def median_min_distance(data, metric):
    """This function computes a graph of nearest-neighbors for each sample point in
        'data' and returns the median of the distribution of distances between those
        nearest-neighbors, the distance metric being specified by 'metric'.
    
    Parameters
    ----------
    data : array of shape (n_samples, n_features)
        The data-set, a fraction of whose sample points will be extracted
        by density sampling.
    
    metric : string
        The distance metric used to determine the nearest-neighbor to each data-point.
        The DistanceMetric class defined in scikit-learn's library lists all available
        metrics.
    
    Returns
    -------
    median_min_dist : float
        The median of the distribution of distances between nearest-neighbors.
    """

    data = np.atleast_2d(data)
    
    nearest_distances = kneighbors_graph(data, 1, mode = 'distance', metric = metric, include_self = False).data
 
    median_min_dist = np.median(nearest_distances, overwrite_input = True)

    return round(median_min_dist, 4)
def median_min_distance(data, metric):
    """This function computes a graph of nearest-neighbors for each sample point in
        'data' and returns the median of the distribution of distances between those
        nearest-neighbors, the distance metric being specified by 'metric'.
    
    Parameters
    ----------
    data : array of shape (n_samples, n_features)
        The data-set, a fraction of whose sample points will be extracted
        by density sampling.
    
    metric : string
        The distance metric used to determine the nearest-neighbor to each data-point.
        The DistanceMetric class defined in scikit-learn's library lists all available
        metrics.
    
    Returns
    -------
    median_min_dist : float
        The median of the distribution of distances between nearest-neighbors.
    """

    data = np.atleast_2d(data)
    
    nearest_distances = kneighbors_graph(data, 1, mode = 'distance', metric = metric, include_self = False).data
 
    median_min_dist = np.median(nearest_distances, overwrite_input = True)

    return round(median_min_dist, 4)
def median_min_distance ( data , metric ) : data = np . atleast_2d ( data ) nearest_distances = kneighbors_graph ( data , 1 , mode = 'distance' , metric = metric , include_self = False ) . data median_min_dist = np . median ( nearest_distances , overwrite_input = True ) return round ( median_min_dist , 4 )
This function computes a graph of nearest-neighbors for each sample point in
        'data' and returns the median of the distribution of distances between those
        nearest-neighbors, the distance metric being specified by 'metric'.
    
    Parameters
    ----------
    data : array of shape (n_samples, n_features)
        The data-set, a fraction of whose sample points will be extracted
        by density sampling.
    
    metric : string
        The distance metric used to determine the nearest-neighbor to each data-point.
        The DistanceMetric class defined in scikit-learn's library lists all available
        metrics.
    
    Returns
    -------
    median_min_dist : float
        The median of the distribution of distances between nearest-neighbors.
This function computes a graph of nearest - neighbors for each sample point in data and returns the median of the distribution of distances between those nearest - neighbors the distance metric being specified by metric . Parameters ---------- data : array of shape ( n_samples n_features ) The data - set a fraction of whose sample points will be extracted by density sampling . metric : string The distance metric used to determine the nearest - neighbor to each data - point . The DistanceMetric class defined in scikit - learn s library lists all available metrics . Returns ------- median_min_dist : float The median of the distribution of distances between nearest - neighbors .