darwin-rs 0.4.0

Evolutionary algorithms library written in Rust.
Documentation 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142

//! This module defines helper functions (builder pattern) to create a valid simulation.
//!
//! darwin-rs: evolutionary algorithms with Rust
//!
//! Written by Willi Kappler, Version 0.4 (2017.06.26)
//!
//! Repository: https://github.com/willi-kappler/darwin-rs
//!
//! License: MIT
//!
//! This library allows you to write evolutionary algorithms (EA) in Rust.
//! Examples provided: TSP, Sudoku, Queens Problem, OCR
//!
//!

use std;

use simulation::{Simulation, SimulationType, SimulationResult};
use individual::{Individual};
use population::Population;

/// This is a helper struct in order to build (configure) a valid simulation.
/// See builder pattern: https://en.wikipedia.org/wiki/Builder_pattern
///
/// Maybe use phantom types, see https://github.com/willi-kappler/darwin-rs/issues/9
pub struct SimulationBuilder<T: Individual + Send + Sync> {
    /// The actual simulation.
    simulation: Simulation<T>,
}

error_chain! {
    errors {
        EndIterationTooLow
    }
}

/// This implementation contains all the helper method to build (configure) a valid simulation.
impl<T: Individual + Send + Sync> SimulationBuilder<T> {
    /// Start with this method, it must always be called as the first one.
    /// It creates a default simulation with some dummy (but invalid) values.
    pub fn new() -> SimulationBuilder<T> {
        SimulationBuilder {
            simulation: Simulation {
                type_of_simulation: SimulationType::EndIteration(10),
                num_of_threads: 2,
                habitat: Vec::new(),
                total_time_in_ms: 0.0,
                simulation_result: SimulationResult {
                    improvement_factor: std::f64::MAX,
                    original_fitness: std::f64::MAX,
                    fittest: Vec::new(),
                    iteration_counter: 0
                },
                share_fittest: false,
                num_of_global_fittest: 10,
                output_every: 10,
                output_every_counter: 0,
                share_every: 10,
                share_counter: 0
            },
        }
    }

    /// Set the total number of iterations for the simulation and thus sets the simulation
    /// type to `EndIteration`. (Only usefull in combination with `EndIteration`).
    pub fn iterations(mut self, iterations: u32) -> SimulationBuilder<T> {
        self.simulation.type_of_simulation = SimulationType::EndIteration(iterations);
        self
    }

    /// Set the improvement factor stop criteria for the simulation and thus sets the simulation
    /// type to `EndFactor`. (Only usefull in combination with `EndFactor`).
    pub fn factor(mut self, factor: f64) -> SimulationBuilder<T> {
        self.simulation.type_of_simulation = SimulationType::EndFactor(factor);
        self
    }

    /// Set the minimum fitness stop criteria for the simulation and thus sets the simulation
    /// type to `EndFitness`. (Only usefull in combination with `EndFactor`).
    pub fn fitness(mut self, fitness: f64) -> SimulationBuilder<T> {
        self.simulation.type_of_simulation = SimulationType::EndFitness(fitness);
        self
    }

    /// Sets the number of threads in order to speed up the simulation.
    pub fn threads(mut self, threads: usize) -> SimulationBuilder<T> {
        self.simulation.num_of_threads = threads;
        self
    }

    /// Add a population to the simulation.
    pub fn add_population(mut self, population: Population<T>) -> SimulationBuilder<T> {
        self.simulation.habitat.push(population);
        self
    }

    /// Add multiple populations to the simulation.
    pub fn add_multiple_populations(mut self, multiple_populations: Vec<Population<T>>) -> SimulationBuilder<T> {
        for population in multiple_populations {
            self.simulation.habitat.push(population);
        }
        self
    }

    /// If this option is enabled (default: off), then the fittest individual of all populations
    /// is shared between all populations.
    pub fn share_fittest(mut self) -> SimulationBuilder<T> {
        self.simulation.share_fittest = true;
        self
    }

    /// How many global fittest should be kept ? (The size of the "high score list")
    pub fn num_of_global_fittest(mut self, num_of_global_fittest: usize) -> SimulationBuilder<T> {
        self.simulation.num_of_global_fittest = num_of_global_fittest;
        self
    }

    /// Do not output every time a new individual is found, only every nth time.
    /// n == output_every
    pub fn output_every(mut self, output_every: u32) -> SimulationBuilder<T> {
        self.simulation.output_every = output_every;
        self
    }

    /// If share fittest is enabled and the number share_every of iteration has passed then
    /// the fittest individual is shared between all populations
    pub fn share_every(mut self, share_every: u32) -> SimulationBuilder<T> {
        self.simulation.share_every = share_every;
        self
    }

    /// This checks the configuration of the simulation and returns an error or Ok if no errors
    /// where found.
    pub fn finalize(self) -> Result<Simulation<T>> {
        match self.simulation {
            Simulation { type_of_simulation: SimulationType::EndIteration(0...9), .. } => {
                Err(ErrorKind::EndIterationTooLow.into())
            }
            _ => Ok(self.simulation),
        }
    }
}