update config

test/forward/5multi_varialbe_function_rely_on_analytical_der.cpp

@@ -9,24 +9,24 @@ using namespace autodiff;
 
 // Define functions A, Ax, Ay using double; analytical derivatives are available.
 double  A(double x, double y) { return x*y; }
-double Ax(double x, double y) { return x; }
-double Ay(double x, double y) { return y; }
+double Ax(double x, double y) { return y; } // \partial A / \partial x = y
+double Ay(double x, double y) { return x; } // \partial A / \partial y = x
 
 // Define functions B, Bx, By using double; analytical derivatives are available.
 double  B(double x, double y) { return x + y; }
 double Bx(double x, double y) { return 1.0; }
 double By(double x, double y) { return 1.0; }
 
-// Wrap A into Adual function so that it can be used within autodiff-enabled codes.
 // A是一个double相关的函数，无法与autodiff混合使用；这里将A包装成Adual
+// Wrap A into Adual function so that it can be used within autodiff-enabled codes.
 dual Adual(dual const& x, dual const& y)
 {
     dual res = A(x.val, y.val);
 
-    if(x.grad != 0.0)
+    if(x.grad != 0.0) // x导数存在，需要对x链式求导
         res.grad += x.grad * Ax(x.val, y.val);
 
-    if(y.grad != 0.0)
+    if(y.grad != 0.0) // y导数存在，需要对y链式求导
         res.grad += y.grad * Ay(x.val, y.val);
 
     return res;
@@ -75,7 +75,7 @@ int main()
     // Compute expected analytical derivatives of C with respect to x and y
     auto x0 = x.val;
     auto y0 = y.val;
-    auto expectedCx = 2.0*A(x0, y0)*Ax(x0, y0) + Bx(x0, y0);
+    auto expectedCx = 2.0*A(x0, y0)*Ax(x0, y0) + Bx(x0, y0); 
     auto expectedCy = 2.0*A(x0, y0)*Ay(x0, y0) + By(x0, y0);
 
     std::cout << "C0 = " << C0 << "\n";
@@ -89,7 +89,7 @@ int main()
 
 // Output:
 // C0 = 7
-// Cx(computed) = 5
-// Cx(expected) = 5
-// Cy(computed) = 9
-// Cy(expected) = 9
+// Cx(computed) = 9
+// Cx(expected) = 9
+// Cy(computed) = 5
+// Cy(expected) = 5

test/forward/test.cpp

@@ -0,0 +1,41 @@
+#include <cmath>
+#include <iostream>
+#include <autodiff/forward/dual.hpp>
+using namespace autodiff;
+
+dual sqrt1(dual x){
+    dual res = 0.0;
+    res.val = std::sqrt(x.val);
+    if (x.grad !=0.0){
+        std::cout << "x.grad: " << x.grad << std::endl;
+        res.grad += 0.5/res.val*x.grad;
+    }
+    return res;
+}
+dual test_exp(dual x){
+    dual res = 0.0;
+    std::cout << "x.val: " << x.val << std::endl;
+    res.val = std::exp(x.val);
+    std::cout << "res.val: " << res.val << std::endl;
+    std::cout << "res.grad: " << res.grad << std::endl;
+    if(x.grad != 0.0) res.grad += x.grad*res.val;
+    std::cout << "res.val: " << res.val << std::endl;
+    std::cout << "res.grad: " << res.grad << std::endl;
+    return res;
+}
+
+dual test_exp0(dual x){
+    return 1.0+exp(x);
+}
+
+int main()
+{
+    dual x = 1.2;
+    dual u = sqrt1(x);
+    double dfdx = derivative(sqrt1, wrt(x), at(x));
+    double dfdx2 = derivative(test_exp, wrt(x), at(x));
+    double dfdx3 = derivative(test_exp0, wrt(x), at(x));
+    std::cout << "dfdx: " << dfdx << std::endl;
+    std::cout << "dfdx2: " << dfdx2 << std::endl;
+    std::cout << "dfdx3: " << dfdx3 << std::endl;
+}