def pca_U(channelDataList,
informations,
centroidList,
clusterAssment,
newDimension=1):
newChannelDataList = []
U2s = []
rates = np.array(np.zeros((len(channelDataList), 2)), dtype=complex)
# 为了输出，要把rates放到list中
rateList = []
# 计算变换矩阵
for i in range(len(centroidList)):
U2 = centroidList[i][:, 0:newDimension]
U2s.append(U2)
# 降维
for i in range(len(channelDataList)):
newChannelData = np.dot(channelDataList[i],
U2s[(int)(clusterAssment[i, 0].real)])
newChannelDataList.append(newChannelData)
newCovMatrixList = tools.getCovMatrixList(newChannelDataList)
newInformation = tools.getInformations(newCovMatrixList)[0]
for i in range(len(channelDataList)):
rate2 = newInformation[0][i] / informations[0][i]
rates[i, 1] = rate2
rateList.append(rates)
return newChannelDataList, newCovMatrixList, U2s, rateList
def elbowCore(channelDataAll, a, k, iRate, schedule):
n = np.shape(channelDataAll[0])[1] # 列数
p = len(channelDataAll) # 页数
sub = n >> a
rates_C = []
rates_U = []
rates_S = []
for g in range(1 << a):
# 显示进度
schedule[1] += 1
tmpSchedule = schedule[1]
print(u'共' + str(schedule[0]) + u'部分，' + u'第' + str(tmpSchedule) + u'部分开始！')
channelData = []
for h in range(p):
channelDataPage = channelDataAll[h]
channelData.append(channelDataPage[:, g * sub:(g + 1) * sub])
covMatrixList = tools.getCovMatrixList(channelData)
allCovMatrix = tools.matrixListToMatrix(covMatrixList)
# 对协方差进行聚类
centroids, clusterAssment = kmeans.KMeansOushi(allCovMatrix, k)
centroidList = tools.matrixToMatrixList(centroids)
# 计算原信道信息量、协方差矩阵特征值、变换矩阵
informations, SigmaList, UList = tools.getInformations(covMatrixList)
# 分析PCA效果,计算信息量保留程度
tmpRates = pca.pca(channelData, informations, centroidList, clusterAssment, iRate)[3][0][:, 1]
rates_C.append(np.mean(tmpRates))
# 对变换矩阵进行聚类
allU = tools.matrixListToMatrix_U(UList)
weights = tools.matrixListToMatrix_U(SigmaList)
centroids, clusterAssment = kmeans.KMeansOushi_U(allU, k, weights, iRate)
centroidList = tools.matrixToMatrixList_U(centroids)
# 分析PCA效果,计算信息量保留程度
tmpRates = pca.pca_U(channelData, informations, centroidList, clusterAssment, iRate)[3][0][:, 1]
rates_U.append(np.mean(tmpRates))
# 不聚类，直接PCA
tmpRates = pca.pca_S(SigmaList, iRate)[0][:, 1]
rates_S.append(np.mean(tmpRates))
# 显示进度
print(u'共' + str(schedule[0]) + u'部分，' + u'第' + str(tmpSchedule) + u'部分完成，' + u'已完成' + str(schedule[1]) + u'部分，' + u'完成度：' + '%.2f%%' % (schedule[1] / schedule[0] * 100) + u'！')
rate_C = np.mean(rates_C)
rate_U = np.mean(rates_U)
rate_S = np.mean(rates_S)
return rate_S.real, rate_C.real, rate_U.real
def clusterCore(channelData1, covMatrixList1, channelData2, centroids, centroidUList, type):
newChannelData1 = []
newChannelData2 = []
newDimension = np.shape(centroidUList[0])[1]
p = np.shape(channelData1)[0]
if type == "C":
# 计算信道相关系数矩阵并输出，然后放到一个矩阵中
allCovMatrix1 = tools.matrixListToMatrix(covMatrixList1)
# 确定每个数据分别属于哪个簇
clusterAssment = kmeans.getClusterAssment(allCovMatrix1, centroids)
# 变换域
for i in range(p):
newChannelData1.append(np.dot(channelData1[i], centroidUList[(int)(clusterAssment[i, 0].real)]))
newChannelData2.append(np.dot(channelData2[i], centroidUList[(int)(clusterAssment[i, 0].real)]))
if type == "U":
informations, SigmaList, UList = tools.getInformations(covMatrixList1)
allU = tools.matrixListToMatrix_U(UList)
weights = tools.matrixListToMatrix_U(SigmaList)
# 确定每个数据分别属于哪个簇
clusterAssment = kmeans.getClusterAssment_U(allU, weights, centroids, newDimension)
# 变换域
for i in range(p):
newChannelData1.append(np.dot(channelData1[i], centroidUList[(int)(clusterAssment[i, 0].real)]))
newChannelData2.append(np.dot(channelData2[i], centroidUList[(int)(clusterAssment[i, 0].real)]))
if type == "S":
covMatrixList2 = tools.getCovMatrixList(channelData2)
UList1 = tools.getInformations(covMatrixList1)[2]
UList2 = tools.getInformations(covMatrixList2)[2]
iRate = np.shape(centroidUList[0])[1]
# 变换域
for i in range(p):
newChannelData1.append(np.dot(channelData1[i], UList1[i][:, 0:iRate]))
newChannelData2.append(np.dot(channelData2[i], UList2[i][:, 0:iRate]))
# 输出处理后的信道数据
# path = u'/Users/jinruimeng/Downloads/keyan/'
# nowTime = time.strftime("%Y-%m-%d.%H.%M.%S", time.localtime(time.time()))
# pathSuffix = type + "_" + slice + "_" + nowTime
#
# outNewChannel1ListPath = path + "clusterAddNoise_outNewChannel1List_" + pathSuffix
# outNewChannel2ListPath = path + "clusterAddNoise_outNewChannel2List_" + pathSuffix
# readAndWriteDataSet.write(newChannelData1, outNewChannel1ListPath, ".xlsx")
# readAndWriteDataSet.write(newChannelData2, outNewChannel2ListPath, ".xlsx")
return newChannelData1, newChannelData2
def cluster(schedule, channelDataAll1, channelDataAll2, allCentroidsC, allCentroidUList, a, low, high, step):
inconsistencyRates_old = []
inconsistencyRates_new_noCom = []
inconsistencyRates_new = []
for g in range(1, (1 << a) + 1):
schedule[1] += 1
tmpSchedule = schedule[1]
print(u'共' + str(schedule[0]) + u'部分，' + u'第' + str(tmpSchedule) + u'部分开始！')
channelData1 = []
channelData2 = []
for i in range(np.shape(channelDataAll1)[0]):
channelData1.append(channelDataAll1[i][:, (g - 1) * sub:g * sub])
channelData2.append(channelDataAll2[i][:, (g - 1) * sub:g * sub])
# 计算信道协方差矩阵呢
covMatrixList1 = tools.getCovMatrixList(channelData1)
newChannelData01, newChannelData02 = clusterCore(channelData1, covMatrixList1, channelData2, allCentroidsC[g - 1], allCentroidUList[g - 1], "S")
newChannelData1, newChannelData2 = clusterCore(channelData1, covMatrixList1, channelData2, allCentroidsC[g - 1], allCentroidUList[g - 1], "C")
# 量化并计算不一致率
for i in range(low, high + 1, step):
bit_inconsistencyRates_old = []
bit_inconsistencyRates_new_noCom = []
bit_inconsistencyRates_new = []
for j in range(np.shape(channelData1)[0]):
oldKey1, oldKey2 = quantification.quantificate(channelData1[j], channelData2[j], i)
newKey01, newKey02 = quantification.quantificate(newChannelData01[j], newChannelData02[j], i)
newKey1, newKey2 = quantification.quantificate(newChannelData1[j], newChannelData2[j], i)
inconsistencyRate_old = quantification.getInconsistencyRate(oldKey1, oldKey2)
inconsistencyRate_new_noCom = quantification.getInconsistencyRate(newKey01, newKey02)
inconsistencyRate_new = quantification.getInconsistencyRate(newKey1, newKey2)
bit_inconsistencyRates_old.append(inconsistencyRate_old)
bit_inconsistencyRates_new_noCom.append(inconsistencyRate_new_noCom)
bit_inconsistencyRates_new.append(inconsistencyRate_new)
if g == 1:
inconsistencyRates_old.append(mean(bit_inconsistencyRates_old))
inconsistencyRates_new_noCom.append(mean(bit_inconsistencyRates_new_noCom))
inconsistencyRates_new.append(mean(bit_inconsistencyRates_new))
else:
inconsistencyRates_old[i - low] += mean(bit_inconsistencyRates_old)
inconsistencyRates_new_noCom[i - low] += mean(bit_inconsistencyRates_new_noCom)
inconsistencyRates_new[i - low] += mean(bit_inconsistencyRates_new)
# 显示进度
print(u'共' + str(schedule[0]) + u'部分，' + u'第' + str(tmpSchedule) + u'部分完成，' + u'已完成' + str(schedule[1]) + u'部分，' + u'完成度：' + '%.2f%%' % (schedule[1] / schedule[0] * 100) + u'！')
for g in range(np.shape(inconsistencyRates_old)[0]):
inconsistencyRates_old[g] = inconsistencyRates_old[g] / (1 << a)
inconsistencyRates_new_noCom[g] = inconsistencyRates_new_noCom[g] / (1 << a)
inconsistencyRates_new[g] = inconsistencyRates_new[g] / (1 << a)
return inconsistencyRates_old, inconsistencyRates_new_noCom, inconsistencyRates_new
def cluster(a, schedule, channelDataAll1, channelDataAll2, allCentroidsC, allCentroidUList, allCentroidsU,
allCentroidUList2):
newPca1 = []
newPca2 = []
newC1 = []
newC2 = []
newU1 = []
newU2 = []
newWt1 = []
newWt2 = []
for g in range(1, (1 << a) + 1):
schedule[1] += 1
tmpSchedule = schedule[1]
print(u'共' + str(schedule[0]) + u'部分，' + u'第' + str(tmpSchedule) + u'部分开始！')
channelData1 = []
channelData2 = []
for i in range(np.shape(channelDataAll1)[0]):
channelData1.append(channelDataAll1[i][:, (g - 1) * sub:g * sub])
channelData2.append(channelDataAll2[i][:, (g - 1) * sub:g * sub])
# 计算信道协方差矩阵
covMatrixList1 = tools.getCovMatrixList(channelData1)
# 无交互PCA
tmpNewPca1, tmpNewPca2 = clusterCore(channelData1, covMatrixList1, channelData2, allCentroidsC[g - 1],
allCentroidUList[g - 1], "general")
newPca1.append(tmpNewPca1)
newPca2.append(tmpNewPca2)
# 聚类协方差矩阵
tmpNewC1, tmpNewC2 = clusterCore(channelData1, covMatrixList1, channelData2, allCentroidsC[g - 1],
allCentroidUList[g - 1], "C")
newC1.append(tmpNewC1)
newC2.append(tmpNewC2)
# 聚类变换矩阵
tmpNewU1, tmpNewU2 = clusterCore(channelData1, covMatrixList1, channelData2, allCentroidsU[g - 1],
allCentroidUList2[g - 1], "U")
newU1.append(tmpNewU1)
newU2.append(tmpNewU2)
# DCT变换
tmpNewWt1, tmpNewWt2 = clusterCore(channelData1, covMatrixList1, channelData2, allCentroidsU[g - 1],
allCentroidUList2[g - 1], "wt")
newWt1.append(tmpNewWt1)
newWt2.append(tmpNewWt2)
# 显示进度
print(u'共' + str(schedule[0]) + u'部分，' + u'第' + str(tmpSchedule) + u'部分完成，' + u'已完成' + str(schedule[1]) + u'部分，' + u'完成度：' + '%.2f%%' % (schedule[1] / schedule[0] * 100) + u'！')
return newPca1, newPca2, newC1, newC2, newU1, newU2, newWt1, newWt2
def elbow2(channelDataAll, low, high, step, a, schedule):
# 检查参数合理性
if low <= 0:
print(u'下限太低：下限小于等于0！')
return
if high >= (shape(channelDataAll[0])[1] / (1 << a)):
print(u'上限太高：降维后维度数大于原数据维度！')
return
# 计算PCA的总次数
time1 = ((int)((high - low) / step + 1))
time2 = 1 << a
schedule[0] = time2
# 利用SSE选择k
SSE_S = [] # 存放所有结果
rates_S = np.array(np.zeros((time2, time1))) # 存放单次结果
n = np.shape(channelDataAll[0])[1] # 列数
p = len(channelDataAll) # 页数
sub = n >> a
for g in range(time2):
channelData = []
for h in range(p):
channelDataPage = channelDataAll[h]
channelData.append(channelDataPage[:, g * sub:(g + 1) * sub])
covMatrixList = tools.getCovMatrixList(channelData)
# 计算原信道信息量、协方差矩阵特征值、变换矩阵
informations, SigmaList, UList = tools.getInformations(covMatrixList)
for h in range(time1):
tmpRates = pca.pca_S(SigmaList, h * step + low)[0][:, 1]
rates_S[g, h] = np.mean(tmpRates).real
# 显示进度
schedule[1] += 1
print(u'共' + str(schedule[0]) + u'轮，' + u'已完成' + str(schedule[1]) + u'轮，' + u'完成度：' + '%.2f%%' % (
schedule[1] / schedule[0] * 100) + u'！')
for h in range(time1):
SSE_S.append(np.mean(rates_S[:, h]))
plt.xlabel(u'保留维度数k')
X = range(low, high + 1, step)
plt.ylabel(u'特征值保留')
plt.plot(X, SSE_S, 'k-s')
plt.show()
print(u'主进程结束！')
def cluster(a, schedule, channelDataAll1, channelDataAll2, allCentroidsC,
allCentroidUList, allCentroidsU, allCentroidUList2):
totalOldCorr = []
totalPcaCorr = []
totalNewCCorr = []
totalNewUCorr = []
for g in range(1, (1 << a) + 1):
schedule[1] += 1
tmpSchedule = schedule[1]
print(u'共' + str(schedule[0]) + u'部分，' + u'第' + str(tmpSchedule) +
u'部分开始！')
channelData1 = []
channelData2 = []
for i in range(np.shape(channelDataAll1)[0]):
channelData1.append(channelDataAll1[i][:, (g - 1) * sub:g * sub])
channelData2.append(channelDataAll2[i][:, (g - 1) * sub:g * sub])
# 计算信道协方差矩阵呢
covMatrixList1 = tools.getCovMatrixList(channelData1)
allOldCorr = clusterCore(channelData1, covMatrixList1, channelData2,
allCentroidsC[g - 1], allCentroidUList[g - 1],
"none")
totalOldCorr.append(allOldCorr)
allNewPcaCorr = clusterCore(channelData1, covMatrixList1, channelData2,
allCentroidsC[g - 1],
allCentroidUList[g - 1], "general")
totalPcaCorr.append(allNewPcaCorr)
allNewCCorr = clusterCore(channelData1, covMatrixList1, channelData2,
allCentroidsC[g - 1],
allCentroidUList[g - 1], "C")
totalNewCCorr.append(allNewCCorr)
allNewUCorr = clusterCore(channelData1, covMatrixList1, channelData2,
allCentroidsU[g - 1],
allCentroidUList2[g - 1], "U")
totalNewUCorr.append(allNewUCorr)
# 显示进度
print(u'共' + str(schedule[0]) + u'部分，' + u'第' + str(tmpSchedule) +
u'部分完成，' + u'已完成' + str(schedule[1]) + u'部分，' + u'完成度：' +
'%.2f%%' % (schedule[1] / schedule[0] * 100) + u'！')
return totalOldCorr, totalPcaCorr, totalNewCCorr, totalNewUCorr
def pca_general(data, newDimension=1):
try:
# 如果输入是单个信道，进行以下步骤
m, n = np.shape(data)
# 计算协方差矩阵 rowvar=False代表每一列是一个变量
covMatrix = np.cov(data, rowvar=False)
# SVD分解协方差矩阵得出变换矩阵
U = np.transpose(np.linalg.svd(covMatrix)[2])
return np.dot(data, U[:, 0:newDimension])
except:
print(u'pca_general')
# 如果输入是列表，进行以下步骤
out = []
covList = tools.getCovMatrixList(data)
UList = tools.getInformations(covList)[2]
for i in range(len(data)):
out.append(np.dot(data[i], UList[i][:, 0:newDimension]))
return out
def pca(channelData, informations, centroidList, clusterAssment, rate=1):
U2s = []
rates = np.array(np.zeros((len(channelData), 2)), dtype=complex)
rateList = []
newChannelDataList = []
# 计算变换矩阵
for i in range(len(centroidList)):
U, Sigma, VT = np.linalg.svd(centroidList[i])
sum = np.sum(Sigma)
curSum = 0
index = 0
if rate <= 1:
for j in range(len(Sigma)):
curSum += Sigma[j]
if rate - (curSum / sum) > 0:
index += 1
else:
break
else:
index = rate - 1
U2 = np.transpose(VT[0:index + 1, :])
U2s.append(U2)
# 降维
for i in range(len(channelData)):
newChannelData = np.dot(channelData[i],
U2s[(int)(clusterAssment[i, 0].real)])
newChannelDataList.append(newChannelData)
index = np.shape(newChannelData)[1]
rates[i, 0] = index
newCovMatrixList = tools.getCovMatrixList(newChannelDataList)
newInformations = tools.getInformations(newCovMatrixList)[0]
for i in range(len(channelData)):
rate2 = newInformations[0][i] / informations[0][i]
rates[i, 1] = rate2
rateList.append(rates)
return newChannelDataList, newCovMatrixList, U2s, rateList
def cluster(a, schedule, channelDataAll1, channelDataAll2, allCentroidsC, allCentroidUList, allCentroidsU, allCentroidUList2):
allOldCorr = []
allNewCCorr = []
allNewUCorr = []
for g in range(1, (1 << a) + 1):
schedule[1] += 1
tmpSchedule = schedule[1]
print(u'共' + str(schedule[0]) + u'部分，' + u'第' + str(tmpSchedule) + u'部分开始！')
channelData1 = []
channelData2 = []
for i in range(p):
channelData1.append(channelDataAll1[i][:, (g - 1) * sub:g * sub])
channelData2.append(channelDataAll2[i][:, (g - 1) * sub:g * sub])
oldCorr = []
channelDatas1 = tools.matrixListToMatrix_U(channelData1)
channelDatas2 = tools.matrixListToMatrix_U(channelData2)
# 计算信道协方差矩阵呢
covMatrixList1 = tools.getCovMatrixList(channelData1)
for i in range(np.shape(channelData1)[0]):
oldCorr.append(np.corrcoef(channelDatas1[i, :], channelDatas2[i, :]))
allOldCorr.append(np.mean(oldCorr))
newCCorrMean = clusterCore(channelData1, covMatrixList1, channelData2, allCentroidsC[g - 1],
allCentroidUList[g - 1], "C")
allNewCCorr.append(newCCorrMean)
newUCorrMean = clusterCore(channelData1, covMatrixList1, channelData2, allCentroidsU[g - 1],
allCentroidUList2[g - 1], "U")
allNewUCorr.append(newUCorrMean)
# 显示进度
print(u'共' + str(schedule[0]) + u'部分，' + u'第' + str(tmpSchedule) + u'部分完成，' + u'已完成' + str(schedule[1]) + u'部分，' + u'完成度：' + '%.2f%%' % (schedule[1] / schedule[0] * 100) + u'！')
return abs(np.mean(allOldCorr)), abs(np.mean(allNewCCorr)), abs(np.mean(allNewUCorr))
def getCentroids(schedule, path, suffix, channelData, g, k, iRate, type=u'C'):
# 校验数据正确性
if k > np.shape(channelData)[0]:
print(u'聚类中心数量不能大于样本数量！')
return
if iRate > np.shape(channelData)[1]:
print(u'降维后维度不能大于样本原有的维度！')
return
if k <= 0 or iRate <= 0:
print(u'聚类中心数量和降维后维度不能小于1！')
return
schedule[1] += 1
tmpSchedule = schedule[1]
print(u'共' + str(schedule[0]) + u'部分，' + u'第' + str(tmpSchedule) +
u'部分开始！')
# 得到相关系数矩阵并输出,然后放到一个矩阵中
covMatrixList = tools.getCovMatrixList(channelData)
informations, SigmaList, UList = tools.getInformations(covMatrixList)
if type == u'total':
# 对协方差进行聚类
getCentroidsCore(path, suffix, channelData, covMatrixList,
informations, SigmaList, UList, g, k, iRate, "C")
# 对变换矩阵进行聚类
getCentroidsCore(path, suffix, channelData, covMatrixList,
informations, SigmaList, UList, g, k, iRate, "U")
else:
getCentroidsCore(path, suffix, channelData, covMatrixList,
informations, SigmaList, UList, g, k, iRate, type)
# 显示进度
print(u'共' + str(schedule[0]) + u'部分，' + u'第' + str(tmpSchedule) +
u'部分完成，' + u'已完成' + str(schedule[1]) + u'部分，' + u'完成度：' + '%.2f%%' %
(schedule[1] / schedule[0] * 100) + u'！')
def cluster(schedule, path, suffix, channelData, g, iRate):
if iRate > np.shape(channelData)[1]:
print(u'降维后维度不能大于样本原有的维度！')
return
if iRate <= 0:
print(u'降维后维度不能小于1！')
return
schedule[1] += 1
tmpSchedule = schedule[1]
print(u'共' + str(schedule[0]) + u'部分，' + u'第' + str(tmpSchedule) +
u'部分开始！')
pathSuffix = "C" + "_" + str(g) + "_"
centroidListPath = path + "getCentroids_outCentroidList_" + pathSuffix
nowTime = time.strftime("%Y-%m-%d.%H.%M.%S", time.localtime(time.time()))
pathSuffix = pathSuffix + str(nowTime)
outOldCovMatrixListPath = path + "cluster_outOldCovMatrixList_" + pathSuffix
outClusterAssmentPath = path + "cluster_outClusterAssment_" + pathSuffix
outNewChannelDataPath = path + "cluster_outNewChannelData_" + pathSuffix
outNewCovMatrixsPath = path + "cluster_outNewCovMatrixList_" + pathSuffix
ratesPath = path + "cluster_rates_" + pathSuffix
UTsPath = path + "cluster_UTs_" + pathSuffix
# 读入聚类中心信息
# 合并多个文件
centroidList = []
for root, dirs, files in os.walk(path, topdown=True):
for file in files:
file = os.path.join(root, file)
if centroidListPath in file:
centroidListTmp = readAndWriteDataSet.excelToMatrixList(file)
for centroid in centroidListTmp:
centroidList.append(centroid)
break
centroids = tools.matrixListToMatrix(centroidList)
# 计算信道相关系数矩阵并输出，然后放到一个矩阵中
covMatrixList = tools.getCovMatrixList(channelData)
allCovMatrix = tools.matrixListToMatrix(covMatrixList)
# 确定每个数据分别属于哪个簇
clusterAssment = kmeans.getClusterAssment(allCovMatrix, centroids)
clusterAssmentList = []
clusterAssmentList.append(clusterAssment)
# 分析PCA效果
newChannelData, newCovMatrixList, UTs, rates = pca.pca(
channelData, covMatrixList, centroidList, clusterAssment, iRate)
# 输出结果
# 输出聚类结果
readAndWriteDataSet.write(clusterAssmentList, outClusterAssmentPath,
suffix)
# 协方差矩阵太大了，先不输出
# readAndWriteDataSet.write(covMatrixList, outOldCovMatrixListPath, suffix)
# 输出PCA结果
readAndWriteDataSet.write(newChannelData, outNewChannelDataPath, suffix)
readAndWriteDataSet.write(newCovMatrixList, outNewCovMatrixsPath, suffix)
readAndWriteDataSet.write(UTs, UTsPath, suffix)
readAndWriteDataSet.write(rates, ratesPath, suffix)
# 显示进度
print(u'共' + str(schedule[0]) + u'部分，' + u'第' + str(tmpSchedule) +
u'部分完成，' + u'已完成' + str(schedule[1]) + u'部分，' + u'完成度：' + '%.2f%%' %
(schedule[1] / schedule[0] * 100) + u'！')
