已知无人机撞击地面点的二维概率密度函数，用MATLAB画出无人机撞击地面点的二维分布图

以下是一个基于pytorch的3DCNN分类代码示例：

import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
import numpy as np

class MyDataset(Dataset):
    def __init__(self, data_path, label_path):
        self.data = np.load(data_path)
        self.labels = np.load(label_path)

    def __len__(self):
        return len(self.labels)

    def __getitem__(self, idx):
        sample = {'data': self.data[idx], 'label': self.labels[idx]}
        return sample

class MyModel(nn.Module):
    def __init__(self):
        super(MyModel, self).__init__()
        self.conv1 = nn.Conv3d(1, 16, kernel_size=3, stride=1, padding=1)
        self.bn1 = nn.BatchNorm3d(16)
        self.relu1 = nn.ReLU(inplace=True)
        self.pool1 = nn.MaxPool3d(kernel_size=2, stride=2)

        self.conv2 = nn.Conv3d(16, 32, kernel_size=3, stride=1, padding=1)
        self.bn2 = nn.BatchNorm3d(32)
        self.relu2 = nn.ReLU(inplace=True)
        self.pool2 = nn.MaxPool3d(kernel_size=2, stride=2)

        self.conv3 = nn.Conv3d(32, 64, kernel_size=3, stride=1, padding=1)
        self.bn3 = nn.BatchNorm3d(64)
        self.relu3 = nn.ReLU(inplace=True)
        self.pool3 = nn.MaxPool3d(kernel_size=2, stride=2)

        self.fc1 = nn.Linear(64 * 4 * 4 * 4, 128)
        self.relu4 = nn.ReLU(inplace=True)
        self.dropout = nn.Dropout(p=0.5)

        self.fc2 = nn.Linear(128, 2)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu1(x)
        x = self.pool1(x)

        x = self.conv2(x)
        x = self.bn2(x)
        x = self.relu2(x)
        x = self.pool2(x)

        x = self.conv3(x)
        x = self.bn3(x)
        x = self.relu3(x)
        x = self.pool3(x)

        x = x.view(x.size(0), -1)
        x = self.fc1(x)
        x = self.relu4(x)
        x = self.dropout(x)

        x = self.fc2(x)
        return x

def train(model, device, train_loader, optimizer, criterion):
    model.train()
    for batch_idx, sample in enumerate(train_loader):
        data, target = sample['data'], sample['label']
        data, target = data.to(device), target.to(device)
        optimizer.zero_grad()
        output = model(data)
        loss = criterion(output, target)
        loss.backward()
        optimizer.step()

def test(model, device, test_loader, criterion):
    model.eval()
    test_loss = 0
    correct = 0
    with torch.no_grad():
        for batch_idx, sample in enumerate(test_loader):
            data, target = sample['data'], sample['label']
            data, target = data.to(device), target.to(device)
            output = model(data)
            test_loss += criterion(output, target).item()
            pred = output.argmax(dim=1, keepdim=True)
            correct += pred.eq(target.view_as(pred)).sum().item()

    test_loss /= len(test_loader.dataset)
    accuracy = correct / len(test_loader.dataset)
    print('Average loss: {:.4f}, Accuracy: {}/{} ({:.2f}%)'.format(
        test_loss, correct, len(test_loader.dataset), 100. * accuracy))

def main():
    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

    train_dataset = MyDataset('train_data.npy', 'train_labels.npy')
    train_loader = DataLoader(train_dataset, batch_size=16, shuffle=True)

    test_dataset = MyDataset('test_data.npy', 'test_labels.npy')
    test_loader = DataLoader(test_dataset, batch_size=16, shuffle=True)

    model = MyModel().to(device)

    optimizer = optim.Adam(model.parameters(), lr=0.001)
    criterion = nn.CrossEntropyLoss()

    for epoch in range(10):
        train(model, device, train_loader, optimizer, criterion)
        test(model, device, test_loader, criterion)

if __name__ == '__main__':
    main()

在这个示例中，我们首先定义了一个`MyDataset`类，这个类用来加载数据集。`MyDataset`类继承自`torch.utils.data.Dataset`类，实现了`__len__`和`__getitem__`方法，分别用来获取数据集的长度和获取数据集中的一个样本。在`__getitem__`方法中，我们返回一个字典，包含了样本数据和标签。

接下来，我们定义了一个`MyModel`类，这个类是我们的模型。这个模型是一个简单的3D卷积神经网络，包含了3个卷积层和2个全连接层。在前向传播中，我们首先进行3次卷积和池化操作，然后将特征图展平并送入2个全连接层中。在第一个全连接层中，我们使用ReLU激活函数和dropout进行非线性变换和正则化。

最后，我们定义了`train`和`test`函数，用来训练和测试模型。在`train`函数中，我们首先将模型设为训练模式，然后对于每个batch，计算输出和损失，并执行反向传播和参数更新。在`test`函数中，我们将模型设为评估模式，然后对于测试集中的每个batch，计算输出和损失，并计算准确率。

最后，在`main`函数中，我们加载数据集，创建模型和优化器，并开始训练和测试模型。