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```//! This module defines the trait and the data structure needed for specifying each individual in a population.
//!
//! darwin-rs: evolutionary algorithms with Rust
//!
//! Written by Willi Kappler, Version 0.4 (2017.06.26)
//!
//! Repository: https://github.com/willi-kappler/darwin-rs
//!
//!
//! This library allows you to write evolutionary algorithms (EA) in Rust.
//! Examples provided: TSP, Sudoku, Queens Problem, OCR
//!
//!

// external modules
use std::cmp::Ordering;

/// A wrapper helper struct for the individuals.
/// It does the book keeping of the fitness and the number of mutations this individual
/// has to run in one iteration.
#[derive(Debug,Clone)]
pub struct IndividualWrapper<T: Individual> {
/// The actual individual, user defined struct.
pub individual: T,
/// The current calculated fitness for this individual.
pub fitness: f64,
/// The number of mutation this individual is doing in one iteration.
pub num_of_mutations: u32,
/// The id of the population that this individual belongs to. Just for statistics.
pub id: u32,
}

/// Implement this for sorting
impl<T: Individual> PartialEq for IndividualWrapper<T> {
fn eq(&self, other: &IndividualWrapper<T>) -> bool {
self.fitness == other.fitness
}
}

/// Implement this for sorting
impl<T: Individual> Eq for IndividualWrapper<T> {}

/// Implement this for sorting
impl<T: Individual> Ord for IndividualWrapper<T> {
fn cmp(&self, other: &IndividualWrapper<T>) -> Ordering {
self.partial_cmp(other).expect("Fitness of Individual is NaN")
}
}

/// Implement this for sorting
impl<T: Individual> PartialOrd for IndividualWrapper<T> {
fn partial_cmp(&self, other: &IndividualWrapper<T>) -> Option<Ordering> {
self.fitness.partial_cmp(&other.fitness)
}
}

/// This trait has to be implemented for the user defined struct.
/// In order to share common data between all individuals use Arc. See TSP and OCR exmaples.
///
/// TODO: add serialization, see https://github.com/willi-kappler/darwin-rs/issues/11
pub trait Individual {
/// This method mutates the individual. Usually this is a cheap and easy to implement
/// function. In order to improve the simulation, the user can make this function a bit
/// "smarter". This is nicely shown in the tsp and tsp2 example. The tsp2 example contains
/// two types of mutation, tsp just one:
///
/// examples/tsp: 1. swap position
///
/// examples/tsp2: 1. swap position, 2. rotate (shift) positions
///
/// By just adding this one additional mutation type the simulation converges much faster
/// to the optimum. Of course rotation can be "simulated" by a number of swaps, but re-doing
/// all these steps takes time and the chances that these steps are taken in the correct
/// order by just randomly swaping positions are very slim. So just start with one simple
/// mutation function (one operation) and add more and more "smarter" mutation types to the
/// mutate function.
fn mutate(&mut self);
/// This method calculates the fitness for the individual. Usually this is an expensive
/// operation and a bit more difficult to implement, compared to the mutation method above.
/// The lower the fitness value, the better (healthier) the individual is and the closer
/// the individual is to the perfect solution. This can also correspont to the number of
/// errors like for example in the sudoku or queens problem case.
fn calculate_fitness(&mut self) -> f64;
/// This method resets each individual to an initial state.
/// For example in the "queens" case it would reset the queens position randomly
/// (or all in the first row).
fn reset(&mut self);
/// This method is called whenever a new fittest individual is found. It is usefull when you
/// want to provide some additional information or do some statistics.
/// It is optional and the default implementation does nothing.
fn new_fittest_found(&mut self) {

}
}

#[cfg(test)]
mod test {
use super::{IndividualWrapper, Individual};

struct IndividualTest1;

impl Individual for IndividualTest1 {
fn mutate(&mut self) {
}

fn calculate_fitness(&mut self) -> f64 {
0.0
}

fn reset(&mut self) {

}
}

#[test]
fn compare1() {
let individual1 = IndividualWrapper{individual: IndividualTest1, fitness: 1.2, num_of_mutations: 21, id: 1};
let individual2 = IndividualWrapper{individual: IndividualTest1, fitness: 5.93, num_of_mutations: 7, id: 1};

assert!(individual2 > individual1);
}

#[test]
fn compare2() {
let individual1 = IndividualWrapper{individual: IndividualTest1, fitness: 3.78, num_of_mutations: 21, id: 1};
let individual2 = IndividualWrapper{individual: IndividualTest1, fitness: 7.12, num_of_mutations: 7, id: 1};

assert!(individual1 < individual2);
}

#[test]
fn compare3() {
let individual1 = IndividualWrapper{individual: IndividualTest1, fitness: 21.996, num_of_mutations: 11, id: 1};
let individual2 = IndividualWrapper{individual: IndividualTest1, fitness: 21.996, num_of_mutations: 34, id: 1};

assert!(individual1 == individual2);
}
}
```