command seccessfully and half for mlp code

integration/Window.py

@@ -23,6 +23,8 @@ from torch.utils.data import DataLoader, TensorDataset, random_split
 import pickle
 import joblib
 
+#this is the file for seperated hardware detectors
+
 class Window:
     def __init__(self, root):
         self.root = root

integration/Window2.py

@@ -14,6 +14,8 @@ import os
 from PIL import Image, ImageTk
 from time import sleep, time
 
+#this is the file for the combined hardware detector
+
 class Window:
     def __init__(self, root):
         self.input_port='COM9'

integration/mlp.py

+import torch
+import torch.nn as nn
+import torch.optim as optim
+from sklearn.model_selection import train_test_split
+
+# 生成一些随机数据
+def generate_data(num_samples):
+    # 生成两类数据
+    label_0 = torch.randn(num_samples, 2) + torch.tensor([1, 1])
+    label_1 = torch.randn(num_samples, 2) + torch.tensor([-1, -1])
+    labels = torch.cat((torch.zeros(num_samples), torch.ones(num_samples)), dim=0)
+    data = torch.cat((label_0, label_1), dim=0)
+    return data, labels
+
+# 定义MLP模型
+class MLP(nn.Module):
+    def __init__(self):
+        super(MLP, self).__init__()
+        self.fc1 = nn.Linear(2, 16)
+        self.fc2 = nn.Linear(16, 16)
+        self.fc3 = nn.Linear(16, 2)  # 二分类问题
+
+    def forward(self, x):
+        x = torch.relu(self.fc1(x))
+        x = torch.relu(self.fc2(x))
+        x = self.fc3(x)
+        return x
+
+# 超参数
+num_samples = 1000
+num_epochs = 50
+batch_size = 32
+learning_rate = 0.01
+
+# 数据准备
+data, labels = generate_data(num_samples)
+train_data, test_data, train_labels, test_labels = train_test_split(data, labels, test_size=0.2)
+
+# 数据集和数据加载器
+train_dataset = torch.utils.data.TensorDataset(train_data, train_labels)
+train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
+
+# 初始化模型、损失函数和优化器
+model = MLP()
+criterion = nn.CrossEntropyLoss()
+optimizer = optim.Adam(model.parameters(), lr=learning_rate)
+
+# 训练模型
+for epoch in range(num_epochs):
+    for inputs, targets in train_loader:
+        optimizer.zero_grad()
+        outputs = model(inputs)
+        loss = criterion(outputs, targets.long())
+        loss.backward()
+        optimizer.step()
+    print(f'Epoch [{epoch + 1}/{num_epochs}], Loss: {loss.item():.4f}')
+
+# 测试模型
+model.eval()
+with torch.no_grad():
+    test_outputs = model(test_data)
+    _, predicted = torch.max(test_outputs, 1)
+    accuracy = (predicted == test_labels).sum().item() / len(test_labels)
+    print(f'Accuracy on test data: {accuracy * 100:.2f}%')