meanshift_poinc_v1.py

def meanshift(data, sigma, steps):
    d1 = np.copy(data)                        # Need to copy the data, don't want to modify the originals
    for it in range(steps):                   # at each step
        for i, p in enumerate(d1):            # for each point
            dists = dist_poinc( p, d1)        # we calculate the distance from that point to all the other ones
            weights = gaussian(dists, sigma)  # then we weight those distances by our gaussian kernel
            d1[i] = (np.expand_dims(weights,1)*d1).sum(0) / weight.sum()     # and substitute the point with the weighted sum
    return d1

on line 7, should be weights.sum( )