Removed walruses
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@ -272,28 +272,14 @@ class Attributes:
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self.crossover_individual_impl = Crossover.Individual.Permutation.ox1
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self.mutation_individual_impl = Mutation.Individual.Permutation.swap_genes
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# Count the number of gene pairs they don't have in common
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def dist(self, chromosome_1, chromosome_2):
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# Used to set values during comprehension
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set_value = lambda arg: True
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# Index of gene from chromosome 1 in chromosome 2
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j = 0
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"""Count the number of gene pairs they don't have in common."""
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return sum(
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# Add 1 if they are different
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int(chromosome_1[i-1] != chromosome_2[j-1])
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# Loop over chromosome 1
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for i
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in range(1-cycle, len(chromosome_1))
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# Find index of gene from chromosome 1 in chromosome 2
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if set_value(j := chromosome_2.index_of(chromosome_1[i]))
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# Additional case to check
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if j + cycle > 0
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1
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for x, y
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in zip(chromosome_1, chromosome_2)
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if x != y
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)
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self.dist = dist
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@ -161,62 +161,3 @@ class Individual:
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ga.population.add_child(gene_list_1)
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@_check_weight
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def partially_mapped(ga, parent_1, parent_2, *, weight = 0.5):
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"""Cross two parents by slicing out a random part of one parent
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and then filling in the rest of the genes from the second parent,
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preserving the ordering of genes wherever possible.
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NOTE: Needs to be fixed, since genes are not hashable..."""
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# Too small to cross
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if len(parent_1) < 2:
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return parent_1.gene_list
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# Unequal parent lengths
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if len(parent_1) != len(parent_2):
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raise ValueError("Parents do not have the same lengths.")
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# Swap with weighted probability so that most of the genes
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# are taken directly from parent 1.
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if random.choices([0, 1], cum_weights = [weight, 1]) == 1:
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parent_1, parent_2 = parent_2, parent_1
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# Extract genes from parent 1 between two random indexes
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index_2 = random.randrange(1, len(parent_1))
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index_1 = random.randrange(index_2)
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# Create copies of the gene lists
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gene_list_1 = [None]*index_1 + parent_1[index_1:index_2] + [None]*(len(parent_1)-index_2)
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gene_list_2 = list(parent_2)
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# Create hash for gene list 2
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hash = {gene:index for index, gene in enumerate(gene_list_2)}
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# For each gene in the copied segment from parent 2
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for i in range(index_1, index_2):
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# If it is not already copied,
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# find where it got displaced to
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j = i
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while gene_list_1[(j := hash[gene_list_1[j]])] is not None:
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pass
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gene_list_1[j] = gene_list_2[i]
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# Fill in whatever is leftover (copied from ox1).
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# For each gene from the second parent
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for _ in range(len(gene_list_2)):
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# Remove it if it is already used
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if gene_list_2[-1] in gene_list_1:
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gene_list_2.pop(-1)
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# Add it if it has not been used
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else:
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if input_index == index_1:
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input_index = index_2
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gene_list_1[input_index] = gene_list_2.pop(-1)
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input_index += 1
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ga.population.add_child(gene_list_1)
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