impl.py 文件源码

def numba_kde_multithread(eval_points, samples, bandwidths):
    result = np.zeros_like(eval_points)

    # SPEEDTIP: Parallelize over evaluation points with prange()
    for i in numba.prange(len(eval_points)):
        eval_x = eval_points[i]
        for sample, bandwidth in zip(samples, bandwidths):
            result[i] += gaussian((eval_x - sample) / bandwidth) / bandwidth
        result[i] /= len(samples)

    return result
#### END: numba_multithread