Removed walruses

src/attributes.py

@@ -272,28 +272,14 @@ class Attributes:
         self.crossover_individual_impl = Crossover.Individual.Permutation.ox1
         self.mutation_individual_impl  = Mutation.Individual.Permutation.swap_genes
 
-        # Count the number of gene pairs they don't have in common
         def dist(self, chromosome_1, chromosome_2):
-
+            """Count the number of gene pairs they don't have in common."""
-            # Used to set values during comprehension
-            set_value = lambda arg: True
-
-            # Index of gene from chromosome 1 in chromosome 2
-            j = 0
 
             return sum(
-                # Add 1 if they are different
+                1
-                int(chromosome_1[i-1] != chromosome_2[j-1])
+                for x, y
-
+                in zip(chromosome_1, chromosome_2)
-                # Loop over chromosome 1
+                if x != y
-                for i
-                in range(1-cycle, len(chromosome_1))
-
-                # Find index of gene from chromosome 1 in chromosome 2
-                if set_value(j := chromosome_2.index_of(chromosome_1[i]))
-
-                # Additional case to check
-                if j + cycle > 0
             )
 
         self.dist = dist

src/crossover/Crossover.py

@@ -161,62 +161,3 @@ class Individual:
 
             ga.population.add_child(gene_list_1)
 
-
-        @_check_weight
-        def partially_mapped(ga, parent_1, parent_2, *, weight = 0.5):
-            """Cross two parents by slicing out a random part of one parent
-            and then filling in the rest of the genes from the second parent,
-            preserving the ordering of genes wherever possible.
-
-            NOTE: Needs to be fixed, since genes are not hashable..."""
-
-            # Too small to cross
-            if len(parent_1) < 2:
-                return parent_1.gene_list
-
-            # Unequal parent lengths
-            if len(parent_1) != len(parent_2):
-                raise ValueError("Parents do not have the same lengths.")
-
-            # Swap with weighted probability so that most of the genes
-            # are taken directly from parent 1.
-            if random.choices([0, 1], cum_weights = [weight, 1]) == 1:
-                parent_1, parent_2 = parent_2, parent_1
-
-            # Extract genes from parent 1 between two random indexes
-            index_2 = random.randrange(1, len(parent_1))
-            index_1 = random.randrange(index_2)
-
-            # Create copies of the gene lists
-            gene_list_1 = [None]*index_1 + parent_1[index_1:index_2] + [None]*(len(parent_1)-index_2)
-            gene_list_2 = list(parent_2)
-
-            # Create hash for gene list 2
-            hash = {gene:index for index, gene in enumerate(gene_list_2)}
-
-            # For each gene in the copied segment from parent 2
-            for i in range(index_1, index_2):
-
-                # If it is not already copied,
-                # find where it got displaced to
-                j = i
-                while gene_list_1[(j := hash[gene_list_1[j]])] is not None:
-                    pass
-                gene_list_1[j] = gene_list_2[i]
-
-            # Fill in whatever is leftover (copied from ox1).
-            # For each gene from the second parent
-            for _ in range(len(gene_list_2)):
-
-                # Remove it if it is already used
-                if gene_list_2[-1] in gene_list_1:
-                    gene_list_2.pop(-1)
-
-                # Add it if it has not been used
-                else:
-                    if input_index == index_1:
-                        input_index = index_2
-                    gene_list_1[input_index] = gene_list_2.pop(-1)
-                    input_index += 1
-
-            ga.population.add_child(gene_list_1)