Under the plot_figures function, what code plots the average of the five curves (that correspond to the polynomial of degree "order")?

import random

import numpy as np

import pandas as pd

import matplotlib.pyplot as plt

from sklearn.preprocessing import PolynomialFeatures from sklearn.linear_model import LinearRegression from sklearn.pipeline import Pipeline

# Ground truth target function def f(x): return 3 * np.cos(1.3 * x) # seed np.random.seed(62) # x x = np.random.uniform(0.0, 5.0, [100, 20]) x = np.sort(x) # Ground truth targets g = f(x) # Add white noise noisy = np.random.normal(0, 1, [100, 20]) # y y = g + noisy # use linspace(0,5,100) as test set to plot the images x_test = np.linspace(0,5,100)

from sklearn.preprocessing import PolynomialFeatures from sklearn.linear_model import LinearRegression from sklearn.pipeline import Pipeline def linear_model_predict(X, Y, order): # fit one polynomial model of degree `order` model = Pipeline([('poly', PolynomialFeatures(degree=order)), ('linear', LinearRegression(fit_intercept=False))]) model.fit(X.reshape(-1,1), Y) return model def plot_figure(x, y, x_test, order): # plot five curves corresponding to the polynomial of degree `order` # plot the average of these five curves plt.title("Order "+str(order)) #plot five curves for n in range(5): model=linear_model_predict(x[n], y[n], order) plt.plot(x_test, model.predict(x_test.reshape(-1,1))) #plot average of curves plt.legend() plt.show # show the plots fig, axs = plt.subplots(2, 2, figsize=(15, 15)) # figure one plt.subplot(2, 2, 1) plt.title("Function and data") plt.plot(x_test, f(x_test)) plt.plot(x[0], y[0], 'o') # figure two plt.subplot(2, 2, 2) plt.ylim(-5, 5) plot_figure(x, y, x_test, order=1) # figure three plt.subplot(2, 2, 3) plt.ylim(-5, 5) plot_figure(x, y, x_test, order=3) # figure four plt.subplot(2, 2, 4) plt.ylim(-5, 5) plot_figure(x, y, x_test, order=5)