def pdist_squareformed_numpy(a):
    """
    Compute spatial distance using pure numpy
    (similar to scipy.spatial.distance.cdist())

    Thanks to Divakar Roy (@droyed) at stackoverflow.com

    Note this needs at least np.float64 precision!

    Returns: dist
    """
    a = np.array(a, dtype=np.float64)
    a_sumrows = np.einsum('ij,ij->i', a, a)
    dist = a_sumrows[:, None] + a_sumrows - 2 * np.dot(a, a.T)
    np.fill_diagonal(dist, 0)
    return dist
def pdist_squareformed_numpy ( a ) : a = np . array ( a , dtype = np . float64 ) a_sumrows = np . einsum ( 'ij,ij->i' , a , a ) dist = a_sumrows [ : , None ] + a_sumrows - 2 * np . dot ( a , a . T ) np . fill_diagonal ( dist , 0 ) return dist
Compute spatial distance using pure numpy
    (similar to scipy.spatial.distance.cdist())

    Thanks to Divakar Roy (@droyed) at stackoverflow.com

    Note this needs at least np.float64 precision!

    Returns: dist