
@@ -71,8 +74,7 @@ pub trait NetworkProcess {
     /// # Return
     ///
     /// * Index of the `node` relative to the network.
-    fn get_param_index_network(&self, node: usize, current_state: &Vec<params::StateType>)
-        -> usize;
+    fn get_param_index_network(&self, node: usize, current_state: &NetworkProcessState) -> usize;
 
     /// Compute the index that must be used to access the parameters of a `node`, given a specific
     /// configuration of the network and a generic `parent_set`.

reCTBN/src/process/ctbn.rs

@@ -8,7 +8,7 @@ use crate::params::{DiscreteStatesContinousTimeParams, Params, ParamsTrait, Stat
 use crate::process;
 
 use super::ctmp::CtmpProcess;
-use super::NetworkProcess;
+use super::{NetworkProcess, NetworkProcessState};
 
 /// It represents both the structure and the parameters of a CTBN.
 ///
@@ -86,7 +86,7 @@ impl CtbnNetwork {
 
         for idx_current_state in 0..state_space {
             let current_state = CtbnNetwork::idx_to_state(&variables_domain, idx_current_state);
-            let current_state_statetype: Vec<StateType> = current_state
+            let current_state_statetype: NetworkProcessState = current_state
                 .iter()
                 .map(|x| StateType::Discrete(*x))
                 .collect();
@@ -98,7 +98,7 @@ impl CtbnNetwork {
                     let mut next_state = current_state.clone();
                     next_state[idx_node] = next_node_state;
 
-                    let next_state_statetype: Vec<StateType> =
+                    let next_state_statetype: NetworkProcessState =
                         next_state.iter().map(|x| StateType::Discrete(*x)).collect();
                     let idx_next_state = self.get_param_index_from_custom_parent_set(
                         &next_state_statetype,
@@ -119,7 +119,6 @@ impl CtbnNetwork {
             BTreeSet::from_iter((0..state_space).map(|x| x.to_string())),
         );
 
-        println!("{:?}", amalgamated_cim);
         amalgamated_param.set_cim(amalgamated_cim).unwrap();
 
         let mut ctmp = CtmpProcess::new();
@@ -186,7 +185,7 @@ impl process::NetworkProcess for CtbnNetwork {
         &mut self.nodes[node_idx]
     }
 
-    fn get_param_index_network(&self, node: usize, current_state: &Vec<StateType>) -> usize {
+    fn get_param_index_network(&self, node: usize, current_state: &NetworkProcessState) -> usize {
         self.adj_matrix
             .as_ref()
             .unwrap()
@@ -205,7 +204,7 @@ impl process::NetworkProcess for CtbnNetwork {
 
     fn get_param_index_from_custom_parent_set(
         &self,
-        current_state: &Vec<StateType>,
+        current_state: &NetworkProcessState,
         parent_set: &BTreeSet<usize>,
     ) -> usize {
         parent_set

reCTBN/src/process/ctmp.rs

@@ -5,7 +5,7 @@ use crate::{
     process,
 };
 
-use super::NetworkProcess;
+use super::{NetworkProcess, NetworkProcessState};
 
 pub struct CtmpProcess {
     param: Option<Params>,
@@ -68,11 +68,7 @@ impl NetworkProcess for CtmpProcess {
         }
     }
 
-    fn get_param_index_network(
-        &self,
-        node: usize,
-        current_state: &Vec<crate::params::StateType>,
-    ) -> usize {
+    fn get_param_index_network(&self, node: usize, current_state: &NetworkProcessState) -> usize {
         if node == 0 {
             match current_state[0] {
                 StateType::Discrete(x) => x,
@@ -84,8 +80,8 @@ impl NetworkProcess for CtmpProcess {
 
     fn get_param_index_from_custom_parent_set(
         &self,
-        _current_state: &Vec<crate::params::StateType>,
-        _parent_set: &std::collections::BTreeSet<usize>,
+        _current_state: &NetworkProcessState,
+        _parent_set: &BTreeSet<usize>,
     ) -> usize {
         unimplemented!("CtmpProcess has only one node")
     }

reCTBN/src/reward_function.rs

@@ -0,0 +1,142 @@
+//! Module for dealing with reward functions
+
+use crate::{
+    params::{self, ParamsTrait},
+    process,
+};
+use ndarray;
+
+/// Instantiation of reward function and instantaneous reward
+///
+///
+/// # Arguments
+///
+/// * `transition_reward`: reward obtained transitioning from one state to another
+/// * `instantaneous_reward`: reward per unit of time obtained staying in a specific state
+
+#[derive(Debug, PartialEq)]
+pub struct Reward {
+    pub transition_reward: f64,
+    pub instantaneous_reward: f64,
+}
+
+/// The trait RewardFunction describe the methods that all the reward functions must satisfy
+
+pub trait RewardFunction {
+    /// Given the current state and the previous state, it compute the reward.
+    ///
+    /// # Arguments
+    ///
+    /// * `current_state`: the current state of the network represented as a `process::NetworkProcessState`
+    /// * `previous_state`: an optional argument representing the previous state of the network
+
+    fn call(
+        &self,
+        current_state: process::NetworkProcessState,
+        previous_state: Option<process::NetworkProcessState>,
+    ) -> Reward;
+
+    /// Initialize the RewardFunction internal accordingly to the structure of a NetworkProcess
+    ///
+    /// # Arguments
+    ///
+    /// * `p`: any structure that implements the trait `process::NetworkProcess`
+    fn initialize_from_network_process<T: process::NetworkProcess>(p: &T) -> Self;
+}
+
+/// Reward function over a factored state space
+///
+/// The `FactoredRewardFunction` assume the reward function is the sum of the reward of each node
+/// of the underling `NetworkProcess`
+///
+/// # Arguments
+///
+/// * `transition_reward`: a vector of two-dimensional arrays. Each array contains the transition
+/// reward of a node
+
+pub struct FactoredRewardFunction {
+    transition_reward: Vec<ndarray::Array2<f64>>,
+    instantaneous_reward: Vec<ndarray::Array1<f64>>,
+}
+
+impl FactoredRewardFunction {
+    pub fn get_transition_reward(&self, node_idx: usize) -> &ndarray::Array2<f64> {
+        &self.transition_reward[node_idx]
+    }
+
+    pub fn get_transition_reward_mut(&mut self, node_idx: usize) -> &mut ndarray::Array2<f64> {
+        &mut self.transition_reward[node_idx]
+    }
+
+    pub fn get_instantaneous_reward(&self, node_idx: usize) -> &ndarray::Array1<f64> {
+        &self.instantaneous_reward[node_idx]
+    }
+
+    pub fn get_instantaneous_reward_mut(&mut self, node_idx: usize) -> &mut ndarray::Array1<f64> {
+        &mut self.instantaneous_reward[node_idx]
+    }
+}
+
+impl RewardFunction for FactoredRewardFunction {
+    fn call(
+        &self,
+        current_state: process::NetworkProcessState,
+        previous_state: Option<process::NetworkProcessState>,
+    ) -> Reward {
+        let instantaneous_reward: f64 = current_state
+            .iter()
+            .enumerate()
+            .map(|(idx, x)| {
+                let x = match x {
+                    params::StateType::Discrete(x) => x,
+                };
+                self.instantaneous_reward[idx][*x]
+            })
+            .sum();
+        if let Some(previous_state) = previous_state {
+            let transition_reward = previous_state
+                .iter()
+                .zip(current_state.iter())
+                .enumerate()
+                .find_map(|(idx, (p, c))| -> Option<f64> {
+                    let p = match p {
+                        params::StateType::Discrete(p) => p,
+                    };
+                    let c = match c {
+                        params::StateType::Discrete(c) => c,
+                    };
+                    if p != c {
+                        Some(self.transition_reward[idx][[*p, *c]])
+                    } else {
+                        None
+                    }
+                })
+                .unwrap_or(0.0);
+            Reward {
+                transition_reward,
+                instantaneous_reward,
+            }
+        } else {
+            Reward {
+                transition_reward: 0.0,
+                instantaneous_reward,
+            }
+        }
+    }
+
+    fn initialize_from_network_process<T: process::NetworkProcess>(p: &T) -> Self {
+        let mut transition_reward: Vec<ndarray::Array2<f64>> = vec![];
+        let mut instantaneous_reward: Vec<ndarray::Array1<f64>> = vec![];
+        for i in p.get_node_indices() {
+            //This works only for discrete nodes!
+            let size: usize = p.get_node(i).get_reserved_space_as_parent();
+            instantaneous_reward.push(ndarray::Array1::zeros(size));
+            transition_reward.push(ndarray::Array2::zeros((size, size)));
+        }
+
+        FactoredRewardFunction {
+            transition_reward,
+            instantaneous_reward,
+        }
+    }
+}

reCTBN/src/sampling.rs

@@ -1,13 +1,19 @@
 //! Module containing methods for the sampling.
 
 use crate::{
-    params::{self, ParamsTrait},
-    process::NetworkProcess,
+    params::ParamsTrait,
+    process::{NetworkProcess, NetworkProcessState},
 };
 use rand::SeedableRng;
 use rand_chacha::ChaCha8Rng;
 
-pub trait Sampler: Iterator {
+#[derive(Clone)]
+pub struct Sample {
+    pub t: f64,
+    pub state: NetworkProcessState,
+}
+
+pub trait Sampler: Iterator<Item = Sample> {
     fn reset(&mut self);
 }
 
@@ -18,7 +24,7 @@ where
     net: &'a T,
     rng: ChaCha8Rng,
     current_time: f64,
-    current_state: Vec<params::StateType>,
+    current_state: NetworkProcessState,
     next_transitions: Vec<Option<f64>>,
 }
 
@@ -43,7 +49,7 @@ impl<'a, T: NetworkProcess> ForwardSampler<'a, T> {
 }
 
 impl<'a, T: NetworkProcess> Iterator for ForwardSampler<'a, T> {
-    type Item = (f64, Vec<params::StateType>);
+    type Item = Sample;
 
     fn next(&mut self) -> Option<Self::Item> {
         let ret_time = self.current_time.clone();
@@ -96,7 +102,10 @@ impl<'a, T: NetworkProcess> Iterator for ForwardSampler<'a, T> {
             self.next_transitions[child] = None;
         }
 
-        Some((ret_time, ret_state))
+        Some(Sample {
+            t: ret_time,
+            state: ret_state,
+        })
     }
 }
 

reCTBN/src/tools.rs

@@ -69,18 +69,18 @@ pub fn trajectory_generator<T: process::NetworkProcess>(
         let mut time: Vec<f64> = Vec::new();
         //Configuration of the process variables at time t initialized with an uniform
         //distribution.
-        let mut events: Vec<Vec<params::StateType>> = Vec::new();
+        let mut events: Vec<process::NetworkProcessState> = Vec::new();
 
         //Current Time and Current State
-        let (mut t, mut current_state) = sampler.next().unwrap();
+        let mut sample = sampler.next().unwrap();
         //Generate new samples until ending time is reached.
-        while t < t_end {
-            time.push(t);
-            events.push(current_state);
-            (t, current_state) = sampler.next().unwrap();
+        while sample.t < t_end {
+            time.push(sample.t);
+            events.push(sample.state);
+            sample = sampler.next().unwrap();
         }
 
-        current_state = events.last().unwrap().clone();
+        let current_state = events.last().unwrap().clone();
         events.push(current_state);
 
         //Add t_end as last time.

reCTBN/tests/reward_function.rs

@@ -0,0 +1,118 @@
+mod utils;
+
+use ndarray::*;
+use utils::generate_discrete_time_continous_node;
+use reCTBN::{process::{NetworkProcess, ctbn::*, NetworkProcessState}, reward_function::*, params};
+
+
+#[test]
+fn simple_factored_reward_function_binary_node() {
+    let mut net = CtbnNetwork::new();
+    let n1 = net
+        .add_node(generate_discrete_time_continous_node(String::from("n1"), 2))
+        .unwrap();
+
+    let mut rf = FactoredRewardFunction::initialize_from_network_process(&net);
+    rf.get_transition_reward_mut(n1).assign(&arr2(&[[12.0, 1.0],[2.0,12.0]]));
+    rf.get_instantaneous_reward_mut(n1).assign(&arr1(&[3.0,5.0]));
+    
+    let s0: NetworkProcessState = vec![params::StateType::Discrete(0)];
+    let s1: NetworkProcessState =  vec![params::StateType::Discrete(1)];
+    assert_eq!(rf.call(s0.clone(), None), Reward{transition_reward: 0.0, instantaneous_reward: 3.0});
+    assert_eq!(rf.call(s1.clone(), None), Reward{transition_reward: 0.0, instantaneous_reward: 5.0});
+
+
+    assert_eq!(rf.call(s0.clone(), Some(s1.clone())), Reward{transition_reward: 2.0, instantaneous_reward: 3.0});
+    assert_eq!(rf.call(s1.clone(), Some(s0.clone())), Reward{transition_reward: 1.0, instantaneous_reward: 5.0});
+
+    assert_eq!(rf.call(s0.clone(), Some(s0.clone())), Reward{transition_reward: 0.0, instantaneous_reward: 3.0});
+    assert_eq!(rf.call(s1.clone(), Some(s1.clone())), Reward{transition_reward: 0.0, instantaneous_reward: 5.0});
+}
+
+
+#[test]
+fn simple_factored_reward_function_ternary_node() {
+    let mut net = CtbnNetwork::new();
+    let n1 = net
+        .add_node(generate_discrete_time_continous_node(String::from("n1"), 3))
+        .unwrap();
+
+    let mut rf = FactoredRewardFunction::initialize_from_network_process(&net);
+    rf.get_transition_reward_mut(n1).assign(&arr2(&[[0.0, 1.0, 3.0],[2.0,0.0, 4.0], [5.0, 6.0, 0.0]]));
+    rf.get_instantaneous_reward_mut(n1).assign(&arr1(&[3.0,5.0, 9.0]));
+    
+    let s0: NetworkProcessState = vec![params::StateType::Discrete(0)];
+    let s1: NetworkProcessState = vec![params::StateType::Discrete(1)];
+    let s2: NetworkProcessState = vec![params::StateType::Discrete(2)];
+
+
+    assert_eq!(rf.call(s0.clone(), Some(s1.clone())), Reward{transition_reward: 2.0, instantaneous_reward: 3.0});
+    assert_eq!(rf.call(s0.clone(), Some(s2.clone())), Reward{transition_reward: 5.0, instantaneous_reward: 3.0});
+
+
+    assert_eq!(rf.call(s1.clone(), Some(s0.clone())), Reward{transition_reward: 1.0, instantaneous_reward: 5.0});
+    assert_eq!(rf.call(s1.clone(), Some(s2.clone())), Reward{transition_reward: 6.0, instantaneous_reward: 5.0});
+
+
+    assert_eq!(rf.call(s2.clone(), Some(s0.clone())), Reward{transition_reward: 3.0, instantaneous_reward: 9.0});
+    assert_eq!(rf.call(s2.clone(), Some(s1.clone())), Reward{transition_reward: 4.0, instantaneous_reward: 9.0});
+}
+
+#[test]
+fn factored_reward_function_two_nodes() {
+    let mut net = CtbnNetwork::new();
+    let n1 = net
+        .add_node(generate_discrete_time_continous_node(String::from("n1"), 3))
+        .unwrap();
+    let n2 = net
+        .add_node(generate_discrete_time_continous_node(String::from("n2"), 2))
+        .unwrap();
+    net.add_edge(n1, n2);
+
+
+    let mut rf = FactoredRewardFunction::initialize_from_network_process(&net);
+    rf.get_transition_reward_mut(n1).assign(&arr2(&[[0.0, 1.0, 3.0],[2.0,0.0, 4.0], [5.0, 6.0, 0.0]]));
+    rf.get_instantaneous_reward_mut(n1).assign(&arr1(&[3.0,5.0, 9.0]));
+
+
+    rf.get_transition_reward_mut(n2).assign(&arr2(&[[12.0, 1.0],[2.0,12.0]]));
+    rf.get_instantaneous_reward_mut(n2).assign(&arr1(&[3.0,5.0]));
+    
+    let s00: NetworkProcessState = vec![params::StateType::Discrete(0), params::StateType::Discrete(0)];
+    let s01: NetworkProcessState = vec![params::StateType::Discrete(1), params::StateType::Discrete(0)];
+    let s02: NetworkProcessState = vec![params::StateType::Discrete(2), params::StateType::Discrete(0)];
+
+
+    let s10: NetworkProcessState = vec![params::StateType::Discrete(0), params::StateType::Discrete(1)];
+    let s11: NetworkProcessState = vec![params::StateType::Discrete(1), params::StateType::Discrete(1)];
+    let s12: NetworkProcessState = vec![params::StateType::Discrete(2), params::StateType::Discrete(1)];
+
+    assert_eq!(rf.call(s00.clone(), Some(s01.clone())), Reward{transition_reward: 2.0, instantaneous_reward: 6.0});
+    assert_eq!(rf.call(s00.clone(), Some(s02.clone())), Reward{transition_reward: 5.0, instantaneous_reward: 6.0});
+    assert_eq!(rf.call(s00.clone(), Some(s10.clone())), Reward{transition_reward: 2.0, instantaneous_reward: 6.0});
+
+
+    assert_eq!(rf.call(s01.clone(), Some(s00.clone())), Reward{transition_reward: 1.0, instantaneous_reward: 8.0});
+    assert_eq!(rf.call(s01.clone(), Some(s02.clone())), Reward{transition_reward: 6.0, instantaneous_reward: 8.0});
+    assert_eq!(rf.call(s01.clone(), Some(s11.clone())), Reward{transition_reward: 2.0, instantaneous_reward: 8.0});
+
+
+    assert_eq!(rf.call(s02.clone(), Some(s00.clone())), Reward{transition_reward: 3.0, instantaneous_reward: 12.0});
+    assert_eq!(rf.call(s02.clone(), Some(s01.clone())), Reward{transition_reward: 4.0, instantaneous_reward: 12.0});
+    assert_eq!(rf.call(s02.clone(), Some(s12.clone())), Reward{transition_reward: 2.0, instantaneous_reward: 12.0});
+
+
+    assert_eq!(rf.call(s10.clone(), Some(s11.clone())), Reward{transition_reward: 2.0, instantaneous_reward: 8.0});
+    assert_eq!(rf.call(s10.clone(), Some(s12.clone())), Reward{transition_reward: 5.0, instantaneous_reward: 8.0});
+    assert_eq!(rf.call(s10.clone(), Some(s00.clone())), Reward{transition_reward: 1.0, instantaneous_reward: 8.0});
+    
+
+    assert_eq!(rf.call(s11.clone(), Some(s10.clone())), Reward{transition_reward: 1.0, instantaneous_reward: 10.0});
+    assert_eq!(rf.call(s11.clone(), Some(s12.clone())), Reward{transition_reward: 6.0, instantaneous_reward: 10.0});
+    assert_eq!(rf.call(s11.clone(), Some(s01.clone())), Reward{transition_reward: 1.0, instantaneous_reward: 10.0});
+
+
+    assert_eq!(rf.call(s12.clone(), Some(s10.clone())), Reward{transition_reward: 3.0, instantaneous_reward: 14.0});
+    assert_eq!(rf.call(s12.clone(), Some(s11.clone())), Reward{transition_reward: 4.0, instantaneous_reward: 14.0});
+    assert_eq!(rf.call(s12.clone(), Some(s02.clone())), Reward{transition_reward: 1.0, instantaneous_reward: 14.0});
+}