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/dports/science/pagmo2/pagmo2-2.18.0/tests/
H A Dwfg.cpp79 BOOST_CHECK_CLOSE(wfg1.fitness(x)[0], 2.67637472191165, 1e-6); in BOOST_AUTO_TEST_CASE()
80 BOOST_CHECK_CLOSE(wfg1.fitness(x)[1], 1.00059019674296, 1e-6); in BOOST_AUTO_TEST_CASE()
81 BOOST_CHECK_CLOSE(wfg1.fitness(x)[2], 1.00158344827345, 1e-6); in BOOST_AUTO_TEST_CASE()
82 BOOST_CHECK_CLOSE(wfg1.fitness(x)[3], 0.999721693168825, 1e-6); in BOOST_AUTO_TEST_CASE()
83 BOOST_CHECK_CLOSE(wfg1.fitness(x)[4], 0.994938703521363, 1e-6); in BOOST_AUTO_TEST_CASE()
93 BOOST_CHECK_CLOSE(wfg2.fitness(x)[3], 3.2410479386539, 1e-6); in BOOST_AUTO_TEST_CASE()
94 BOOST_CHECK_CLOSE(wfg2.fitness(x)[4], 6.7367724867725, 1e-6); in BOOST_AUTO_TEST_CASE()
103 BOOST_CHECK_CLOSE(wfg3.fitness(x)[2], 1.66007950505305, 1e-6); in BOOST_AUTO_TEST_CASE()
104 BOOST_CHECK_CLOSE(wfg3.fitness(x)[3], 4.09523809523809, 1e-6); in BOOST_AUTO_TEST_CASE()
105 BOOST_CHECK_CLOSE(wfg3.fitness(x)[4], 2.98677248677249, 1e-6); in BOOST_AUTO_TEST_CASE()
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H A Dzdt.cpp72 BOOST_CHECK_CLOSE(zdt1.fitness(x)[0], 0.25, 1e-13); in BOOST_AUTO_TEST_CASE()
78 BOOST_CHECK_CLOSE(zdt1.fitness(x)[0], 0.33, 1e-13); in BOOST_AUTO_TEST_CASE()
88 BOOST_CHECK_CLOSE(zdt2.fitness(x)[0], 0.25, 1e-13); in BOOST_AUTO_TEST_CASE()
94 BOOST_CHECK_CLOSE(zdt2.fitness(x)[0], 0.33, 1e-13); in BOOST_AUTO_TEST_CASE()
104 BOOST_CHECK_CLOSE(zdt3.fitness(x)[0], 0.25, 1e-13); in BOOST_AUTO_TEST_CASE()
110 BOOST_CHECK_CLOSE(zdt3.fitness(x)[0], 0.33, 1e-13); in BOOST_AUTO_TEST_CASE()
137 BOOST_CHECK_CLOSE(zdt5.fitness(x)[0], 31., 1e-13); in BOOST_AUTO_TEST_CASE()
144 BOOST_CHECK_CLOSE(zdt5.fitness(x)[0], 31., 1e-13); in BOOST_AUTO_TEST_CASE()
152 BOOST_CHECK_CLOSE(zdt5.fitness(x)[0], 31., 1e-13); in BOOST_AUTO_TEST_CASE()
159 BOOST_CHECK_CLOSE(zdt5.fitness(x)[0], 31., 1e-13); in BOOST_AUTO_TEST_CASE()
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H A Dunconstrain.cpp87 vector_double fitness(const vector_double &x) const in fitness() function
124 BOOST_CHECK(p0.fitness(vector_double(6, 0.)) == vector_double(2, 0.)); in BOOST_AUTO_TEST_CASE()
130 BOOST_CHECK(p0.fitness(vector_double(6, 0.)) == vector_double(2, 0.)); in BOOST_AUTO_TEST_CASE()
131 BOOST_CHECK(p0.fitness(vector_double{0., 0., 1., 1., -1., 1.}) in BOOST_AUTO_TEST_CASE()
133 BOOST_CHECK(p0.fitness(vector_double{0., 0., 1., 1., -1., -1.}) in BOOST_AUTO_TEST_CASE()
135 BOOST_CHECK(p0.fitness(vector_double{0., 0., 0., 1., 0., 0.}) in BOOST_AUTO_TEST_CASE()
140 BOOST_CHECK(p0.fitness(vector_double(6, 0.)) == vector_double(2, 0.)); in BOOST_AUTO_TEST_CASE()
151 BOOST_CHECK(p0.fitness(vector_double(6, 0.)) == vector_double(2, 0.)); in BOOST_AUTO_TEST_CASE()
182 p.fitness({1., 1., 1., 1., 1., 1.}); in BOOST_AUTO_TEST_CASE()
213 vector_double fitness(const vector_double &) const in fitness() function
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H A Ddtlz.cpp80 BOOST_CHECK_CLOSE(udp.fitness(dv1)[i], f1[i], 1e-12); in BOOST_AUTO_TEST_CASE()
83 BOOST_CHECK_CLOSE(udp.fitness(dv2)[i], f2[i], 1e-12); in BOOST_AUTO_TEST_CASE()
97 BOOST_CHECK_CLOSE(udp.fitness(dv1)[i], f1[i], 1e-12); in BOOST_AUTO_TEST_CASE()
100 BOOST_CHECK_CLOSE(udp.fitness(dv2)[i], f2[i], 1e-12); in BOOST_AUTO_TEST_CASE()
114 BOOST_CHECK_CLOSE(udp.fitness(dv1)[i], f1[i], 1e-12); in BOOST_AUTO_TEST_CASE()
117 BOOST_CHECK_CLOSE(udp.fitness(dv2)[i], f2[i], 1e-12); in BOOST_AUTO_TEST_CASE()
131 BOOST_CHECK_CLOSE(udp.fitness(dv1)[i], f1[i], 1e-12); in BOOST_AUTO_TEST_CASE()
134 BOOST_CHECK_CLOSE(udp.fitness(dv2)[i], f2[i], 1e-12); in BOOST_AUTO_TEST_CASE()
148 BOOST_CHECK_CLOSE(udp.fitness(dv1)[i], f1[i], 1e-12); in BOOST_AUTO_TEST_CASE()
151 BOOST_CHECK_CLOSE(udp.fitness(dv2)[i], f2[i], 1e-12); in BOOST_AUTO_TEST_CASE()
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H A Dgradients_and_hessians.cpp44 vector_double fitness(const vector_double &dv) const in fitness() function
66 vector_double fitness(const vector_double &dv) const in fitness() function
94 …= estimate_sparsity([udp](const vector_double &x) { return udp.fitness(x); }, {0.1, 0.2, 0.3, 0.4}… in BOOST_AUTO_TEST_CASE()
97 …estimate_sparsity([udp2](const vector_double &x) { return udp2.fitness(x); }, {0.1, 0.2, 0.3, 0.4}… in BOOST_AUTO_TEST_CASE()
104 …= estimate_sparsity([prob](const vector_double &x) { return prob.fitness(x); }, {0.1, 0.2, 0.3, 0.… in BOOST_AUTO_TEST_CASE()
107 …estimate_sparsity([prob2](const vector_double &x) { return prob2.fitness(x); }, {0.1, 0.2, 0.3, 0.… in BOOST_AUTO_TEST_CASE()
113 vector_double fitness(const vector_double &dv) const in fitness() function
139 …estimate_gradient([udp2](const vector_double &x) { return udp2.fitness(x); }, {0.1, 0.2, 0.3, 0.4}… in BOOST_AUTO_TEST_CASE()
142 …estimate_gradient_h([udp2](const vector_double &x) { return udp2.fitness(x); }, {0.1, 0.2, 0.3, 0.… in BOOST_AUTO_TEST_CASE()
144 …auto g = estimate_gradient([udp](const vector_double &x) { return udp.fitness(x); }, {0.1, 0.2, 0.… in BOOST_AUTO_TEST_CASE()
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/dports/math/py-deap/deap-1.3.1/deap/tests/
H A Dtest_pickle.py39 fitness = creator.FitnessMax()
40 fitness.values = (1.0,)
47 ind.fitness.values = (4.0,)
55 ind.fitness.values = (4.0,)
64 ind.fitness.values = (4.0,)
76 ind.fitness.values = (1.0,)
81 msg = "Unpickled fitness %s != pickled fitness %s" % (str(ind.fitness), str(ind_l.fitness))
82 self.assertEqual(ind.fitness, ind_l.fitness, msg)
91 ind.fitness.values = (1.0,)
97 self.assertEqual(ind.fitness, ind_l.fitness, msg)
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/dports/devel/gaul/gaul-devel-0.1849-0/src/
H A Dga_qsort.c90 a = array_of_ptrs[first]->fitness;
91 b = array_of_ptrs[last]->fitness;
143 while (array_of_ptrs[k]->fitness == array_of_ptrs[first]->fitness && first<last)
151 while (array_of_ptrs[k]->fitness == array_of_ptrs[last]->fitness && last>first)
214 if ( array_of_ptrs[k]->fitness < array_of_ptrs[k+1]->fitness )
225 if ( array_of_ptrs[k]->fitness > array_of_ptrs[k-1]->fitness )
240 if ( array_of_ptrs[k-1]->fitness < array_of_ptrs[k]->fitness )
294 if ( array_of_ptrs[k]->fitness > array_of_ptrs[k-1]->fitness ) in sort_population()
306 if ( array_of_ptrs[k]->fitness > array_of_ptrs[k-1]->fitness ) in sort_population()
317 if ( array_of_ptrs[k]->fitness < array_of_ptrs[k+1]->fitness ) in sort_population()
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H A Dga_optim.c3067 if (son->fitness > father->fitness) in ga_evolution_with_stats()
3084 if (son->fitness > MAX(mother->fitness,father->fitness)) in ga_evolution_with_stats()
3085 crossover_gain += son->fitness-MAX(mother->fitness,father->fitness); in ga_evolution_with_stats()
3086 if (daughter->fitness > MAX(mother->fitness,father->fitness)) in ga_evolution_with_stats()
3589 if (son->fitness > father->fitness) in ga_evolution_steady_state_with_stats()
3597 if (son->fitness > mother->fitness) in ga_evolution_steady_state_with_stats()
3606 if (son->fitness > MAX(mother->fitness,father->fitness)) in ga_evolution_steady_state_with_stats()
3607 crossover_gain += son->fitness-MAX(mother->fitness,father->fitness); in ga_evolution_steady_state_with_stats()
3608 if (daughter->fitness > MAX(mother->fitness,father->fitness)) in ga_evolution_steady_state_with_stats()
3860 if (best->fitness < current->fitness) in ga_random_mutation_hill_climbing()
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H A Dga_simplex.c222 if ( putative[j]->fitness > putative[j-1]->fitness ) in ga_simplex()
238 if ( putative[j]->fitness < putative[j+1]->fitness ) in ga_simplex()
334 if (new1->fitness > putative[0]->fitness) in ga_simplex()
349 if (new2->fitness > putative[0]->fitness) in ga_simplex()
442 while (putative[i]->fitness > new1->fitness) i++; in ga_simplex()
508 while (putative[i]->fitness > new1->fitness) i++; in ga_simplex()
677 if ( putative[j]->fitness > putative[j-1]->fitness ) in ga_simplex_double()
780 if (new1->fitness > putative[0]->fitness) in ga_simplex_double()
795 if (new2->fitness > putative[0]->fitness) in ga_simplex_double()
876 while (putative[i]->fitness > new1->fitness) i++; in ga_simplex_double()
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H A Dga_select.c123 sum += pop->entity_iarray[i]->fitness; in gaul_select_sum_fitness()
145 sum += ( pop->entity_iarray[i]->fitness * pop->entity_iarray[i]->fitness ); in gaul_select_sum_sq_fitness()
364 if (mother2->fitness > (*mother)->fitness) in ga_select_one_bestof3()
366 if (mother3->fitness > (*mother)->fitness) in ga_select_one_bestof3()
405 if (challenger1->fitness > (*mother)->fitness) in ga_select_two_bestof3()
407 if (challenger2->fitness > (*mother)->fitness) in ga_select_two_bestof3()
418 if (challenger1 != *mother && challenger1->fitness > (*father)->fitness) in ga_select_two_bestof3()
421 if (challenger2 != *mother && challenger2->fitness > (*father)->fitness) in ga_select_two_bestof3()
456 if (mother2->fitness > (*mother)->fitness) in ga_select_one_bestof2()
493 if (challenger->fitness > (*mother)->fitness) in ga_select_two_bestof2()
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H A Dga_deterministiccrowding.c118 if (pop->entity_iarray[i]->fitness == GA_MIN_FITNESS) in ga_deterministiccrowding()
136 pop->entity_iarray[0]->fitness, in ga_deterministiccrowding()
137 pop->entity_iarray[pop->size-1]->fitness ); in ga_deterministiccrowding()
170 ga_get_entity_rank(pop, mother), mother->fitness, in ga_deterministiccrowding()
172 ga_get_entity_rank(pop, father), father->fitness); in ga_deterministiccrowding()
224 if (daughter->fitness < mother->fitness) in ga_deterministiccrowding()
233 if (son->fitness < father->fitness) in ga_deterministiccrowding()
244 if (son->fitness < mother->fitness) in ga_deterministiccrowding()
253 if (daughter->fitness < father->fitness) in ga_deterministiccrowding()
269 pop->entity_iarray[0]->fitness, in ga_deterministiccrowding()
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H A Dga_sa.c47 return ( original->fitness < putative->fitness || in ga_sa_boltzmann_acceptance()
48 random_boolean_prob(exp((putative->fitness-original->fitness) in ga_sa_boltzmann_acceptance()
66 return ( original->fitness < putative->fitness+pop->sa_params->temperature ); in ga_sa_linear_acceptance()
210 if (best->fitness==GA_MIN_FITNESS) pop->evaluate(pop, best); in ga_sa()
214 best->fitness ); in ga_sa()
265 if ( initial->fitness<best->fitness ) in ga_sa()
277 best->fitness ); in ga_sa()
/dports/science/py-pygmo2/pygmo2-2.18.0/pygmo/
H A D_problem_test.py18 def fitness(self, a): member in _prob
72 def fitness(self, a): member in problem_test_case.run_basic_tests.np0
87 fitness = 42 variable in problem_test_case.run_basic_tests.np2
95 def fitness(self, a): member in problem_test_case.run_basic_tests.np3
109 def fitness(self, a): member in problem_test_case.run_basic_tests.p
146 prob.fitness([0, 0])
147 prob.fitness([0, 0])
160 def fitness(self, a): member in problem_test_case.run_basic_tests.p
171 def fitness(self, a): member in problem_test_case.run_basic_tests.p
181 def fitness(self, a): member in problem_test_case.run_basic_tests.p
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/dports/astro/py-astropy/astropy-5.0/astropy/stats/tests/
H A Dtest_bayesian_blocks.py43 bins = bayesian_blocks(t, x, sigma, fitness='measures')
55 bins = bayesian_blocks(t, x, sigma, fitness='measures')
73 bins2 = bayesian_blocks(t, fitness=RegularEvents, dt=dt)
77 bins3 = bayesian_blocks(t, fitness=RegularEvents(dt=dt))
87 bayesian_blocks(t, fitness='events', x=t)
95 bayesian_blocks(t, fitness='measures')
99 bayesian_blocks(t, fitness='events', sigma=0.5)
103 bayesian_blocks(t, fitness='measures', x=t[:-1])
109 bayesian_blocks(t2, fitness='measures', x=t)
122 edges = bayesian_blocks(t, fitness='events')
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/dports/comms/yagiuda/yagiuda-1.19/src/
H A Dgenetic_algorithm_lib.c16 double fitness ; member
126 Pop1[a].fitness=0.0 ; in Initialise()
141 Pop2[a].fitness=0.0 ; in Initialise()
216 if (Pop1[inner].fitness<Pop1[inner+1].fitness) in Sort()
253 if (Pop1[a].fitness<minfit) minfit=Pop1[a].fitness ; in Selection()
254 if (Pop1[a].fitness>maxfit) maxfit=Pop1[a].fitness ; in Selection()
255 sigma+=Pop1[a].fitness ; in Selection()
264 Pop1[a].fitness=((Pop1[a].fitness-minfit)*99.0/(maxfit-minfit))+1.0 ; in Selection()
311 if (Pop1[a].fitness<Pop1[b].fitness) in Selection()
313 if (Pop1[b].fitness<Pop1[c].fitness) in Selection()
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/dports/science/py-pygmo2/pygmo2-2.18.0/doc/tutorials/
H A Dudp_meta_decorator.rst13 in the fitness function of :class:`~pygmo.decompose`, which first invokes the fitness function
15 the resulting multi-dimensional fitness vector into a scalar fitness. From a pythonic point of view,
67 Has batch fitness evaluation: false
97 ... return fitness
132 Has batch fitness evaluation: false
147 fitness
160 Logging fitness evaluations
170 The fitness logging decorator is rather simple:
210 >>> drb.fitness([1, 2])
212 >>> drb.fitness([3, 4])
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/dports/math/py-deap/deap-1.3.1/examples/coev/
H A Dsymbreg.py30 creator.create("IndGA", list, fitness=creator.FitnessMax)
48 stats = tools.Statistics(lambda ind: ind.fitness.values)
61 ind.fitness.values = toolbox_gp.evaluate(ind, points=best_ga)
64 ind.fitness.values = toolbox_gp.evaluate(best_gp, points=ind)
89 del ind1.fitness.values
90 del ind2.fitness.values
95 del ind1.fitness.values
96 del ind2.fitness.values
101 del ind.fitness.values
106 del ind.fitness.values
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/dports/math/R-cran-igraph/igraph/man/
H A Dsample_fitness.Rd5 \alias{static.fitness.game}
6 \title{Random graphs from vertex fitness scores}
10 fitness.out,
11 fitness.in = NULL,
19 \item{fitness.out}{A numeric vector containing the fitness of each vertex.
20 For directed graphs, this specifies the out-fitness of each vertex.}
22 \item{fitness.in}{If \code{NULL} (the default), the generated graph will be
24 specifies the in-fitness of each vertex.
39 proportional to node fitness scores.
46 out- and an in-fitness, and the probability of an edge from \eqn{i} to
[all …]
/dports/math/py-deap/deap-1.3.1/examples/de/
H A Ddynamic.py45 creator.create("Individual", array.array, typecode='d', fitness=creator.FitnessMax)
62 stats = tools.Statistics(lambda ind: ind.fitness.values)
78 ind.fitness.values = fit
90 if any(b.fitness.values != toolbox.evaluate(b) for b in bests):
92 del individual.fitness.values
97 if bests[i].fitness.valid and bests[j].fitness.valid:
102 if bests[i].fitness < bests[j].fitness:
113 ind.fitness.values = fit
133 offspring.fitness.values = toolbox.evaluate(offspring)
134 if offspring.fitness >= individual.fitness:
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/dports/textproc/fop/fop-2.6/fop-core/src/main/java/org/apache/fop/layoutmgr/
H A DBreakingAlgorithm.java235 public final int fitness; field in BreakingAlgorithm.KnuthNode
294 this.fitness = fitness; in KnuthNode()
353 bestNode[fitness] = node; in addRecord()
354 bestAdjust[fitness] = adjust; in addRecord()
359 bestIndex = fitness; in addRecord()
382 return bestDemerits[fitness]; in getDemerits()
390 return bestNode[fitness]; in getNode()
398 return bestAdjust[fitness]; in getAdjust()
422 return bestDifference[fitness]; in getDifference()
720 best.getAvailableShrink(fitness), best.getAvailableStretch(fitness), in createNode()
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/dports/math/py-deap/deap-1.3.1/deap/
H A Dalgorithms.py75 del offspring[i - 1].fitness.values, offspring[i].fitness.values
80 del offspring[i].fitness.values
152 ind.fitness.values = fit
174 ind.fitness.values = fit
235 del ind1.fitness.values
240 del ind.fitness.values
303 ind.fitness.values = fit
322 ind.fitness.values = fit
401 ind.fitness.values = fit
423 ind.fitness.values = fit
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/dports/science/libgeodecomp/libgeodecomp-0.4.0/src/misc/
H A Dsimplexoptimizer.h23 fitness(-1) in SimplexVertex()
28 return fitness; in getFitness()
33 fitness = eval(*this); in evaluate()
34 return fitness; in evaluate()
39 void setFitness(const double fitness) in setFitness() argument
41 this->fitness = fitness; in setFitness()
45 double fitness;
71 int comperator(double fitness);
/dports/math/py-deap/deap-1.3.1/examples/ga/
H A Donemax.py27 creator.create("Individual", list, fitness=creator.FitnessMax)
90 ind.fitness.values = fit
95 fits = [ind.fitness.values[0] for ind in pop]
120 del child1.fitness.values
121 del child2.fitness.values
128 del mutant.fitness.values
131 invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
134 ind.fitness.values = fit
142 fits = [ind.fitness.values[0] for ind in pop]
157 print("Best individual is %s, %s" % (best_ind, best_ind.fitness.values))
/dports/math/py-deap/deap-1.3.1/examples/pso/
H A Dbasic_numpy.py27 creator.create("Particle", numpy.ndarray, fitness=creator.FitnessMax, speed=list,
58 stats = tools.Statistics(lambda ind: ind.fitness.values)
72 part.fitness.values = toolbox.evaluate(part)
73 if part.best is None or part.best.fitness < part.fitness:
75 part.best.fitness.values = part.fitness.values
76 if best is None or best.fitness < part.fitness:
78 best.fitness.values = part.fitness.values
/dports/math/py-deap/deap-1.3.1/doc/code/tutorials/part_3/
H A D3_next_step.py11 creator.create("Individual", list, fitness=creator.FitnessMin)
21 print ind1.fitness.valid # False
30 ind1.fitness.values = evaluate(ind1)
31 print ind1.fitness.valid # True
32 print ind1.fitness # (2.73, 0.2)
37 del mutant.fitness.values
45 del child1.fitness.values
46 del child2.fitness.values

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