Implemented reward_evaluation for an entire process.

reCTBN/src/params.rs

@@ -20,7 +20,7 @@ pub enum ParamsError {
 }
 
 /// Allowed type of states
-#[derive(Clone)]
+#[derive(Clone, Hash, PartialEq, Eq, Debug)]
 pub enum StateType {
     Discrete(usize),
 }

reCTBN/src/reward.rs

@@ -1,6 +1,8 @@
 pub mod reward_function;
 pub mod reward_evaluation;
 
+use std::collections::HashMap;
+
 use crate::process;
 use ndarray;
 
@@ -43,12 +45,13 @@ pub trait RewardFunction {
 }
 
 pub trait RewardEvaluation {
-    fn call<N: process::NetworkProcess, R: RewardFunction>(
+    fn evaluate_state_space<N: process::NetworkProcess, R: RewardFunction>(
         &self,
         network_process: &N,
         reward_function: &R,
-    ) -> ndarray::Array1<f64>;
+    ) -> HashMap<process::NetworkProcessState, f64>;
-    fn call_state<N: process::NetworkProcess, R: RewardFunction>(
+
+    fn evaluate_state<N: process::NetworkProcess, R: RewardFunction>(
         &self,
         network_process: &N,
         reward_function: &R,

reCTBN/src/reward/reward_evaluation.rs

@@ -1,7 +1,12 @@
+use std::collections::HashMap;
+
+use crate::params::{self, ParamsTrait};
+use crate::process;
+
 use crate::{
+    process::NetworkProcessState,
     reward::RewardEvaluation,
     sampling::{ForwardSampler, Sampler},
-    process::NetworkProcessState
 };
 
 pub struct MonteCarloDiscountedRward {
@@ -28,21 +33,42 @@ impl MonteCarloDiscountedRward {
 }
 
 impl RewardEvaluation for MonteCarloDiscountedRward {
-    fn call<N: crate::process::NetworkProcess, R: super::RewardFunction>(
+    fn evaluate_state_space<N: process::NetworkProcess, R: super::RewardFunction>(
         &self,
         network_process: &N,
         reward_function: &R,
-    ) -> ndarray::Array1<f64> {
+    ) -> HashMap<process::NetworkProcessState, f64> {
-        todo!()
+        let variables_domain: Vec<Vec<params::StateType>> = network_process
+            .get_node_indices()
+            .map(|x| match network_process.get_node(x) {
+                params::Params::DiscreteStatesContinousTime(x) => 
+                    (0..x.get_reserved_space_as_parent()).map(|s| params::StateType::Discrete(s)).collect()
+                }).collect();
+
+        let n_states:usize = variables_domain.iter().map(|x| x.len()).product();
+        
+        (0..n_states).map(|s| {
+            let state: process::NetworkProcessState = variables_domain.iter().fold((s, vec![]), |acc, x| {
+                let mut acc = acc;
+                let idx_s = acc.0%x.len();
+                acc.1.push(x[idx_s].clone());
+                acc.0 = acc.0 / x.len();
+                acc
+            }).1;
+
+            let r = self.evaluate_state(network_process, reward_function, &state);
+            (state, r)
+        }).collect()
     }
 
-    fn call_state<N: crate::process::NetworkProcess, R: super::RewardFunction>(
+    fn evaluate_state<N: crate::process::NetworkProcess, R: super::RewardFunction>(
         &self,
         network_process: &N,
         reward_function: &R,
         state: &NetworkProcessState,
     ) -> f64 {
-        let mut sampler = ForwardSampler::new(network_process, self.seed.clone(), Some(state.clone()));
+        let mut sampler =
+            ForwardSampler::new(network_process, self.seed.clone(), Some(state.clone()));
         let mut ret = 0.0;
 
         for _i in 0..self.n_iterations {
@@ -60,7 +86,8 @@ impl RewardEvaluation for MonteCarloDiscountedRward {
                     let discount = std::f64::consts::E.powf(-self.discount_factor * previous.t)
                         - std::f64::consts::E.powf(-self.discount_factor * current.t);
                     ret += discount * r.instantaneous_reward;
-                    ret += std::f64::consts::E.powf(-self.discount_factor * current.t) * r.transition_reward;
+                    ret += std::f64::consts::E.powf(-self.discount_factor * current.t)
+                        * r.transition_reward;
                 }
                 previous = current;
             }

reCTBN/tests/reward_evaluation.rs

@@ -34,8 +34,13 @@ fn simple_factored_reward_function_binary_node_MC() {
     let s1: NetworkProcessState = vec![params::StateType::Discrete(1)];
 
     let mc = MonteCarloDiscountedRward::new(100, 10.0, 1.0, Some(215));
-    assert_abs_diff_eq!(3.0, mc.call_state(&net, &rf, &s0), epsilon = 1e-2);
+    assert_abs_diff_eq!(3.0, mc.evaluate_state(&net, &rf, &s0), epsilon = 1e-2);
-    assert_abs_diff_eq!(3.0, mc.call_state(&net, &rf, &s1), epsilon = 1e-2);
+    assert_abs_diff_eq!(3.0, mc.evaluate_state(&net, &rf, &s1), epsilon = 1e-2);
+    
+    let rst = mc.evaluate_state_space(&net, &rf);
+    assert_abs_diff_eq!(3.0, rst[&s0], epsilon = 1e-2);
+    assert_abs_diff_eq!(3.0, rst[&s1], epsilon = 1e-2);
+
 }
 
 #[test]
@@ -102,6 +107,10 @@ fn simple_factored_reward_function_chain_MC() {
         params::StateType::Discrete(0),
     ];
 
-    let mc = MonteCarloDiscountedRward::new(10000, 100.0, 1.0, Some(215));
+    let mc = MonteCarloDiscountedRward::new(1000, 10.0, 1.0, Some(215));
-    assert_abs_diff_eq!(2.447, mc.call_state(&net, &rf, &s000), epsilon = 1e-1);
+    assert_abs_diff_eq!(2.447, mc.evaluate_state(&net, &rf, &s000), epsilon = 1e-1);
+
+    let rst = mc.evaluate_state_space(&net, &rf);
+    assert_abs_diff_eq!(2.447, rst[&s000], epsilon = 1e-1);
+
 }