answer:import numpy as np from skopt.space import Integer, Real from skopt import Optimizer from sklearn.model_selection import cross_val_score # Define the search space search_space = [ Integer(2, 10, name="num_layers"), Integer(4, 32, name="num_units"), Real(10**-5, 10**0, "log-uniform", name="learning_rate"), Real(10**-5, 10**0, "log-uniform", name="weight_regularization"), Integer(2, 5, name="dropout_rate") ] # Define the optimizer opt = Optimizer(search_space, n_random_starts=100, acq_func="EI") # Define the objective @opt.objective def objective(parameters): # Set the parameters of the model model.set_params(parameters) # Calculate the cross-validation score cv_score = cross_val_score(model, X, y).mean() # Return the negative score to minimize return -cv_score # Optimize the model opt.maximize(init_points=10, n_iter=100)

answer:def pascal_triangle(n): rows = [] for i in range(1, n + 1): row = [1] for j in range(1, i): row.append(rows[i-2][j-1] + rows[i-2][j]) row.append(1) rows.append(row) return rows n = 8 result = pascal_triangle(n) sum_of_row = sum(result[n-1]) print(f"The sum of the {n}th row of Pascal's Triangle is {sum_of_row}.")

answer:def remove_char(given_string, character): new_string = given_string.replace(character, "") return new_string

answer:import json data = json.loads('{ "name": "John Smith", "age": 35 }') customer_name = data["name"] print(customer_name)