x1 = features(:,1); % 第一个特征 x2 = features(:,2); % 第二个特征 % 假设已经对数据进行了聚类，将每个数据点的簇标签存储在idx中 unique_labels = unique(idx); colors = hsv(length(unique_labels)); % 生成不同颜色 figure; for i = 1:length(unique_labels) cluster_i = find(idx == unique_labels(i)); fprintf('Cluster %d includes %d samples: %s\n', i, length(cluster_i), num2str(cluster_i)); scatter3(x1(cluster_i), x2(cluster_i), [], colors(i,:), 'filled'); % scatter3(x1(cluster_i), x2(cluster_i), x3(cluster_i), x4(cluster_i), x5(cluster_i), [], colors(i,:), 'filled'); % 绘制散点图 hold on; end xlabel('Feature 1'); ylabel('Feature 2'); legend('Cluster 1', 'Cluster 2');检查代码，修改为有两个特征聚类的

修改后的代码如下：

x1 = features(:,1); % 第一个特征
x2 = features(:,2); % 第二个特征

% 假设已经对数据进行了聚类，将每个数据点的簇标签存储在idx中
unique_labels = unique(idx);
colors = hsv(length(unique_labels)); % 生成不同颜色

figure;
for i = 1:length(unique_labels)
    cluster_i = find(idx == unique_labels(i));
    fprintf('Cluster %d includes %d samples: %s\n', i, length(cluster_i), num2str(cluster_i));
    scatter(x1(cluster_i), x2(cluster_i), [], colors(i,:), 'filled'); % 绘制散点图
    hold on;
end

xlabel('Feature 1');
ylabel('Feature 2');

legend('Cluster 1', 'Cluster 2');

这里只需要将 scatter3 函数改为 scatter 函数，并且去掉多余的参数即可。同时，也需要将 xlabel 和 ylabel 函数的参数修改为对应的特征名称。

相关问题

def forward(self, x): input_shape = x.shape[-2:] x1 = x[:, :3, ...] x2 = x[:, 3:, ...] features1 = self.backbone1(x1) features2 = self.backbone2(x2)

这段代码是一个 PyTorch 的模型的前向传播函数。该模型输入一个张量 x，将其拆分成两个部分 x1 和 x2，并分别对它们进行特征提取。x1 和 x2 的维度分别为 (batch_size, 3, H, W) 和 (batch_size, C-3, H, W)，其中 C 是 x 的通道数，H 和 W 分别是 x 的高度和宽度。特征提取是通过调用该模型中的两个 backbone 进行的，分别是 self.backbone1 和 self.backbone2。最终，该函数返回两个特征张量 features1 和 features2。

class Net(nn.Module): def __init__(self): super(Net, self).__init__() self.conv1 = nn.Conv1d(in_channels=1, out_channels=64, kernel_size=32, stride=8, padding=12) self.pool1 = nn.MaxPool1d(kernel_size=2, stride=2) self.BN = nn.BatchNorm1d(num_features=64) self.conv3_1 = nn.Conv1d(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1) self.pool3_1 = nn.MaxPool1d(kernel_size=2, stride=2) self.conv3_2 = nn.Conv1d(in_channels=64, out_channels=128, kernel_size=3, stride=1, padding=1) self.pool3_2 = nn.MaxPool1d(kernel_size=2, stride=2) self.conv3_3 = nn.Conv1d(in_channels=128, out_channels=256, kernel_size=3, stride=1, padding=1) self.pool3_3 = nn.MaxPool1d(kernel_size=2, stride=2) self.conv5_1 = nn.Conv1d(in_channels=64, out_channels=64, kernel_size=5, stride=1, padding=2) self.pool5_1 = nn.MaxPool1d(kernel_size=2, stride=2) self.conv5_2 = nn.Conv1d(in_channels=64, out_channels=128, kernel_size=5, stride=1, padding=2) self.pool5_2 = nn.MaxPool1d(kernel_size=2, stride=2) self.conv5_3 = nn.Conv1d(in_channels=128, out_channels=256, kernel_size=5, stride=1, padding=2) self.pool5_3 = nn.MaxPool1d(kernel_size=2, stride=2) self.conv7_1 = nn.Conv1d(in_channels=64, out_channels=64, kernel_size=7, stride=1, padding=3) self.pool7_1 = nn.MaxPool1d(kernel_size=2, stride=2) self.conv7_2 = nn.Conv1d(in_channels=64, out_channels=128, kernel_size=7, stride=1, padding=3) self.pool7_2 = nn.MaxPool1d(kernel_size=2, stride=2) self.conv7_3 = nn.Conv1d(in_channels=128, out_channels=256, kernel_size=7, stride=1, padding=3) self.pool7_3 = nn.MaxPool1d(kernel_size=2, stride=2) self.pool2 = nn.MaxPool1d(kernel_size=8, stride=1) self.fc = nn.Linear(in_features=256 * 3, out_features=4) ##这里的256*3是计算出来的 self.softmax = nn.Softmax() def forward(self, x): x = self.conv1(x) ## x:Batch, 1, 1024 x = self.pool1(x) x1 = self.conv3_1(x) x1 = self.pool3_1(x1) x1 = self.conv3_2(x1) x1 = self.pool3_2(x1) x1 = self.conv3_3(x1) x1 = self.pool3_3(x1) x2 = self.conv5_1(x) x2 = self.pool5_1(x2) x2 = self.conv5_2(x2) x2 = self.pool5_2(x2) x2 = self.conv5_3(x2) x2 = self.pool5_3(x2) x3 = self.conv7_1(x) x3 = self.pool7_1(x3) x3 = self.conv7_2(x3) x3 = self.pool7_2(x3) x3 = self.conv7_3(x3) x3 = self.pool7_3(x3) x1 = self.pool2(x1) x2 = self.pool2(x2) x3 = self.pool2(x3) Batch, Channel, Length = x1.size() x1 = x1.view(Batch, -1) Batch, Channel, Length = x2.size() x2 = x2.view(Batch, -1) Batch, Channel, Length = x3.size() x3 = x3.view(Batch, -1) x = torch.cat((x1, x2, x3), dim=1) x = self.fc(x) # x = self.softmax(x) return x，解释代码和参数

这是一个神经网络模型，在初始化时包含了多个卷积层和池化层。其中包括使用不同卷积核大小的卷积层以及对应的池化层，以及最后的全连接层。这个模型可以用于分类任务，输出4个类别的预测结果。网络中还包括了BatchNorm1d层，用于规范化输入数据，以提高训练效果。