meanshift_poinc_parallel.py

def __shift(points, sigma, beg, end):
    dists=dist_batch(points, beg, end)
    weights = gaussian(dists, sigma)
    expd_w=np.dot(weights.T, points)
    summed_weight=np.sum(weights,0)
    return expd_w/np.expand_dims(summed_weight,1), beg, end # we return also the position of the starting and ending vectors

def meanshift_parallel(points, batches)
    with concurrent.futures.ThreadPoolExecutor(max_workers=4) as executor:
        future_shifting = {executor.submit(__shift, points, sigma, d_beg, min(d_beg+batches, n_samples)):
                         d_beg for d_beg in range(0, n_samples, batches)} # the dictionary with the functions which are going to be executed
        for future in concurrent.futures.as_completed(future_shifting):   # for each function we wait for the execution
            points[future.result()[1]:future.result()[2]]=future.result()[0] # when a function is actually executed, we update the dataset with the results
    return points