File: differential_evolution_test.cpp

package info (click to toggle)
scipy 1.16.0-1exp7
  • links: PTS, VCS
  • area: main
  • in suites: experimental
  • size: 234,820 kB
  • sloc: cpp: 503,145; python: 344,611; ansic: 195,638; javascript: 89,566; fortran: 56,210; cs: 3,081; f90: 1,150; sh: 848; makefile: 785; pascal: 284; csh: 135; lisp: 134; xml: 56; perl: 51
file content (220 lines) | stat: -rw-r--r-- 8,502 bytes parent folder | download | duplicates (6)
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
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
/*
 * Copyright Nick Thompson, 2023
 * Use, modification and distribution are subject to the
 * Boost Software License, Version 1.0. (See accompanying file
 * LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt)
 */

#include "math_unit_test.hpp"
#include "test_functions_for_optimization.hpp"
#include <boost/math/optimization/differential_evolution.hpp>
#include <random>

using boost::math::optimization::differential_evolution;
using boost::math::optimization::differential_evolution_parameters;
using boost::math::optimization::validate_differential_evolution_parameters;
using boost::math::optimization::detail::best_indices;
using boost::math::optimization::detail::random_initial_population;
using boost::math::optimization::detail::validate_initial_guess;

void test_random_initial_population() {
  std::array<double, 2> lower_bounds = {-5, -5};
  std::array<double, 2> upper_bounds = {5, 5};
  size_t n = 500;
  std::mt19937_64 gen(12345);
  auto population = random_initial_population(lower_bounds, upper_bounds, n, gen);
  CHECK_EQUAL(population.size(), n);
  for (auto const & individual : population) {
    validate_initial_guess(individual, lower_bounds, upper_bounds);
  }
  // Reproducibility:
  gen.seed(12345);
  auto population2 = random_initial_population(lower_bounds, upper_bounds, n, gen);
  for (size_t i = 0; i < n; ++i) {
    for (size_t j = 0; j < 2; ++j) {
      CHECK_EQUAL(population[i][j], population2[i][j]);
    }
  }
}
void test_nan_sorting() {
  auto nan = std::numeric_limits<double>::quiet_NaN();
  std::vector<double> v{-1.2, nan, -3.5, 2.3, nan, 8.7, -4.2};
  auto indices = best_indices(v);
  CHECK_EQUAL(indices[0], size_t(6));
  CHECK_EQUAL(indices[1], size_t(2));
  CHECK_EQUAL(indices[2], size_t(0));
  CHECK_EQUAL(indices[3], size_t(3));
  CHECK_EQUAL(indices[4], size_t(5));
  CHECK_NAN(v[indices[5]]);
  CHECK_NAN(v[indices[6]]);
}

void test_parameter_checks() {
  using ArgType = std::array<double, 2>;
  auto de_params = differential_evolution_parameters<ArgType>();
  de_params.threads = 0;
  bool caught = false;
  try {
    validate_differential_evolution_parameters(de_params);
  } catch(std::exception const &) {
    caught = true;
  }
  CHECK_TRUE(caught);
  caught = false;
  de_params = differential_evolution_parameters<ArgType>();
  de_params.NP = 1;
  try {
    validate_differential_evolution_parameters(de_params);
  } catch(std::exception const &) {
    caught = true;
  }
  CHECK_TRUE(caught);
}
template <class Real> void test_ackley() {
  std::cout << "Testing differential evolution on the Ackley function . . .\n";
  using ArgType = std::array<Real, 2>;
  auto de_params = differential_evolution_parameters<ArgType>();
  de_params.lower_bounds = {-5, -5};
  de_params.upper_bounds = {5, 5};

  std::mt19937_64 gen(12345);
  auto local_minima = differential_evolution(ackley<Real>, de_params, gen);
  CHECK_LE(std::abs(local_minima[0]), 10 * std::numeric_limits<Real>::epsilon());
  CHECK_LE(std::abs(local_minima[1]), 10 * std::numeric_limits<Real>::epsilon());

  // Does it work with a lambda?
  auto ack = [](std::array<Real, 2> const &x) { return ackley<Real>(x); };
  local_minima = differential_evolution(ack, de_params, gen);
  CHECK_LE(std::abs(local_minima[0]), 10 * std::numeric_limits<Real>::epsilon());
  CHECK_LE(std::abs(local_minima[1]), 10 * std::numeric_limits<Real>::epsilon());

  // Test that if an intial guess is the exact solution, the returned solution is the exact solution:
  std::array<Real, 2> initial_guess{0, 0};
  de_params.initial_guess = &initial_guess;
  local_minima = differential_evolution(ack, de_params, gen);
  CHECK_EQUAL(local_minima[0], Real(0));
  CHECK_EQUAL(local_minima[1], Real(0));
}

template <class Real> void test_rosenbrock_saddle() {
  std::cout << "Testing differential evolution on the Rosenbrock saddle . . .\n";
  using ArgType = std::array<Real, 2>;
  auto de_params = differential_evolution_parameters<ArgType>();
  de_params.lower_bounds = {0.5, 0.5};
  de_params.upper_bounds = {2.048, 2.048};
  std::mt19937_64 gen(234568);
  auto local_minima = differential_evolution(rosenbrock_saddle<Real>, de_params, gen);

  CHECK_ABSOLUTE_ERROR(Real(1), local_minima[0], 10 * std::numeric_limits<Real>::epsilon());
  CHECK_ABSOLUTE_ERROR(Real(1), local_minima[1], 10 * std::numeric_limits<Real>::epsilon());

  // Does cancellation work?
  std::atomic<bool> cancel = true;
  gen.seed(12345);
  local_minima =
      differential_evolution(rosenbrock_saddle<Real>, de_params, gen, std::numeric_limits<Real>::quiet_NaN(), &cancel);
  CHECK_GE(std::abs(local_minima[0] - Real(1)), std::sqrt(std::numeric_limits<Real>::epsilon()));
}

template <class Real> void test_rastrigin() {
  std::cout << "Testing differential evolution on the Rastrigin function . . .\n";
  using ArgType = std::vector<Real>;
  auto de_params = differential_evolution_parameters<ArgType>();
  de_params.lower_bounds.resize(8, static_cast<Real>(-5.12));
  de_params.upper_bounds.resize(8, static_cast<Real>(5.12));
  std::mt19937_64 gen(34567);
  auto local_minima = differential_evolution(rastrigin<Real>, de_params, gen);
  for (auto x : local_minima) {
    CHECK_ABSOLUTE_ERROR(x, Real(0), Real(2e-4));
  }

  // By definition, the value of the function which a target value is provided must be <= target_value.
  auto target_value = static_cast<Real>(1e-3);
  local_minima = differential_evolution(rastrigin<Real>, de_params, gen, target_value);
  CHECK_LE(rastrigin(local_minima), target_value);
}

// Tests NaN return types and return type != input type:
void test_sphere() {
  std::cout << "Testing differential evolution on the sphere function . . .\n";
  using ArgType = std::vector<float>;
  auto de_params = differential_evolution_parameters<ArgType>();
  de_params.lower_bounds.resize(3, -1);
  de_params.upper_bounds.resize(3, 1);
  de_params.NP *= 10;
  de_params.max_generations *= 10;
  de_params.crossover_probability = 0.9;
  double target_value = 1e-8;
  de_params.threads = 1;
  std::mt19937_64 gen(56789);
  auto local_minima = differential_evolution(sphere, de_params, gen, target_value);
  CHECK_LE(sphere(local_minima), target_value);
  // Check computational reproducibility:
  gen.seed(56789);
  auto local_minima_2 = differential_evolution(sphere, de_params, gen, target_value);
  for (size_t i = 0; i < local_minima.size(); ++i) {
    CHECK_EQUAL(local_minima[i], local_minima_2[i]);
  }
}

template<typename Real>
void test_three_hump_camel() {
  std::cout << "Testing differential evolution on the three hump camel . . .\n";
  using ArgType = std::array<Real, 2>;
  auto de_params = differential_evolution_parameters<ArgType>();
  de_params.lower_bounds[0] = -5.0;
  de_params.lower_bounds[1] = -5.0;
  de_params.upper_bounds[0] = 5.0;
  de_params.upper_bounds[1] = 5.0;
  std::mt19937_64 gen(56789);
  auto local_minima = differential_evolution(three_hump_camel<Real>, de_params, gen);
  for (auto x : local_minima) {
    CHECK_ABSOLUTE_ERROR(0.0f, x, 2e-4f);
  }
}

template<typename Real>
void test_beale() {
  std::cout << "Testing differential evolution on the Beale function . . .\n";
  using ArgType = std::array<Real, 2>;
  auto de_params = differential_evolution_parameters<ArgType>();
  de_params.lower_bounds[0] = -5.0;
  de_params.lower_bounds[1] = -5.0;
  de_params.upper_bounds[0]= 5.0;
  de_params.upper_bounds[1]= 5.0;
  std::mt19937_64 gen(56789);
  auto local_minima = differential_evolution(beale<Real>, de_params, gen);
  CHECK_ABSOLUTE_ERROR(Real(3), local_minima[0], Real(2e-4));
  CHECK_ABSOLUTE_ERROR(Real(1)/Real(2), local_minima[1], Real(2e-4));
}

#if BOOST_MATH_TEST_UNITS_COMPATIBILITY
void test_dimensioned_sphere() {
  std::cout << "Testing differential evolution on dimensioned sphere . . .\n";
  using ArgType = std::vector<quantity<length>>;
  auto params = differential_evolution_parameters<ArgType>();
  params.lower_bounds.resize(4, -1.0*meter);
  params.upper_bounds.resize(4, 1*meter);
  params.threads = 2;
  std::mt19937_64 gen(56789);
  auto local_minima = differential_evolution(dimensioned_sphere, params, gen);
}
#endif

int main() {

#if defined(__clang__) || defined(_MSC_VER)
  test_ackley<float>();
  test_ackley<double>();
  test_rosenbrock_saddle<double>();
  test_rastrigin<float>();
  test_three_hump_camel<float>();
  test_beale<double>();
#endif
#if BOOST_MATH_TEST_UNITS_COMPATIBILITY
  test_dimensioned_sphere();
#endif
  test_sphere();
  test_parameter_checks();
  return boost::math::test::report_errors();
}