```

feat(examples): 更新simple_model_example.go以使用gomatrix包

- 导入gomatrix包替代部分gotensor功能
- 修改权重张量梯度初始化使用gomatrix.NewZeros
- 更新must函数为泛型实现
- 重构损失函数实现使用gomatrix操作
- 优化输出格式化避免重复数据访问

refactor(model_test): 更新测试用例使用gomatrix构造函数

- 修改TestSequential测试使用gomatrix.NewMatrix和gomatrix.NewVector
- 更新TestSaveLoadModel测试使用gomatrix构造函数
- 修改TestLinearLayer测试使用NewTensor构造权重矩阵

refactor(trainer_test): 将Must函数改为泛型实现

- 更新Must函数为泛型版本支持任意类型
```

examples/simple_model_example.go

@@ -3,6 +3,7 @@ package main
 import (
 	"fmt"
 
+	"git.kingecg.top/kingecg/gomatrix"
 	"git.kingecg.top/kingecg/gotensor"
 )
 
@@ -38,7 +39,7 @@ func (l *LinearLayer) Forward(inputs *gotensor.Tensor) (*gotensor.Tensor, error)
 	// 创建转置后的权重张量
 	weightTransposedTensor := &gotensor.Tensor{
 		Data: weightTransposed,
-		Grad: must(gotensor.NewZeros(l.Weight.Shape())),
+		Grad: must(gomatrix.NewZeros(l.Weight.Shape())),
 	}
 
 	// 矩阵乘法
@@ -83,7 +84,7 @@ func (m *SimpleModel) ZeroGrad() {
 }
 
 // must 是一个辅助函数，用于处理可能的错误
-func must(t *gotensor.Tensor, err error) *gotensor.Tensor {
+func must[T any](t *T, err error) *T {
 	if err != nil {
 		panic(err)
 	}
@@ -122,12 +123,12 @@ func main() {
 	// 定义损失函数 (MSE)
 	lossFn := func(output, target *gotensor.Tensor) *gotensor.Tensor {
 		// 计算均方误差
-		diff, _ := output.Sub(target)
-		squared, _ := diff.Mul(diff)
-		sum, _ := squared.Sum()
+		diff, _ := output.Data.Subtract(target.Data)
+		squared, _ := diff.Multiply(diff)
+		sum := squared.Sum()
 		size := float64(output.Size())
-		result, _ := sum.DivScalar(size)
-		return result
+		result := sum / size
+		return must(gotensor.NewTensor([]float64{result}, []int{1}))
 	}
 
 	fmt.Println("开始训练模型...")
@@ -155,10 +156,11 @@ func main() {
 		inputVal1, _ := input.Data.Get(1)
 		outputVal0, _ := output.Data.Get(0)
 		outputVal1, _ := output.Data.Get(1)
-
+		targetVal0, _ := trainTargets[i].Data.Get(0)
+		targetVal1, _ := trainTargets[i].Data.Get(1)
 		fmt.Printf("输入: [%.0f, %.0f] -> 输出: [%.3f, %.3f], 目标: [%.0f, %.0f]\n",
 			inputVal0, inputVal1, outputVal0, outputVal1,
-			trainTargets[i].Data.Get(0), trainTargets[i].Data.Get(1))
+			targetVal0, targetVal1)
 	}
 
 	// 保存模型

go.sum

@@ -1,6 +1,2 @@
-git.kingecg.top/kingecg/gomatrix v0.0.0-20251230141944-2ff4dcfb0fcd h1:vn3LW38hQPGig0iqofIaIMYXVp3Uqb5QX6eH5B5lVxU=
-git.kingecg.top/kingecg/gomatrix v0.0.0-20251230141944-2ff4dcfb0fcd/go.mod h1:CHH1HkVvXrpsb+uDrsoyjx0lTwQ3oSSMbIRJmwvO6z8=
-git.kingecg.top/kingecg/gomatrix v0.0.0-20251231092627-f40960a855c7 h1:tutkcVKwpzNYxZRXkunhnkrDGRfMYgvwGAbCBCtO62c=
-git.kingecg.top/kingecg/gomatrix v0.0.0-20251231092627-f40960a855c7/go.mod h1:CHH1HkVvXrpsb+uDrsoyjx0lTwQ3oSSMbIRJmwvO6z8=
 git.kingecg.top/kingecg/gomatrix v0.0.0-20251231094846-bfcfba4e3f99 h1:sV3rEZIhYwU1TLmqFybT6Lwf6lA4oiITX/HC7i+JsiA=
 git.kingecg.top/kingecg/gomatrix v0.0.0-20251231094846-bfcfba4e3f99/go.mod h1:CHH1HkVvXrpsb+uDrsoyjx0lTwQ3oSSMbIRJmwvO6z8=

model_test.go

@@ -65,11 +65,11 @@ func TestSequential(t *testing.T) {
 	linearLayer := &Linear{
 		Weight: &Tensor{
 			Data: weight,
-			Grad: Must(NewMatrix([][]float64{{0, 0}, {0, 0}})),
+			Grad: Must(gomatrix.NewMatrix([]float64{0, 0, 0, 0}, []int{2, 2})),
 		},
 		Bias: &Tensor{
 			Data: bias,
-			Grad: Must(NewVector([]float64{0, 0})),
+			Grad: Must(gomatrix.NewVector([]float64{0, 0})),
 		},
 	}
 
@@ -117,11 +117,11 @@ func TestSequential(t *testing.T) {
 
 // TestSaveLoadModel 测试模型保存和加载功能
 func TestSaveLoadModel(t *testing.T) {
-	weight, err := NewMatrix([][]float64{{1, 2}, {3, 4}})
+	weight, err := gomatrix.NewMatrix([]float64{1, 2, 3, 4}, []int{2, 2})
 	if err != nil {
 		t.Fatalf("Failed to create weight matrix: %v", err)
 	}
-	bias, err := NewVector([]float64{0.5, 0.5})
+	bias, err := gomatrix.NewVector([]float64{0.5, 0.5})
 	if err != nil {
 		t.Fatalf("Failed to create bias vector: %v", err)
 	}
@@ -129,11 +129,11 @@ func TestSaveLoadModel(t *testing.T) {
 	linearLayer := &Linear{
 		Weight: &Tensor{
 			Data: weight,
-			Grad: Must(NewMatrix([][]float64{{0, 0}, {0, 0}})),
+			Grad: Must(gomatrix.NewMatrix([]float64{0, 0, 0, 0}, []int{2, 2})),
 		},
 		Bias: &Tensor{
 			Data: bias,
-			Grad: Must(NewVector([]float64{0, 0})),
+			Grad: Must(gomatrix.NewVector([]float64{0, 0})),
 		},
 	}
 
@@ -169,7 +169,7 @@ func TestSaveLoadModel(t *testing.T) {
 
 // TestLinearLayer 测试线性层功能
 func TestLinearLayer(t *testing.T) {
-	weight := Must(NewMatrix([][]float64{{2, 0}, {0, 3}}))
+	weight := Must(NewTensor([]float64{2, 0, 0, 3}, []int{2, 2}))
 	bias := Must(NewVector([]float64{0.5, 0.5}))
 
 	layer := NewLinear(weight, bias)

trainer_test.go

@@ -63,7 +63,7 @@ func NewVector(data []float64) (*Tensor, error) {
 
 }
 
-func Must(t *Tensor, err error) *Tensor {
+func Must[T any](t *T, err error) *T {
 	if err != nil {
 		panic(err)
 	}