fix(CI): various minor changes for satisfying CI (#432)

- Run cargo fmt
- Cleanup comparator impl
- Make some types public instead of pub(self)
- Disable incorrect clippy lint
- Apply fixes suggested by clippy

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IF THERE IS NO LINKED ISSUE) -->

## Description

<!-- Please describe the motivation & changes introduced by this PR -->

## Linked issues

<!-- Please use "Resolves #<issue_no> syntax in case this PR should be
linked to an issue -->

## Important implementation details

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examples/jssp/logging.rs

@@ -9,7 +9,6 @@ use log4rs::{
     encode::pattern::PatternEncoder,
 };
 
-
 #[derive(serde::Serialize, Clone, Debug)]
 pub struct OutputData {
     pub solution_string: String,
@@ -19,7 +18,10 @@ pub struct OutputData {
     pub total_time: u128,
 }
 
-pub fn init_logging(event_log_files: &HashMap<String, PathBuf>, metadata_log_file: &PathBuf) -> Result<log4rs::Handle, log::SetLoggerError> {
+pub fn init_logging(
+    event_log_files: &HashMap<String, PathBuf>,
+    metadata_log_file: &PathBuf,
+) -> Result<log4rs::Handle, log::SetLoggerError> {
     let log_pattern = String::from("[{l}] {m}{n}");
     let csv_log_pattern = String::from("{m}{n}");
     let csv_encoder = Box::new(PatternEncoder::new(&csv_log_pattern));
@@ -33,7 +35,6 @@ pub fn init_logging(event_log_files: &HashMap<String, PathBuf>, metadata_log_fil
     // Register console appender
     cfg_builder = cfg_builder.appender(Appender::builder().build("main", Box::new(stdout_appender)));
 
-
     // Register appenders & loggers for given events
     if !event_log_files.is_empty() {
         let csv_encoder = Box::new(PatternEncoder::new(&csv_log_pattern));
@@ -70,7 +71,6 @@ pub fn init_logging(event_log_files: &HashMap<String, PathBuf>, metadata_log_fil
                 .build("metadata", log::LevelFilter::Info),
         );
 
-
     let config = cfg_builder
         .build(
             log4rs::config::Root::builder()

examples/jssp/problem/individual.rs

@@ -358,6 +358,7 @@ impl PartialEq for JsspIndividual {
 impl Eq for JsspIndividual {}
 
 impl PartialOrd for JsspIndividual {
+    #[allow(clippy::incorrect_partial_ord_impl_on_ord_type)]
     fn partial_cmp(&self, other: &Self) -> Option<std::cmp::Ordering> {
         self.fitness.partial_cmp(&other.fitness)
     }

examples/jssp/problem/probe.rs

@@ -115,27 +115,24 @@ impl Probe<JsspIndividual> for JsspProbe {
         metadata: &ecrs::ga::GAMetadata,
         _population: &[JsspIndividual],
         best_individual: &JsspIndividual,
-    ) { 
+    ) {
         let mut ops = best_individual.operations.clone();
         ops.sort_unstable_by(|a, b| {
-            if a.finish_time == b.finish_time {
-                if a.duration == 0 && b.duration != 0 {
-                    return Ordering::Greater;
-                } else if a.duration != 0 && b.duration == 0 {
-                    return Ordering::Less;
-                } else if a.machine < b.machine {
-                    return Ordering::Less;
-                } else {
-                    return Ordering::Greater;
-                }
-            } else if a.finish_time < b.finish_time {
-                return Ordering::Less;
+            if a.finish_time < b.finish_time {
+                Ordering::Less
+            } else if a.finish_time > b.finish_time {
+                Ordering::Greater
+            } else if a.duration != 0 && b.duration != 0 {
+                a.machine.cmp(&b.machine)
+            } else if a.duration != 0 && b.duration == 0 {
+                Ordering::Less
             } else {
-                return Ordering::Greater;
+                Ordering::Greater
             }
         });
         let n = ops.len();
-        let solution_string = ops.into_iter()
+        let solution_string = ops
+            .into_iter()
             .filter(|op| op.id != 0 && op.id != n + 1)
             .map(|op| op.id.to_string())
             .join("_");

src/aco/tsp/goodness.rs

@@ -78,7 +78,7 @@ mod tests {
         let beta = 3.0;
         let pheromone = FMatrix::from_vec(2, 2, vec![4.0, 2.0, 8.0, 0.5]);
 
-        let goodness = vec![16.0, 32.0, 4096.0, 128.0];
+        let goodness = [16.0, 32.0, 4096.0, 128.0];
 
         let mut g_op = CanonicalGoodness::new(alpha, beta, heuristic);
         for (a, b) in goodness.iter().zip(g_op.apply(&pheromone).iter()) {

src/ga/builder.rs

@@ -26,7 +26,7 @@ type FitnessFn<S, R> = fn(&S) -> R;
 
 /// Error type for internal use
 #[derive(Debug, Clone)]
-enum ConfigError {
+pub enum ConfigError {
     MissingParam(String),
     MissingOperator(String),
     MissingPopulationFactory,
@@ -53,7 +53,7 @@ impl Error for ConfigError {}
 // TODO: We should really consider creating a macro here, so that we
 // don't have to write it by hand...
 #[derive(Debug, Clone)]
-pub(self) struct GAParamsOpt {
+pub struct GAParamsOpt {
     pub selection_rate: Option<f64>,
     pub mutation_rate: Option<f64>,
     pub population_size: Option<usize>,
@@ -88,24 +88,28 @@ impl TryFrom<GAParamsOpt> for GAParams {
 
     fn try_from(params_opt: GAParamsOpt) -> Result<Self, Self::Error> {
         let Some(selection_rate) = params_opt.selection_rate else {
-			return Err(ConfigError::MissingParam("Unspecified selection rate".to_owned()));
-		};
+            return Err(ConfigError::MissingParam("Unspecified selection rate".to_owned()));
+        };
 
         let Some(mutation_rate) = params_opt.mutation_rate else {
-			return Err(ConfigError::MissingParam("Unspecified mutation rate".to_owned()));
-		};
+            return Err(ConfigError::MissingParam("Unspecified mutation rate".to_owned()));
+        };
 
         let Some(population_size) = params_opt.population_size else {
-			return Err(ConfigError::MissingParam("Unspecified population size".to_owned()));
-		};
+            return Err(ConfigError::MissingParam(
+                "Unspecified population size".to_owned(),
+            ));
+        };
 
         let Some(generation_limit) = params_opt.generation_limit else {
-			return Err(ConfigError::MissingParam("Unspecified generation_limit".to_owned()));
-		};
+            return Err(ConfigError::MissingParam(
+                "Unspecified generation_limit".to_owned(),
+            ));
+        };
 
         let Some(max_duration) = params_opt.max_duration else {
-			return Err(ConfigError::MissingParam("Unspecified max duration".to_owned()));
-		};
+            return Err(ConfigError::MissingParam("Unspecified max duration".to_owned()));
+        };
 
         Ok(GAParams {
             selection_rate,
@@ -121,7 +125,7 @@ impl TryFrom<GAParamsOpt> for GAParams {
 /// inside `Option` type, so that builders can incrementally fill it up.
 // TODO: We should really consider creating a macro here, so that we
 // don't have to write it by hand...
-pub(self) struct GAConfigOpt<IndividualT, MutOpT, CrossOpT, SelOpT, ReplOpT, PopGenT, FitnessT, ProbeT>
+pub struct GAConfigOpt<IndividualT, MutOpT, CrossOpT, SelOpT, ReplOpT, PopGenT, FitnessT, ProbeT>
 where
     IndividualT: IndividualTrait,
     MutOpT: MutationOperator<IndividualT>,
@@ -192,32 +196,42 @@ where
         let params = GAParams::try_from(config_opt.params)?;
 
         let Some(fitness_fn) = config_opt.fitness_fn else {
-			return Err(ConfigError::MissingOperator("No fitness function specified".to_owned()));
-		};
+            return Err(ConfigError::MissingOperator(
+                "No fitness function specified".to_owned(),
+            ));
+        };
 
         let Some(mutation_operator) = config_opt.mutation_operator else {
-			return Err(ConfigError::MissingOperator("No mutation operator specified".to_owned()));
-		};
+            return Err(ConfigError::MissingOperator(
+                "No mutation operator specified".to_owned(),
+            ));
+        };
 
         let Some(crossover_operator) = config_opt.crossover_operator else {
-			return Err(ConfigError::MissingOperator("No crossover operator specified".to_owned()));
-		};
+            return Err(ConfigError::MissingOperator(
+                "No crossover operator specified".to_owned(),
+            ));
+        };
 
         let Some(selection_operator) = config_opt.selection_operator else {
-			return Err(ConfigError::MissingOperator("No selection operator specified".to_owned()));
-		};
+            return Err(ConfigError::MissingOperator(
+                "No selection operator specified".to_owned(),
+            ));
+        };
 
         let Some(replacement_operator) = config_opt.replacement_operator else {
-			return Err(ConfigError::MissingOperator("No replacement operator specified".to_owned()));
-		};
+            return Err(ConfigError::MissingOperator(
+                "No replacement operator specified".to_owned(),
+            ));
+        };
 
         let Some(population_factory) = config_opt.population_factory else {
-			return Err(ConfigError::MissingPopulationFactory);
-		};
+            return Err(ConfigError::MissingPopulationFactory);
+        };
 
         let Some(probe) = config_opt.probe else {
-			return Err(ConfigError::NoProbe);
-		};
+            return Err(ConfigError::NoProbe);
+        };
 
         Ok(GAConfig {
             params,

src/ga/individual.rs

@@ -146,6 +146,7 @@ impl<T: Chromosome> PartialEq<Self> for Individual<T> {
 impl<T: Chromosome> Eq for Individual<T> {}
 
 impl<T: Chromosome> PartialOrd<Self> for Individual<T> {
+    #[allow(clippy::incorrect_partial_ord_impl_on_ord_type)]
     fn partial_cmp(&self, other: &Self) -> Option<std::cmp::Ordering> {
         self.fitness.partial_cmp(&other.fitness)
     }

src/ga/operators/crossover.rs

@@ -933,7 +933,7 @@ mod test {
         child
             .chromosome()
             .iter()
-            .zip(vec![1, 3, 2, 4, 6, 5].iter())
+            .zip([1, 3, 2, 4, 6, 5].iter())
             .for_each(|(x, x_expected)| assert_eq!(x, x_expected))
     }
 

src/ga/operators/selection.rs

@@ -583,9 +583,11 @@ where
             weights.push((-idv.fitness() / temp).exp())
         }
 
-        let Ok(indices) = rand::seq::index::sample_weighted(&mut self.rng, population.len(), |i| weights[i], count) else {
-			panic!("Some error occured while generating indices. This is most likely an library implementation error. Please file an issue: https://github.com/kkafar/evolutionary-algorithms");
-		};
+        let Ok(indices) =
+            rand::seq::index::sample_weighted(&mut self.rng, population.len(), |i| weights[i], count)
+        else {
+            panic!("Some error occured while generating indices. This is most likely an library implementation error. Please file an issue: https://github.com/kkafar/evolutionary-algorithms");
+        };
 
         for i in indices {
             selected.push(&population[i]);

src/test_functions/mod.rs

@@ -120,7 +120,7 @@ pub fn brad(x: &Vec<f64>) -> f64 {
     let x1 = x[0];
     let x2 = x[1];
     let x3 = x[2];
-    let y = vec![
+    let y = [
         0.14, 0.18, 0.22, 0.25, 0.29, 0.32, 0.35, 0.39, 0.37, 0.58, 0.73, 0.96, 1.34, 2.10, 4.39,
     ];
     let mut res = 0_f64;
@@ -1252,7 +1252,7 @@ pub fn hartmann_3d(x: &Vec<f64>) -> f64 {
         x.len(),
         "Hartmann 3-dimensional function takes only a three dimensional vector as a parameter."
     );
-    let alfa = vec![1.0, 1.2, 3.0, 3.2];
+    let alfa = [1.0, 1.2, 3.0, 3.2];
     let a = vec![
         vec![3.0, 10.0, 30.0],
         vec![0.1, 10.0, 35.0],
@@ -1285,7 +1285,7 @@ pub fn hartmann_4d(x: &Vec<f64>) -> f64 {
         x.len(),
         "Hartmann 4-dimensional function takes only a three dimensional vector as a parameter."
     );
-    let alfa = vec![1.0, 1.2, 3.0, 3.2];
+    let alfa = [1.0, 1.2, 3.0, 3.2];
     let a = vec![
         vec![10.0, 3.0, 17.0, 3.5, 1.7, 8.0],
         vec![0.05, 10.0, 17.0, 0.1, 8.0, 14.0],
@@ -1320,7 +1320,7 @@ pub fn hartmann_6d(x: &Vec<f64>) -> f64 {
         x.len(),
         "Hartmann 6-dimensional function takes only a six dimensional vector as a parameter."
     );
-    let alfa = vec![1.0, 1.2, 3.0, 3.2];
+    let alfa = [1.0, 1.2, 3.0, 3.2];
     let a = vec![
         vec![10.0, 3.0, 17.0, 3.5, 1.7, 8.0],
         vec![0.05, 10.0, 17.0, 0.1, 8.0, 14.0],