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use v6.c; use Test; use Algorithm::Evolutionary::Simple; my $population-size = 32; # TODO: add 64 here when regression in Rakudo is fixed. for <32 48> -> $length { for ^$population-size { my @random-chromosome = random-chromosome( $length ); is( @random-chromosome.elems, $length, "Chromosome length OK" ); my $packed-in-an-int = pack-individual( @random-chromosome); isa-ok( $packed-in-an-int, Int, "Individual with @random-chromosome[0] is packed in $packed-in-an-int" ); is-deeply( unpack-individual( $packed-in-an-int, $length), @random-chromosome, "Individual unpacks OK" ); } my @initial-population = initialize( size => $population-size, genome-length => $length ); is( @initial-population.elems, $population-size, "Pop is the right size"); my $packed-pop = pack-population( @initial-population); does-ok( $packed-pop, Buf[uint64], "Population is packed"); is( $packed-pop.elems, $population-size, "Buf is the right size"); my @unpacked-pop = unpack-population( $packed-pop, $length); is( @unpacked-pop.elems, $population-size, "Population unpacked OK"); is-deeply( @unpacked-pop[0], @initial-population[0].Array, "Unpacking works"); } my $length = 32; my @population = initialize( size => $population-size, genome-length => $length ); my $evaluated-pop = evaluate-nocache(:@population, evaluator => &max-ones ); is best-one( $evaluated-pop).value, best-fitness( $evaluated-pop), "Best fitness OK"; cmp-ok best-fitness( $evaluated-pop ), "≥", $evaluated-pop.sort(*.value).reverse.[1].value, "Best is equal or better than second"; for $evaluated-pop.keys -> $k { is( $evaluated-pop{$k}, max-ones( $k ), "Evaluation is correct, {$evaluated-pop{$k}}"); } does-ok($evaluated-pop, Mix, "Evaluated pop is the right class" ); my @population-prime = initialize( size => $population-size, genome-length => $length ); my $new-pop = mix-raw( @population, @population-prime, $population-size, &max-ones); is( $new-pop.elems, $population-size, "Size is correct" ); my @fake-population = [ [True,True,True,True],[True,True,True,False],[True,True,False,False],[True,False,False,False] ]; my @frequencies = frequencies( @fake-population); is-deeply(@frequencies, [1.0,0.75,0.5,0.25], "Frequencies OK" ); # Check on real pop my $best-one = best-one( $new-pop ); @frequencies = frequencies( $new-pop ); is( @frequencies.elems, $length, "Size is correct" ); cmp-ok( any(@frequencies), ">", 0, "Some frequencies are not null" ); my @freqs-other-way = frequencies( $new-pop.keys ); is-deeply( @freqs-other-way, @frequencies, "Checking frequencies both ways" ); @population = generate-by-frequencies( $population-size, @frequencies ); is( @population.elems, $population-size, "Size is correct" ); for @population -> @p { is( @p.elems, $length, "Size of generated elem is correct" ); } @population = generate-with-best( $population-size, @frequencies, $best-one); is( @population.elems, $population-size, "Size is correct" ); my @new-frequencies = frequencies( @population ); my $difference = [+] @new-frequencies Z- @frequencies; cmp-ok( $difference, "<", $population-size * 0.3, "Frequencies differ in $difference" ); my @freqs-best = frequencies-best( $new-pop ); is( @freqs-best.elems, $length, "Size of freqs-best is correct" ); cmp-ok((sum @freqs-best), ">", (sum @frequencies), "Frequencies of the best are better"); my @crossed = crossover-frequencies( @frequencies, @new-frequencies ); is @crossed.elems, @frequencies.elems, "Same length frequencies"; is( @crossed[0], any(@frequencies[0],@new-frequencies[0]), "Crossing OK"); is( @crossed[*-1], any(@frequencies[*-1],@new-frequencies[*-1]), "Crossing OK"); # Test no-change for ^2 { is( no-change-during( 3, 3 ), False, "No change for $_ generations" ); } is( no-change-during( 3, 3 ), True, "No change for 3 generations" ); is( no-change-during( 3, 4 ), False, "There's been change" ); done-testing;