#LOF异常检测算法主要用异常点的检测输出

from scipy.spatial.distance import cdist

import numpy as np

class LOF:

def __init__(self, data, k, epsilon=1.0):

self.data = data

self.k = k

self.epsilon = epsilon

self.N = self.data.shape[0]

def get_dist(self):

# 计算欧式距离矩阵

return cdist(self.data, self.data)

def _kdist(self, arr):

# 计算k距离

inds_sort = np.argsort(arr)

neighbor_ind = inds_sort[1:self.k + 1] # 邻域内点索引

return neighbor_ind, arr[neighbor_ind[-1]]

def get_rdist(self):

# 计算可达距离

dist = self.get_dist()

nei_kdist = np.apply_along_axis(self._kdist, 1, dist)

nei_inds, kdist = zip(*nei_kdist)

for i, k in enumerate(kdist):

ind = np.where(dist[i] < k) # 实际距离小于k距离，则可达距离为k距离

dist[i][ind] = k

return nei_inds, dist

def get_lrd(self, nei_inds, rdist):

# 计算局部可达密度

lrd = np.zeros(self.N)

for i, inds in enumerate(nei_inds):

s = 0

for j in inds:

s += rdist[j, i]

lrd[i] = self.k / s

return lrd

def run(self):

# 计算局部离群因子

nei_inds, rdist = self.get_rdist()

lrd = self.get_lrd(nei_inds, rdist)

score = np.zeros(self.N)

for i, inds in enumerate(nei_inds):

N = len(inds)

lrd_nei = sum(lrd[inds])

score[i] = lrd_nei / self.k / lrd[i]

return score, np.where(score > self.epsilon)[0]

if __name__ == ‘__main__‘:

np.random.seed(42)

import pandas as pd

data=pd.read_excel("finaldata.xlsx")

data=data.loc[:,["p1","p2","p3"]]

print(data)

x = np.random.normal(2, 1, size=(10000, 2))

print(x)

y = np.random.normal(5, 1, size=(20, 2))

print(y)

z = np.vstack((x, y))

print(z)

data=z

data=np.array(data)

lof = LOF(data,5, epsilon=3)

score, out_ind = lof.run()

outliers = data[out_ind]

import matplotlib.pyplot as plt

plt.scatter(data[:, 0], data[:, 1], color=‘b‘)

plt.scatter(outliers[:, 0], outliers[:, 1], color=‘r‘)

plt.show()

print(data)

print(outliers)

print(out_ind)

原文：https://www.cnblogs.com/Yanjy-OnlyOne/p/13367505.html