探讨：Golang在人工智能领域的应用潜力

package main

import (
    "fmt"
    "math"
)

type NeuralNetwork struct {
    inputNodes  int
    hiddenNodes int
    outputNodes int
    weightsIH   [][]float64
    weightsHO   [][]float64
}

func NewNeuralNetwork(inputNodes, hiddenNodes, outputNodes int) *NeuralNetwork {
    weightsIH := make([][]float64, hiddenNodes)
    weightsHO := make([][]float64, outputNodes)
    return &NeuralNetwork{
        inputNodes:  inputNodes,
        hiddenNodes: hiddenNodes,
        outputNodes: outputNodes,
        weightsIH:   weightsIH,
        weightsHO:   weightsHO,
    }
}

func (nn *NeuralNetwork) FeedForward(input []float64) []float64 {
    hiddenOutputs := make([]float64, nn.hiddenNodes)
    outputs := make([]float64, nn.outputNodes)

    // Calculate hidden layer outputs
    for i := 0; i < nn.hiddenNodes; i++ {
        hiddenValue := 0.0
        for j := 0; j < nn.inputNodes; j++ {
            hiddenValue += nn.weightsIH[i][j] * input[j]
        }
        hiddenOutputs[i] = sigmoid(hiddenValue)
    }

    // Calculate output layer outputs
    for i := 0; i < nn.outputNodes; i++ {
        outputValue := 0.0
        for j := 0; j < nn.hiddenNodes; j++ {
            outputValue += nn.weightsHO[i][j] * hiddenOutputs[j]
        }
        outputs[i] = sigmoid(outputValue)
    }

    return outputs
}

func sigmoid(x float64) float64 {
    return 1 / (1 + math.Exp(-x))
}

func main() {
    // 创建一个具有2个输入节点、2个隐藏节点和1个输出节点的神经网络
    nn := NewNeuralNetwork(2, 2, 1)

    // 设置权重
    nn.weightsIH = [][]float64{{0.5, -0.3}, {0.2, 0.8}}
    nn.weightsHO = [][]float64{{0.9, 0.4}}

    // 输入数据
    input := []float64{0.5, 0.8}

    // 进行前向传播
    output := nn.FeedForward(input)

    // 输出结果
    fmt.Println("Output:", output)
}