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143 lines
6.8 KiB
143 lines
6.8 KiB
import numpy as np
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import time
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import copy
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from AANN_Derivative import AANN_Derivative
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from AANN_Fit import AANN_Fit
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def AANN_SF_SBSRB(fault_sample, fault_magnitude, v1, v2, w1, w2, sigma, mindata, maxdata):
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"""
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前向后向序列
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:param fault_sample:
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:param fault_magnitude:
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:param model:
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:return:
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"""
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# print(type(fault_sample))
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a = 0
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[m, n] = fault_sample.shape
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isolated = np.zeros((m, n))
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delt_ff=np.zeros((m, n))
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detected = 0 # 检测出故障样本数量
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rod_n = 0
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mdata = (2*fault_sample-np.tile(maxdata, (m, 1))-np.tile(mindata, (m, 1)))/(np.tile(maxdata, (m, 1))-np.tile(mindata, (m, 1)))
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time1 = int(round(time.time()*1000))
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confidence = 2*sigma
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index = []
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nodenum_record = np.zeros(m)
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spe = np.zeros(m)
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for sample_id in range(m):
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# output = model(mdata[sample_id,:])
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if sample_id==225:
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print(1)
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output = AANN_Fit(mdata[sample_id, :], v1, v2, w1, w2)
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index.append(np.sum((mdata[sample_id, :]-output)*(mdata[sample_id, :]-output)))
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spe[sample_id] = index[sample_id]
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if index[sample_id] > sigma:
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vnum = 1
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detected += 1
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node_num = 0
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# 前向序列
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variables = [i for i in range(n)]
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selected = np.zeros((n, 1))
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selected_v_num = -1
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while selected_v_num <= n:
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selected_variables = [variables[i] for i in (np.where(selected == 1)[0])]
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unselected_variables = [variables[i] for i in (np.where(selected == 0)[0])]
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cur_rbindex = np.zeros(len(unselected_variables))
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for v_id in range(len(unselected_variables)):
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cur_fault_variables = copy.deepcopy(selected_variables)
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cur_fault_variables.append(unselected_variables[v_id])
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cur_fault_variables = np.array(cur_fault_variables).astype(np.float64)
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cur_rbindex[v_id], B, C, SPE = AANN_Derivative(v1.astype(np.float64), v2.astype(np.float64), w1.astype(np.float64), w2.astype(np.float64), mdata[sample_id, :], cur_fault_variables)
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node_num += node_num
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min_rbindex, op_vc_id = min(cur_rbindex), np.argmin(cur_rbindex)
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selected[unselected_variables[op_vc_id]] = 1
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if min_rbindex < confidence:
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isolated_variable = [variables[i] for i in (np.where(selected == 1)[0])]#有问题
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isolated[sample_id, isolated_variable] = 1
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break
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else:
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selected_v_num += 1
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# 后向序列
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remained_v_num = len(np.where(selected == 1)[0])
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while remained_v_num > 1:
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remained_variables = [variables[i] for i in (np.where(selected == 1)[0])]
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cur_rbindex = np.zeros(len(remained_variables))
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for v_id in range(len(remained_variables)):
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cur_fault_variables = copy.deepcopy(remained_variables)
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cur_fault_variables = np.delete(cur_fault_variables, v_id)
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cur_fault_variables = np.array(cur_fault_variables).astype(np.float64)
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cur_rbindex[v_id], B, C, SPE = AANN_Derivative(v1, v2, w1, w2, mdata[sample_id, :], cur_fault_variables)
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node_num += node_num
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min_rbindex, op_vc_id = min(cur_rbindex), np.argmin(cur_rbindex)
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if min_rbindex > confidence:
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isolated_variable = [variables[i] for i in (np.where(selected == 1)[0])] # 有问题
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isolated[sample_id, :] = 0
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isolated[sample_id, isolated_variable] = 1
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isolated_variable = np.array(isolated_variable).astype(np.float64)
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A, delt_ff[sample_id,:], C, D = AANN_Derivative(v1, v2, w1, w2, mdata[sample_id, :],
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isolated_variable)
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# spe[sample_id] = min_rbindex
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break
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else:
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selected[remained_variables[op_vc_id], :] = 0
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remained_v_num -= 1
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# 后向序列结束
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nodenum_record[sample_id] = node_num
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false_alarm = np.where((((fault_magnitude[sample_id, :]>0).astype('int')-isolated[sample_id, :]) == -1) == True)
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print('sample_id-->{};false alarm:{}'.format(sample_id, false_alarm))
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maxvalue1, maxindex1 = max(isolated[sample_id, :]), np.argmax(isolated[sample_id, :])
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maxvalue2, maxindex2 = max(abs(fault_magnitude[sample_id, :])), np.argmax(abs(fault_magnitude[sample_id, :]))
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if maxindex1 == maxindex2:
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rod_n +=1
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time2 = int(round(time.time()*1000))
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tc = time2-time1
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real_fault = (abs(fault_magnitude) > 0).astype('int')
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fdr = np.sum(((real_fault-(isolated == 0).astype('int')) == 1).astype('int'))/np.sum(real_fault)
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far = np.sum(((real_fault-isolated) == -1).astype('int'))/np.sum((real_fault == 0).astype('int'))
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#fdr_variable, far_variable
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# fdr_variable = []
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# far_variable = []
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# for i in range(real_fault.shape[1]):
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# if np.sum(real_fault[:,i]) != 0:
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# fdr_variable.append(np.sum(((real_fault[:,i]-(isolated[:,i] == 0).astype('int')) == 1).astype('int'))/np.sum(real_fault[:,i]))*100
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# else:
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# fdr_variable.append(0)
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# if np.sum((real_fault[:,i] == 0).astype('int')) != 0:
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# far_variable.append(np.sum(((real_fault[:,i]-isolated[:,i]) == -1).astype('int'))/np.sum((real_fault[:,i] == 0).astype('int')))*100
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# else:
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# far_variable.append(0)
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fdr_variable = fdr
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far_variable = far
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delt_ff=delt_ff/2*(np.tile(maxdata, (m, 1))-np.tile(mindata, (m, 1))) #对偏差值进行反归一化
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Reconstruction_precision=np.sum(np.average(abs(fault_magnitude-delt_ff), axis=0))
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#为AANN_test_offline准备,2021-6-16 rsj&lf
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Data_origin=fault_sample
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errorData = delt_ff #测量值-重构值
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reconData = Data_origin-errorData
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SPE_list = np.sum(errorData ** 2)
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R = 0
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for index in range(0, reconData.shape[1]):
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vector1 = Data_origin[:, index]
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vector2 = np.array(reconData)[:, index]
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R += np.dot(vector1, vector2.T) / (np.sqrt(np.sum(vector1 ** 2)) * np.sqrt(np.sum(vector2 ** 2)))
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R /= reconData.shape[1]
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items = [('reconData', reconData.tolist())
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, ('errorData', errorData.tolist()), ('R', R.tolist()), ('SPE', SPE_list), ('FAI', SPE_list),
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('paraState', paraState.tolist())]
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ReconRes=json.dumps(dict(items)) # json.dumps(result)
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# 为AANN_test_offline准备
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dr = detected/np.sum((np.sum(real_fault, axis=1) > 0).astype('int'))*100
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rod = rod_n/detected*100
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nodes = np.mean(nodenum_record)
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return delt_ff,fdr,far,fdr_variable,far_variable,Reconstruction_precision,spe,ReconRes
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