euclidean distance is too slow

[yiitozer]

We might want to change the computation of euclidean_distance:

from sklearn.metrics.pairwise import euclidean_distances

#@jit(nopython=True)
def euclidean_distance(f1, f2, l2_meas_max=1.0, l2_meas_min=0.0):
    """Computes euclidean distances between the vectors in f1 and f2, and
    rescales the results to lie in the range [cos_meas_min, cos_meas_max]."""

    #S1 = np.zeros((f1.shape[1], f2.shape[1]))
    #for n in range(f2.shape[1]):
    #    S1[:, n] = np.sqrt(np.sum((f1.T - f2[:, n]) ** 2, axis=1))
    S1 = euclidean_distances(f1.T, f2.T)

    return S1 * (l2_meas_max - l2_meas_min) + l2_meas_min

[yiitozer]

Thank you @nctamer for pointing this out.