import numpy as npfrom numba.experimental import jitclassfrom numba import types, typed

data_input = np.load("data/ci_inputs.npy")data_output = np.load("data/ci_outputs.npy")print("Begin compiling!")begin_time = time.time_ns()compile_nn = make_neural_network(layer_sizes=[data_input.shape[1], data_output.shape[1]], layer_activations=["sigmoid"])compile_nn.train(data_input[:1], data_output[:1], data_input[1: 2], data_output[1: 2])end_time = time.time_ns()print("Compile time:", (end_time-begin_time) / 1e9)for i in range(10):    random_seed = np.random.randint(10, 1010)    np.random.seed(random_seed)    train_input, validate_input, test_input = h.kfold(4, data_input, random_seed)    train_output, validate_output, test_output = h.kfold(4, data_output, random_seed)    nn = make_neural_network(layer_sizes=[train_input.shape[1], 20, train_output.shape[1]], layer_activations=["sigmoid", "sigmoid"])    begin_time = time.time_ns()    epochs, current_mse = nn.train(train_input, train_output, validate_input, validate_output)    end_time = time.time_ns()    train_mse = nn.calculate_MSE(train_input, train_output)    test_mse = nn.calculate_MSE(test_input, test_output)    accuracy_test = nn.evaluate(test_input, test_output)    print("Seed:", random_seed, "Epochs:", epochs, "Time:", (end_time-begin_time)/1e9, "Accuracy:", accuracy_test, "Tr:", train_mse, "V:", current_mse, "T:", test_mse)