``` class Matrix { constructor(rows, cols) { this.rows = rows; this.cols = cols; this.data = Array(rows).fill().map(() => Array(cols).fill(0)); } static multiply(m1, m2) { console.log(`[Matrix Multiply] ${m1.rows}x${m1.cols} * ${m2.rows}x${m2.cols}`); if (m1.cols !== m2.rows) throw "Columns of A must match rows of B"; let result = new Matrix(m1.rows, m2.cols); for (let i = 0; i < result.rows; i++) { for (let j = 0; j < result.cols; j++) { let sum = 0; for (let k = 0; k < m1.cols; k++) { sum += m1.data[i][k] * m2.data[k][j]; } result.data[i][j] = sum; } } return result; } static transpose(matrix) { let result = new Matrix(matrix.cols, matrix.rows); for (let i = 0; i < matrix.rows; i++) { for (let j = 0; j < matrix.cols; j++) { result.data[j][i] = matrix.data[i][j]; } } return result; } static map(matrix, func) { let result = new Matrix(matrix.rows, matrix.cols); for (let i = 0; i < matrix.rows; i++) { for (let j = 0; j < matrix.cols; j++) { result.data[i][j] = func(matrix.data[i][j]); } } return result; } static subtract(a, b) { let result = new Matrix(a.rows, a.cols); for (let i = 0; i < a.rows; i++) { for (let j = 0; j < a.cols; j++) { result.data[i][j] = a.data[i][j] - b.data[i][j]; } } return result; } static randomize() { const lcg = (seed) => { const a = 1664525; const c = 1013904223; const m = 4294967296; // 2^32 let state = seed || Date.now() % m; return () => { state = (a * state + c) % m; return state / m; }; }; } } class NeuralNetwork { constructor(input, hidden, output) { // 初始化权重矩阵 this.weights_ih = new Matrix(hidden, input); this.weights_ho = new Matrix(output, hidden); this.weights_ih.randomize(); this.weights_ho.randomize(); // 初始化偏置 this.bias_h = new Matrix(hidden, 1); this.bias_o = new Matrix(output, 1); this.bias_h.randomize(); this.bias_o.randomize(); this.learningRate = 0.1; } // 前向传播过程 feedforward(input_array) { console.log("\n=== Forward Propagation Start ==="); // 输入层 → 隐藏层 let inputs = Matrix.fromArray(input_array); console.log("Input layer:\n" + inputs.toString()); let hidden = Matrix.multiply(this.weights_ih, inputs); console.log("\nHidden layer weighted sum:\n" + hidden.toString()); hidden.add(this.bias_h); console.log("\nAfter adding bias:\n" + hidden.toString()); hidden.map(sigmoid); console.log("\nAfter activation:\n" + hidden.toString()); // 隐藏层 → 输出层 let output = Matrix.multiply(this.weights_ho, hidden); output.add(this.bias_o); output.map(sigmoid); console.log("\n=== Forward Propagation End ==="); return output.toArray(); } } }```以上代码不依赖第三方库比如Math库，来实现自己的神经网络训练过程，请完善以上代码中的hidden.add(this.bias_h);操作对应的add函数