You are an expert in writing code in many different languages. Your goal is to perform code completion for the following code, keeping in mind the rest of the code and the file meta data.
    Metadata details:
    filename: vl_pr.m
    path of file: ./repos/2016_person_re-ID/vlfeat/toolbox/plotop
    
    the code of the file until where you have to start completion: function [recall, precision, info] = vl_pr(labels, scores, varargin)
%VL_PR   Precision-recall curve.
%   [RECALL, PRECISION] = VL_PR(LABELS, SCORES) computes the
%   precision-recall (PR) curve. LABELS are the ground truth labels,
%   greather than zero for a positive sample and smaller than zero for
%   a negative one. SCORES are the scores of the samples obtained from
%   a classifier, where lager scores should correspond to positive
%   samples.
%
%   Samples are ranked by decreasing scores, starting from rank 1.
%   PRECISION(K) and RECALL(K) are the precison and recall when
%   samples of rank smaller or equal to K-1 are predicted to be
%   positive and the remaining to be negative. So for example
%   PRECISION(3) is the percentage of positive samples among the two
%   samples with largest score. PRECISION(1) is the precision when no
%   samples are predicted to be positive and is conventionally set to
%   the value 1.
%
%   Set to zero the lables of samples that should be ignored in the
%   evaluation. Set to -INF the scores of samples which are not
%   retrieved. If there are samples with -INF score, then the PR curve
%   may have maximum recall smaller than 1, unless the INCLUDEINF
%   option is used (see below). The options NUMNEGATIVES and
%   NUMPOSITIVES can be used to add additional surrogate samples with
%   -INF score (see below).
%
%   [RECALL, PRECISION, INFO] = VL_PR(...) returns an additional
%   structure INFO with the following fields:
%
%   info.auc::
%     The area under the precision-recall curve. If the INTERPOLATE
%     option is set to FALSE, then trapezoidal interpolation is used
%     to integrate the PR curve. If the INTERPOLATE option is set to
%     TRUE, then the curve is piecewise constant and no other
%     approximation is introduced in the calculation of the area. In
%     the latter case, INFO.AUC is the same as INFO.AP.
%
%   info.ap::
%     Average precision as defined by TREC. This is the average of the
%     precision observed each time a new positive sample is
%     recalled. In this calculation, any sample with -INF score
%     (unless INCLUDEINF is used) and any additional positive induced
%     by NUMPOSITIVES has precision equal to zero. If the INTERPOLATE
%     option is set to true, the AP is computed from the interpolated
%     precision and the result is the same as INFO.AUC. Note that AP
%     as defined by TREC normally does not use interpolation [1].
%
%   info.ap_interp_11::
%     11-points interpolated average precision as defined by TREC.
%     This is the average of the maximum precision for recall levels
%     greather than 0.0, 0.1, 0.2, ..., 1.0. This measure was used in
%     the PASCAL VOC challenge up to the 2008 edition.
%
%   info.auc_pa08::
%     Deprecated. It is the same of INFO.AP_INTERP_11.
%
%   VL_PR(...) with no output arguments plots the PR curve in the
%   current axis.

    
function [recall, precision, info] = vl_pr(labels, scores, varargin)
%VL_PR   Precision-recall curve.
%   [RECALL, PRECISION] = VL_PR(LABELS, SCORES) computes the
%   precision-recall (PR) curve. LABELS are the ground truth labels,
%   greather than zero for a positive sample and smaller than zero for
%   a negative one. SCORES are the scores of the samples obtained from
%   a classifier, where lager scores should correspond to positive
%   samples.
%
%   Samples are ranked by decreasing scores, starting from rank 1.
%   PRECISION(K) and RECALL(K) are the precison and recall when
%   samples of rank smaller or equal to K-1 are predicted to be
%   positive and the remaining to be negative. So for example
%   PRECISION(3) is the percentage of positive samples among the two
%   samples with largest score. PRECISION(1) is the precision when no
%   samples are predicted to be positive and is conventionally set to
%   the value 1.
%
%   Set to zero the lables of samples that should be ignored in the
%   evaluation. Set to -INF the scores of samples which are not
%   retrieved. If there are samples with -INF score, then the PR curve
%   may have maximum recall smaller than 1, unless the INCLUDEINF
%   option is used (see below). The options NUMNEGATIVES and
%   NUMPOSITIVES can be used to add additional surrogate samples with
%   -INF score (see below).
%
%   [RECALL, PRECISION, INFO] = VL_PR(...) returns an additional
%   structure INFO with the following fields:
%
%   info.auc::
%     The area under the precision-recall curve. If the INTERPOLATE
%     option is set to FALSE, then trapezoidal interpolation is used
%     to integrate the PR curve. If the INTERPOLATE option is set to
%     TRUE, then the curve is piecewise constant and no other
%     approximation is introduced in the calculation of the area. In
%     the latter case, INFO.AUC is the same as INFO.AP.
%
%   info.ap::
%     Average precision as defined by TREC. This is the average of the
%     precision observed each time a new positive sample is
%     recalled. In this calculation, any sample with -INF score
%     (unless INCLUDEINF is used) and any additional positive induced
%     by NUMPOSITIVES has precision equal to zero. If the INTERPOLATE
%     option is set to true, the AP is computed from the interpolated
%     precision and the result is the same as INFO.AUC. Note that AP
%     as defined by TREC normally does not use interpolation [1].
%
%   info.ap_interp_11::
%     11-points interpolated average precision as defined by TREC.
%     This is the average of the maximum precision for recall levels
%     greather than 0.0, 0.1, 0.2, ..., 1.0. This measure was used in
%     the PASCAL VOC challenge up to the 2008 edition.
%
%   info.auc_pa08::
%     Deprecated. It is the same of INFO.AP_INTERP_11.
%
%   VL_PR(...) with no output arguments plots the PR curve in the
%   current axis.
%
%   VL_PR() accepts the following options:
%
%   Interpolate:: false
%     If set to true, use interpolated precision. The interpolated
%     precision is defined as the maximum precision for a given recall
%     level and onwards. Here it is implemented as the culumative
%     maximum from low to high scores of the precision.
%
%   NumPositives:: []
%   NumNegatives:: []
%     If set to a number, pretend