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: roc.m
    path of file: ./repos/panorama/lib/vlfeat-0.9.20/toolbox/noprefix
    
    the code of the file until where you have to start completion: function varargout = roc(varargin)
%VL_ROC   ROC curve.
%   [TPR,TNR] = VL_ROC(LABELS, SCORES) computes the Receiver Operating
%   Characteristic (ROC) curve [1]. LABELS is a row vector of ground
%   truth labels, greater than zero for a positive sample and smaller
%   than zero for a negative one. SCORES is a row vector of
%   corresponding sample scores, usually obtained from a
%   classifier. The scores induce a ranking of the samples where
%   larger scores should correspond to positive labels.
%
%   Without output arguments, the function plots the ROC graph of the
%   specified data in the current graphical axis.
%
%   Otherwise, the function returns the true positive and true
%   negative rates TPR and TNR. These are vectors of the same size of
%   LABELS and SCORES and are computed as follows. Samples are ranked
%   by decreasing scores, starting from rank 1. TPR(K) and TNR(K) are
%   the true positive and true negative rates when samples of rank
%   smaller or equal to K-1 are predicted to be positive. So for
%   example TPR(3) is the true positive rate when the two samples with
%   largest score are predicted to be positive. Similarly, TPR(1) is
%   the true positive rate when no samples are predicted to be
%   positive, i.e. the constant 0.
%
%   Setting a label to zero ignores the corresponding sample in the
%   calculations, as if the sample was removed from the data. Setting
%   the score of a sample to -INF causes the function to assume that
%   that sample was never retrieved. If there are samples with -INF
%   score, the ROC curve is incomplete as the maximum recall is less
%   than 1.
%
%   [TPR,TNR,INFO] = VL_ROC(...) returns an additional structure INFO
%   with the following fields:
%
%   info.auc:: Area under the ROC curve (AUC).
%     This is the area under the ROC plot, the parametric curve
%     (FPR(S), TPR(S)). The PLOT option can be used to plot variants
%     of this curve, which affects the calculation of a corresponding
%     AUC.
%
%   info.eer:: Equal error rate (EER).
%     The equal error rate is the value of FPR (or FNR) when the ROC

    
function varargout = roc(varargin)
%VL_ROC   ROC curve.
%   [TPR,TNR] = VL_ROC(LABELS, SCORES) computes the Receiver Operating
%   Characteristic (ROC) curve [1]. LABELS is a row vector of ground
%   truth labels, greater than zero for a positive sample and smaller
%   than zero for a negative one. SCORES is a row vector of
%   corresponding sample scores, usually obtained from a
%   classifier. The scores induce a ranking of the samples where
%   larger scores should correspond to positive labels.
%
%   Without output arguments, the function plots the ROC graph of the
%   specified data in the current graphical axis.
%
%   Otherwise, the function returns the true positive and true
%   negative rates TPR and TNR. These are vectors of the same size of
%   LABELS and SCORES and are computed as follows. Samples are ranked
%   by decreasing scores, starting from rank 1. TPR(K) and TNR(K) are
%   the true positive and true negative rates when samples of rank
%   smaller or equal to K-1 are predicted to be positive. So for
%   example TPR(3) is the true positive rate when the two samples with
%   largest score are predicted to be positive. Similarly, TPR(1) is
%   the true positive rate when no samples are predicted to be
%   positive, i.e. the constant 0.
%
%   Setting a label to zero ignores the corresponding sample in the
%   calculations, as if the sample was removed from the data. Setting
%   the score of a sample to -INF causes the function to assume that
%   that sample was never retrieved. If there are samples with -INF
%   score, the ROC curve is incomplete as the maximum recall is less
%   than 1.
%
%   [TPR,TNR,INFO] = VL_ROC(...) returns an additional structure INFO
%   with the following fields:
%
%   info.auc:: Area under the ROC curve (AUC).
%     This is the area under the ROC plot, the parametric curve
%     (FPR(S), TPR(S)). The PLOT option can be used to plot variants
%     of this curve, which affects the calculation of a corresponding
%     AUC.
%
%   info.eer:: Equal error rate (EER).
%     The equal error rate is the value of FPR (or FNR) when the ROC
%     curves intersects the line connecting (0,0) to (1,1).
%
%   info.eerThreshold:: EER threshold.
%     The value of the score for which the EER is attained.
%
%   VL_ROC() accepts the following options:
%
%   Plot:: []
%     Setting this option turns on plotting unconditionally. The
%     following plot variants are supported:
%
%     tntp:: Plot TPR against TNR (standard ROC plot).
%     tptn:: Plot TNR against TPR (recall on the horizontal axis).
%     fptp:: Plot TPR against FPR.
%     fpfn:: Plot FNR against FPR (similar to a DET curve).
%
%     Note that this option will affect the INFO.AUC value computation
%     too.
%
%   NumPositives:: []
%   NumNegatives:: []
%     If either of these parameters is set to a number, the function
%     pretend