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Added iterable features

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SimpleArt
2020-11-19 11:46:47 -05:00
parent 6e95ff5d9d
commit 0ee545429c
7 changed files with 167 additions and 97 deletions
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58
src/crossover/crossover_methods.py
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@ -43,14 +43,14 @@ class Crossover_Methods:
class Individual:
"""Methods for crossing parents."""
def single_point(ga, parent_one, parent_two):
def single_point(ga, parent_1, parent_2):
"""Cross two parents by swapping genes at one random point."""
swap_index = random.randint(0, parent_one.size()-1)
return ga.make_chromosome(parent_one.get_gene_list()[:swap_index] + parent_two.get_gene_list()[swap_index:])
swap_index = random.randint(0, len(parent_1)-1)
return ga.make_chromosome(parent_1[:swap_index] + parent_2[swap_index:])
def multi_point(ga, parent_one, parent_two):
def multi_point(ga, parent_1, parent_2):
"""Cross two parents by swapping genes at multiple points."""
pass
@ -58,9 +58,9 @@ class Crossover_Methods:
def uniform(ga, parent_1, parent_2):
"""Cross two parents by swapping all genes randomly."""
return ga.make_chromosome([ # Make a new chromosome
random.choice([gene_1, gene_2]) # by randomly selecting genes
for gene_1, gene_2 in zip(parent_1.gene_list, parent_2.gene_list)]) # from each parent
return ga.make_chromosome([ # Make a new chromosome
random.choice([gene_1, gene_2]) # by randomly selecting genes
for gene_1, gene_2 in zip(parent_1, parent_2)]) # from each parent
class Arithmetic:
@ -69,9 +69,12 @@ class Crossover_Methods:
def int_random(ga, parent_1, parent_2):
"""Cross two parents by taking a random integer value between each of the genes."""
return ga.make_chromosome([ # Make a new chromosome
ga.make_gene(random.randint(*sorted([data_1, data_2]))) # by randomly selecting integer genes between
for data_1, data_2 in zip(iter(parent_1), iter(parent_2))]) # each parents' genes
value_list_1 = parent_1.gene_value_list
value_list_2 = parent_2.gene_value_list
return ga.make_chromosome([ # Make a new chromosome
ga.make_gene(random.randint(*sorted([value_1, value_2]))) # by randomly selecting integer genes between
for value_1, value_2 in zip(value_list_1, value_list_2)]) # each parents' genes
def int_weighted(ga, parent_1, parent_2):
@ -80,19 +83,25 @@ class Crossover_Methods:
# the percentage of genes taken from the first gene
weight = 0.25
return ga.make_chromosome([ # Make a new chromosome
ga.make_gene(int( # filled with new integer genes
weight*data_1+(1-weight)*data_2 # with weight% from gene 1 and
)) # (100-weight)% from gene 2
for data_1, data_2 in zip(iter(parent_1), iter(parent_2))]) # from each parents' genes
value_list_1 = parent_1.gene_value_list
value_list_2 = parent_2.gene_value_list
return ga.make_chromosome([ # Make a new chromosome
ga.make_gene(int( # filled with new integer genes
weight*value_1+(1-weight)*value_2 # with weight% from gene 1 and
)) # (100-weight)% from gene 2
for value_1, value_2 in zip(value_list_1, value_list_2)]) # from each parents' genes
def float_random(ga, parent_one, parent_two):
"""Cross two parents by taking a random numeric value between each of the genes."""
return ga.make_chromosome([ # Make a new chromosome
ga.make_gene(random.uniform([data_1, data_2])) # by randomly selecting integer genes between
for data_1, data_2 in zip(iter(parent_1), iter(parent_2))]) # from each parents' genes
value_list_1 = parent_1.gene_value_list
value_list_2 = parent_2.gene_value_list
return ga.make_chromosome([ # Make a new chromosome
ga.make_gene(random.uniform([value_1, value_2])) # by randomly selecting integer genes between
for value_1, value_2 in zip(value_list_1, value_list_2)]) # from each parents' genes
def float_weighted(ga, parent_one, parent_two):
@ -101,8 +110,11 @@ class Crossover_Methods:
# the percentage of genes taken from the first gene
weight = 0.25
return ga.make_chromosome([ # Make a new chromosome
ga.make_gene( # filled with new float genes
weight*data_1+(1-weight)*data_2 # with weight% from gene 1 and
) # (100-weight)% from gene 2
for data_1, data_2 in zip(iter(parent_1), iter(parent_2))]) # from each parents' genes
value_list_1 = parent_1.gene_value_list
value_list_2 = parent_2.gene_value_list
return ga.make_chromosome([ # Make a new chromosome
ga.make_gene( # filled with new float genes
weight*value_1+(1-weight)*value_2 # with weight% from gene 1 and
) # (100-weight)% from gene 2
for value_1, value_2 in zip(value_list_1, value_list_2)]) # from each parents' genes