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()