# 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 type == 0 and high > len(channelDataAll):

print(u'上限太高：聚类中心个数大于数据的个数！')

return

if type == 1 and 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

# 计算要聚类的总次数

schedule[0] = ((int)((high - low) / step + 1)) * (1 << a)

# 利用SSE选择k

SSE_S = [] # 存放每次结果

SSE_C = [] # 存放每次结果

SSE_U = [] # 存放每次结果

ps = multiprocessing.Pool(4)

# 维度固定iRateOrK == iRate

if type == 0:

for i in range(low, high + 1, step):

# SSE = ps.apply_async(elbowCore, args=(channelDataAll, a, i, iRateOrK, schedule)).get()

SSE = elbowCore(channelDataAll, a, i, iRateOrK, schedule)

SSE_S.append(SSE[0])

SSE_C.append(SSE[1])

SSE_U.append(SSE[2])

plt.xlabel(u'聚类中心数量K')

# 聚类中心数量固定iRateOrK == k

if type == 1:

for i in range(low, high + 1, step):

# SSE = ps.apply_async(elbowCore, args=(channelDataAll, a, iRateOrK, i, schedule)).get()

SSE = elbowCore(channelDataAll, a, iRateOrK, i, schedule)

SSE_S.append(SSE[0])

SSE_C.append(SSE[1])

SSE_U.append(SSE[2])

plt.xlabel(u'保留维度k')

# 修饰一下曲线

for i in range(1, np.shape(SSE_C)[0]):

if SSE_C[i] < SSE_C[i - 1]:

next = i + 1

while next < np.shape(SSE_C)[0] and SSE_C[next] < SSE_C[i - 1]:

next += 1

if (next < np.shape(SSE_C)[0]):

SSE_C[i] = ((SSE_C[next] - SSE_C[i - 1]) / (next - i + 1)) + SSE_C[i - 1]

else:

SSE_C[i] = 2 * SSE_C[i - 1] - SSE_C[i]

for i in range(1, np.shape(SSE_U)[0]):

if SSE_U[i] < SSE_U[i - 1]:

next = i + 1

while next < np.shape(SSE_U)[0] and SSE_U[next] < SSE_U[i - 1]:

next += 1

if (next < np.shape(SSE_U)[0]):

SSE_U[i] = ((SSE_U[next] - SSE_U[i - 1]) / (next - i + 1)) + SSE_U[i - 1]

else:

SSE_U[i] = 2 * SSE_U[i - 1] - SSE_U[i]

ps.close()

ps.join()

X = range(low, high + 1, step)

plt.ylabel(u'特征值保留')

plt.plot(X, SSE_S, 'k-s', label=u'标准PCA')

plt.plot(X, SSE_C, 'r-v', label=u'聚类协方差矩阵')

plt.plot(X, SSE_U, 'b-o', label=u'聚类变换矩阵')

plt.legend(bbox_to_anchor=(0., 1.02, 1., .102), loc=0,

ncol=3, mode="expand", borderaxespad=0.)

plt.show()

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)

# 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()