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How to use change_one_value method in avocado

Best Python code snippet using avocado_python

p4-2-Automater.py

Source:p4-2-Automater.py

1from problem import maxone,knapsack,vertex_cover2from solver.ga import GeneticSolver3from fn.ga import *4from fn.fitness import *5from utils import *6def select_problem(problem_name,config):7    if problem_name == 'maxone':8        problem = maxone.problem(16)9        config.fitness_fn = maxone_fitness10        config.feasibility_minimum = 011        config.best_possible_score = problem.N12    elif problem_name == 'knapsack':13        problem = knapsack.problem(99)14        config.fitness_fn = knapsack_fitness15        config.feasibility_minimum = 10016        config.best_possible_score = problem.total_value17    elif problem_name == 'vertex_cover':18        problem = vertex_cover.problem(99)19        config.fitness_fn = vertex_cover_fitness20        config.feasibility_minimum = 10021        config.best_possible_score = len(problem.variables)22    return problem23def main():24    bestConfigScore = 0;25    bestConfigList = [];26    configIterations = 0;27    # LISTS28    populationSizeList = [20,50]29    populationModelList = [(generational, "generational"), (choose_best, "choose_best")]30    31    parentSimilarityList = [0.9, 0.5]32    parentSelectionList = [ (fitness_proportionate, "fitness_proportionate") , (tournament_selection, "tournament_selection")]33    crossoverProbabilityList = [0.90, 0.60]34    crossoverTypeList = [(one_point_crossover,"one_point_crossover"), (two_point_crossover,"two_point_crossover"), (uniform_crossover, "uniform_crossover")]35    mutationProbabilityList = [0.3, 0.5]36    changekval2 = change_k_values(2)37    mutationTypeList = [(change_one_value, "change_one_value"), (changekval2, "changekval2"), (swap_two_values, "swap_two_values")]38    39    for popSize in populationSizeList:40        for popModel in populationModelList:41            for parentSim in parentSimilarityList:42                for parentSel in parentSelectionList: 43                    for crossoverProb in crossoverProbabilityList:44                        for crossoverType in crossoverTypeList:45                            for mutationProb in mutationProbabilityList:46                                for mutationType in mutationTypeList:47                                    config = Config()48                                    # problem_name = 'maxone'49                                    # problem_name = 'knapsack'50                                    problem_name = 'vertex_cover'51                                    problem = select_problem(problem_name,config)52                                    config.best_possible_score += config.feasibility_minimum53                                    config.initial_solution = 'random'54                                    config.max_parent_try = 50055                                    config.max_iterations =  20056                                    config.max_flat_iterations = 5057                                    config.random_seed =   12345678958                                    config.explain = False59                                    configList = []60                                    ################## CONFIG CHANGES STARTS HERE ########################################61                                    62                                    # POPULATION SIZE63                                    config.population_size = popSize     # 20, 5064                                    configList.append(("popSize", popSize))65                                    # POPULATION MODEL66                                    config.replace_population = popModel[0]67                                    # config.replace_population = choose_best68                                    configList.append(("popModel", popModel[1]))69                                    70                                    # SELECTION (PARENT SIMILARITY)71                                    config.max_parent_similarity = parentSim # 0.9, 0.572                                    configList.append(("parentSim", parentSim))73                                    74                                    # SELECTION (PARENT SELECTION)75                                    config.select_parents = parentSel[0]76                                    # config.select_parents = tournament_selection77                                    configList.append(("parentSel", parentSel[1]))78                                    # TOURNAMENT (NO CHANGE)79                                    config.tournament_k = int(0.2 * config.population_size)80                                    # CROSSOVER PROBABILITY81                                    config.prob_crossover = crossoverProb   # 0.90, 0.6082                                    configList.append(("crossoverProb", crossoverProb))83                                    # CROSSOVER TYPE84                                    config.crossover = crossoverType[0]85                                    # config.crossover = two_point_crossover86                                    # config.crossover =" uniform_crossover87                                    configList.append(("crossoverType", crossoverType[1]))88                                    # MUTATION PROBABILITY89                                    config.prob_mutate =  mutationProb   # 0.3, 0.590                                    configList.append(("mutationProb", mutationProb))91                                    # MUTATION TYPE92                                    config.mutate =  mutationType[0]93                                    # config.mutate =  change_k_values(2)94                                    # config.mutate =  swap_two_values95                                    configList.append(("mutationType", mutationType[1]))96                                    ################## CONFIG CHANGES ENDS HERE ########################################97                                    98                                    print("\n\nSOLVING: ")99                                    for item in configList:100                                        print(str(item[0]) + ": " + str(item[1]))101                                    solver = GeneticSolver(problem,config)102                                    solver.solve()103                                    #Get max config104                                    currentConfigScore = solver.getBestScore()105                                    print("SCORE: " + str(currentConfigScore))106                                    if(currentConfigScore > bestConfigScore):107                                        bestConfigScore = currentConfigScore108                                        bestConfigList = configList109                                    configIterations+=1110    print("\n\n\n\n\nTESTS DONE!" + problem_name)111    print("CONFIGS TESTED: " + str(configIterations))112    print("BEST CONFIGURATION SCORE: " + str(bestConfigScore))113    for item in bestConfigList:114        print(str(item[0]) + ": " + str(item[1]))115if __name__ == '__main__':116    import time117    start = time.time()118    main()119    end = time.time()120    elapsed = end - start...

p4_automated.py

Source:p4_automated.py

1from problem import maxone,knapsack,vertex_cover2from solver.ga import GeneticSolver3from fn.ga import *4from fn.fitness import *5from utils import *6def select_problem(problem_name,config):7    if problem_name == 'maxone':8        problem = maxone.problem(16)9        config.fitness_fn = maxone_fitness10        config.feasibility_minimum = 011        config.best_possible_score = problem.N12    elif problem_name == 'knapsack':13        problem = knapsack.problem(99)14        config.fitness_fn = knapsack_fitness15        config.feasibility_minimum = 10016        config.best_possible_score = problem.total_value17    elif problem_name == 'vertex_cover':18        problem = vertex_cover.problem(99)19        config.fitness_fn = vertex_cover_fitness20        config.feasibility_minimum = 10021        config.best_possible_score = len(problem.variables)22    return problem23def main():24    # Initialization of parameters to be used for automation25    # POPULATION26    population_size = [20, 50]27    replace_population = [(generational, "generational"), (choose_best, "choose_best")]28    # SELECTION29    max_parent_similarity = [0.9, 0.5] 30    select_parents = [(fitness_proportionate, "fitness_proportionate"), (tournament_selection, "tournament_selection")]31    # CROSSOVER32    prob_crossover = [0.90, 0.60]    33    crossover = [(one_point_crossover, "one_point_crossover"), (two_point_crossover, "two_point_crossover"), (uniform_crossover, "uniform_crossover")]34    # MUTATION35    prob_mutate =  [0.5, 0.3]   36    mutate =  [(change_one_value, "change_one_value"), (change_k_values(2), "change_k_values"), (swap_two_values, "swap_two_values")]37    best_config_list = []38    best_score = 039    # Configuration starts40    config = Config()41    # problem_name = 'maxone'42    # problem_name = 'knapsack'43    problem_name = 'vertex_cover'44    problem = select_problem(problem_name,config)45    config.best_possible_score += config.feasibility_minimum46    config.initial_solution = 'random'47    config.max_parent_try = 50048    config.max_iterations =  20049    config.max_flat_iterations = 5050    config.random_seed =   12345678951    config.explain = False # Initially True. Set to False to avoid lots of printing in the terminal.52    # Automation starts53    count = 054    for populationSize in population_size:55        for replacePopulation in replace_population:56            for maxParentSimilarity in max_parent_similarity:57                for selectParents in select_parents:58                    for probCrossover in prob_crossover:59                        for Crossover in crossover:60                            for probMutate in prob_mutate:61                                for Mutate in mutate:62                                    # POPULATION63                                    config.population_size = populationSize     # 20, 5064                                    config.replace_population = replacePopulation[0]65                                    # config.replace_population = choose_best66                                    # SELECTION67                                    config.max_parent_similarity = maxParentSimilarity # 0.9, 0.568                                    config.select_parents = selectParents[0]69                                    # config.select_parents = tournament_selection70                                    config.tournament_k = int(0.2 * config.population_size)71                                    # CROSSOVER72                                    config.prob_crossover = probCrossover    # 0.90, 0.6073                                    config.crossover = Crossover[0]74                                    # config.crossover = two_point_crossover75                                    # config.crossover = uniform_crossover76                                    # MUTATION77                                    config.prob_mutate = probMutate   # 0.3, 0.578                                    config.mutate =  Mutate[0]79                                    # config.mutate =  change_k_values(2)80                                    # config.mutate =  swap_two_values81                                    config_list = [82                                                            ("config.population_size",populationSize),83                                                            ("config.replace_population",replacePopulation[1]),84                                                            ("config.max_parent_similarity",maxParentSimilarity),85                                                            ("config.select_parents",selectParents[1]),86                                                            ("config.prob_crossover",probCrossover),87                                                            ("config.crossover",Crossover[1]),88                                                            ("config.prob_mutate",probMutate),89                                                            ("config.mutate",Mutate[1])90                                                        ]91                                    count+=192                                    # print(count)93                                    # print(config_list)94                                    solver = GeneticSolver(problem,config)95                                    solver.solve()96                                    score = solver.get_best_score()97                                    iterations = solver.get_iterations()98                                    99                                    if score >= best_score:100                                        best_score = score101                                        best_config_list = config_list102                                        print(count)103                                        print(best_config_list)104                                        print("Score:",score)105                                        print("Iterations:",iterations)106                                        print("\n")107                                    # display_solutions(problem,solver)108                                  109if __name__ == '__main__':110    import time111    start = time.time()112    main()113    end = time.time()114    elapsed = end - start...

configchanges.py

Source:configchanges.py

1    # POPULATION SIZE2    config.population_size = 20     # 20, 503    4    # POPULATION MODEL5    config.replace_population = generational6    # config.replace_population = choose_best7    # SELECTION (PARENT SIMILARITY)8    config.max_parent_similarity = 0.9 # 0.9, 0.59    10    11    # SELECTION (PARENT SELECTION)12    config.select_parents = fitness_proportionate13    # config.select_parents = tournament_selection14    # TOURNAMENT (NO CHANGE)15    config.tournament_k = int(0.2 * config.population_size)16    # CROSSOVER PROBABILITY17    config.prob_crossover = 0.90    # 0.90, 0.6018    # CROSSOVER TYPE19    config.crossover = one_point_crossover20    # config.crossover = two_point_crossover21    # config.crossover = uniform_crossover22    # MUTATION PROBABILITY23    config.prob_mutate =  0.5   # 0.3, 0.524    # MUTATION TYPE25    config.mutate =  change_one_value26    # config.mutate =  change_k_values(2)...

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