写出MLP、CNN和RNN算法的伪代码。

当然，我可以为你提供简单的线性回归神经网络（Multilayer Perceptron, MLP）、卷积神经网络(Convolutional Neural Network, CNN) 和循环神经网络(Recurrent Neural Network, RNN) 的伪代码概述。这里假设我们使用Python语言和NumPy库。

1. **多层感知机 (MLP)**:

class MLP:
    def __init__(self, layers):
        self.weights = []
        for i in range(len(layers) - 1):
            w = np.random.randn(layers[i], layers[i+1])
            b = np.zeros((1, layers[i+1]))
            self.weights.append((w, b))

    def forward(self, X):
        activations = [X]
        for layer_weights, biases in self.weights:
            z = np.dot(X, layer_weights) + biases
            A = sigmoid(z)
            activations.append(A)
            X = A
        return A

def sigmoid(x):  # 激活函数
    return 1 / (1 + np.exp(-x))

2. **卷积神经网络 (CNN) 示例**:

class ConvNet:
    def __init__(self, conv_layers, fc_layers):
        self.conv_layers = [(filter_size, num_filters, stride)] * len(conv_layers)
        self.fc_layers = [num_neurons] * len(fc_layers)

    def conv_forward(self, X, W, b):
        # ... 进行卷积操作 ...

    def max_pooling(self, X, pool_size, stride):
        # ... 进行最大池化 ...

    def forward(self, X):
        # 遍历卷积层和全连接层
        for conv_params, fc_neuron_count in zip(self.conv_layers, self.fc_layers):
            # 对每个阶段执行卷积、激活和池化
            # 然后将结果馈送到下一层全连接
        return Z  # 最终全连接层的输出

3. **循环神经网络 (RNN)**

class RNNCell:
    def __init__(self, input_dim, hidden_dim):
        self.Wxh = np.random.randn(hidden_dim, input_dim)
        self.Whh = np.random.randn(hidden_dim, hidden_dim)
        self.bh = np.zeros((1, hidden_dim))
        self.by = np.zeros((1, hidden_dim))

    def step(self, X_t, h_t_minus_1):
        # 计算当前时间步的隐藏状态
        h_t = tanh(np.dot(h_t_minus_1, self.Whh) + np.dot(X_t, self.Wxh) + self.bh)
        y_t = softmax(np.dot(h_t, self.Wyh) + self.by)
        return h_t, y_t

class RNN:
    def __init__(self, cell, seq_length):
        self.cell = cell
        self.seq_length = seq_length

    def forward(self, X):
        # 初始化隐藏状态
        h = np.zeros((seq_length, cell.hidden_dim))
        outputs = []
        for X_t in X:
            h, y = self.cell.step(X_t, h)
            outputs.append(y)
        return outputs

注意：以上代码仅为简化版示例，实际应用中需要处理数据预处理、损失函数计算、反向传播等更多细节。