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: smooth_knn_dist.m
    path of file: ./repos/CanlabCore/CanlabCore/External/umap/umap
    
    the code of the file until where you have to start completion: function [knn_dist, nn_dist] = smooth_knn_dist(distances, k, varargin)
%SMOOTH_KNN_DIST Compute a continuous version of the distance to the kth
% nearest neighbor. That is, this is similar to knn-distance but allows
% continuous k values rather than requiring an integral k. In esscence we
% are simply computing the distance such that the cardinality of fuzzy set
% we generate is k.
% 
% [knn_dist, nn_dist] = SMOOTH_KNN_DIST(distances, k, local_connectivity,
% n_iter, bandwidth)
% 
% Parameters
% ----------
% distances: array of size (n_samples, n_neighbors)
%     Distances to nearest neighbors for each samples. Each row should be a
%     sorted list of distances to a given sample's nearest neighbors.
% 
% k: double

    
function [knn_dist, nn_dist] = smooth_knn_dist(distances, k, varargin)
%SMOOTH_KNN_DIST Compute a continuous version of the distance to the kth
% nearest neighbor. That is, this is similar to knn-distance but allows
% continuous k values rather than requiring an integral k. In esscence we
% are simply computing the distance such that the cardinality of fuzzy set
% we generate is k.
% 
% [knn_dist, nn_dist] = SMOOTH_KNN_DIST(distances, k, local_connectivity,
% n_iter, bandwidth)
% 
% Parameters
% ----------
% distances: array of size (n_samples, n_neighbors)
%     Distances to nearest neighbors for each samples. Each row should be a
%     sorted list of distances to a given sample's nearest neighbors.
% 
% k: double
%     The number of nearest neighbors to approximate for.
% 
% n_iter: double (optional, default 64)
%     We need to binary search for the correct distance value. This is the
%     max number of iterations to use in such a search.
% 
% local_connectivity: double (optional, default 1)
%     The local connectivity required -- i.e. the number of nearest
%     neighbors that should be assumed to be connected at a local level.
%     The higher this value the more connected the manifold becomes
%     locally. In practice this should be not more than the local intrinsic
%     dimension of the manifold.
% 
% bandwidth: double (optional, default 1)
%     The target bandwidth of the kernel, larger values will produce
%     larger return values.
% 
% Returns
% -------
% knn_dist: array of size (n_samples, 1)
%     The distance to kth nearest neighbor, as suitably approximated.
% 
% nn_dist: array of size (n_samples, 1)
%     The distance to the 1st nearest neighbor for each point.
%
%   AUTHORSHIP
%   Math Lead & Primary Developer:  Connor Meehan <connor.gw.meehan@gmail.com>
%   Secondary Developer: Stephen Meehan <swmeehan@stanford.edu>
%   Bioinformatics Lead:  Wayne Moore <wmoore@stanford.edu>
%   Provided by the Herzenberg Lab at Stanford University 
%   License: BSD 3 clause
%
    p=parseArguments();
    parse(p,varargin{:});
    args=p.Results;
    local_connectivity = args.local_connectivity;
    n_iter = args.n_iter;
    bandwidth = args.bandwidth;
    same_set = args.same_set;
    
    SMOOTH_K_TOLERANCE = 1e-5;
    MIN_K_DIST_SCALE = 1e-3;
    
    height = size(distances,1);
    
    target = log2(k)*bandwidth;

    lo = zeros(height,1);
    hi = Inf(height,1);
    mid = ones(height,1);

    zero_dists = sum(distances == 0, 2);
    
    if any(zero_dists==size(distances,2))
        warning('There are at least n_neighbors identical data points in the raw data. Results may be inaccurate.');
    end