用Go语言+Gorgonia，让AI为洗煤厂加药房“开药方

go get -u gorgonia.org/gorgonia
go get -u gorgonia.org/tensor

package main

import (
	"fmt"
	"log"
	"math/rand"
	"time"

	"gorgonia.org/gorgonia"
	"gorgonia.org/tensor"
)

func main() {
	rand.Seed(time.Now().UnixNano())

	g := gorgonia.NewGraph()

	// 定义输入节点
	pH := gorgonia.NewScalar(g, tensor.Float64, gorgonia.WithName("pH"))
	conductivity := gorgonia.NewScalar(g, tensor.Float64, gorgonia.WithName("conductivity"))

	// 定义模型参数（权重和偏置）
	wAnion := gorgonia.NewScalar(g, tensor.Float64, gorgonia.WithName("wAnion"), gorgonia.WithValue(tensor.NewScalar(tensor.Float64, tensor.WithBacking(rand.NormFloat64()*0.1))))
	wCation := gorgonia.NewScalar(g, tensor.Float64, gorgonia.WithName("wCation"), gorgonia.WithValue(tensor.NewScalar(tensor.Float64, tensor.WithBacking(rand.NormFloat64()*0.1))))
	bAnion := gorgonia.NewScalar(g, tensor.Float64, gorgonia.WithName("bAnion"), gorgonia.WithValue(tensor.NewScalar(tensor.Float64, tensor.WithBacking(rand.NormFloat64()*0.1))))
	bCation := gorgonia.NewScalar(g, tensor.Float64, gorgonia.WithName("bCation"), gorgonia.WithValue(tensor.NewScalar(tensor.Float64, tensor.WithBacking(rand.NormFloat64()*0.1))))

	// 简单的线性模型：y = wx + b
	anionPred := gorgonia.Must(gorgonia.Add(gorgonia.Must(gorgonia.Mul(wAnion, pH)), bAnion))
	cationPred := gorgonia.Must(gorgonia.Add(gorgonia.Must(gorgonia.Mul(wCation, conductivity)), bCation))

	// 创建一个虚拟机来执行图
	machine := gorgonia.NewTapeMachine(g)
	defer machine.Close()

	// 假设有一些训练数据（这里用随机数据模拟）
	trainingData := []struct {
		pH, Conductivity, Anion, Cation float64
	}{
		{6.8, 250.0, 8.0, 5.0},
		{7.2, 300.0, 7.0, 6.0},
		{6.5, 200.0, 9.0, 4.0},
		// 添加更多数据...
	}

	// 简单的梯度下降训练循环
	learningRate := 0.01
	for i := 0; i < 1000; i++ {
		for _, data := range trainingData {
			gorgonia.Let(pH, data.pH)
			gorgonia.Let(conductivity, data.Conductivity)

			// 假设真实值（在实际应用中，这些应该是从数据集中获取的）
			expectedAnion := tensor.NewScalar(tensor.Float64, tensor.WithBacking(data.Anion))
			expectedCation := tensor.NewScalar(tensor.Float64, tensor.WithBacking(data.Cation))

			// 执行图
			if err := machine.RunAll(); err != nil {
				log.Fatalf("Failed to run graph: %v", err)
			}

			// 计算损失（这里用简单的平方差）
			anionLoss := gorgonia.Must(gorgonia.Square(gorgonia.Must(gorgonia.Sub(anionPred, expectedAnion))))
			cationLoss := gorgonia.Must(gorgonia.Square(gorgonia.Must(gorgonia.Sub(cationPred, expectedCation))))
			totalLoss := gorgonia.Must(gorgonia.Add(anionLoss, cationLoss))

			// 反向传播
			if err := gorgonia.Linalg.Gradient(totalLoss, []gorgonia.ValueGrad{wAnion, wCation, bAnion, bCation}); err != nil {
				log.Fatalf("Failed to compute gradients: %v", err)
			}

			// 更新参数
			gorgonia.Read(wAnion, gorgonia.WithInplace(true)).(gorgonia.Value).(gorgonia.Node).Value().(*tensor.Dense).Apply(func(v interface{}) interface{} {
				return v.(float64) - learningRate*wAnion.Grad().Data().(float64)
			})
			gorgonia.Read(wCation, gorgonia.WithInplace(true)).(gorgonia.Value).(gorgonia.Node).Value().(*tensor.Dense).Apply(func(v interface{}) interface{} {
				return v.(float64) - learningRate*wCation.Grad().Data().(float64)
			})
			gorgonia.Read(bAnion, gorgonia.WithInplace(true)).(gorgonia.Value).(gorgonia.Node).Value().(*tensor.Dense).Apply(func(v interface{}) interface{} {
				return v.(float64) - learningRate*bAnion.Grad().Data().(float64)
			})
			gorgonia.Read(bCation, gorgonia.WithInplace(true)).(gorgonia.Value).(gorgonia.Node).Value().(*tensor.Dense).Apply(func(v interface{}) interface{} {
				return v.(float64) - learningRate*bCation.Grad().Data().(float64)
			})

			// 重置梯度
			wAnion.ZeroGrad()
			wCation.ZeroGrad()
			bAnion.ZeroGrad()
			bCation.ZeroGrad()
		}
	}

	// 使用训练好的模型进行预测
	testpH := 7.0
	testConductivity := 300.0
	gorgonia.Let(pH, testpH)
	gorgonia.Let(conductivity, testConductivity)

	if err := machine.RunAll(); err != nil {
		log.Fatalf("Failed to run graph: %v", err)
	}

	fmt.Printf("预测阴离子添加量: %.2f\n", anionPred.Value().Data().(float64))
	fmt.Printf("预测阳离子添加量: %.2f\n", cationPred.Value().Data().(float64))
}