Added roc_point and compute_roc_curve().

dlib/statistics/lda.h

@@ -180,6 +180,55 @@ namespace dlib
         return std::make_pair((low_error+high_error)/2, thresh);
     }
 
+// ----------------------------------------------------------------------------------------
+
+    struct roc_point
+    {
+        double true_positive_rate;
+        double false_positive_rate;
+        double detection_threshold;
+    };
+
+    inline std::vector<roc_point> compute_roc_curve (
+        const std::vector<double>& true_detections,
+        const std::vector<double>& false_detections 
+    )
+    {
+        DLIB_CASSERT(true_detections.size() != 0);
+        DLIB_CASSERT(false_detections.size() != 0);
+
+        std::vector<std::pair<double,int> > temp;
+        temp.reserve(true_detections.size()+false_detections.size());
+        for (unsigned long i = 0; i < true_detections.size(); ++i)
+            temp.push_back(std::make_pair(true_detections[i], +1));
+        for (unsigned long i = 0; i < false_detections.size(); ++i)
+            temp.push_back(std::make_pair(false_detections[i], -1));
+
+        std::sort(temp.rbegin(), temp.rend());
+
+
+        std::vector<roc_point> roc_curve;
+        roc_curve.reserve(temp.size());
+
+        double num_false_included = 0;
+        double num_true_included = 0;
+        for (unsigned long i = 0; i < temp.size(); ++i)
+        {
+            if (temp[i].second > 0)
+                num_true_included++;
+            else
+                num_false_included++;
+
+            roc_point p;
+            p.true_positive_rate = num_true_included/true_detections.size();
+            p.false_positive_rate = num_false_included/false_detections.size();
+            p.detection_threshold = temp[i].first;
+            roc_curve.push_back(p);
+        }
+
+        return roc_curve;
+    }
+
 // ----------------------------------------------------------------------------------------
 
 }

dlib/statistics/lda_abstract.h

@@ -74,6 +74,41 @@ namespace dlib
             - returns make_pair(ERR,T) 
     !*/
 
+// ----------------------------------------------------------------------------------------
+
+    struct roc_point
+    {
+        double true_positive_rate;
+        double false_positive_rate;
+        double detection_threshold;
+    };
+
+    std::vector<roc_point> compute_roc_curve (
+        const std::vector<double>& true_detections,
+        const std::vector<double>& false_detections 
+    );
+    /*!
+        requires
+            - true_detections.size() != 0
+            - false_detections.size() != 0
+        ensures
+            - This function computes the ROC curve (receiver operating characteristic)
+              curve of the given data.  Therefore, we interpret true_detections as
+              containing detection scores for a bunch of true detections and
+              false_detections as detection scores from a bunch of false detections.  A
+              perfect detector would always give higher scores to true detections than to
+              false detections, resulting in a true positive rate of 1 and a false positive
+              rate of 0, for some appropriate detection threshold.
+            - Returns an array, ROC, such that:
+                - ROC.size() == true_detections.size()+false_detections.size()
+                - for all valid i:
+                    - If you were to accept all detections with a score >= ROC[i].detection_threshold 
+                      then you would obtain a true positive rate of ROC[i].true_positive_rate and a 
+                      false positive rate of ROC[i].false_positive_rate.
+                - ROC is ordered such that low detection rates come first.  That is, the
+                  curve is swept from a high detection threshold to a low threshold.
+    !*/
+
 // ----------------------------------------------------------------------------------------
 
 }