230523

2024-05-23 20:43:52 +08:00
parent e6da567437
commit 9a33bd6f49
11 changed files with 292 additions and 1 deletions
--- a/regression/1.py
+++ b/regression/1.py
@@ -0,0 +1,56 @@
+import numpy as np
+import torch
+
+
+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, dtype=torch.float32)
+    w_gradient = torch.tensor(0.0, device=points.device, dtype=torch.float32)
+    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) + b_current)
+        w_gradient += -(2 / N) * x * (y - (w_current * x + b_current + 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, dtype=torch.float32)
+    w = torch.tensor(starting_w, device=points.device, dtype=torch.float32)
+    for i in range(num_iterations):
+        b, w = step_gradient(b, w, points, learningRate)
+    return [b, w]
+
+
+def run():
+    # 修改为生成数据的文件路径
+    points_np = np.genfromtxt("data1.csv", delimiter=',').astype(np.float32)
+    points = torch.tensor(points_np, device='mps')
+    learning_rate = 0.0001  # 使用较小的学习率
+    initial_b = 0.0
+    initial_w = 0.0
+    num_iterations = 1000
+    print("Starting gradient descent at b={0},w={1},error={2}".format(initial_b, initial_w,
+                                                                      compute_error_for_line_given_points(initial_b,
+                                                                                                          initial_w,
+                                                                                                          points)))
+    print("running...")
+    [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)))
+
+
+if __name__ == '__main__':
+    run()