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FC_ML_NN/NN_Polynomial_Test.py

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+#多项式拟合
+import torch
+import numpy as np
+
+from FC_ML_Data.FC_ML_Data_Output.Data_Output_File import tensor_to_excel
+
+# 真实多项式系数
+true_w = torch.tensor([0.5, 3.0, 2.4])  # 对应x, x², x³项
+true_b = 0.9
+
+# 生成训练数据
+def make_features(x):
+    return torch.stack([x**i for i in range(1,4)], dim=1)  # 构建x, x², x³特征矩阵
+
+x = torch.linspace(-3, 3, 100)
+X = make_features(x)
+y = X @ true_w + true_b + torch.randn(x.size()) * 0.5  # 添加噪声
+print(x,X,y)
+# tensor_to_excel(torch.cat([x, y], dim=-1),"./")
+
+
+class PolyModel(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.linear = torch.nn.Linear(3, 1)  # 输入3维(x,x²,x³)，输出1维
+
+    def forward(self, x):
+        return self.linear(x)
+
+
+model = PolyModel()
+criterion = torch.nn.MSELoss()
+optimizer = torch.optim.SGD(model.parameters(), lr=1e-3)
+
+for epoch in range(1000):
+    pred = model(X)
+    loss = criterion(pred.squeeze(), y)
+
+    optimizer.zero_grad()
+    loss.backward()
+    optimizer.step()
+
+    if epoch % 100 == 0:
+        print(f'Epoch {epoch}, Loss: {loss.item():.4f}')
+
+# 获取训练后的参数
+w_pred = model.linear.weight.detach().numpy().flatten()
+b_pred = model.linear.bias.detach().numpy()
+
+# print(f"真实参数: w={true_w.numpy()}, b={true_b}")
+# print(f"预测参数: w={w_pred}, b={b_pred:.2f}")
+
+# 可视化
+import matplotlib.pyplot as plt
+plt.scatter(x, y, label='ori')
+plt.plot(x, model(X).detach().numpy(), 'r', label='fit')
+plt.legend()
+plt.show()