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https://github.com/kuhyx/WUT_Computer_Science.git
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feat: make code conform to pylint
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parent
0271bfb1eb
commit
8cc2316ce7
3
.vscode/settings.json
vendored
Normal file
3
.vscode/settings.json
vendored
Normal file
@ -0,0 +1,3 @@
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{
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"python.linting.pylintArgs": ["--generate-members"]
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}
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264
lab3/main.py
264
lab3/main.py
@ -13,119 +13,154 @@ import numpy as np
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def rastrigin(x_argument, y_argument):
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""" Define the Rastrigin function """
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return 20 + x_argument**2 - 10 * np.cos(2 * np.pi * x_argument) + \
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y_argument**2 - 10 * np.cos(2 * np.pi * y_argument)
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"""Define the Rastrigin function"""
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return (
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20
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+ x_argument**2
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- 10 * np.cos(2 * np.pi * x_argument)
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+ y_argument**2
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- 10 * np.cos(2 * np.pi * y_argument)
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)
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def generate(population,
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number_of_parents=5,
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size_of_population=20,
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mutation_strength=0.1,
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):
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""" Run single generation """
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def generate(
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population,
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arguments
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):
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"""Run single generation"""
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# Evaluate the fitness of each individual
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fitness = np.array([rastrigin(x_point_value, y_point_value)
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for x_point_value, y_point_value in population])
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fitness = np.array(
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[
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rastrigin(x_point_value, y_point_value)
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for x_point_value, y_point_value in population
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]
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)
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# Select the top number_of_parents individuals
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parents = population[np.argsort(fitness)[:number_of_parents]]
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# Select the top arguments["number_of_parents"] individuals
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parents = population[np.argsort(fitness)[:arguments["number_of_parents"]]]
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# Generate the next generation of lambda individuals by recombination
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children = np.concatenate(
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[np.random.permutation(parents) for i in range((size_of_population//number_of_parents)+1)])
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children = children[:size_of_population]
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[
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np.random.permutation(parents)
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for i in range((arguments["size_of_population"] // arguments["number_of_parents"]) + 1)
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]
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)
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children = children[:arguments["size_of_population"]]
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# Add mutation to the children
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mutation = np.random.normal(
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loc=0, scale=mutation_strength, size=(
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size_of_population, 2))
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loc=0, scale=arguments["mutation_strength"], size=(arguments["size_of_population"], 2)
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)
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population = children + mutation
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return fitness, population
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def evolution_strategy(
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number_of_parents=5,
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size_of_population=20,
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mutation_strength=0.1,
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number_of_generations=123,
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min_max=(-5.12, 5.12),
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number_of_outputs = 10,
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no_display = False,
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save_results = False
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arguments,
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no_display=False,
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):
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""" Define the Evolutionary Strategy (μ, λ) algorithm """
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"""Define the Evolutionary Strategy (μ, λ) algorithm"""
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# Initialize the population
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print_info = []
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population = np.random.uniform(
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low=min_max[0], high=min_max[1], size=(
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size_of_population, 2))
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low=arguments["min"], high=arguments["max"], size=(arguments["size_of_population"], 2)
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)
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summary = []
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if not no_display:
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print_info.append((population, 0, f"0:nop-{number_of_parents}:sop-{size_of_population}:ms-{mutation_strength}:nog-{number_of_generations}:min-max-{min_max}:noo-{number_of_outputs}"))
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number_of_outputs = min([number_of_outputs-1, number_of_generations])
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print_info.append(
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(population,
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0,
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f"""0:nop-{arguments["number_of_parents"]}:sop-{arguments["size_of_population"]}:
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ms-{arguments["mutation_strength"]}:nog-{arguments["number_of_generations"]}:
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min-max-{arguments["min"], arguments["max"]}:noo-{arguments["number_of_outputs"]}""",
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))
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arguments["number_of_outputs"] = min(
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[arguments["number_of_outputs"] - 1, arguments["number_of_generations"]])
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# Iterate until we reach max number of generate and terminate
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for generation_number in range(1, number_of_generations+1):
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for generation_number in range(1, arguments["number_of_generations"] + 1):
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fitness, population = generate(
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population,
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number_of_parents,
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size_of_population,
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mutation_strength)
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step = number_of_generations//number_of_outputs \
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if number_of_generations % number_of_outputs == 0 \
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else number_of_generations//(number_of_outputs-1)
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offset = number_of_generations % step
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population, arguments)
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step = (
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arguments["number_of_generations"] // arguments["number_of_outputs"]
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if arguments["number_of_generations"] % arguments["number_of_outputs"] == 0
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else arguments["number_of_generations"] // (arguments["number_of_outputs"] - 1)
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)
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offset = arguments["number_of_generations"] % step
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if (generation_number - offset) % step == 0 and not no_display:
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print_info.append((population, generation_number, f"{generation_number}:nop_{number_of_parents}:sop_{size_of_population}:ms_{mutation_strength}:nog_{number_of_generations}:min_max_{min_max}:noo_{number_of_outputs}"))
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print_info.append(
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(population,
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generation_number,
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f"""{generation_number}:nop_{arguments["number_of_parents"]}:
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sop_{arguments["size_of_population"]}:ms_{arguments["mutation_strength"]}:
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nog_{arguments["number_of_generations"]}:
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min_max_{arguments["min"], arguments["max"]}:
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noo_{arguments["number_of_outputs"]}""",
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))
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summary.append(population)
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# Evaluate the fitness of the final population
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fitness = np.array([rastrigin(x_point_value, y_point_value)
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for x_point_value, y_point_value in population])
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fitness = np.array(
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[
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rastrigin(x_point_value, y_point_value)
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for x_point_value, y_point_value in population
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]
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)
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# Return the best individual found
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best_idx = np.argmin(fitness)
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return population[best_idx], fitness[best_idx], population, total_time, print_info, summary
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return (
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population[best_idx],
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fitness[best_idx],
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population,
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print_info,
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summary,
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)
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def print_help():
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""" Print program functionality and how to access it """
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print("""
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python main.py - Default functionality optimizing Rastrigin function file_ (x_point_value, y_point_value) =
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"""Print program functionality and how to access it"""
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print(
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"""
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python main.py - Default functionality optimizing
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Rastrigin function file_ (x_point_value, y_point_value) =
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20 + (x_point_value^2 - 10cos(2πx)) + (y_point_value^2 - 10 cos(2πy))
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using Evolutionary Strategy (μ, λ), using only default values
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Default values:
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number_of_parents=5,
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size_of_population=20,
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mutation_strength=0.1,
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number_of_generations=100,
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arguments["number_of_parents"]=5,
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arguments["size_of_population"]=20,
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arguments["mutation_strength"]=0.1,
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arguments["number_of_generations"]=100,
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min_value=-5.12,
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max_value=5.12
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number_of_outputs = 100
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arguments["number_of_outputs"] = 100
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python main.py -h --help print this prompt
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Any of the default values an be changed using arguments:
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-nop --number_of_parents [number]
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-sop --size_of_population [number]
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-ms --mutation_strength [number]
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-nog --number_of_generations [number]
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-nop --arguments["number_of_parents"] [number]
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-sop --arguments["size_of_population"] [number]
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-ms --arguments["mutation_strength"] [number]
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-nog --arguments["number_of_generations"] [number]
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-min --min_value [number]
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-max --max_value [number]
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-noo, --number_of_outputs [number]
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Those arguments can be given in any order and any argument which was not entered will be replaced with default value,
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-noo, --arguments["number_of_outputs"] [number]
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Those arguments can be given in any order and any argument
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which was not entered will be replaced with default value,
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Additional flags:
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-nd, --no-display (does not show the plots)
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-s, --save (if issued WILL save the files)
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exemplary use:
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python main.py -nop 5 -sop 20 -ms 0.1 -i 100 -min -5.12 -max 5.12 -noo 100
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""")
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"""
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)
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def get_output_bounds(x_data, y_data):
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"""Get x and y output limits for pyplot"""
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# min_size = 0.2
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min_output_size = ARGUMENTS["mutation_strength"]*10
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min_output_size = ARGUMENTS["mutation_strength"] * 10
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xmin = min(x_data)
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xmax = max(x_data)
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@ -137,21 +172,25 @@ def get_output_bounds(x_data, y_data):
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if min_output_size is None:
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min_output_size = max(x_diff, y_diff)
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margin = max(x_diff, y_diff)/5
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margin = max(x_diff, y_diff) / 5
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if x_diff < min_output_size:
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xmax += (min_output_size - x_diff)/2
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xmin -= (min_output_size - x_diff)/2
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xmax += (min_output_size - x_diff) / 2
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xmin -= (min_output_size - x_diff) / 2
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if y_diff < min_output_size:
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ymax += (min_output_size - y_diff)/2
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ymin -= (min_output_size - y_diff)/2
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x_bounds = [xmin-margin, xmax+margin]
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y_bounds = [ymin-margin, ymax+margin]
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ymax += (min_output_size - y_diff) / 2
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ymin -= (min_output_size - y_diff) / 2
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x_bounds = [xmin - margin, xmax + margin]
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y_bounds = [ymin - margin, ymax + margin]
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return x_bounds, y_bounds
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def output(population_output, generation_number, file_name = "temp", save_results = False):
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""" Draw result of our function """
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def output(
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population_output,
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generation_number,
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file_name="temp",
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save_results=False):
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"""Draw result of our function"""
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# define the visualization params
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colors = np.random.rand(len(population_output))
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@ -193,12 +232,8 @@ def output(population_output, generation_number, file_name = "temp", save_result
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os.unlink(file_.name)
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def print_summary(populations, file_name = "temp_summary", save_results = False):
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""" Draw result of our function for chosen generations """
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# define the visualization params
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main_color = [[1, 1, 1]] * len(populations[0])
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final_color = [[0, 1, 0]] * len(populations[0])
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def print_summary(populations, file_name="temp_summary", save_results=False):
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"""Draw result of our function for chosen generations"""
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with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as file_:
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# iterate over the optimization steps
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@ -217,10 +252,14 @@ def print_summary(populations, file_name = "temp_summary", save_results = False)
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if ind == 0:
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bounds = get_output_bounds(x_data, y_data)
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# plot the data
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transparency = ind/(len(populations)-1)
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transparency = ind / (len(populations) - 1)
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color = [[transparency, 0, 0]] * len(pop)
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plt.scatter(x_data, y_data, c=color,
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alpha=transparency, label=f"{ind}")
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plt.scatter(
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x_data,
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y_data,
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c=color,
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alpha=transparency,
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label=f"{ind}")
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plt.xlim(bounds[0])
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plt.ylim(bounds[1])
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plt.savefig(file_.name)
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@ -231,8 +270,7 @@ def print_summary(populations, file_name = "temp_summary", save_results = False)
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cv2.imwrite("SUMMARY:" + file_name + ".jpg", image)
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# show the image, provide window name first
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cv2.imshow(f"Summary", image)
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cv2.imshow("Summary", image)
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# add wait key. window waits until user presses a key and quits if
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# the key is 'q'
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@ -247,7 +285,7 @@ def print_summary(populations, file_name = "temp_summary", save_results = False)
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def user_input():
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""" Handle user terminal arguments"""
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"""Handle user terminal arguments"""
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arguments = {
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"number_of_parents": 5,
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"size_of_population": 20,
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@ -257,61 +295,65 @@ def user_input():
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"max": 5.12,
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"number_of_outputs": 10,
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"no_display": False,
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"save": False}
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"save": False,
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}
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for index, argument in enumerate(sys.argv):
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if argument in ('-h', '--help'):
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if argument in ("-h", "--help"):
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print_help()
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sys.exit()
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if argument in ('-nop', '--number_of_parents'):
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arguments["number_of_parents"] = int(sys.argv[index+1])
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if argument in ('-sop', '--size_of_population'):
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arguments["size_of_population"] = int(sys.argv[index+1])
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if argument in ('-ms', '--mutation_strength'):
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arguments["mutation_strength"] = float(sys.argv[index+1])
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if argument in ('-nog', '--number_of_generations'):
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arguments["number_of_generations"] = int(sys.argv[index+1])
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if argument in ('-min', '--min_value'):
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arguments["min"] = float(sys.argv[index+1])
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if argument in ('-max', '--max_value'):
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arguments["max"] = float(sys.argv[index+1])
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if argument in ('-noo', '--number_of_outputs'):
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if argument in ("-nop", "--number_of_parents"):
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arguments["number_of_parents"] = int(sys.argv[index + 1])
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if argument in ("-sop", "--size_of_population"):
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arguments["size_of_population"] = int(sys.argv[index + 1])
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if argument in ("-ms", "--mutation_strength"):
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arguments["mutation_strength"] = float(sys.argv[index + 1])
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if argument in ("-nog", "--number_of_generations"):
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arguments["number_of_generations"] = int(sys.argv[index + 1])
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if argument in ("-min", "--min_value"):
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arguments["min"] = float(sys.argv[index + 1])
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if argument in ("-max", "--max_value"):
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arguments["max"] = float(sys.argv[index + 1])
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if argument in ("-noo", "--number_of_outputs"):
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arguments["number_of_outputs"] = int(sys.argv[index + 1])
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if argument in ('-nd', '--no_display'):
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if argument in ("-nd", "--no_display"):
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arguments["no_display"] = True
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if argument in ('-s', '--save'):
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if argument in ("-s", "--save"):
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arguments["save"] = True
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return arguments
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def print_output(print_info, save_results, summary):
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""" Prints out population and summary plots """
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for population, generation_number, file_name in print_info:
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output(population, generation_number, file_name, save_results)
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summary_file_name = file_name
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print_summary(summary, summary_file_name, save_results)
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# Ran first in the code
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if __name__ == "__main__":
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# Run the Evolutionary Strategy algorithm
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ARGUMENTS = user_input()
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total_time = 0
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TOTAL_TIME = 0
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start_time = time.perf_counter()
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best_individual, best_fitness, output_population, generation_time, print_info, summary = evolution_strategy(
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ARGUMENTS["number_of_parents"],
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ARGUMENTS["size_of_population"],
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ARGUMENTS["mutation_strength"],
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ARGUMENTS["number_of_generations"],
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(ARGUMENTS["min"], ARGUMENTS["max"]),
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ARGUMENTS["number_of_outputs"],
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(
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best_individual,
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best_fitness,
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output_population,
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PRINT_INFO,
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SUMMARY,
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) = evolution_strategy(
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ARGUMENTS,
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ARGUMENTS["no_display"],
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ARGUMENTS["save"])
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)
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end_time = time.perf_counter()
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if not ARGUMENTS["no_display"]:
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print_output(print_info, ARGUMENTS["save"], summary)
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total_time = end_time - start_time
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time_per_generation = total_time / \
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ARGUMENTS["number_of_generations"]
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print_output(PRINT_INFO, ARGUMENTS["save"], SUMMARY)
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TOTAL_TIME = end_time - start_time
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time_per_generation = TOTAL_TIME / ARGUMENTS["number_of_generations"]
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print("Best individual found:", best_individual)
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print("Best fitness found:", best_fitness)
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print("total_generation_time: ", total_time)
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print("total_generation_time: ", TOTAL_TIME)
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print("time_per_generation: ", time_per_generation)
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