230603

linear regression/1_cuda.py

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