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Commit 2c6c6d8

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1 parent 468d36a commit 2c6c6d8

16 files changed

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lines changed

‎example2.py

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import pygad
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import numpy
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function_inputs = [4,-2,3.5,5,-11,-4.7]
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desired_output = 44
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def fitness_func(ga_instance, solution, solution_idx):
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output = numpy.sum(solution*function_inputs)
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fitness = 1.0 / (numpy.abs(output - desired_output) + 0.000001)
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return fitness
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num_genes = len(function_inputs)
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ga_instance = pygad.GA(num_generations=100,
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num_parents_mating=10,
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sol_per_pop=20,
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num_genes=num_genes,
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mutation_num_genes=6,
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fitness_func=fitness_func,
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init_range_low=1,
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init_range_high=100,
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# suppress_warnings=True,
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random_mutation_min_val=1,
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random_mutation_max_val=100,
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mutation_by_replacement=True,
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gene_type=[float, 1],
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save_solutions=True,
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allow_duplicate_genes=False,
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# gene_space=numpy.unique(numpy.random.uniform(1, 100, size=100)),
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gene_space=[range(0, 100), {"low": 0, "high": 100, 'step': 1}, 2.5891221, [1,2,3,4], None, numpy.unique(numpy.random.uniform(1, 100, size=4))],
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gene_constraint=[lambda x: x[0]>=95,lambda x: x[1]>=95,lambda x: x[2]<98,lambda x: x[3]<98,lambda x: x[4]<98,lambda x: x[5]<98],
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)
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print(ga_instance.initial_population)
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# ga_instance.run()
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# print(ga_instance.gene_space_unpacked)
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# print(ga_instance.population)
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‎pygad/__pycache__/pygad.cpython-310.pyc

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‎pygad/helper/misc.py

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-An array with number of maximum number of values equal to sample_size if sample_size>1.
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"""
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range_min, range_max = self.get_random_mutation_range(gene_idx)
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if gene_value is None:
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# Use the initial population range.
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range_min, range_max = self.get_initial_population_range(gene_index=gene_idx)
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else:
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# Use the mutation range.
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range_min, range_max = self.get_random_mutation_range(gene_idx)
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if self.gene_space_nested:
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# Returning the current gene space from the 'gene_space' attribute.

‎pygad/helper/unique.py

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‎pygad/pygad.py

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