36 lines
1.2 KiB
C++
36 lines
1.2 KiB
C++
/*前向自动微分的声明部分*/
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#pragma once
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#include <vector>
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#include "dual_ops.hpp"
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#include "types/common.hpp"
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namespace forwardad{
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template <typename Func, typename... Args>
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ADResult diff(const Func& f, Args... args) {
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ADResult res;
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constexpr size_t N = sizeof...(Args);
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res.gradient.resize(N);
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// 1. 计算函数值（所有导数为 0）
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Dual value_res = f(Dual((double)args, 0.0)...);
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res.value = value_res.value;
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// 2. 对每个输入变量求偏导（seed 依次设为 1）
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double args_arr[] = { (double)args... };
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[&]<size_t... Is>(std::index_sequence<Is...>) {
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([&]() {
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// 创建 Dual 输入，第 Is 个变量导数=1，其余=0
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Dual inputs[N];
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for (size_t j = 0; j < N; ++j)
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inputs[j] = Dual(args_arr[j], j == Is ? 1.0 : 0.0);
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// 用内层 index_sequence 展开所有 N 个参数传给 f
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res.gradient[Is] = [&]<size_t... Js>(std::index_sequence<Js...>) {
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return f(inputs[Js]...).deriv;
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}(std::make_index_sequence<N>{});
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}(), ...);
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}(std::make_index_sequence<N>{});
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return res;
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}
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} // namespace forwardad
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