refactor: Refactor algorithm trait

examples/iov/main.rs

@@ -52,8 +52,7 @@ fn main() -> Result<()> {
     let data = data::read_pmetrics("examples/iov/test.csv").unwrap();
     let mut algorithm = dispatch_algorithm(settings, sde, data).unwrap();
     algorithm.initialize().unwrap();
-    while !algorithm.next_cycle().unwrap() {}
-    let result = algorithm.into_npresult();
+    let result = algorithm.fit().unwrap();
     result.write_outputs().unwrap();
 
     Ok(())

examples/meta/main.rs

@@ -64,7 +64,6 @@ fn main() {
     let mut algorithm = dispatch_algorithm(settings, eq, data).unwrap();
     // let result = algorithm.fit().unwrap();
     algorithm.initialize().unwrap();
-    while !algorithm.next_cycle().unwrap() {}
-    let result = algorithm.into_npresult();
+    let result = algorithm.fit().unwrap();
     result.write_outputs().unwrap();
 }

examples/new_iov/main.rs

@@ -55,7 +55,6 @@ fn main() {
     let data = data::read_pmetrics("examples/new_iov/data.csv").unwrap();
     let mut algorithm = dispatch_algorithm(settings, sde, data).unwrap();
     algorithm.initialize().unwrap();
-    while !algorithm.next_cycle().unwrap() {}
-    let result = algorithm.into_npresult();
+    let result = algorithm.fit().unwrap();
     result.write_outputs().unwrap();
 }

examples/theophylline/main.rs

@@ -53,7 +53,6 @@ fn main() {
     let mut algorithm = dispatch_algorithm(settings, eq, data).unwrap();
     // let result = algorithm.fit().unwrap();
     algorithm.initialize().unwrap();
-    while !algorithm.next_cycle().unwrap() {}
-    let result = algorithm.into_npresult();
+    let result = algorithm.fit().unwrap();
     result.write_outputs().unwrap();
 }

examples/vanco_sde/main.rs

@@ -78,7 +78,6 @@ fn main() {
 
     let mut algorithm = dispatch_algorithm(settings, sde, data).unwrap();
     algorithm.initialize().unwrap();
-    while !algorithm.next_cycle().unwrap() {}
-    let result = algorithm.into_npresult();
+    let result = algorithm.fit().unwrap();
     result.write_outputs().unwrap();
 }

src/algorithms/mod.rs

@@ -75,7 +75,7 @@ pub trait Algorithms<E: Equation + Send + 'static>: Sync + Send + 'static {
             .collect::<Vec<_>>();
 
         if !indices.is_empty() {
-            let subject: Vec<&Subject> = self.get_data().subjects();
+            let subject: Vec<&Subject> = self.data().subjects();
             let zero_probability_subjects: Vec<&String> =
                 indices.iter().map(|&i| subject[i].id()).collect();
 
@@ -89,7 +89,7 @@ pub trait Algorithms<E: Equation + Send + 'static>: Sync + Send + 'static {
             for index in &indices {
                 tracing::debug!("Subject with zero probability: {}", subject[*index].id());
 
-                let error_model = self.get_settings().errormodels().clone();
+                let error_model = self.settings().errormodels().clone();
 
                 // Simulate all support points in parallel
                 let spp_results: Vec<_> = self
@@ -207,54 +207,103 @@ pub trait Algorithms<E: Equation + Send + 'static>: Sync + Send + 'static {
 
         Ok(())
     }
-    fn get_settings(&self) -> &Settings;
+
+    fn settings(&self) -> &Settings;
+    /// Get the equation used in the algorithm
     fn equation(&self) -> &E;
-    fn get_data(&self) -> &Data;
+    /// Get the data used in the algorithm
+    fn data(&self) -> &Data;
     fn get_prior(&self) -> Theta;
-    fn inc_cycle(&mut self) -> usize;
-    fn get_cycle(&self) -> usize;
+    /// Increment the cycle counter and return the new value
+    fn increment_cycle(&mut self) -> usize;
+    /// Get the current cycle number
+    fn cycle(&self) -> usize;
+    /// Set the current [Theta]
     fn set_theta(&mut self, theta: Theta);
+    /// Get the current [Theta]
     fn theta(&self) -> &Theta;
+    /// Get the current [Psi]
     fn psi(&self) -> &Psi;
+    /// Get the current likelihood
     fn likelihood(&self) -> f64;
+    /// Get the current negative two log-likelihood
     fn n2ll(&self) -> f64 {
         -2.0 * self.likelihood()
     }
+    /// Get the current [Status] of the algorithm
     fn status(&self) -> &Status;
+    /// Set the current [Status] of the algorithm
     fn set_status(&mut self, status: Status);
-    fn convergence_evaluation(&mut self);
-    fn converged(&self) -> bool;
+    /// Evaluate convergence criteria and update status
+    fn evaluation(&mut self) -> Result<Status>;
+
+    /// Create and log a cycle state with the current algorithm state
+    fn log_cycle_state(&mut self);
+
+    /// Initialize the algorithm, setting up initial [Theta] and [Status]
     fn initialize(&mut self) -> Result<()> {
         // If a stop file exists in the current directory, remove it
         if Path::new("stop").exists() {
             tracing::info!("Removing existing stop file prior to run");
             fs::remove_file("stop").context("Unable to remove previous stop file")?;
         }
-        self.set_status(Status::InProgress);
+        self.set_status(Status::Continue);
         self.set_theta(self.get_prior());
         Ok(())
     }
-    fn evaluation(&mut self) -> Result<()>;
+    fn estimation(&mut self) -> Result<()>;
+    /// Performs condensation of [Theta] and updates [Psi]
+    ///
+    /// This step reduces the number of support points in [Theta] based on the current weights,
+    /// and updates the [Psi] matrix accordingly to reflect the new set of support points.
+    /// It is typically performed after the estimation step in each cycle of the algorithm.
     fn condensation(&mut self) -> Result<()>;
+
+    /// Performs optimizations on the current [ErrorModels] and updates [Psi] accordingly
+    ///
+    /// This step refines the error model parameters to better fit the data,
+    /// and subsequently updates the [Psi] matrix to reflect these changes.
     fn optimizations(&mut self) -> Result<()>;
-    fn logs(&self);
+
+    /// Performs expansion of [Theta]
+    ///
+    /// This step increases the number of support points in [Theta] based on the current distribution,
+    /// allowing for exploration of the parameter space.
     fn expansion(&mut self) -> Result<()>;
-    fn next_cycle(&mut self) -> Result<bool> {
-        if self.inc_cycle() > 1 {
+
+    /// Proceed to the next cycle of the algorithm
+    ///
+    /// This method increments the cycle counter, performs expansion if necessary,
+    /// and then runs the estimation, condensation, optimization, logging, and evaluation steps
+    /// in sequence. It returns the current [Status] of the algorithm after completing these steps.
+    fn next_cycle(&mut self) -> Result<Status> {
+        let cycle = self.increment_cycle();
+
+        if cycle > 1 {
             self.expansion()?;
         }
-        let span = tracing::info_span!("", "{}", format!("Cycle {}", self.get_cycle()));
+
+        let span = tracing::info_span!("", "{}", format!("Cycle {}", self.cycle()));
         let _enter = span.enter();
-        self.evaluation()?;
+        self.estimation()?;
         self.condensation()?;
         self.optimizations()?;
-        self.logs();
-        self.convergence_evaluation();
-        Ok(self.converged())
+        self.evaluation()
     }
+
+    /// Fit the model until convergence or stopping criteria are met
+    ///
+    /// This method runs the full fitting process, starting with initialization,
+    /// followed by iterative cycles of estimation, condensation, optimization, and evaluation
+    /// until the algorithm converges or meets a stopping criteria.
     fn fit(&mut self) -> Result<NPResult<E>> {
         self.initialize().unwrap();
-        while !self.next_cycle()? {}
+        loop {
+            match self.next_cycle()? {
+                Status::Continue => continue,
+                Status::Stop(_) => break,
+            }
+        }
         Ok(self.into_npresult())
     }
 
@@ -274,32 +323,27 @@ pub fn dispatch_algorithm<E: Equation + Send + 'static>(
     }
 }
 
-/// Represents the status of the algorithm
+/// Represents the status/result of the algorithm
 #[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
 pub enum Status {
-    /// Algorithm is starting up
-    Starting,
-    /// Algorithm has converged to a solution
-    Converged,
-    /// Algorithm stopped due to reaching maximum cycles
-    MaxCycles,
-    /// Algorithm is currently running
-    InProgress,
-    /// Algorithm was manually stopped by user
-    ManualStop,
-    /// Other status with custom message
-    Other(String),
+    Continue,
+    Stop(StopReason),
 }
 
 impl std::fmt::Display for Status {
     fn fmt(&self, f: &mut std::fmt::Formatter<'_>) -> std::fmt::Result {
         match self {
-            Status::Starting => write!(f, "Starting"),
-            Status::Converged => write!(f, "Converged"),
-            Status::MaxCycles => write!(f, "Maximum cycles reached"),
-            Status::InProgress => write!(f, "In progress"),
-            Status::ManualStop => write!(f, "Manual stop requested"),
-            Status::Other(msg) => write!(f, "{}", msg),
+            Status::Continue => write!(f, "Continue"),
+            Status::Stop(s) => write!(f, "Stop: {:?}", s),
         }
     }
 }
+
+#[derive(Debug, Clone, PartialEq, Eq, Serialize, Deserialize)]
+
+pub enum StopReason {
+    Converged,
+    MaxCycles,
+    Stopped,
+    Completed,
+}