Revert "新建仓库维护数据预测项目"

This reverts commit 516126d2a5.

FC_ML_Linear/FC_ML_Linear.py

@@ -1,41 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.optim as optim
-import numpy as np
-import matplotlib.pyplot as plt
-
-# 生成数据
-np.random.seed(0)
-x = np.random.rand(100, 1) * 10  # 生成100个在0到10之间的随机数
-y = 3.5 * x + 2 + np.random.randn(100, 1) * 2  # 真实的线性关系加上一些噪声
-
-# 将numpy数组转换为torch张量
-x_tensor = torch.from_numpy(x).float()
-y_tensor = torch.from_numpy(y).float()
-
-
-model = nn.Linear(in_features=1, out_features=1, bias=True)
-
-criterion = nn.MSELoss()
-optimizer = optim.SGD(model.parameters(), lr=0.01)
-
-epochs = 100
-for epoch in range(epochs):
-    # 前向传播
-    outputs = model(x_tensor)
-    loss = criterion(outputs, y_tensor)
-
-    # 反向传播和优化
-    optimizer.zero_grad()
-    loss.backward()
-    optimizer.step()
-
-    if (epoch + 1) % 10 == 0:
-        print(f'Epoch [{epoch + 1}/{epochs}], Loss: {loss.item():.4f}')
-
-# 可视化原始数据和拟合结果
-predicted = model(x_tensor).detach().numpy()  # detach()用于从计算图中移除张量，避免未来的梯度计算干扰。
-plt.scatter(x, y, label='Original data')
-plt.plot(x, predicted, color='red', label='Fitted line')
-plt.legend()
-plt.show()