refactor: 分离测试代码

CMakeLists.txt

@@ -9,4 +9,5 @@ set(CMAKE_POSITION_INDEPENDENT_CODE ON)
 include_directories(include)
 
 # 测试可执行文件
-add_executable(test_basic tests/basic.cpp)
+add_executable(test_forward tests/test_forward.cpp)
+add_executable(test_backward tests/test_backward.cpp)

tests/basic.cpp

@@ -1,75 +0,0 @@
-#include <iostream>
-#include "forwardad.hpp"
-#include "backwardad.hpp"
-#include "types/dual.hpp"
-
-// 一阶测试函数
-forwardad::Dual f1(forwardad::Dual x) {
-    return x + x*x + pow(x,3);
-}
-
-// 二阶测试函数
-forwardad::Dual g1(forwardad::Dual x, forwardad::Dual y) {
-    return exp(x) * log(y);
-}
-
-// 高阶测试函数
-forwardad::Dual h1(forwardad::Dual x, forwardad::Dual y, forwardad::Dual z) {
-    return pow(x, 3) + pow(y, 2) + z + cos(x * y * z);
-}
-
-backwardad::Node f2(backwardad::Node x) {
-    return x + x*x + pow(x,3);
-}
-
-backwardad::Node g2(backwardad::Node x, backwardad::Node y) {
-    return exp(x) * log(y);
-}   
-
-backwardad::Node h2(backwardad::Node x, backwardad::Node y, backwardad::Node z) {
-    return pow(x, 3) + pow(y, 2) + z + cos(x * y * z);
-}
-
-int test_forward_ad() {
-    std::cout << "Testing f(x) = x^2 + sin(x) at x=2.0\n";
-    auto r = forwardad::diff(f1, 2.0);
-    std::cout << "value = " << r.value << "\n";
-    std::cout << "grad  = " << r.gradient[0] << "\n";
-
-    std::cout << "\nTesting g(x,y) = exp(x)*log(y) at (x,y)=(1.0, 2.0)\n";
-    auto r2 = forwardad::diff(g1, 1.0, 2.0);
-    std::cout << "value = " << r2.value << "\n";
-    std::cout << "grad  = (" << r2.gradient[0] << ", " << r2.gradient[1] << ")\n";
-
-    std::cout << "\nTesting h(x,y,z) = x^3 + y^2 + z + cos(x*y*z) at (x,y,z)=(1.0, 2.0, 3.0)\n";
-    auto r3 = forwardad::diff(h1, 1.0, 2.0, 3.0);
-    std::cout << "value = " << r3.value << "\n";
-    std::cout << "grad  = (" << r3.gradient[0] << ", " << r3.gradient[1] << ", " << r3.gradient[2] << ")\n";
-    return 0;
-}
-
-int test_backward_ad() {
-    std::cout << "Testing f(x) = x^2 + sin(x) at x=2.0\n";
-    auto r = backwardad::diff(f2, 2.0);
-    std::cout << "value = " << r.value << "\n";
-    std::cout << "grad  = " << r.gradient[0] << "\n";
-
-    std::cout << "\nTesting g(x,y) = exp(x)*log(y) at (x,y)=(1.0, 2.0)\n";
-    auto r2 = backwardad::diff(g2, 1.0, 2.0);
-    std::cout << "value = " << r2.value << "\n";
-    std::cout << "grad  = (" << r2.gradient[0] << ", " << r2.gradient[1] << ")\n";
-
-    std::cout << "\nTesting h(x,y,z) = x^3 + y^2 + z + cos(x*y*z) at (x,y,z)=(1.0, 2.0, 3.0)\n";
-    auto r3 = backwardad::diff(h2, 1.0, 2.0, 3.0);
-    std::cout << "value = " << r3.value << "\n";
-    std::cout << "grad  = (" << r3.gradient[0] << ", " << r3.gradient[1] << ", " << r3.gradient[2] << ")\n";
-    return 0;
-}
-
-int main() {
-    std::cout << "=== Forward AD Tests ===\n";
-    test_forward_ad();
-    std::cout << "\n=== Backward AD Tests ===\n";
-    test_backward_ad();
-    return 0;
-}

tests/test_backward.cpp

@@ -0,0 +1,41 @@
+#include <iostream>
+#include "backwardad.hpp"
+
+// 一阶反向自动微分测试函数
+backwardad::Node f2(backwardad::Node x) {
+    return x + x*x + pow(x,3);
+}
+
+// 二阶反向自动微分测试函数
+backwardad::Node g2(backwardad::Node x, backwardad::Node y) {
+    return exp(x) * log(y);
+}   
+
+// 高阶反向自动微分测试函数
+backwardad::Node h2(backwardad::Node x, backwardad::Node y, backwardad::Node z) {
+    return pow(x, 3) + pow(y, 2) + z + cos(x * y * z);
+}
+
+int test_backward_ad() {
+    std::cout << "Testing f(x) = x^2 + sin(x) at x=2.0\n";
+    auto r = backwardad::diff(f2, 2.0);
+    std::cout << "value = " << r.value << "\n";
+    std::cout << "grad  = " << r.gradient[0] << "\n";
+
+    std::cout << "\nTesting g(x,y) = exp(x)*log(y) at (x,y)=(1.0, 2.0)\n";
+    auto r2 = backwardad::diff(g2, 1.0, 2.0);
+    std::cout << "value = " << r2.value << "\n";
+    std::cout << "grad  = (" << r2.gradient[0] << ", " << r2.gradient[1] << ")\n";
+
+    std::cout << "\nTesting h(x,y,z) = x^3 + y^2 + z + cos(x*y*z) at (x,y,z)=(1.0, 2.0, 3.0)\n";
+    auto r3 = backwardad::diff(h2, 1.0, 2.0, 3.0);
+    std::cout << "value = " << r3.value << "\n";
+    std::cout << "grad  = (" << r3.gradient[0] << ", " << r3.gradient[1] << ", " << r3.gradient[2] << ")\n";
+    return 0;
+}
+
+int main() {
+    std::cout << "\n=== Backward AD Tests ===\n";
+    test_backward_ad();
+    return 0;
+}

tests/test_forward.cpp

@@ -0,0 +1,41 @@
+#include <iostream>
+#include "forwardad.hpp"
+
+// 一阶前向自动微分测试函数
+forwardad::Dual f1(forwardad::Dual x) {
+    return x + x*x + pow(x,3);
+}
+
+// 二阶前向自动微分测试函数
+forwardad::Dual g1(forwardad::Dual x, forwardad::Dual y) {
+    return exp(x) * log(y);
+}
+
+// 高阶前向自动微分测试函数
+forwardad::Dual h1(forwardad::Dual x, forwardad::Dual y, forwardad::Dual z) {
+    return pow(x, 3) + pow(y, 2) + z + cos(x * y * z);
+}
+
+int test_forward_ad() {
+    std::cout << "Testing f(x) = x^2 + sin(x) at x=2.0\n";
+    auto r = forwardad::diff(f1, 2.0);
+    std::cout << "value = " << r.value << "\n";
+    std::cout << "grad  = " << r.gradient[0] << "\n";
+
+    std::cout << "\nTesting g(x,y) = exp(x)*log(y) at (x,y)=(1.0, 2.0)\n";
+    auto r2 = forwardad::diff(g1, 1.0, 2.0);
+    std::cout << "value = " << r2.value << "\n";
+    std::cout << "grad  = (" << r2.gradient[0] << ", " << r2.gradient[1] << ")\n";
+
+    std::cout << "\nTesting h(x,y,z) = x^3 + y^2 + z + cos(x*y*z) at (x,y,z)=(1.0, 2.0, 3.0)\n";
+    auto r3 = forwardad::diff(h1, 1.0, 2.0, 3.0);
+    std::cout << "value = " << r3.value << "\n";
+    std::cout << "grad  = (" << r3.gradient[0] << ", " << r3.gradient[1] << ", " << r3.gradient[2] << ")\n";
+    return 0;
+}
+
+int main() {
+    std::cout << "=== Forward AD Tests ===\n";
+    test_forward_ad();
+    return 0;
+}