59ec6660464bd378b20d4ae200c7614133a9a38b,pyntcloud/samplers/points.py,FarthestPointsSampler,compute,#FarthestPointsSampler#,54

Before Change

        solution_set.append(remaining_points.pop(
            random.randint(0, len(remaining_points) - 1)))
        for _ in range(self.n - 1):
            distances = [self.cal_distance(p, solution_set[0]) for p in remaining_points]
            for i, p in enumerate(remaining_points):
                for j, s in enumerate(solution_set):
                    distances[i] = min(distances[i], self.cal_distance(p, s))
            solution_set.append(remaining_points.pop(distances.index(max(distances))))
        return pd.DataFrame(solution_set)

After Change

        remaining_points = self.points.values

        // the sampled points set as the return
        select_idx = np.random.randint(low=0, high=len(self.points))
        // to remain the shape as (1, n) instead of (n, )
        solution_set = remaining_points[select_idx: select_idx+1]
        remaining_points = np.delete(remaining_points, select_idx)

        for _ in range(self.n - 1):
            distance_sum = self.cal_distance(remaining_points, solution_set)
            select_idx = np.argmax(distance_sum)
            solution_set = np.concatenate([solution_set, remaining_points[select_idx:select_idx+1]], axis=0)
            remaining_points = np.delete(remaining_points, select_idx)

        return pd.DataFrame(solution_set, columns=self.points.columns)

Instances