import numpy as np import pandas as pd import matplotlib.pyplot as plt from linear_regression import LinearRegression data = pd.read_csv('../data/non-linear-regression-x-y.csv') x = data['x'].values.reshape((data.shape[0], 1)) y = data['y'].values.reshape((data.shape[0], 1)) data.head(10) plt.plot(x, y) plt.show() num_iterations = 50000 learning_rate = 0.02 polynomial_degree = 15 sinusoid_degree = 15 normalize_data = True linear_regression = LinearRegression(x, y, polynomial_degree, sinusoid_degree, normalize_data) (theta, cost_history) = linear_regression.train( learning_rate, num_iterations ) print('开始损失: {:.2f}'.format(cost_history[0])) print('结束损失: {:.2f}'.format(cost_history[-1])) theta_table = pd.DataFrame({'Model Parameters': theta.flatten()}) plt.plot(range(num_iterations), cost_history) plt.xlabel('Iterations') plt.ylabel('Cost') plt.title('Gradient Descent Progress') plt.show() predictions_num = 1000 x_predictions = np.linspace(x.min(), x.max(), predictions_num).reshape(predictions_num, 1); y_predictions = linear_regression.predict(x_predictions) plt.scatter(x, y, label='Training Dataset') plt.plot(x_predictions, y_predictions, 'r', label='Prediction') plt.show()