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: CalcObjVal.m
    path of file: ./repos/Projects/Clustering
    
    the code of the file until where you have to start completion: function [ objVal ] = CalcObjVal( mD, vMedoidIdx )
% ----------------------------------------------------------------------------------------------- %
%[ vClusterIdx, vMedoidIdx ] = ClusterLpStability( mD, paramMu, debugMode )
% Clusters data according to its Distance Matrix by solving the relaxation
% of the Integer Linear Programming of Clustering with regularization on
% the number of clusters.
% Input:
%   - mD            -   Distance Matrix.
%                       The distance matrix of the data. The 'mD(ii, jj)'
%                       elements represent the distance between the 'ii'
%                       and the 'jj' samples. The matrix is assumed to be
%                       Symmetric.
%                       Structure: Matrix (numSamples x numSamples).
%                       Type: 'Single' / 'Double'.
%                       Range: [0, inf).
%   - paramMu       -   Parameter Mu.
%                       Scales the Median of distances in order to set the
%                       penalty / regularization on the number of clusters
%                       / medoids. The method use the penalty to measure
%                       the stability of the clusters. Hence it shouldn't
%                       be to sensitive to this.
%                       Structure: Scalar.
%                       Type: 'Single' / 'Double'.
%                       Range: (0, inf).
%   - maxNumMedoids -   Maximum Number of Medoids.
%                       Scales the maximum number of medoids. In case
%                       'paramMu' is low enough this will be the number of
%                       medoids (The K most dominant medoids).
%                       Structure: Scalar.
%                       Type: 'Single' / 'Double'.

    
function [ objVal ] = CalcObjVal( mD, vMedoidIdx )
% ----------------------------------------------------------------------------------------------- %
%[ vClusterIdx, vMedoidIdx ] = ClusterLpStability( mD, paramMu, debugMode )
% Clusters data according to its Distance Matrix by solving the relaxation
% of the Integer Linear Programming of Clustering with regularization on
% the number of clusters.
% Input:
%   - mD            -   Distance Matrix.
%                       The distance matrix of the data. The 'mD(ii, jj)'
%                       elements represent the distance between the 'ii'
%                       and the 'jj' samples. The matrix is assumed to be
%                       Symmetric.
%                       Structure: Matrix (numSamples x numSamples).
%                       Type: 'Single' / 'Double'.
%                       Range: [0, inf).
%   - paramMu       -   Parameter Mu.
%                       Scales the Median of distances in order to set the
%                       penalty / regularization on the number of clusters
%                       / medoids. The method use the penalty to measure
%                       the stability of the clusters. Hence it shouldn't
%                       be to sensitive to this.
%                       Structure: Scalar.
%                       Type: 'Single' / 'Double'.
%                       Range: (0, inf).
%   - maxNumMedoids -   Maximum Number of Medoids.
%                       Scales the maximum number of medoids. In case
%                       'paramMu' is low enough this will be the number of
%                       medoids (The K most dominant medoids).
%                       Structure: Scalar.
%                       Type: 'Single' / 'Double'.
%                       Range: (0, inf).
%   - debugMode     -   Debug Mode.
%                       Sets Debug Mode which prints to screen the
%                       information about the Margin between the Primal and
%                       Dual problems and finding new cluster.
%                       Structure: Scalar.
%                       Type: 'Single' / 'Double'.
%                       Range: {0, 1}.
% Output:
%   - vClusterIdx   -   Cluster Index.
%                       Describe per sample to which cluster it was
%                       assigned. If 'vClusterIdx(ii) = k' the the 'ii'
%                       samples is assigned to the 'k' cluster represented
%                       by the 'k' sample which is a medoid.
%                       Number of clusters is 'length(unique(vClusterIdx))'
%                       or 'length(vMedoidIdx)'.
%                       Structure: Vector (numSamples x 1).
%                       Type: 'Single' / 'Double'.
%                       Range: {1, 2, ..., numSamples}.
%   - vMedoidIdx    -   Medoid Index.
%                       Vector of samples indices which are assigned as
%                       medoids. If 'vMedoidIdx(ii) = jj' it means that the
%                       'jj' sample is the medoid of the cluster it was
%                       assigned to.
%                       Structure: Vector (numClusters x 1).
%                       Type: 'Single' / 'Double'.
%                       Range: {1, 2, ..., numSamples}.
% References:
%   1.  Nikos Komodakis - Clustering via LP Based Stabilities (NIPS 2009).
% Remarks:
%   1.  V
% TODO:
%   1.  C
% Release Notes:
%   -   1.0.000     04/09/2020  Or Yair
%       *   First release version.
% ----------------------------------------------------------------------------------------------- %

arguments
    mD {mustBeNumeric, mustBeReal, mustBeSquareMatrix}
    vMedoidIdx {mustBeNumeric, mustBeReal, mustBePositive}
end