(x, y, sigma=_mock_ones)
return np.sqrt(result)
r"""Euclidean distance standardised against a vector of standard
    deviations per coordinate.

    ..math::
        D(x, y) = \sqrt{\sum_i \frac{(x_i - y_i)**2}{v_i}}
r"""Euclidean distance standardised against a vector of standard
    deviations per coordinate.
r Euclidean distance standardised against a vector of standard deviations per coordinate .
def standardised_euclidean(x, y, sigma=_mock_ones):
    r"""Euclidean distance standardised against a vector of standard
    deviations per coordinate.

    ..math::
        D(x, y) = \sqrt{\sum_i \frac{(x_i - y_i)**2}{v_i}}
    """
    result = 0.0
    for i in range(x.shape[0]):
        result += ((x[i] - y[i]) ** 2) / sigma[i]

    return np.sqrt(result)
def standardised_euclidean ( x , y , sigma = _mock_ones ) : result = 0.0 for i in range ( x . shape [ 0 ] ) : result += ( ( x [ i ] - y [ i ] ) ** 2 ) / sigma [ i ] return np . sqrt ( result )