(X, i, metric)
return ds
Return the distance from the ith point in a Euclidean point cloud
    to the rest of the points
    Parameters
    ----------
    X: ndarray (n_samples, n_features)
        A numpy array of data 
    i: int
        The index of the point from which to return all distances
    metric: string or callable
        The metric to use when calculating distance between instances in a 
        feature array
Return the distance from the ith point in a Euclidean point cloud
    to the rest of the points
    Parameters
    ----------
    X: ndarray (n_samples, n_features)
        A numpy array of data 
    i: int
        The index of the point from which to return all distances
    metric: string or callable
        The metric to use when calculating distance between instances in a 
        feature array
Return the distance from the ith point in a Euclidean point cloud to the rest of the points Parameters ---------- X : ndarray ( n_samples n_features ) A numpy array of data i : int The index of the point from which to return all distances metric : string or callable The metric to use when calculating distance between instances in a feature array
def dpoint2pointcloud(X, i, metric):
    """
    Return the distance from the ith point in a Euclidean point cloud
    to the rest of the points
    Parameters
    ----------
    X: ndarray (n_samples, n_features)
        A numpy array of data 
    i: int
        The index of the point from which to return all distances
    metric: string or callable
        The metric to use when calculating distance between instances in a 
        feature array
    """
    ds = pairwise_distances(X, X[i, :][None, :], metric=metric).flatten()
    ds[i] = 0
    return ds
def dpoint2pointcloud ( X , i , metric ) : ds = pairwise_distances ( X , X [ i , : ] [ None , : ] , metric = metric ) . flatten ( ) ds [ i ] = 0 return ds