Added comments

reCTBN/src/lib.rs

@@ -6,7 +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;
-pub mod reward_function;

reCTBN/src/reward_function.rs

@@ -1,22 +1,62 @@
-use crate::{process, sampling, params::{ParamsTrait, self}};
+//! Module for dealing with reward functions
+
+use crate::{
+    params::{self, ParamsTrait},
+    process, sampling,
+};
 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
+    pub instantaneous_reward: f64,
 }
 
+/// The trait RewardFunction describe the methods that all the reward functions must satisfy
+
 pub trait RewardFunction {
-    fn call(&self, current_state: sampling::Sample, previous_state: Option<sampling::Sample>) -> Reward;
+    /// Given the current state and the previous state, it compute the reward.
+    ///
+    /// # Arguments
+    ///
+    /// * `current_state`: the current state of the network represented as a `sampling::Sample`
+    /// * `previous_state`: an optional argument representing the previous state of the network
+
+    fn call(
+        &self,
+        current_state: sampling::Sample,
+        previous_state: Option<sampling::Sample>,
+    ) -> 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>>
+    instantaneous_reward: Vec<ndarray::Array1<f64>>,
 }
 
 impl FactoredRewardFunction {
@@ -35,30 +75,54 @@ impl FactoredRewardFunction {
     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(
-    fn call(&self, current_state: sampling::Sample, previous_state: Option<sampling::Sample>) -> Reward {
+        &self,
-        let instantaneous_reward: f64 =  current_state.state.iter().enumerate().map(|(idx, x)| {
+        current_state: sampling::Sample,
-            let x = match x {params::StateType::Discrete(x) => x};
+        previous_state: Option<sampling::Sample>,
+    ) -> Reward {
+        let instantaneous_reward: f64 = current_state
+            .state
+            .iter()
+            .enumerate()
+            .map(|(idx, x)| {
+                let x = match x {
+                    params::StateType::Discrete(x) => x,
+                };
                 self.instantaneous_reward[idx][*x]
-        }).sum();
+            })
+            .sum();
         if let Some(previous_state) = previous_state {
-            let transition_reward = previous_state.state.iter().zip(current_state.state.iter()).enumerate().find_map(|(idx,(p,c))|->Option<f64> {
+            let transition_reward = previous_state
-            let p = match p {params::StateType::Discrete(p) => p};
+                .state
-            let c = match c {params::StateType::Discrete(c) => c};
+                .iter()
+                .zip(current_state.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]])
+                        Some(self.transition_reward[idx][[*p, *c]])
                     } else {
                         None
                     }
-            }).unwrap_or(0.0);
+                })
-            Reward {transition_reward, instantaneous_reward}
+                .unwrap_or(0.0);
+            Reward {
+                transition_reward,
+                instantaneous_reward,
+            }
         } else {
-            Reward { transition_reward: 0.0, instantaneous_reward}
+            Reward {
+                transition_reward: 0.0,
+                instantaneous_reward,
+            }
         }
     }
 
@@ -72,9 +136,9 @@ impl RewardFunction for FactoredRewardFunction {
             transition_reward.push(ndarray::Array2::zeros((size, size)));
         }
 
-        FactoredRewardFunction { transition_reward, instantaneous_reward }
+        FactoredRewardFunction {
-        
+            transition_reward,
+            instantaneous_reward,
+        }
     }
-
 }
-

reCTBN/src/sampling.rs

@@ -10,15 +10,13 @@ use rand_chacha::ChaCha8Rng;
 #[derive(Clone)]
 pub struct Sample {
     pub t: f64,
-    pub state: Vec<params::StateType>
+    pub state: Vec<params::StateType>,
 }
 
 pub trait Sampler: Iterator<Item = Sample> {
     fn reset(&mut self);
 }
 
-
-
 pub struct ForwardSampler<'a, T>
 where
     T: NetworkProcess,
@@ -104,7 +102,10 @@ impl<'a, T: NetworkProcess> Iterator for ForwardSampler<'a, T> {
             self.next_transitions[child] = None;
         }
 
-        Some(Sample{t: ret_time, state: ret_state})
+        Some(Sample {
+            t: ret_time,
+            state: ret_state,
+        })
     }
 }