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