You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
78 lines
3.3 KiB
78 lines
3.3 KiB
2 weeks ago
|
import numpy as np
|
||
|
|
import time
|
||
|
|
from AANN_Fit import AANN_Fit
|
||
|
|
import itertools
|
||
|
|
from AANN_Derivative import AANN_Derivative
|
||
|
|
|
||
|
|
|
||
|
|
def AANN_RB(fault_sample, fault_magnitude,model):
|
||
|
|
"""
|
||
|
|
:param fault_sample:
|
||
|
|
:param fault_magnitude:
|
||
|
|
:param model:
|
||
|
|
:return:
|
||
|
|
"""
|
||
|
|
[m, n] = fault_sample.shape
|
||
|
|
v1 = model.v1
|
||
|
|
v2 = model.v2
|
||
|
|
w1 = model.w1
|
||
|
|
w2 = model.w2
|
||
|
|
sigma = model.sigma
|
||
|
|
isolated = np.zeros((m, n))
|
||
|
|
detected = 0
|
||
|
|
rod_n = 0
|
||
|
|
mdata = (2*fault_sample-np.tile(model.maxdata, (m, 1))-np.tile(model.mindata, (m, 1)))/(np.tile(model.maxdata, (m, 1))-np.tile(model.mindata, (m, 1)))
|
||
|
|
# 数据正则化
|
||
|
|
mdata1 = (2 * (fault_sample-fault_magnitude) - np.tile(model.maxdata, (m, 1)) - np.tile(model.mindata, (m, 1))) / (
|
||
|
|
np.tile(model.maxdata, (m, 1)) - np.tile(model.mindata, (m, 1)))
|
||
|
|
# mdata = mdata/np.tile(pow(np.sum(pow(mdata1, 2), axis=1), 1/2), (n, 1)).T
|
||
|
|
time1 = time.time()*1000
|
||
|
|
index = np.zeros(m)
|
||
|
|
index2 = np.zeros(m)
|
||
|
|
index_v = np.zeros((m, n))
|
||
|
|
nodenum_record = np.zeros(m)
|
||
|
|
for sample_id in range(m):
|
||
|
|
output = AANN_Fit(mdata[sample_id, :], v1, v2, w1, w2)
|
||
|
|
output2 = AANN_Fit(mdata1[sample_id, :], v1, v2, w1, w2)
|
||
|
|
index[sample_id] = np.sum((mdata[sample_id, :]-output)*(mdata[sample_id, :]-output))
|
||
|
|
index2[sample_id] = np.sum((mdata1[sample_id, :] - output2) * (mdata1[sample_id, :] - output2))
|
||
|
|
index_v[sample_id, :] = (mdata[sample_id, :]-output)*(mdata[sample_id, :]-output)
|
||
|
|
if index[sample_id] > sigma:
|
||
|
|
vnum = 1
|
||
|
|
detected += 1
|
||
|
|
nodenum = 0
|
||
|
|
while vnum <= n:
|
||
|
|
variablecombination = itertools.combinations([i for i in range(n)], vnum)
|
||
|
|
combi_list =np.array(list(variablecombination))
|
||
|
|
cur_rbindex = np.zeros(combi_list.shape[0])
|
||
|
|
vc_id = 0
|
||
|
|
for v_combination in combi_list:
|
||
|
|
cur_fault_varibles =v_combination
|
||
|
|
cur_fault_variables = np.array(cur_fault_variables).astype(np.float64)
|
||
|
|
cur_rbindex[vc_id], B, C, spe = AANN_Derivative(v1, v2, w1, w2, mdata[sample_id, :], cur_fault_varibles)
|
||
|
|
nodenum = nodenum+1
|
||
|
|
vc_id = vc_id+1
|
||
|
|
min_rbindex, op_vc_id = np.min(cur_rbindex), np.argmin(cur_rbindex)
|
||
|
|
cur_vc = combi_list[op_vc_id, :]
|
||
|
|
if min_rbindex < sigma*2:
|
||
|
|
isolated[sample_id, combi_list[op_vc_id, :]] = 1
|
||
|
|
break
|
||
|
|
else:
|
||
|
|
vnum = vnum+1
|
||
|
|
nodenum_record[sample_id] = nodenum
|
||
|
|
maxindex1 = np.argmax(isolated[sample_id, :])
|
||
|
|
maxindex2 = np.argmax(np.abs(fault_magnitude[sample_id, :]))
|
||
|
|
if maxindex1 == maxindex2:
|
||
|
|
rod_n = rod_n+1
|
||
|
|
time2 = time.time() * 1000
|
||
|
|
tc = round(time2 - time1)
|
||
|
|
real_fault = (fault_magnitude > 0).astype('int')
|
||
|
|
fdr = np.sum(((real_fault - (isolated == 0).astype('int')) == 1).astype('int')) / np.sum(real_fault) * 100
|
||
|
|
far = np.sum(((real_fault - isolated) == -1).astype('int')) / np.sum((real_fault == 0).astype('int')) * 100
|
||
|
|
dr = detected / np.sum((np.sum(real_fault, axis=1) > 0).astype('int')) * 100
|
||
|
|
rod = rod_n / detected * 100
|
||
|
|
nodes = np.mean(nodenum_record)
|
||
|
|
|
||
|
|
return fdr, far, tc, dr, rod
|
||
|
|
|