# 信噪比的上下限
low = 5
high = 60
step = 5
# 显示进度
manager = multiprocessing.Manager()
schedule = manager.Array('i', [1, 0])
time1 = ((int)((high - low) / step + 1))
time2 = 1 << a
schedule[0] = time1 * time2
# 读入信道数据
channelDataPath = path + u'channelDataP.xlsx'
channelDataAll = readAndWriteDataSet.excelToMatrixList(channelDataPath)
n = np.shape(channelDataAll[0])[1] # 列数
p = np.shape(channelDataAll)[0] # 页数
sub = n >> a
if iRate > sub:
print(u'降维后维度不能大于样本原有的维度！')
sys.exit()
if iRate <= 0:
print(u'降维后维度不能小于1！')
sys.exit()
# 读入协方差聚类中心，计算变换矩阵
allCentroidsC, allCentroidUList = readAndWriteDataSet.readCentroids(path, iRate, u'C', a)
# 读入变换矩阵聚类中心，计算变换矩阵
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'！')
