{"id":118864,"date":"2026-10-28T09:24:29","date_gmt":"2026-10-28T01:24:29","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/118864.html"},"modified":"2026-10-28T09:24:29","modified_gmt":"2026-10-28T01:24:29","slug":"c%e4%bb%a3%e7%a0%81%e5%ae%9e%e7%8e%b0matlab%e4%b8%ad%e7%9a%84rocmetrics%e5%87%bd%e6%95%b0%e5%8a%9f%e8%83%bd","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/118864.html","title":{"rendered":"C++\u4ee3\u7801\u5b9e\u73b0MATLAB\u4e2d\u7684rocmetrics\u51fd\u6570\u529f\u80fd"},"content":{"rendered":"

\/\/ rocmetrics.cpp<\/span>
\n\/\/ g++ -std=c++17 -O2 -o rocmetrics rocmetrics.cpp<\/span><\/p>\n

#<\/span>include<\/span> <algorithm><\/span><\/span>
\n#<\/span>include<\/span> <cmath><\/span><\/span>
\n#<\/span>include<\/span> <cstddef><\/span><\/span>
\n#<\/span>include<\/span> <iostream><\/span><\/span>
\n#<\/span>include<\/span> <limits><\/span><\/span>
\n#<\/span>include<\/span> <numeric><\/span><\/span>
\n#<\/span>include<\/span> <random><\/span><\/span>
\n#<\/span>include<\/span> <stdexcept><\/span><\/span>
\n#<\/span>include<\/span> <string><\/span><\/span>
\n#<\/span>include<\/span> <vector><\/span><\/span><\/p>\n

namespace<\/span> rocmetrics {<\/span><\/p>\n

\/\/ ————————————————————<\/span>
\n\/\/ \u57fa\u7840\u6570\u636e\u7ed3\u6784<\/span>
\n\/\/ ————————————————————<\/span>
\nstruct<\/span> ROCCurve<\/span> {<\/span>
\n std::<\/span>vector<<\/span>double<\/span>><\/span> thresholds;<\/span>
\n std::<\/span>vector<<\/span>double<\/span>><\/span> fpr;<\/span>
\n std::<\/span>vector<<\/span>double<\/span>><\/span> tpr;<\/span>
\n double<\/span> auc =<\/span> 0.0<\/span>;<\/span>
\n}<\/span>;<\/span><\/p>\n

struct<\/span> ConfidenceInterval<\/span> {<\/span>
\n double<\/span> lower =<\/span> std::<\/span>numeric_limits<\/span><<\/span>double<\/span>><\/span>::<\/span>quiet_NaN<\/span>(<\/span>)<\/span>;<\/span>
\n double<\/span> upper =<\/span> std::<\/span>numeric_limits<\/span><<\/span>double<\/span>><\/span>::<\/span>quiet_NaN<\/span>(<\/span>)<\/span>;<\/span>
\n double<\/span> level =<\/span> 0.95<\/span>;<\/span>
\n}<\/span>;<\/span><\/p>\n

struct<\/span> ROCMetricsOptions<\/span> {<\/span>
\n bool<\/span> compute_bootstrap_ci =<\/span> false<\/span>;<\/span>
\n int<\/span> num_bootstrap =<\/span> 1000<\/span>;<\/span>
\n double<\/span> ci_level =<\/span> 0.95<\/span>;<\/span>
\n unsigned<\/span> int<\/span> seed =<\/span> 0<\/span>;<\/span>
\n bool<\/span> use_nearest_neighbor =<\/span> false<\/span>;<\/span>
\n std::<\/span>string nan_flag =<\/span> "omitan"<\/span>;<\/span>
\n}<\/span>;<\/span><\/p>\n

\/\/ ————————————————————<\/span>
\n\/\/ \u5185\u90e8\u5de5\u5177<\/span>
\n\/\/ ————————————————————<\/span>
\nnamespace<\/span> detail {<\/span><\/p>\n

inline<\/span> void<\/span> validate_labels<\/span>(<\/span>const<\/span> std::<\/span>vector<<\/span>int<\/span>><\/span>&<\/span> labels)<\/span> {<\/span>
\n for<\/span> (<\/span>int<\/span> y :<\/span> labels)<\/span> {<\/span>
\n if<\/span> (<\/span>y !=<\/span> 0<\/span> &&<\/span> y !=<\/span> 1<\/span>)<\/span>
\n throw<\/span> std::<\/span>invalid_argument<\/span>(<\/span>"labels must be 0 (negative) or 1 (positive)"<\/span>)<\/span>;<\/span>
\n }<\/span>
\n}<\/span><\/p>\n

inline<\/span> ROCCurve compute_roc_points_from_sorted<\/span>(<\/span>
\n const<\/span> std::<\/span>vector<<\/span>int<\/span>><\/span>&<\/span> sorted_labels,<\/span>
\n const<\/span> std::<\/span>vector<<\/span>double<\/span>><\/span>&<\/span> sorted_scores)<\/span>
\n{<\/span>
\n const<\/span> std::<\/span>size_t n =<\/span> sorted_labels.<\/span>size<\/span>(<\/span>)<\/span>;<\/span>
\n if<\/span> (<\/span>n ==<\/span> 0<\/span>)<\/span> return<\/span> {<\/span>}<\/span>;<\/span><\/p>\n

long<\/span> P =<\/span> 0<\/span>,<\/span> N =<\/span> 0<\/span>;<\/span>
\n for<\/span> (<\/span>int<\/span> y :<\/span> sorted_labels)<\/span> (<\/span>y ==<\/span> 1<\/span> ?<\/span> P :<\/span> N)<\/span>++<\/span>;<\/span>
\n if<\/span> (<\/span>P ==<\/span> 0<\/span> ||<\/span> N ==<\/span> 0<\/span>)<\/span>
\n throw<\/span> std::<\/span>invalid_argument<\/span>(<\/span>"both positive and negative samples are required"<\/span>)<\/span>;<\/span><\/p>\n

ROCCurve curve;<\/span>
\n long<\/span> tp =<\/span> 0<\/span>,<\/span> fp =<\/span> 0<\/span>;<\/span><\/p>\n

curve.<\/span>thresholds.<\/span>push_back<\/span>(<\/span>std::<\/span>numeric_limits<\/span><<\/span>double<\/span>><\/span>::<\/span>infinity<\/span>(<\/span>)<\/span>)<\/span>;<\/span>
\n curve.<\/span>fpr.<\/span>push_back<\/span>(<\/span>0.0<\/span>)<\/span>;<\/span>
\n curve.<\/span>tpr.<\/span>push_back<\/span>(<\/span>0.0<\/span>)<\/span>;<\/span><\/p>\n

std::<\/span>size_t i =<\/span> 0<\/span>;<\/span>
\n while<\/span> (<\/span>i <<\/span> n)<\/span> {<\/span>
\n double<\/span> score =<\/span> sorted_scores[<\/span>i]<\/span>;<\/span>
\n while<\/span> (<\/span>i <<\/span> n &&<\/span> sorted_scores[<\/span>i]<\/span> ==<\/span> score)<\/span> {<\/span>
\n if<\/span> (<\/span>sorted_labels[<\/span>i]<\/span> ==<\/span> 1<\/span>)<\/span> ++<\/span>tp;<\/span> else<\/span> ++<\/span>fp;<\/span>
\n ++<\/span>i;<\/span>
\n }<\/span>
\n curve.<\/span>thresholds.<\/span>push_back<\/span>(<\/span>score)<\/span>;<\/span>
\n curve.<\/span>fpr.<\/span>push_back<\/span>(<\/span>static_cast<\/span><<\/span>double<\/span>><\/span><\/span><\/span>(<\/span>fp)<\/span> \/<\/span> static_cast<\/span><<\/span>double<\/span>><\/span><\/span><\/span>(<\/span>N)<\/span>)<\/span>;<\/span>
\n curve.<\/span>tpr.<\/span>push_back<\/span>(<\/span>static_cast<\/span><<\/span>double<\/span>><\/span><\/span><\/span>(<\/span>tp)<\/span> \/<\/span> static_cast<\/span><<\/span>double<\/span>><\/span><\/span><\/span>(<\/span>P)<\/span>)<\/span>;<\/span>
\n }<\/span><\/p>\n

if<\/span> (<\/span>curve.<\/span>fpr.<\/span>back<\/span>(<\/span>)<\/span> <<\/span> 1.0<\/span> ||<\/span> curve.<\/span>tpr.<\/span>back<\/span>(<\/span>)<\/span> <<\/span> 1.0<\/span>)<\/span> {<\/span>
\n curve.<\/span>thresholds.<\/span>push_back<\/span>(<\/span>–<\/span>std::<\/span>numeric_limits<\/span><<\/span>double<\/span>><\/span>::<\/span>infinity<\/span>(<\/span>)<\/span>)<\/span>;<\/span>
\n curve.<\/span>fpr.<\/span>push_back<\/span>(<\/span>1.0<\/span>)<\/span>;<\/span>
\n curve.<\/span>tpr.<\/span>push_back<\/span>(<\/span>1.0<\/span>)<\/span>;<\/span>
\n }<\/span>
\n return<\/span> curve;<\/span>
\n}<\/span><\/p>\n

inline<\/span> double<\/span> trapezoid_auc<\/span>(<\/span>const<\/span> std::<\/span>vector<<\/span>double<\/span>><\/span>&<\/span> fpr,<\/span>
\n const<\/span> std::<\/span>vector<<\/span>double<\/span>><\/span>&<\/span> tpr)<\/span> {<\/span>
\n double<\/span> area =<\/span> 0.0<\/span>;<\/span>
\n for<\/span> (<\/span>std::<\/span>size_t i =<\/span> 1<\/span>;<\/span> i <<\/span> fpr.<\/span>size<\/span>(<\/span>)<\/span>;<\/span> ++<\/span>i)<\/span> {<\/span>
\n double<\/span> dx =<\/span> fpr[<\/span>i]<\/span> –<\/span> fpr[<\/span>i –<\/span> 1<\/span>]<\/span>;<\/span>
\n area +=<\/span> 0.5<\/span> *<\/span> dx *<\/span> (<\/span>tpr[<\/span>i –<\/span> 1<\/span>]<\/span> +<\/span> tpr[<\/span>i]<\/span>)<\/span>;<\/span>
\n }<\/span>
\n return<\/span> area;<\/span>
\n}<\/span><\/p>\n

inline<\/span> double<\/span> auc_mann_whitney<\/span>(<\/span>const<\/span> std::<\/span>vector<<\/span>int<\/span>><\/span>&<\/span> labels,<\/span>
\n const<\/span> std::<\/span>vector<<\/span>double<\/span>><\/span>&<\/span> scores)<\/span> {<\/span>
\n std::<\/span>size_t n =<\/span> labels.<\/span>size<\/span>(<\/span>)<\/span>;<\/span>
\n std::<\/span>vector<<\/span>std::<\/span>size_t><\/span> idx<\/span>(<\/span>n)<\/span>;<\/span>
\n std::<\/span>iota<\/span>(<\/span>idx.<\/span>begin<\/span>(<\/span>)<\/span>,<\/span> idx.<\/span>end<\/span>(<\/span>)<\/span>,<\/span> 0<\/span>)<\/span>;<\/span>
\n std::<\/span>sort<\/span>(<\/span>idx.<\/span>begin<\/span>(<\/span>)<\/span>,<\/span> idx.<\/span>end<\/span>(<\/span>)<\/span>,<\/span>
\n [<\/span>&<\/span>]<\/span>(<\/span>std::<\/span>size_t a,<\/span> std::<\/span>size_t b)<\/span> {<\/span> return<\/span> scores[<\/span>a]<\/span> <<\/span> scores[<\/span>b]<\/span>;<\/span> }<\/span>)<\/span>;<\/span><\/p>\n

long<\/span> P =<\/span> 0<\/span>,<\/span> N =<\/span> 0<\/span>;<\/span>
\n for<\/span> (<\/span>int<\/span> y :<\/span> labels)<\/span> (<\/span>y ==<\/span> 1<\/span> ?<\/span> P :<\/span> N)<\/span>++<\/span>;<\/span>
\n if<\/span> (<\/span>P ==<\/span> 0<\/span> ||<\/span> N ==<\/span> 0<\/span>)<\/span> return<\/span> std::<\/span>numeric_limits<\/span><<\/span>double<\/span>><\/span>::<\/span>quiet_NaN<\/span>(<\/span>)<\/span>;<\/span><\/p>\n

double<\/span> rank_sum_pos =<\/span> 0.0<\/span>;<\/span>
\n std::<\/span>size_t i =<\/span> 0<\/span>;<\/span>
\n while<\/span> (<\/span>i <<\/span> n)<\/span> {<\/span>
\n std::<\/span>size_t j =<\/span> i;<\/span>
\n while<\/span> (<\/span>j <<\/span> n &&<\/span> scores[<\/span>idx[<\/span>j]<\/span>]<\/span> ==<\/span> scores[<\/span>idx[<\/span>i]<\/span>]<\/span>)<\/span> ++<\/span>j;<\/span>
\n double<\/span> avg_rank =<\/span> 0.5<\/span> *<\/span> (<\/span>i +<\/span> 1<\/span> +<\/span> j)<\/span>;<\/span>
\n for<\/span> (<\/span>std::<\/span>size_t k =<\/span> i;<\/span> k <<\/span> j;<\/span> ++<\/span>k)<\/span> {<\/span>
\n if<\/span> (<\/span>labels[<\/span>idx[<\/span>k]<\/span>]<\/span> ==<\/span> 1<\/span>)<\/span> rank_sum_pos +=<\/span> avg_rank;<\/span>
\n }<\/span>
\n i =<\/span> j;<\/span>
\n }<\/span><\/p>\n

double<\/span> U =<\/span> rank_sum_pos –<\/span> 0.5<\/span> *<\/span> P *<\/span> (<\/span>P +<\/span> 1<\/span>)<\/span>;<\/span>
\n return<\/span> U \/<\/span> (<\/span>static_cast<\/span><<\/span>double<\/span>><\/span><\/span><\/span>(<\/span>P)<\/span> *<\/span> static_cast<\/span><<\/span>double<\/span>><\/span><\/span><\/span>(<\/span>N)<\/span>)<\/span>;<\/span>
\n}<\/span><\/p>\n

inline<\/span> double<\/span> quantile<\/span>(<\/span>const<\/span> std::<\/span>vector<<\/span>double<\/span>><\/span>&<\/span> sorted,<\/span> double<\/span> q)<\/span> {<\/span>
\n if<\/span> (<\/span>sorted.<\/span>empty<\/span>(<\/span>)<\/span>)<\/span> return<\/span> std::<\/span>numeric_limits<\/span><<\/span>double<\/span>><\/span>::<\/span>quiet_NaN<\/span>(<\/span>)<\/span>;<\/span>
\n if<\/span> (<\/span>q <=<\/span> 0.0<\/span>)<\/span> return<\/span> sorted.<\/span>front<\/span>(<\/span>)<\/span>;<\/span>
\n if<\/span> (<\/span>q >=<\/span> 1.0<\/span>)<\/span> return<\/span> sorted.<\/span>back<\/span>(<\/span>)<\/span>;<\/span>
\n double<\/span> pos =<\/span> q *<\/span> (<\/span>sorted.<\/span>size<\/span>(<\/span>)<\/span> –<\/span> 1<\/span>)<\/span>;<\/span>
\n std::<\/span>size_t lo =<\/span> static_cast<\/span><<\/span>std::<\/span>size_t><\/span><\/span><\/span>(<\/span>std::<\/span>floor<\/span>(<\/span>pos)<\/span>)<\/span>;<\/span>
\n std::<\/span>size_t hi =<\/span> static_cast<\/span><<\/span>std::<\/span>size_t><\/span><\/span><\/span>(<\/span>std::<\/span>ceil<\/span>(<\/span>pos)<\/span>)<\/span>;<\/span>
\n double<\/span> w =<\/span> pos –<\/span> lo;<\/span>
\n return<\/span> sorted[<\/span>lo]<\/span> *<\/span> (<\/span>1.0<\/span> –<\/span> w)<\/span> +<\/span> sorted[<\/span>hi]<\/span> *<\/span> w;<\/span>
\n}<\/span><\/p>\n

}<\/span> \/\/ namespace detail<\/span><\/p>\n

\/\/ ————————————————————<\/span>
\n\/\/ \u4e8c\u5206\u7c7b ROC<\/span>
\n\/\/ ————————————————————<\/span>
\ninline<\/span> ROCCurve compute_binary_roc<\/span>(<\/span>const<\/span> std::<\/span>vector<<\/span>int<\/span>><\/span>&<\/span> labels,<\/span>
\n const<\/span> std::<\/span>vector<<\/span>double<\/span>><\/span>&<\/span> scores,<\/span>
\n const<\/span> ROCMetricsOptions&<\/span> opt =<\/span> {<\/span>}<\/span>)<\/span> {<\/span>
\n if<\/span> (<\/span>labels.<\/span>size<\/span>(<\/span>)<\/span> !=<\/span> scores.<\/span>size<\/span>(<\/span>)<\/span>)<\/span>
\n throw<\/span> std::<\/span>invalid_argument<\/span>(<\/span>"labels and scores must have same length"<\/span>)<\/span>;<\/span>
\n detail::<\/span>validate_labels<\/span>(<\/span>labels)<\/span>;<\/span><\/p>\n

std::<\/span>vector<<\/span>int<\/span>><\/span> y;<\/span>
\n std::<\/span>vector<<\/span>double<\/span>><\/span> s;<\/span>
\n y.<\/span>reserve<\/span>(<\/span>labels.<\/span>size<\/span>(<\/span>)<\/span>)<\/span>;<\/span>
\n s.<\/span>reserve<\/span>(<\/span>scores.<\/span>size<\/span>(<\/span>)<\/span>)<\/span>;<\/span>
\n for<\/span> (<\/span>std::<\/span>size_t i =<\/span> 0<\/span>;<\/span> i <<\/span> labels.<\/span>size<\/span>(<\/span>)<\/span>;<\/span> ++<\/span>i)<\/span> {<\/span>
\n if<\/span> (<\/span>opt.<\/span>nan_flag ==<\/span> "omitan"<\/span> &&<\/span> !<\/span>std::<\/span>isfinite<\/span>(<\/span>scores[<\/span>i]<\/span>)<\/span>)<\/span> continue<\/span>;<\/span>
\n y.<\/span>push_back<\/span>(<\/span>labels[<\/span>i]<\/span>)<\/span>;<\/span>
\n s.<\/span>push_back<\/span>(<\/span>scores[<\/span>i]<\/span>)<\/span>;<\/span>
\n }<\/span>
\n if<\/span> (<\/span>y.<\/span>empty<\/span>(<\/span>)<\/span>)<\/span>
\n throw<\/span> std::<\/span>invalid_argument<\/span>(<\/span>"no valid samples after NaN handling"<\/span>)<\/span>;<\/span><\/p>\n

std::<\/span>vector<<\/span>std::<\/span>size_t><\/span> idx<\/span>(<\/span>y.<\/span>size<\/span>(<\/span>)<\/span>)<\/span>;<\/span>
\n std::<\/span>iota<\/span>(<\/span>idx.<\/span>begin<\/span>(<\/span>)<\/span>,<\/span> idx.<\/span>end<\/span>(<\/span>)<\/span>,<\/span> 0<\/span>)<\/span>;<\/span>
\n std::<\/span>sort<\/span>(<\/span>idx.<\/span>begin<\/span>(<\/span>)<\/span>,<\/span> idx.<\/span>end<\/span>(<\/span>)<\/span>,<\/span>
\n [<\/span>&<\/span>]<\/span>(<\/span>std::<\/span>size_t a,<\/span> std::<\/span>size_t b)<\/span> {<\/span> return<\/span> s[<\/span>a]<\/span> ><\/span> s[<\/span>b]<\/span>;<\/span> }<\/span>)<\/span>;<\/span><\/p>\n

std::<\/span>vector<<\/span>int<\/span>><\/span> sorted_y<\/span>(<\/span>y.<\/span>size<\/span>(<\/span>)<\/span>)<\/span>;<\/span>
\n std::<\/span>vector<<\/span>double<\/span>><\/span> sorted_s<\/span>(<\/span>y.<\/span>size<\/span>(<\/span>)<\/span>)<\/span>;<\/span>
\n for<\/span> (<\/span>std::<\/span>size_t i =<\/span> 0<\/span>;<\/span> i <<\/span> idx.<\/span>size<\/span>(<\/span>)<\/span>;<\/span> ++<\/span>i)<\/span> {<\/span>
\n sorted_y[<\/span>i]<\/span> =<\/span> y[<\/span>idx[<\/span>i]<\/span>]<\/span>;<\/span>
\n sorted_s[<\/span>i]<\/span> =<\/span> s[<\/span>idx[<\/span>i]<\/span>]<\/span>;<\/span>
\n }<\/span><\/p>\n

ROCCurve curve =<\/span> detail::<\/span>compute_roc_points_from_sorted<\/span>(<\/span>sorted_y,<\/span> sorted_s)<\/span>;<\/span>
\n curve.<\/span>auc =<\/span> detail::<\/span>trapezoid_auc<\/span>(<\/span>curve.<\/span>fpr,<\/span> curve.<\/span>tpr)<\/span>;<\/span>
\n return<\/span> curve;<\/span>
\n}<\/span><\/p>\n

\/\/ ————————————————————<\/span>
\n\/\/ Bootstrap AUC \u7f6e\u4fe1\u533a\u95f4<\/span>
\n\/\/ ————————————————————<\/span>
\ninline<\/span> ConfidenceInterval bootstrap_auc_ci<\/span>(<\/span>
\n const<\/span> std::<\/span>vector<<\/span>int<\/span>><\/span>&<\/span> labels,<\/span>
\n const<\/span> std::<\/span>vector<<\/span>double<\/span>><\/span>&<\/span> scores,<\/span>
\n const<\/span> ROCMetricsOptions&<\/span> opt =<\/span> {<\/span>}<\/span>)<\/span>
\n{<\/span>
\n if<\/span> (<\/span>labels.<\/span>size<\/span>(<\/span>)<\/span> !=<\/span> scores.<\/span>size<\/span>(<\/span>)<\/span>)<\/span>
\n throw<\/span> std::<\/span>invalid_argument<\/span>(<\/span>"labels and scores must have same length"<\/span>)<\/span>;<\/span>
\n detail::<\/span>validate_labels<\/span>(<\/span>labels)<\/span>;<\/span>
\n if<\/span> (<\/span>labels.<\/span>empty<\/span>(<\/span>)<\/span>)<\/span> throw<\/span> std::<\/span>invalid_argument<\/span>(<\/span>"empty input"<\/span>)<\/span>;<\/span><\/p>\n

std::<\/span>mt19937 rng<\/span>(<\/span>opt.<\/span>seed ==<\/span> 0<\/span> ?<\/span> std::<\/span>random_device{<\/span>}<\/span>(<\/span>)<\/span> :<\/span> opt.<\/span>seed)<\/span>;<\/span>
\n std::<\/span>uniform_int_distribution<<\/span>std::<\/span>size_t><\/span> dist<\/span>(<\/span>0<\/span>,<\/span> labels.<\/span>size<\/span>(<\/span>)<\/span> –<\/span> 1<\/span>)<\/span>;<\/span><\/p>\n

std::<\/span>vector<<\/span>double<\/span>><\/span> boot_auc;<\/span>
\n boot_auc.<\/span>reserve<\/span>(<\/span>opt.<\/span>num_bootstrap)<\/span>;<\/span><\/p>\n

for<\/span> (<\/span>int<\/span> b =<\/span> 0<\/span>;<\/span> b <<\/span> opt.<\/span>num_bootstrap;<\/span> ++<\/span>b)<\/span> {<\/span>
\n std::<\/span>vector<<\/span>int<\/span>><\/span> by;<\/span>
\n std::<\/span>vector<<\/span>double<\/span>><\/span> bs;<\/span>
\n by.<\/span>reserve<\/span>(<\/span>labels.<\/span>size<\/span>(<\/span>)<\/span>)<\/span>;<\/span>
\n bs.<\/span>reserve<\/span>(<\/span>scores.<\/span>size<\/span>(<\/span>)<\/span>)<\/span>;<\/span>
\n for<\/span> (<\/span>std::<\/span>size_t i =<\/span> 0<\/span>;<\/span> i <<\/span> labels.<\/span>size<\/span>(<\/span>)<\/span>;<\/span> ++<\/span>i)<\/span> {<\/span>
\n std::<\/span>size_t k =<\/span> dist<\/span>(<\/span>rng)<\/span>;<\/span>
\n by.<\/span>push_back<\/span>(<\/span>labels[<\/span>k]<\/span>)<\/span>;<\/span>
\n bs.<\/span>push_back<\/span>(<\/span>scores[<\/span>k]<\/span>)<\/span>;<\/span>
\n }<\/span>
\n long<\/span> P =<\/span> 0<\/span>,<\/span> N =<\/span> 0<\/span>;<\/span>
\n for<\/span> (<\/span>int<\/span> y :<\/span> by)<\/span> (<\/span>y ==<\/span> 1<\/span> ?<\/span> P :<\/span> N)<\/span>++<\/span>;<\/span>
\n if<\/span> (<\/span>P ==<\/span> 0<\/span> ||<\/span> N ==<\/span> 0<\/span>)<\/span> continue<\/span>;<\/span>
\n double<\/span> a =<\/span> detail::<\/span>auc_mann_whitney<\/span>(<\/span>by,<\/span> bs)<\/span>;<\/span>
\n if<\/span> (<\/span>std::<\/span>isfinite<\/span>(<\/span>a)<\/span>)<\/span> boot_auc.<\/span>push_back<\/span>(<\/span>a)<\/span>;<\/span>
\n }<\/span><\/p>\n

if<\/span> (<\/span>boot_auc.<\/span>empty<\/span>(<\/span>)<\/span>)<\/span> return<\/span> {<\/span>}<\/span>;<\/span>
\n std::<\/span>sort<\/span>(<\/span>boot_auc.<\/span>begin<\/span>(<\/span>)<\/span>,<\/span> boot_auc.<\/span>end<\/span>(<\/span>)<\/span>)<\/span>;<\/span><\/p>\n

double<\/span> alpha =<\/span> 1.0<\/span> –<\/span> opt.<\/span>ci_level;<\/span>
\n ConfidenceInterval ci;<\/span>
\n ci.<\/span>level =<\/span> opt.<\/span>ci_level;<\/span>
\n ci.<\/span>lower =<\/span> detail::<\/span>quantile<\/span>(<\/span>boot_auc,<\/span> alpha \/<\/span> 2.0<\/span>)<\/span>;<\/span>
\n ci.<\/span>upper =<\/span> detail::<\/span>quantile<\/span>(<\/span>boot_auc,<\/span> 1.0<\/span> –<\/span> alpha \/<\/span> 2.0<\/span>)<\/span>;<\/span>
\n return<\/span> ci;<\/span>
\n}<\/span><\/p>\n

\/\/ ————————————————————<\/span>
\n\/\/ \u591a\u5206\u7c7b OVA<\/span>
\n\/\/ ————————————————————<\/span>
\nstruct<\/span> MulticlassROCMetrics<\/span> {<\/span>
\n std::<\/span>vector<<\/span>int<\/span>><\/span> class_names;<\/span>
\n std::<\/span>vector<<\/span>ROCCurve><\/span> roc_per_class;<\/span>
\n std::<\/span>vector<<\/span>double<\/span>><\/span> auc_per_class;<\/span>
\n std::<\/span>vector<<\/span>ConfidenceInterval><\/span> ci_per_class;<\/span>
\n double<\/span> macro_auc =<\/span> 0.0<\/span>;<\/span>
\n}<\/span>;<\/span><\/p>\n

inline<\/span> MulticlassROCMetrics compute_rocmetrics_multiclass<\/span>(<\/span>
\n const<\/span> std::<\/span>vector<<\/span>int<\/span>><\/span>&<\/span> true_labels,<\/span>
\n const<\/span> std::<\/span>vector<<\/span>std::<\/span>vector<<\/span>double<\/span>>><\/span>&<\/span> scores,<\/span>
\n const<\/span> std::<\/span>vector<<\/span>int<\/span>><\/span>&<\/span> class_names,<\/span>
\n const<\/span> ROCMetricsOptions&<\/span> opt =<\/span> {<\/span>}<\/span>)<\/span>
\n{<\/span>
\n const<\/span> std::<\/span>size_t N =<\/span> true_labels.<\/span>size<\/span>(<\/span>)<\/span>;<\/span>
\n const<\/span> std::<\/span>size_t K =<\/span> class_names.<\/span>size<\/span>(<\/span>)<\/span>;<\/span>
\n if<\/span> (<\/span>scores.<\/span>size<\/span>(<\/span>)<\/span> !=<\/span> N)<\/span>
\n throw<\/span> std::<\/span>invalid_argument<\/span>(<\/span>"scores row count must equal label count"<\/span>)<\/span>;<\/span>
\n for<\/span> (<\/span>const<\/span> auto<\/span>&<\/span> row :<\/span> scores)<\/span>
\n if<\/span> (<\/span>row.<\/span>size<\/span>(<\/span>)<\/span> !=<\/span> K)<\/span>
\n throw<\/span> std::<\/span>invalid_argument<\/span>(<\/span>"each score row must have K elements"<\/span>)<\/span>;<\/span><\/p>\n

MulticlassROCMetrics result;<\/span>
\n result.<\/span>class_names =<\/span> class_names;<\/span>
\n result.<\/span>roc_per_class.<\/span>resize<\/span>(<\/span>K)<\/span>;<\/span>
\n result.<\/span>auc_per_class.<\/span>resize<\/span>(<\/span>K)<\/span>;<\/span>
\n result.<\/span>ci_per_class.<\/span>resize<\/span>(<\/span>K)<\/span>;<\/span><\/p>\n

for<\/span> (<\/span>std::<\/span>size_t k =<\/span> 0<\/span>;<\/span> k <<\/span> K;<\/span> ++<\/span>k)<\/span> {<\/span>
\n int<\/span> pos_class =<\/span> class_names[<\/span>k]<\/span>;<\/span>
\n std::<\/span>vector<<\/span>int<\/span>><\/span> binary_y<\/span>(<\/span>N)<\/span>;<\/span>
\n std::<\/span>vector<<\/span>double<\/span>><\/span> binary_s<\/span>(<\/span>N)<\/span>;<\/span>
\n for<\/span> (<\/span>std::<\/span>size_t i =<\/span> 0<\/span>;<\/span> i <<\/span> N;<\/span> ++<\/span>i)<\/span> {<\/span>
\n binary_y[<\/span>i]<\/span> =<\/span> (<\/span>true_labels[<\/span>i]<\/span> ==<\/span> pos_class)<\/span> ?<\/span> 1<\/span> :<\/span> 0<\/span>;<\/span>
\n binary_s[<\/span>i]<\/span> =<\/span> scores[<\/span>i]<\/span>[<\/span>k]<\/span>;<\/span>
\n }<\/span><\/p>\n

long<\/span> P =<\/span> 0<\/span>,<\/span> Neg =<\/span> 0<\/span>;<\/span>
\n for<\/span> (<\/span>int<\/span> y :<\/span> binary_y)<\/span> (<\/span>y ==<\/span> 1<\/span> ?<\/span> P :<\/span> Neg)<\/span>++<\/span>;<\/span>
\n if<\/span> (<\/span>P ==<\/span> 0<\/span> ||<\/span> Neg ==<\/span> 0<\/span>)<\/span> {<\/span>
\n result.<\/span>auc_per_class[<\/span>k]<\/span> =<\/span> std::<\/span>numeric_limits<\/span><<\/span>double<\/span>><\/span>::<\/span>quiet_NaN<\/span>(<\/span>)<\/span>;<\/span>
\n continue<\/span>;<\/span>
\n }<\/span><\/p>\n

result.<\/span>roc_per_class[<\/span>k]<\/span> =<\/span> compute_binary_roc<\/span>(<\/span>binary_y,<\/span> binary_s,<\/span> opt)<\/span>;<\/span>
\n result.<\/span>auc_per_class[<\/span>k]<\/span> =<\/span> result.<\/span>roc_per_class[<\/span>k]<\/span>.<\/span>auc;<\/span><\/p>\n

if<\/span> (<\/span>opt.<\/span>compute_bootstrap_ci)<\/span>
\n result.<\/span>ci_per_class[<\/span>k]<\/span> =<\/span> bootstrap_auc_ci<\/span>(<\/span>binary_y,<\/span> binary_s,<\/span> opt)<\/span>;<\/span>
\n }<\/span><\/p>\n

double<\/span> sum =<\/span> 0.0<\/span>;<\/span>
\n int<\/span> count =<\/span> 0<\/span>;<\/span>
\n for<\/span> (<\/span>double<\/span> a :<\/span> result.<\/span>auc_per_class)<\/span> {<\/span>
\n if<\/span> (<\/span>std::<\/span>isfinite<\/span>(<\/span>a)<\/span>)<\/span> {<\/span> sum +=<\/span> a;<\/span> ++<\/span>count;<\/span> }<\/span>
\n }<\/span>
\n result.<\/span>macro_auc =<\/span> count ><\/span> 0<\/span> ?<\/span> sum \/<\/span> count :<\/span> 0.0<\/span>;<\/span>
\n return<\/span> result;<\/span>
\n}<\/span><\/p>\n

}<\/span> \/\/ namespace rocmetrics<\/span><\/p>\n

\/\/ ————————————————————<\/span>
\n\/\/ main<\/span>
\n\/\/ ————————————————————<\/span>
\nint<\/span> main<\/span>(<\/span>)<\/span> {<\/span>
\n \/\/ —- \u4e8c\u5206\u7c7b\u793a\u4f8b —-<\/span>
\n std::<\/span>vector<<\/span>int<\/span>><\/span> labels =<\/span> {<\/span>0<\/span>,<\/span> 1<\/span>,<\/span> 0<\/span>,<\/span> 1<\/span>,<\/span> 1<\/span>,<\/span> 0<\/span>,<\/span> 1<\/span>,<\/span> 0<\/span>}<\/span>;<\/span>
\n std::<\/span>vector<<\/span>double<\/span>><\/span> scores =<\/span> {<\/span>0.1<\/span>,<\/span> 0.8<\/span>,<\/span> 0.3<\/span>,<\/span> 0.7<\/span>,<\/span> 0.9<\/span>,<\/span> 0.2<\/span>,<\/span> 0.6<\/span>,<\/span> 0.4<\/span>}<\/span>;<\/span><\/p>\n

rocmetrics::<\/span>ROCMetricsOptions opt;<\/span>
\n opt.<\/span>compute_bootstrap_ci =<\/span> true<\/span>;<\/span>
\n opt.<\/span>num_bootstrap =<\/span> 2000<\/span>;<\/span>
\n opt.<\/span>ci_level =<\/span> 0.95<\/span>;<\/span><\/p>\n

auto<\/span> curve =<\/span> rocmetrics::<\/span>compute_binary_roc<\/span>(<\/span>labels,<\/span> scores,<\/span> opt)<\/span>;<\/span><\/p>\n

std::<\/span>cout <<<\/span> "AUC = "<\/span> <<<\/span> curve.<\/span>auc <<<\/span> "\\\\n"<\/span>;<\/span>
\n std::<\/span>cout <<<\/span> "Threshold\\\\tFPR\\\\tTPR\\\\n"<\/span>;<\/span>
\n for<\/span> (<\/span>std::<\/span>size_t i =<\/span> 0<\/span>;<\/span> i <<\/span> curve.<\/span>thresholds.<\/span>size<\/span>(<\/span>)<\/span>;<\/span> ++<\/span>i)<\/span> {<\/span>
\n std::<\/span>cout <<<\/span> curve.<\/span>thresholds[<\/span>i]<\/span> <<<\/span> "\\\\t"<\/span>
\n <<<\/span> curve.<\/span>fpr[<\/span>i]<\/span> <<<\/span> "\\\\t"<\/span>
\n <<<\/span> curve.<\/span>tpr[<\/span>i]<\/span> <<<\/span> "\\\\n"<\/span>;<\/span>
\n }<\/span><\/p>\n

auto<\/span> ci =<\/span> rocmetrics::<\/span>bootstrap_auc_ci<\/span>(<\/span>labels,<\/span> scores,<\/span> opt)<\/span>;<\/span>
\n std::<\/span>cout <<<\/span> "95% CI: ["<\/span> <<<\/span> ci.<\/span>lower <<<\/span> ", "<\/span> <<<\/span> ci.<\/span>upper <<<\/span> "]\\\\n"<\/span>;<\/span><\/p>\n

\/\/ —- \u591a\u5206\u7c7b OVA \u793a\u4f8b —-<\/span>
\n std::<\/span>vector<<\/span>int<\/span>><\/span> y_multi =<\/span> {<\/span>0<\/span>,<\/span> 1<\/span>,<\/span> 2<\/span>,<\/span> 0<\/span>,<\/span> 1<\/span>,<\/span> 2<\/span>,<\/span> 0<\/span>,<\/span> 1<\/span>,<\/span> 2<\/span>,<\/span> 0<\/span>}<\/span>;<\/span>
\n std::<\/span>vector<<\/span>std::<\/span>vector<<\/span>double<\/span>>><\/span> s_multi =<\/span> {<\/span>
\n {<\/span>0.7<\/span>,<\/span> 0.2<\/span>,<\/span> 0.1<\/span>}<\/span>,<\/span> {<\/span>0.1<\/span>,<\/span> 0.8<\/span>,<\/span> 0.1<\/span>}<\/span>,<\/span> {<\/span>0.1<\/span>,<\/span> 0.2<\/span>,<\/span> 0.7<\/span>}<\/span>,<\/span>
\n {<\/span>0.6<\/span>,<\/span> 0.3<\/span>,<\/span> 0.1<\/span>}<\/span>,<\/span> {<\/span>0.2<\/span>,<\/span> 0.7<\/span>,<\/span> 0.1<\/span>}<\/span>,<\/span> {<\/span>0.1<\/span>,<\/span> 0.1<\/span>,<\/span> 0.8<\/span>}<\/span>,<\/span>
\n {<\/span>0.8<\/span>,<\/span> 0.1<\/span>,<\/span> 0.1<\/span>}<\/span>,<\/span> {<\/span>0.1<\/span>,<\/span> 0.9<\/span>,<\/span> 0.0<\/span>}<\/span>,<\/span> {<\/span>0.0<\/span>,<\/span> 0.1<\/span>,<\/span> 0.9<\/span>}<\/span>,<\/span>
\n {<\/span>0.5<\/span>,<\/span> 0.4<\/span>,<\/span> 0.1<\/span>}<\/span>
\n }<\/span>;<\/span>
\n std::<\/span>vector<<\/span>int<\/span>><\/span> classes =<\/span> {<\/span>0<\/span>,<\/span> 1<\/span>,<\/span> 2<\/span>}<\/span>;<\/span><\/p>\n

auto<\/span> multi =<\/span> rocmetrics::<\/span>compute_rocmetrics_multiclass<\/span>(<\/span>
\n y_multi,<\/span> s_multi,<\/span> classes,<\/span> opt)<\/span>;<\/span><\/p>\n

std::<\/span>cout <<<\/span> "Macro AUC = "<\/span> <<<\/span> multi.<\/span>macro_auc <<<\/span> "\\\\n"<\/span>;<\/span>
\n for<\/span> (<\/span>std::<\/span>size_t k =<\/span> 0<\/span>;<\/span> k <<\/span> classes.<\/span>size<\/span>(<\/span>)<\/span>;<\/span> ++<\/span>k)<\/span> {<\/span>
\n std::<\/span>cout <<<\/span> "Class "<\/span> <<<\/span> classes[<\/span>k]<\/span>
\n <<<\/span> " AUC = "<\/span> <<<\/span> multi.<\/span>auc_per_class[<\/span>k]<\/span> <<<\/span> "\\\\n"<\/span>;<\/span>
\n }<\/span>
\n return<\/span> 0<\/span>;<\/span>
\n}<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

\/\/ rocmetrics.cpp
\n\/\/ g -stdc17 -O2 -o rocmetrics rocmetrics.cpp#include
\n#include
\n#include
\n#include
\n#include
\n#include
\n#include
\n#include
\n#include
\n#inclu<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[55,1284,190,427],"topic":[],"class_list":["post-118864","post","type-post","status-publish","format-standard","hentry","category-server","tag-c","tag-matlab","tag-190","tag-427"],"yoast_head":"\nC++\u4ee3\u7801\u5b9e\u73b0MATLAB\u4e2d\u7684rocmetrics\u51fd\u6570\u529f\u80fd - \u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.wsisp.com\/helps\/118864.html\" \/>\n<meta property=\"og:locale\" content=\"zh_CN\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"C++\u4ee3\u7801\u5b9e\u73b0MATLAB\u4e2d\u7684rocmetrics\u51fd\u6570\u529f\u80fd - \u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3\" \/>\n<meta property=\"og:description\" content=\"\/\/ rocmetrics.cpp \/\/ g -stdc17 -O2 -o rocmetrics rocmetrics.cpp#include #include #include #include #include #include #include #include #include #inclu\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.wsisp.com\/helps\/118864.html\" \/>\n<meta property=\"og:site_name\" content=\"\u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3\" \/>\n<meta property=\"article:published_time\" content=\"2026-10-28T01:24:29+00:00\" \/>\n<meta name=\"author\" content=\"admin\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"\u4f5c\u8005\" \/>\n\t<meta name=\"twitter:data1\" content=\"admin\" \/>\n\t<meta name=\"twitter:label2\" content=\"\u9884\u8ba1\u9605\u8bfb\u65f6\u95f4\" \/>\n\t<meta name=\"twitter:data2\" content=\"8 \u5206\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/www.wsisp.com\/helps\/118864.html\",\"url\":\"https:\/\/www.wsisp.com\/helps\/118864.html\",\"name\":\"C++\u4ee3\u7801\u5b9e\u73b0MATLAB\u4e2d\u7684rocmetrics\u51fd\u6570\u529f\u80fd - 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