Python 粒子滤波算法异常点检测与剔除

以下是使用粒子滤波算法找出并剔除异常点的 Python 代码：

import numpy as np

def particle_filter(x, y, t, num_particles=100, threshold=2):
    ' 初始化粒子 '
    particles = np.random.rand(num_particles, 3)
    particles[:, 0] *= np.max(x)
    particles[:, 1] *= np.max(y)
    particles[:, 2] *= np.max(t)

    ' 计算每个粒子的权重 '
    weights = np.zeros(num_particles)
    for i in range(num_particles):
        weights[i] = np.sqrt((particles[i, 0] - x)**2 + (particles[i, 1] - y)**2 + (particles[i, 2] - t)**2)

    ' 根据权重排序粒子 '
    sorted_indices = np.argsort(weights)
    particles = particles[sorted_indices]
    weights = weights[sorted_indices]

    ' 计算异常点的阈值 '
    threshold = np.percentile(weights, threshold)

    ' 剔除异常点 '
    filtered_particles = particles[weights <= threshold]

    return filtered_particles[:, 0], filtered_particles[:, 1], filtered_particles[:, 2]

x = [0.0, 1.0, 3.0, 4.0, 7.0, 8.0, 13.0, 9.0, 11.0, 11.0]
y = [0.0, 2.0, 5.0, 5.0, 5.0, 4.0, 20.0, 3.0, 1.0, 0.0]
t = [0.0, 0.1, 0.3, 0.4, 0.7, 0.8, 0.9, 1.0, 1.2, 1.3]

filtered_x, filtered_y, filtered_t = particle_filter(x, y, t)

print('Filtered x:', filtered_x)
print('Filtered y:', filtered_y)
print('Filtered t:', filtered_t)

运行以上代码，将得到剔除异常点后的x坐标、y坐标和时间。