神经归纳补全矩阵代码实现

这里是一个简单的神经归纳补全矩阵代码实现，使用Python语言和numpy库：

import numpy as np

def neural_inductive_completion(matrix, hidden_size, learning_rate, epochs):
    # 初始化权重矩阵
    input_size = matrix.shape[0]
    weights1 = np.random.rand(input_size, hidden_size)
    weights2 = np.random.rand(hidden_size, input_size)

    # 迭代训练
    for epoch in range(epochs):
        # 前向传播
        hidden = np.dot(matrix, weights1)
        hidden = np.maximum(hidden, 0)
        output = np.dot(hidden, weights2)

        # 计算损失函数
        loss = np.sum((matrix - output) ** 2)

        # 反向传播
        derivative_output = 2 * (output - matrix)
        derivative_weights2 = np.dot(hidden.T, derivative_output)
        derivative_hidden = np.dot(derivative_output, weights2.T)
        derivative_hidden[hidden <= 0] = 0
        derivative_weights1 = np.dot(matrix.T, derivative_hidden)

        # 更新权重矩阵
        weights1 -= learning_rate * derivative_weights1
        weights2 -= learning_rate * derivative_weights2

        # 输出损失函数值
        print("Epoch:", epoch, "Loss:", loss)

    # 返回补全后的矩阵
    hidden = np.dot(matrix, weights1)
    hidden = np.maximum(hidden, 0)
    completion = np.dot(hidden, weights2)
    return completion

其中，matrix为输入的矩阵，hidden_size为隐藏层的大小，learning_rate为学习率，epochs为迭代次数。函数会返回补全后的矩阵