68 lines
2.3 KiB
Python
68 lines
2.3 KiB
Python
import matplotlib.pyplot as plt
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import numpy as np
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import torch
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# 线性回归训练代码
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def compute_error_for_line_given_points(b, w, points):
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totalError = 0
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N = float(len(points))
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for i in range(len(points)):
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x = points[i][0]
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y = points[i][1]
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totalError += (y - (w * x + b)) ** 2
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return totalError / N
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def step_gradient(b_current, w_current, points, learningRate):
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b_gradient = torch.tensor(0.0, device=points.device)
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w_gradient = torch.tensor(0.0, device=points.device)
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N = float(len(points))
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for i in range(len(points)):
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x = points[i][0]
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y = points[i][1]
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b_gradient += -(2 / N) * (y - (w_current * x + b_current))
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w_gradient += -(2 / N) * x * (y - (w_current * x + b_current))
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new_b = b_current - (learningRate * b_gradient)
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new_w = w_current - (learningRate * w_gradient)
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return [new_b, new_w]
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def gradient_descent_runner(points, starting_b, starting_w, learningRate, num_iterations):
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b = torch.tensor(starting_b, device=points.device)
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w = torch.tensor(starting_w, device=points.device)
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for i in range(num_iterations):
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b, w = step_gradient(b, w, points, learningRate)
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return [b, w]
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def run():
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points_np = np.genfromtxt("data1.csv", delimiter=',').astype(np.float32)
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points = torch.tensor(points_np, device='cuda')
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learning_rate = 0.0001
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initial_b = 0.0
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initial_w = 0.0
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num_iterations = 100000
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[b, w] = gradient_descent_runner(points, initial_b, initial_w, learning_rate, num_iterations)
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print("After gradient descent at b={0}, w={1}, error={2}".format(b.item(), w.item(),
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compute_error_for_line_given_points(b, w, points)))
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return b.item(), w.item()
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# 运行线性回归
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final_b, final_w = run()
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# 绘制图像
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points_np = np.genfromtxt("data1.csv", delimiter=',').astype(np.float32)
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x = points_np[:, 0]
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y = points_np[:, 1]
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x_range = np.linspace(min(x), max(x), 100)
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y_pred = final_w * x_range + final_b
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plt.figure(figsize=(8, 6))
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plt.scatter(x, y, color='blue', label='Original data')
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plt.plot(x_range, y_pred, color='red', label='Fitted line')
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plt.xlabel('X')
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plt.ylabel('Y')
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plt.title('Fitting a line to random data')
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plt.legend()
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plt.grid(True)
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plt.savefig('print1.png')
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plt.show()
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