Be Androidian

 import numpy as np import random def distance(city1, city2):     return np.linalg.norm(city1 - city2)  num_cities = 4 num_ants = 4 evaporation_rate = 0.05 alpha = 1 beta = 1 cities = np.random.rand(num_cities, 2) distances = [] for city1 in cities:     temp = []     for city2 in cities:          temp.append(distance(city1, city2))     distances.append(temp) phermones = np.random.rand(num_cities, num_cities) tour_lengths = [] tour_paths = [] for _ in range(num_ants):     randomly_selected_city = random.randint(0, num_cities-1)     selected_city = randomly_selected_city     tour_length = 0     tour_path = []     for j in range(num_cities):          tour_path.append(selected_city)         dist = distances[selected_city]         phero = phermones[selected_city]         stochastic_gra...

def create_server(name, weight):     return {"name": name, "weight": weight} def create_load_balancer(servers):     return {"servers": servers, "current_index": 0} def add_server(load_balancer, server):     load_balancer["servers"].append(server) def get_next_server(load_balancer):     next_server = load_balancer["servers"][load_balancer["current_index"]]     load_balancer["current_index"] = (load_balancer["current_index"] + 1) % len(load_balancer["servers"])     return next_server def prompt_server_info(index):     name = input("Enter the name of server " + str(index) + ": ")     weight = int(input("Enter the weight of server " + str(index) + ": "))     return create_server(name, weight) def assign_load(load_balancer, i):     next_server = get_next_server(load_balancer)     print("Load", i, "assigned to server:", next_server[...

 import xmlrpc.client n = int(input("Enter an integer value: ")) proxy = xmlrpc.client.ServerProxy('http://localhost:8000/') result = proxy.factorial(n) print(f"The factorial of {n} is {result}") import xmlrpc.server def factorial(n):     if n == 0:         return 1     else:         return n * factorial(n-1) server = xmlrpc.server.SimpleXMLRPCServer(('localhost', 8000)) server.register_function(factorial) print("Server started on http://localhost:8000") server.serve_forever()

 import random from deap import creator, base, tools, algorithms creator.create("FitnessMax", base.Fitness, weights=(1.0,)) creator.create("Individual", list, fitness=creator.FitnessMax) toolbox = base.Toolbox() toolbox.register("attr_bool", random.randint, 0, 1) toolbox.register("individual", tools.initRepeat, creator.Individual, toolbox.attr_bool, n=100) toolbox.register("population", tools.initRepeat, list, toolbox.individual) def evalOneMax(individual):     return sum(individual), toolbox.register("evaluate", evalOneMax) toolbox.register("mate", tools.cxTwoPoint) toolbox.register("mutate", tools.mutFlipBit, indpb=0.05) toolbox.register("select", tools.selTournament, tournsize=3) population = toolbox.population(n=300) NGEN=40 for gen in range(NGEN):     offspring = algorithms.varAnd(population, toolbox, cxpb=0.5, mutpb=0.1)     fits = toolbox.map(toolbox.evaluate, offspring)     for fit, ind in...

import numpy as np def create_antibody(size):   return np.random.rand(size)  def affinity(antibody, datapoint):   distance = np.linalg.norm(antibody - datapoint)        return 1 / (1 + distance) def get_key(pair):    return pair[1] size = 3 healthy_data = np.array([[1.0, 2.0, 3.0], [1.1, 1.9, 3.2]])      num_antibodies = 10 antibody_population = [] for i in range(num_antibodies):     antibody_population.append(create_antibody(size)) damaged_data = np.array([[1.2, 1.7, 2.8], [1.4, 1.5, 3.5]]) for i in range(2):     healthy_affinities = []     for ab in antibody_population:        for datapoint in healthy_data:           healthy_affinities.append(affinity(ab, datapoint))          top_antibodies = []     for i in range(len(antibody_population)):         pair = [antibody_population[i], healthy_affinities...

 import numpy as np def sphere_function(x):     return np.sum(x**3) def clonal_selection_algorithm(objective_function, dim, pop_size, max_iter, mutation_rate):          population = np.random.uniform(-5, 5, size=(pop_size, dim))          for iter in range(max_iter):                  fitness = np.array([objective_function(ind) for ind in population])                           sorted_indices = np.argsort(fitness)         population = population[sorted_indices]         fitness = fitness[sorted_indices]                           num_clones = int(pop_size * 0.5)         clones = population[:num_clones]                       ...

import numpy as np from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.neural_network import MLPRegressor from geneticalgorithm import geneticalgorithm as ga import warnings def fitness_function(params):          hidden_layer_sizes = (int(params[0]),) * int(params[1])     activation = ['identity', 'logistic', 'tanh', 'relu'][int(params[2])]     solver = ['lbfgs', 'sgd', 'adam'][int(params[3])]              model = MLPRegressor(hidden_layer_sizes=hidden_layer_sizes, activation=activation, solver=solver)     model.fit(X_train, y_train)               fitness = -model.score(X_val, y_val)          return fitness def train_neural_network(params):          hidden_layer_sizes = (int(params[0]),) * int(params[1])     activation = ['identity', 'log...

A = {'x1': 0.2, 'x2': 0.4, 'x3': 0.6, 'x4': 0.8} B = {'x1': 0.3, 'x2': 0.5, 'x3': 0.7, 'x4': 0.9} R = {('x1', 'y1'): 0.2, ('x1', 'y2'): 0.4, ('x2', 'y1'): 0.6, ('x2', 'y2'): 0.8} S = {('x1', 'y1'): 0.3, ('x1', 'y2'): 0.5, ('x2', 'y1'): 0.7, ('x2', 'y2'): 0.9} def fuzzy_union(A, B):     union = {}     for key in A.keys():         union[key] = max(A[key], B[key])     return union # Example usage union_result = fuzzy_union(A, B) print("Union:", union_result) def fuzzy_intersection(A, B):     intersection = {}     for key in A.keys():         intersection[key] = min(A[key], B[key])     return intersection # Example usage intersection_result = fuzzy_intersection(A, B) print("Intersection:", intersection_result) def fuzzy_complement(A):     complement = {}     for key in A.keys():   ...