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Main_chest_clinic.scala
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Main_chest_clinic.scala
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package BNLV_learning
import java.io.FileWriter
import java.util.Date
import BNLV_learning.Global._
import BNLV_learning.Incremental.IncrementalLearning
import BNLV_learning.Input.BN_Input
import BNLV_learning.Learning.{SEM, StructureLearning, StructureLearning2, StructureLearning_chest_clinic}
import BNLV_learning.Output.BN_Output
object Main_chest_clinic {
def main(args: Array[String]) {
val t_start = new Date() //获取起始时间对象
println("BNLV Learning Based on Spark")
inputFile.primitiveData = "data/chest_clinic/chest-clinic-1000.txt"
// inputFile.primitiveData = "data/chest_clinic/chest-clinic-1000-origin.txt"
// inputFile.primitiveData = "data/chest_clinic/chest-clinic-1000.txt"
// inputFile.primitiveData = "data/chest_clinic/chest-clinic-1.txt"
// inputFile.primitiveData = "data/chest_clinic/add-1-edge_chest-clinic-5000.txt"
// inputFile.primitiveData = "data/chest_clinic/delete-1-edge_chest-clinic-5000.txt"
// inputFile.primitiveData = "data/chest_clinic/add-delete-1-edge_chest-clinic-7500.txt"
// inputFile.primitiveData = "data/chest_clinic/changeCPT-add-delete-1-edge_chest-clinic-7500.txt"
reference.inFile = SparkConf.sc.textFile(inputFile.primitiveData, SparkConf.partitionNum) //读取原始数据为RDD
println("数据量: " + reference.inFile.count())
// System.exit(0)
reference.NodeNum_Total = 8 //设置BN总变量数
reference.NodeNum = 8 //设置BN总变量数
// Smoking = 1
// Bronchitis = 2
// LungCancer = 3
// VisitToAsia = 4
// TB = 5
// TBorCancer = 6
// Dys = 7
// Xray = 8
var bn = new BayesianNetwork(reference.NodeNum)
for (i <- 0 until reference.NodeNum) { //创建数组中每个对象
bn.r(i) = 2;
}
bn.l(1-1) = "C";bn.l(2-1) = "D";bn.l(3-1) = "D";bn.l(4-1) = "C";bn.l(5-1) = "D";bn.l(6-1) = "D";bn.l(7-1) = "S";bn.l(8-1) = "S";
var operation = 7
println("当前操作为 ： " + operation)
operation match {
case 0 => // 无隐变量
reference.LatentNum_Total = 0 //设置BN隐变量数
reference.Data = reference.inFile
StructureLearning_chest_clinic.learningProcess_no_latent(bn)
case 10 => // 无隐变量
reference.NodeNum_Total = 3 //设置BN总变量数
reference.NodeNum = 3 //设置BN总变量数
var bn = new BayesianNetwork(reference.NodeNum)
for (i <- 0 until reference.NodeNum) { //创建数组中每个对象
bn.r(i) = 2;
}
reference.LatentNum_Total = 0 //设置BN隐变量数
reference.Data = reference.inFile.map {
line =>
val data_Array = line.split(separator.data)
var lines = ""
lines += data_Array(1-1) + separator.data
lines += data_Array(2-1) + separator.data
lines += data_Array(7-1)
lines
}
reference.Data.count()
StructureLearning_chest_clinic.learningProcess_no_latent(bn)
case 1 => // 无隐变量 2个子图学习
reference.inFile = SparkConf.sc.textFile(inputFile.primitiveData, SparkConf.partitionNum) //读取原始数据为RDD
reference.LatentNum_Total = 2 //设置BN隐变量数
reference.NodeNum_Total = 4 //设置BN变量数
var bn_Array = new Array[BayesianNetwork](reference.LatentNum_Total)
reference.NodeNum = 4 //设置BN节点数
for (i <- 0 until reference.LatentNum_Total) { //创建数组中每个对象
bn_Array(i) = new BayesianNetwork(reference.NodeNum)
bn_Array(i).r(0) = 2;
bn_Array(i).r(1) = 2;
bn_Array(i).r(2) = 2;
bn_Array(i).r(3) = 2;
}
for(i <- 0 until reference.LatentNum_Total){
var temp_string = (i+1).toString + ".txt"
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
// outputFile.initial_structure = "/mnt/shared/Datasets/wuxinran/SEM_logB/initial_structure" + temp_string
// outputFile.initial_theta = "/mnt/shared/Datasets/wuxinran/SEM_logB/initial_theta" + temp_string
// outputFile.optimal_structure = "/mnt/shared/Datasets/wuxinran/SEM_logB/optimal_structure" + temp_string
// outputFile.optimal_theta = "/mnt/shared/Datasets/wuxinran/SEM_logB/optimal_theta" + temp_string
// logFile.candidateStructure = "/mnt/shared/Datasets/wuxinran/SEM_logB/candidateStructure_file" + temp_string
// logFile.parameterLearning = "/mnt/shared/Datasets/wuxinran/SEM_logB/parameterLearning_log" + temp_string
// logFile.structureLearning = "/mnt/shared/Datasets/wuxinran/SEM_logB/structureLeaning_log" + temp_string
// logFile.BIC_score = "/mnt/shared/Datasets/wuxinran/SEM_logB/BIC_score" + temp_string
// logFile.structureLearningTime = "/mnt/shared/Datasets/wuxinran/SEM_logB/structureLeaning_time" + temp_string
// logFile.parameterLearningTime = "/mnt/shared/Datasets/wuxinran/SEM_logB/parameterLeaning_time" + temp_string
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
outputFile.initial_structure = "out/Log/initial_structure" + temp_string
outputFile.initial_theta = "out/Log/initial_theta" + temp_string
outputFile.optimal_structure = "out/Log/optimal_structure" + temp_string
outputFile.optimal_theta = "out/Log/optimal_theta" + temp_string
logFile.candidateStructure = "out/Log/candidateStructure_file" + temp_string
logFile.parameterLearning = "out/Log/parameterLearning_log" + temp_string
logFile.structureLearning = "out/Log/structureLeaning_log" + temp_string
logFile.BIC_score = "out/Log/BIC_score" + temp_string
logFile.structureLearningTime = "out/Log/structureLeaning_time" + temp_string
logFile.parameterLearningTime = "out/Log/parameterLeaning_time" + temp_string
//////////////////////////////////////////////////////////////////////////////////////////////////////////
//截取与子图相关的数据
if(i==0){
reference.Data = reference.inFile.map {
line =>
val data_Array = line.split(separator.data)
var lines = ""
lines += data_Array(1-1) + separator.data
lines += data_Array(2-1) + separator.data
lines += data_Array(3-1) + separator.data
lines += data_Array(7-1)
lines
}
}else if(i==1){
reference.Data = reference.inFile.map {
line =>
val data_Array = line.split(separator.data)
var lines = ""
lines += data_Array(4-1) + separator.data
lines += data_Array(5-1) + separator.data
lines += data_Array(6-1) + separator.data
lines += data_Array(8-1)
lines
}
}
reference.Data.count()
// println()
// System.exit(0)
// 结构学习
StructureLearning_chest_clinic.learningProcess_no_latent(bn_Array(i))
}
val t_end = new Date() //getTime获取得到的数值单位：数
val runtime = (t_end.getTime - t_start.getTime)/1000.0
val outTime = new FileWriter(logFile.totalTime)
outTime.write(runtime + "\r\n")
outTime.close()
case 21=> // 1个隐变量
reference.inFile = SparkConf.sc.textFile(inputFile.primitiveData, SparkConf.partitionNum) //读取原始数据为RDD
reference.LatentNum_Total = 1 //设置BN隐变量数
var bn_Array = new Array[BayesianNetwork](reference.LatentNum_Total)
reference.Latent_r = Array(2)
println("隐变量的势：")
reference.Latent_r.foreach(println(_))
reference.LatentNum = 1 //设置BN隐变量数
var bn = new BayesianNetwork(reference.NodeNum)
for (i <- 0 until reference.LatentNum_Total) { //创建数组中每个对象
bn_Array(i) = new BayesianNetwork(reference.NodeNum)
bn_Array(i).r(0) = 2;
bn_Array(i).r(1) = 2;
bn_Array(i).r(2) = 2;
bn_Array(i).r(3) = 2;
bn_Array(i).r(4) = 2
bn_Array(i).r(5) = 2
bn_Array(i).r(6) = 2
bn_Array(i).r(7) = 2
}
for(i <- 0 until reference.LatentNum_Total){
reference.NodeNum_Total = 8 //设置BN变量数
reference.NodeNum = 8 //设置BN节点数
reference.R_type = "chest_clinic_1L".toString
var temp_string = (i+1).toString + ".txt"
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
// outputFile.initial_structure = "/mnt/shared/Datasets/wuxinran/SEM_logB/initial_structure" + temp_string
// outputFile.initial_theta = "/mnt/shared/Datasets/wuxinran/SEM_logB/initial_theta" + temp_string
// outputFile.optimal_structure = "/mnt/shared/Datasets/wuxinran/SEM_logB/optimal_structure" + temp_string
// outputFile.optimal_theta = "/mnt/shared/Datasets/wuxinran/SEM_logB/optimal_theta" + temp_string
// logFile.candidateStructure = "/mnt/shared/Datasets/wuxinran/SEM_logB/candidateStructure_file" + temp_string
// logFile.parameterLearning = "/mnt/shared/Datasets/wuxinran/SEM_logB/parameterLearning_log" + temp_string
// logFile.structureLearning = "/mnt/shared/Datasets/wuxinran/SEM_logB/structureLeaning_log" + temp_string
// logFile.BIC_score = "/mnt/shared/Datasets/wuxinran/SEM_logB/BIC_score" + temp_string
// logFile.structureLearningTime = "/mnt/shared/Datasets/wuxinran/SEM_logB/structureLeaning_time" + temp_string
// logFile.parameterLearningTime = "/mnt/shared/Datasets/wuxinran/SEM_logB/parameterLeaning_time" + temp_string
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
outputFile.initial_structure = "out/Log/initial_structure" + temp_string
outputFile.initial_theta = "out/Log/initial_theta" + temp_string
outputFile.optimal_structure = "out/Log/optimal_structure" + temp_string
outputFile.optimal_theta = "out/Log/optimal_theta" + temp_string
logFile.candidateStructure = "out/Log/candidateStructure_file" + temp_string
logFile.parameterLearning = "out/Log/parameterLearning_log" + temp_string
logFile.structureLearning = "out/Log/structureLeaning_log" + temp_string
logFile.BIC_score = "out/Log/BIC_score" + temp_string
logFile.structureLearningTime = "out/Log/structureLeaning_time" + temp_string
logFile.parameterLearningTime = "out/Log/parameterLeaning_time" + temp_string
//////////////////////////////////////////////////////////////////////////////////////////////////////////
//截取与子图相关的数据
if(i==0){
reference.inFile_Data = reference.inFile.map {
line =>
val data_Array = line.split(separator.data)
var lines = ""
lines += data_Array(1-1) + separator.data
lines += data_Array(3-1) + separator.data
lines += data_Array(4-1) + separator.data
lines += data_Array(5-1) + separator.data
lines += data_Array(6-1) + separator.data
lines += data_Array(7-1) + separator.data
lines += data_Array(8-1)
lines
}
}else{
println("运行错误！")
}
reference.inFile_Data.count()
// reference.inFile_Data.foreach(println(_))
// println()
// println("11111")
// System.exit(0)
// 结构学习
StructureLearning.learningProcess(bn_Array(i))
}
reference.BIC = 1.0
reference.BIC_latent = 1.0
var PD_log:Double = SEM.PD(bn_Array(0))
var BIC_score = StructureLearning.BIC_PD(bn_Array(0), PD_log)
println("最后的BIC评分为：" + BIC_score)
val t_end = new Date() //getTime获取得到的数值单位：数
val runtime = (t_end.getTime - t_start.getTime)/1000.0
val outTime = new FileWriter(logFile.totalTime)
outTime.write(runtime + "\r\n")
outTime.close()
case 22=> // 2个隐变量
reference.inFile = SparkConf.sc.textFile(inputFile.primitiveData, SparkConf.partitionNum) //读取原始数据为RDD
reference.LatentNum_Total = 2 //设置BN隐变量数
var bn_Array = new Array[BayesianNetwork](1)
reference.Latent_r = Array(2,2)
println("隐变量的势：")
reference.Latent_r.foreach(println(_))
reference.LatentNum = 2 //设置BN隐变量数
var bn = new BayesianNetwork(reference.NodeNum)
for (i <- 0 until 1) { //创建数组中每个对象
bn_Array(i) = new BayesianNetwork(reference.NodeNum)
bn_Array(i).r(0) = 2;
bn_Array(i).r(1) = 2;
bn_Array(i).r(2) = 2;
bn_Array(i).r(3) = 2;
bn_Array(i).r(4) = 2
bn_Array(i).r(5) = 2
bn_Array(i).r(6) = 2
bn_Array(i).r(7) = 2
}
for(i <- 0 until 1){
reference.NodeNum_Total = 8 //设置BN变量数
reference.NodeNum = 8 //设置BN节点数
reference.R_type = "chest_clinic_2L".toString
var temp_string = (i+1).toString + ".txt"
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
// outputFile.initial_structure = "/mnt/shared/Datasets/wuxinran/SEM_logB/initial_structure" + temp_string
// outputFile.initial_theta = "/mnt/shared/Datasets/wuxinran/SEM_logB/initial_theta" + temp_string
// outputFile.optimal_structure = "/mnt/shared/Datasets/wuxinran/SEM_logB/optimal_structure" + temp_string
// outputFile.optimal_theta = "/mnt/shared/Datasets/wuxinran/SEM_logB/optimal_theta" + temp_string
// logFile.candidateStructure = "/mnt/shared/Datasets/wuxinran/SEM_logB/candidateStructure_file" + temp_string
// logFile.parameterLearning = "/mnt/shared/Datasets/wuxinran/SEM_logB/parameterLearning_log" + temp_string
// logFile.structureLearning = "/mnt/shared/Datasets/wuxinran/SEM_logB/structureLeaning_log" + temp_string
// logFile.BIC_score = "/mnt/shared/Datasets/wuxinran/SEM_logB/BIC_score" + temp_string
// logFile.structureLearningTime = "/mnt/shared/Datasets/wuxinran/SEM_logB/structureLeaning_time" + temp_string
// logFile.parameterLearningTime = "/mnt/shared/Datasets/wuxinran/SEM_logB/parameterLeaning_time" + temp_string
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
outputFile.initial_structure = "out/Log/initial_structure" + temp_string
outputFile.initial_theta = "out/Log/initial_theta" + temp_string
outputFile.optimal_structure = "out/Log/optimal_structure" + temp_string
outputFile.optimal_theta = "out/Log/optimal_theta" + temp_string
logFile.candidateStructure = "out/Log/candidateStructure_file" + temp_string
logFile.parameterLearning = "out/Log/parameterLearning_log" + temp_string
logFile.structureLearning = "out/Log/structureLeaning_log" + temp_string
logFile.BIC_score = "out/Log/BIC_score" + temp_string
logFile.structureLearningTime = "out/Log/structureLeaning_time" + temp_string
logFile.parameterLearningTime = "out/Log/parameterLeaning_time" + temp_string
//////////////////////////////////////////////////////////////////////////////////////////////////////////
//截取与子图相关的数据
if(i==0){
reference.inFile_Data = reference.inFile.map {
line =>
val data_Array = line.split(separator.data)
var lines = ""
lines += data_Array(1-1) + separator.data
lines += data_Array(3-1) + separator.data
lines += data_Array(4-1) + separator.data
lines += data_Array(6-1) + separator.data
lines += data_Array(7-1) + separator.data
lines += data_Array(8-1)
lines
}
}else{
println("运行错误！")
}
reference.inFile_Data.count()
// reference.inFile_Data.foreach(println(_))
// println()
// println("11111")
// System.exit(0)
// 结构学习
StructureLearning.learningProcess(bn_Array(i))
}
reference.BIC = 1.0
reference.BIC_latent = 1.0
var PD_log:Double = SEM.PD(bn_Array(0))
var BIC_score = StructureLearning.BIC_PD(bn_Array(0), PD_log)
println("最后的BIC评分为：" + BIC_score)
val t_end = new Date() //getTime获取得到的数值单位：数
val runtime = (t_end.getTime - t_start.getTime)/1000.0
val outTime = new FileWriter(logFile.totalTime)
outTime.write(runtime + "\r\n")
outTime.close()
case 23=> // 3个隐变量
reference.inFile = SparkConf.sc.textFile(inputFile.primitiveData, SparkConf.partitionNum) //读取原始数据为RDD
reference.LatentNum_Total = 3 //设置BN隐变量数
var bn_Array = new Array[BayesianNetwork](reference.LatentNum_Total)
reference.Latent_r = Array(2,2,2)
println("隐变量的势：")
reference.Latent_r.foreach(println(_))
reference.LatentNum = 3 //设置BN隐变量数
var bn = new BayesianNetwork(reference.NodeNum)
for (i <- 0 until 1) { //创建数组中每个对象
bn_Array(i) = new BayesianNetwork(reference.NodeNum)
bn_Array(i).r(0) = 2;
bn_Array(i).r(1) = 2;
bn_Array(i).r(2) = 2;
bn_Array(i).r(3) = 2;
bn_Array(i).r(4) = 2
bn_Array(i).r(5) = 2
bn_Array(i).r(6) = 2
bn_Array(i).r(7) = 2
}
for(i <- 0 until 1){
reference.NodeNum_Total = 8 //设置BN变量数
reference.NodeNum = 8 //设置BN节点数
reference.R_type = "chest_clinic_3L".toString
var temp_string = (i+1).toString + ".txt"
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
// outputFile.initial_structure = "/mnt/shared/Datasets/wuxinran/SEM_logB/initial_structure" + temp_string
// outputFile.initial_theta = "/mnt/shared/Datasets/wuxinran/SEM_logB/initial_theta" + temp_string
// outputFile.optimal_structure = "/mnt/shared/Datasets/wuxinran/SEM_logB/optimal_structure" + temp_string
// outputFile.optimal_theta = "/mnt/shared/Datasets/wuxinran/SEM_logB/optimal_theta" + temp_string
// logFile.candidateStructure = "/mnt/shared/Datasets/wuxinran/SEM_logB/candidateStructure_file" + temp_string
// logFile.parameterLearning = "/mnt/shared/Datasets/wuxinran/SEM_logB/parameterLearning_log" + temp_string
// logFile.structureLearning = "/mnt/shared/Datasets/wuxinran/SEM_logB/structureLeaning_log" + temp_string
// logFile.BIC_score = "/mnt/shared/Datasets/wuxinran/SEM_logB/BIC_score" + temp_string
// logFile.structureLearningTime = "/mnt/shared/Datasets/wuxinran/SEM_logB/structureLeaning_time" + temp_string
// logFile.parameterLearningTime = "/mnt/shared/Datasets/wuxinran/SEM_logB/parameterLeaning_time" + temp_string
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
outputFile.initial_structure = "out/Log/initial_structure" + temp_string
outputFile.initial_theta = "out/Log/initial_theta" + temp_string
outputFile.optimal_structure = "out/Log/optimal_structure" + temp_string
outputFile.optimal_theta = "out/Log/optimal_theta" + temp_string
logFile.candidateStructure = "out/Log/candidateStructure_file" + temp_string
logFile.parameterLearning = "out/Log/parameterLearning_log" + temp_string
logFile.structureLearning = "out/Log/structureLeaning_log" + temp_string
logFile.BIC_score = "out/Log/BIC_score" + temp_string
logFile.structureLearningTime = "out/Log/structureLeaning_time" + temp_string
logFile.parameterLearningTime = "out/Log/parameterLeaning_time" + temp_string
//////////////////////////////////////////////////////////////////////////////////////////////////////////
//截取与子图相关的数据
if(i==0){
reference.inFile_Data = reference.inFile.map {
line =>
val data_Array = line.split(separator.data)
var lines = ""
lines += data_Array(1-1) + separator.data
lines += data_Array(4-1) + separator.data
lines += data_Array(6-1) + separator.data
lines += data_Array(7-1) + separator.data
lines += data_Array(8-1)
lines
}
}else{
println("运行错误！")
}
reference.inFile_Data.count()
// reference.inFile_Data.foreach(println(_))
// println()
// println("11111")
// System.exit(0)
// 结构学习
StructureLearning.learningProcess(bn_Array(i))
}
reference.BIC = 1.0
reference.BIC_latent = 1.0
var PD_log:Double = SEM.PD(bn_Array(0))
var BIC_score = StructureLearning.BIC_PD(bn_Array(0), PD_log)
println("最后的BIC评分为：" + BIC_score)
val t_end = new Date() //getTime获取得到的数值单位：数
val runtime = (t_end.getTime - t_start.getTime)/1000.0
val outTime = new FileWriter(logFile.totalTime)
outTime.write(runtime + "\r\n")
outTime.close()
case 32 => // 2个隐变量 各子图学习
reference.inFile = SparkConf.sc.textFile(inputFile.primitiveData, SparkConf.partitionNum) //读取原始数据为RDD
reference.LatentNum_Total = 2 //设置BN隐变量数
reference.NodeNum_Total = 4 //设置BN变量数
var bn_Array = new Array[BayesianNetwork](reference.LatentNum_Total)
reference.Latent_r = Array(2,2)
println("隐变量的势：")
reference.Latent_r.foreach(println(_))
reference.NodeNum = 4 //设置BN节点数
reference.LatentNum = 1 //设置BN隐变量数
var bn = new BayesianNetwork(reference.NodeNum)
for (i <- 0 until reference.LatentNum_Total) { //创建数组中每个对象
bn_Array(i) = new BayesianNetwork(reference.NodeNum)
bn_Array(i).r(0) = 2;
bn_Array(i).r(1) = 2;
bn_Array(i).r(2) = 2;
bn_Array(i).r(3) = 2;
}
for(i <- 0 until reference.LatentNum_Total){
reference.R_type = "chest_clinic_2L_" + (i+1).toString
var temp_string = (i+1).toString + ".txt"
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
// outputFile.initial_structure = "/mnt/shared/Datasets/wuxinran/SEM_logB/initial_structure" + temp_string
// outputFile.initial_theta = "/mnt/shared/Datasets/wuxinran/SEM_logB/initial_theta" + temp_string
// outputFile.optimal_structure = "/mnt/shared/Datasets/wuxinran/SEM_logB/optimal_structure" + temp_string
// outputFile.optimal_theta = "/mnt/shared/Datasets/wuxinran/SEM_logB/optimal_theta" + temp_string
// logFile.candidateStructure = "/mnt/shared/Datasets/wuxinran/SEM_logB/candidateStructure_file" + temp_string
// logFile.parameterLearning = "/mnt/shared/Datasets/wuxinran/SEM_logB/parameterLearning_log" + temp_string
// logFile.structureLearning = "/mnt/shared/Datasets/wuxinran/SEM_logB/structureLeaning_log" + temp_string
// logFile.BIC_score = "/mnt/shared/Datasets/wuxinran/SEM_logB/BIC_score" + temp_string
// logFile.structureLearningTime = "/mnt/shared/Datasets/wuxinran/SEM_logB/structureLeaning_time" + temp_string
// logFile.parameterLearningTime = "/mnt/shared/Datasets/wuxinran/SEM_logB/parameterLeaning_time" + temp_string
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
outputFile.initial_structure = "out/Log/initial_structure" + temp_string
outputFile.initial_theta = "out/Log/initial_theta" + temp_string
outputFile.optimal_structure = "out/Log/optimal_structure" + temp_string
outputFile.optimal_theta = "out/Log/optimal_theta" + temp_string
logFile.candidateStructure = "out/Log/candidateStructure_file" + temp_string
logFile.parameterLearning = "out/Log/parameterLearning_log" + temp_string
logFile.structureLearning = "out/Log/structureLeaning_log" + temp_string
logFile.BIC_score = "out/Log/BIC_score" + temp_string
logFile.structureLearningTime = "out/Log/structureLeaning_time" + temp_string
logFile.parameterLearningTime = "out/Log/parameterLeaning_time" + temp_string
//////////////////////////////////////////////////////////////////////////////////////////////////////////
//截取与子图相关的数据
if(i==0){
reference.inFile_Data = reference.inFile.map {
line =>
val data_Array = line.split(separator.data)
var lines = ""
lines += data_Array(1-1) + separator.data
lines += data_Array(3-1) + separator.data
lines += data_Array(7-1)
lines
}
}else if(i==1){
reference.inFile_Data = reference.inFile.map {
line =>
val data_Array = line.split(separator.data)
var lines = ""
lines += data_Array(4-1) + separator.data
lines += data_Array(6-1) + separator.data
lines += data_Array(8-1)
lines
}
}
reference.inFile_Data.count()
// println()
// System.exit(0)
// 结构学习
StructureLearning.learningProcess(bn_Array(i))
}
val t_end = new Date() //getTime获取得到的数值单位：数
val runtime = (t_end.getTime - t_start.getTime)/1000.0
val outTime = new FileWriter(logFile.totalTime)
outTime.write(runtime + "\r\n")
outTime.close()
case 322 => // 2个隐变量 各子图学习
reference.inFile = SparkConf.sc.textFile(inputFile.primitiveData, SparkConf.partitionNum) //读取原始数据为RDD
reference.LatentNum_Total = 2 //设置BN隐变量数
var bn_Array = new Array[BayesianNetwork](reference.LatentNum_Total)
reference.Latent_r = Array(2,2)
println("隐变量的势：")
reference.Latent_r.foreach(println(_))
reference.LatentNum = 1 //设置BN隐变量数
for(i <- 0 until reference.LatentNum_Total){
reference.R_type = "chest_clinic_2L_3+5_" + (i+1).toString
if(i == 0) { //创建数组中每个对象
reference.NodeNum = 3 //设置BN节点数
bn_Array(i) = new BayesianNetwork(reference.NodeNum)
bn_Array(i).r(0) = 2
bn_Array(i).r(1) = 2
bn_Array(i).r(2) = 2
bn_Array(i).latent(3-1) = 1
}else if(i == 1) { //创建数组中每个对象
reference.NodeNum = 5 //设置BN节点数
bn_Array(i) = new BayesianNetwork(reference.NodeNum)
bn_Array(i).r(0) = 2
bn_Array(i).r(1) = 2
bn_Array(i).r(2) = 2
bn_Array(i).r(3) = 2
bn_Array(i).r(4) = 2
bn_Array(i).latent(5-1) = 1
}
var temp_string = (i+1).toString + ".txt"
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
// outputFile.initial_structure = "/mnt/shared/Datasets/wuxinran/SEM_logB/initial_structure" + temp_string
// outputFile.initial_theta = "/mnt/shared/Datasets/wuxinran/SEM_logB/initial_theta" + temp_string
// outputFile.optimal_structure = "/mnt/shared/Datasets/wuxinran/SEM_logB/optimal_structure" + temp_string
// outputFile.optimal_theta = "/mnt/shared/Datasets/wuxinran/SEM_logB/optimal_theta" + temp_string
// logFile.candidateStructure = "/mnt/shared/Datasets/wuxinran/SEM_logB/candidateStructure_file" + temp_string
// logFile.parameterLearning = "/mnt/shared/Datasets/wuxinran/SEM_logB/parameterLearning_log" + temp_string
// logFile.structureLearning = "/mnt/shared/Datasets/wuxinran/SEM_logB/structureLeaning_log" + temp_string
// logFile.BIC_score = "/mnt/shared/Datasets/wuxinran/SEM_logB/BIC_score" + temp_string
// logFile.structureLearningTime = "/mnt/shared/Datasets/wuxinran/SEM_logB/structureLeaning_time" + temp_string
// logFile.parameterLearningTime = "/mnt/shared/Datasets/wuxinran/SEM_logB/parameterLeaning_time" + temp_string
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
outputFile.initial_structure = "out/Log/initial_structure" + temp_string
outputFile.initial_theta = "out/Log/initial_theta" + temp_string
outputFile.optimal_structure = "out/Log/optimal_structure" + temp_string
outputFile.optimal_theta = "out/Log/optimal_theta" + temp_string
logFile.candidateStructure = "out/Log/candidateStructure_file" + temp_string
logFile.parameterLearning = "out/Log/parameterLearning_log" + temp_string
logFile.structureLearning = "out/Log/structureLeaning_log" + temp_string
logFile.BIC_score = "out/Log/BIC_score" + temp_string
logFile.structureLearningTime = "out/Log/structureLeaning_time" + temp_string
logFile.parameterLearningTime = "out/Log/parameterLeaning_time" + temp_string
//////////////////////////////////////////////////////////////////////////////////////////////////////////
//截取与子图相关的数据
if(i==0){
reference.inFile_Data = reference.inFile.map {
line =>
val data_Array = line.split(separator.data)
var lines = ""
lines += data_Array(1-1) + separator.data
lines += data_Array(7-1)
lines
}
}else if(i==1){
reference.inFile_Data = reference.inFile.map {
line =>
val data_Array = line.split(separator.data)
var lines = ""
lines += data_Array(3-1) + separator.data
lines += data_Array(4-1) + separator.data
lines += data_Array(6-1) + separator.data
lines += data_Array(8-1)
lines
}
}
reference.inFile_Data.count()
// println()
// System.exit(0)
// 结构学习
StructureLearning.learningProcess(bn_Array(i))
}
val t_end = new Date() //getTime获取得到的数值单位：数
val runtime = (t_end.getTime - t_start.getTime)/1000.0
val outTime = new FileWriter(logFile.totalTime)
outTime.write(runtime + "\r\n")
outTime.close()
case 42 => // 2个隐变量 子图合并
reference.inFile = SparkConf.sc.textFile(inputFile.primitiveData, SparkConf.partitionNum) //读取原始数据为RDD
reference.LatentNum_Total = 2 //设置BN隐变量数
reference.NodeNum_Total = 8 //设置BN变量数
var bn_Array = new Array[BayesianNetwork](reference.LatentNum_Total)
reference.Latent_r = Array(2,2)
println("隐变量的势：")
reference.Latent_r.foreach(println(_))
reference.NodeNum = 4 //设置BN节点数
reference.LatentNum = 1 //设置BN隐变量数
var bn = new BayesianNetwork(reference.NodeNum)
for (i <- 0 until reference.LatentNum_Total) { //创建数组中每个对象
bn_Array(i) = new BayesianNetwork(reference.NodeNum)
bn_Array(i).r(0) = 2;
bn_Array(i).r(1) = 2;
bn_Array(i).r(2) = 2;
bn_Array(i).r(3) = 2;
}
reference.R_type = "chest_clinic_2L_merge"
for(i <- 0 until reference.LatentNum_Total){
//修改文件名
var temp_string = (i+1).toString + ".txt"
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
// outputFile.optimal_structure = "/mnt/shared/Datasets/wuxinran/SEM_logB/optimal_structure" + temp_string
// outputFile.optimal_theta = "/mnt/shared/Datasets/wuxinran/SEM_logB/optimal_theta" + temp_string
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
outputFile.optimal_structure = "out/Log/optimal_structure" + temp_string
outputFile.optimal_theta = "out/Log/optimal_theta" + temp_string
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
val input = new BN_Input
//从文件中读取表示BN初始结构的邻接矩阵到bn.structure中
input.structureFromFile(outputFile.optimal_structure, separator.structure, bn_Array(i))
bn_Array(i).qi_computation()
bn_Array(i).create_CPT()
//从文件中读取参数θ到动态三维数组bn.theta
input.CPT_FromFile(outputFile.optimal_theta, separator.theta, bn_Array(i))
// val output = new BN_Output
// output.CPT_FormatToConsole(bn_Array(i))
}
// System.exit(0)
//修改文件名
var temp_string = ".txt"
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
// outputFile.initial_structure = "/mnt/shared/Datasets/wuxinran/SEM_logB/initial_structure" + temp_string
// outputFile.initial_theta = "/mnt/shared/Datasets/wuxinran/SEM_logB/initial_theta" + temp_string
// outputFile.optimal_structure = "/mnt/shared/Datasets/wuxinran/SEM_logB/optimal_structure" + temp_string
// outputFile.optimal_theta = "/mnt/shared/Datasets/wuxinran/SEM_logB/optimal_theta" + temp_string
// logFile.candidateStructure = "/mnt/shared/Datasets/wuxinran/SEM_logB/candidateStructure_file" + temp_string
// logFile.parameterLearning = "/mnt/shared/Datasets/wuxinran/SEM_logB/parameterLearning_log" + temp_string
// logFile.structureLearning = "/mnt/shared/Datasets/wuxinran/SEM_logB/structureLeaning_log" + temp_string
// logFile.BIC_score = "/mnt/shared/Datasets/wuxinran/SEM_logB/BIC_score" + temp_string
// logFile.structureLearningTime = "/mnt/shared/Datasets/wuxinran/SEM_logB/structureLeaning_time" + temp_string
// logFile.parameterLearningTime = "/mnt/shared/Datasets/wuxinran/SEM_logB/parameterLeaning_time" + temp_string
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
outputFile.initial_structure = "out/Log/initial_structure" + temp_string
outputFile.initial_theta = "out/Log/initial_theta" + temp_string
outputFile.optimal_structure = "out/Log/optimal_structure" + temp_string
outputFile.optimal_theta = "out/Log/optimal_theta" + temp_string
logFile.candidateStructure = "out/Log/candidateStructure_file" + temp_string
logFile.parameterLearning = "out/Log/parameterLearning_log" + temp_string
logFile.structureLearning = "out/Log/structureLeaning_log" + temp_string
logFile.BIC_score = "out/Log/BIC_score" + temp_string
logFile.structureLearningTime = "out/Log/structureLeaning_time" + temp_string
logFile.parameterLearningTime = "out/Log/parameterLeaning_time" + temp_string
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
var incremental_change_mark = new Array[Array[Int]](reference.LatentNum_Total)
// 结构合并
StructureLearning2.learningProcess1(bn, bn_Array, 0, incremental_change_mark)
val t_end = new Date() //getTime获取得到的数值单位：数
val runtime = (t_end.getTime - t_start.getTime)/1000.0
val outTime = new FileWriter(logFile.totalTime)
outTime.write(runtime + "\r\n")
outTime.close()
case 422 => // 2个隐变量 子图合并
reference.inFile = SparkConf.sc.textFile(inputFile.primitiveData, SparkConf.partitionNum) //读取原始数据为RDD
reference.LatentNum_Total = 2 //设置BN隐变量数
reference.NodeNum_Total = 8 //设置BN变量数
var bn_Array = new Array[BayesianNetwork](reference.LatentNum_Total)
reference.Latent_r = Array(2,2)
println("隐变量的势：")
reference.Latent_r.foreach(println(_))
reference.NodeNum = 4 //设置BN节点数
reference.LatentNum = 1 //设置BN隐变量数
for (i <- 0 until reference.LatentNum_Total) { //创建数组中每个对象
if(i == 0) { //创建数组中每个对象
reference.NodeNum = 3 //设置BN节点数
bn_Array(i) = new BayesianNetwork(reference.NodeNum)
bn_Array(i).r(0) = 2
bn_Array(i).r(1) = 2
bn_Array(i).r(2) = 2
bn_Array(i).latent(3-1) = 1
}else if(i == 1) { //创建数组中每个对象
reference.NodeNum = 5 //设置BN节点数
bn_Array(i) = new BayesianNetwork(reference.NodeNum)
bn_Array(i).r(0) = 2
bn_Array(i).r(1) = 2
bn_Array(i).r(2) = 2
bn_Array(i).r(3) = 2
bn_Array(i).r(4) = 2
bn_Array(i).latent(5-1) = 1
}
}
reference.R_type = "chest_clinic_2L_3+5_merge"
for(i <- 0 until reference.LatentNum_Total){
//修改文件名
var temp_string = (i+1).toString + ".txt"
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
// outputFile.optimal_structure = "/mnt/shared/Datasets/wuxinran/SEM_logB/optimal_structure" + temp_string
// outputFile.optimal_theta = "/mnt/shared/Datasets/wuxinran/SEM_logB/optimal_theta" + temp_string
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
outputFile.optimal_structure = "out/Log/optimal_structure" + temp_string
outputFile.optimal_theta = "out/Log/optimal_theta" + temp_string
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
val input = new BN_Input
//从文件中读取表示BN初始结构的邻接矩阵到bn.structure中
input.structureFromFile(outputFile.optimal_structure, separator.structure, bn_Array(i))
bn_Array(i).qi_computation()
bn_Array(i).create_CPT()
//从文件中读取参数θ到动态三维数组bn.theta
input.CPT_FromFile(outputFile.optimal_theta, separator.theta, bn_Array(i))
// val output = new BN_Output
// output.CPT_FormatToConsole(bn_Array(i))
}
// System.exit(0)
//修改文件名
var temp_string = ".txt"
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
// outputFile.initial_structure = "/mnt/shared/Datasets/wuxinran/SEM_logB/initial_structure" + temp_string
// outputFile.initial_theta = "/mnt/shared/Datasets/wuxinran/SEM_logB/initial_theta" + temp_string
// outputFile.optimal_structure = "/mnt/shared/Datasets/wuxinran/SEM_logB/optimal_structure" + temp_string
// outputFile.optimal_theta = "/mnt/shared/Datasets/wuxinran/SEM_logB/optimal_theta" + temp_string
// logFile.candidateStructure = "/mnt/shared/Datasets/wuxinran/SEM_logB/candidateStructure_file" + temp_string
// logFile.parameterLearning = "/mnt/shared/Datasets/wuxinran/SEM_logB/parameterLearning_log" + temp_string
// logFile.structureLearning = "/mnt/shared/Datasets/wuxinran/SEM_logB/structureLeaning_log" + temp_string
// logFile.BIC_score = "/mnt/shared/Datasets/wuxinran/SEM_logB/BIC_score" + temp_string
// logFile.structureLearningTime = "/mnt/shared/Datasets/wuxinran/SEM_logB/structureLeaning_time" + temp_string
// logFile.parameterLearningTime = "/mnt/shared/Datasets/wuxinran/SEM_logB/parameterLeaning_time" + temp_string
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
outputFile.initial_structure = "out/Log/initial_structure" + temp_string
outputFile.initial_theta = "out/Log/initial_theta" + temp_string
outputFile.optimal_structure = "out/Log/optimal_structure" + temp_string
outputFile.optimal_theta = "out/Log/optimal_theta" + temp_string
logFile.candidateStructure = "out/Log/candidateStructure_file" + temp_string
logFile.parameterLearning = "out/Log/parameterLearning_log" + temp_string
logFile.structureLearning = "out/Log/structureLeaning_log" + temp_string
logFile.BIC_score = "out/Log/BIC_score" + temp_string
logFile.structureLearningTime = "out/Log/structureLeaning_time" + temp_string
logFile.parameterLearningTime = "out/Log/parameterLeaning_time" + temp_string
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
var incremental_change_mark = new Array[Array[Int]](reference.LatentNum_Total)
// 结构合并
StructureLearning2.learningProcess1(bn, bn_Array, 0, incremental_change_mark)
val t_end = new Date() //getTime获取得到的数值单位：数
val runtime = (t_end.getTime - t_start.getTime)/1000.0
val outTime = new FileWriter(logFile.totalTime)
outTime.write(runtime + "\r\n")
outTime.close()
case 7 => // 无隐变量 增量学习
reference.BIC_latent = 0.1
reference.LatentNum_Total = 0 //设置BN隐变量数
var incremental_change_flag = 0
var incremental_change_mark = new Array[Int](reference.NodeNum)
//修改文件名
var temp_string = ".txt"
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
// inputFile.incremental_structure = "/mnt/shared/Datasets/wuxinran/SEM_logB/optimal_structure" + temp_string
// inputFile.incremental_theta = "/mnt/shared/Datasets/wuxinran/SEM_logB/optimal_theta" + temp_string
//
// outputFile.incremental_optimal_structure = "/mnt/shared/Datasets/wuxinran/SEM_logB/Incremental/incremental_optimal_structure" + temp_string
// outputFile.incremental_optimal_theta = "/mnt/shared/Datasets/wuxinran/SEM_logB/Incremental/incremental_optimal_theta" + temp_string
// outputFile.incremental_optimal_structure_S = "/mnt/shared/Datasets/wuxinran/SEM_logB/Incremental/incremental_optimal_structure_S" + temp_string
// outputFile.incremental_optimal_theta_S = "/mnt/shared/Datasets/wuxinran/SEM_logB/Incremental/incremental_optimal_theta_S" + temp_string
//
// logFile.candidateStructure = "/mnt/shared/Datasets/wuxinran/SEM_logB/Incremental/incremental_candidateStructure" + temp_string
// logFile.parameterLearning = "/mnt/shared/Datasets/wuxinran/SEM_logB/parameterLearning_log" + temp_string
// logFile.incremental_Learning = "/mnt/shared/Datasets/wuxinran/SEM_logB/Incremental/incremental_log" + temp_string
// logFile.incremental_BIC_score = "/mnt/shared/Datasets/wuxinran/SEM_logB/Incremental/incremental_BIC_score" + temp_string
// // logFile.incremental_LearningTime = "/mnt/shared/Datasets/wuxinran/SEM_logB/Incremental/incremental_LearningTime" + temp_string
// logFile.parameterLearningTime = "/mnt/shared/Datasets/wuxinran/SEM_logB/parameterLeaning_time" + temp_string
// logFile.structureLearningTime = "/mnt/shared/Datasets/wuxinran/SEM_logB/structureLeaning_time" + temp_string
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
inputFile.incremental_structure = "out/Log/incremental/optimal_structure" + temp_string
inputFile.incremental_theta = "out/Log/Incremental/optimal_theta" + temp_string
outputFile.incremental_optimal_structure = "out/Log/Incremental/incremental_optimal_structure" + temp_string
outputFile.incremental_optimal_theta = "out/Log/Incremental/incremental_optimal_theta" + temp_string
outputFile.incremental_optimal_structure_S = "out/Log/Incremental/incremental_optimal_structure_S" + temp_string
outputFile.incremental_optimal_theta_S = "out/Log/Incremental/incremental_optimal_theta_S" + temp_string
// logFile.incremental_candidateStructure = "out/Log/Incremental/incremental_candidateStructure" + temp_string
logFile.parameterLearning = "out/Log/Incremental/parameterLearning_log" + temp_string
logFile.incremental_Learning = "out/Log/Incremental/incremental_log" + temp_string
logFile.incremental_BIC_score = "out/Log/Incremental/incremental_BIC_score" + temp_string
logFile.incremental_LearningTime = "out/Log/Incremental/incremental_LearningTime" + temp_string
logFile.parameterLearningTime = "out/Log/Incremental/parameterLeaning_time" + temp_string
//////////////////////////////////////////////////////////////////////////////////////////////////////////
//截取与子图相关的数据
reference.inFile_Data = reference.inFile.map {
line =>
val data_Array = line.split(separator.data)
var lines = ""
lines += data_Array(1-1) + separator.data
lines += data_Array(2-1) + separator.data
lines += data_Array(3-1) + separator.data
lines += data_Array(4-1) + separator.data
lines += data_Array(5-1) + separator.data
lines += data_Array(6-1) + separator.data
lines += data_Array(7-1) + separator.data
lines += data_Array(8-1) + separator.data
lines
}
reference.inFile_Data.count()
// 增量学习
incremental_change_mark = new Array[Int](reference.NodeNum)
incremental_change_mark = IncrementalLearning.learningProcess(bn)
if(incremental_change_mark.sum > 0)
incremental_change_flag = 1
println("incremental_change_flag : " + incremental_change_flag)
for(j <- 0 until reference.NodeNum){
print(incremental_change_mark(j) + " ")
}
println()
if(incremental_change_flag == 1 && reference.LatentNum_Total > 1){
print("无子图需要增量学习，无需合并增量学习！")
}else if (incremental_change_flag == 0){
print("无子图需要增量学习，无需合并增量学习！")
}else if (reference.LatentNum_Total == 1){
print("仅有一个隐变量，无需合并增量学习！")
}else{
print("无隐变量，无需合并增量学习！")
}
val t_end = new Date() //getTime获取得到的数值单位：数
val runtime = (t_end.getTime - t_start.getTime)/1000.0
val outTime = new FileWriter(logFile.totalTime)
outTime.write(runtime + "\r\n")
outTime.close()
case 8 => // 无隐变量 各子图增量学习
reference.BIC_latent = 0.1
reference.LatentNum_Total = 0 //子图个数
var bn_Array = new Array[BayesianNetwork](2)
reference.Latent_r = Array(0,0)
println("隐变量的势：")
reference.Latent_r.foreach(println(_))
reference.LatentNum = 0 //设置BN隐变量数
var incremental_change_flag = 0
var incremental_change_mark = new Array[Array[Int]](2)
for(i <- 0 until 2){
reference.R_type = "chest_clinic_2L_3+5_" + (i+1).toString
if(i == 0) { //创建数组中每个对象
reference.NodeNum = 3 //设置BN节点数
bn_Array(i) = new BayesianNetwork(reference.NodeNum)
bn_Array(i).r(0) = 2; bn_Array(i).r(1) = 2; bn_Array(i).r(2) = 2 ;
bn_Array(i).l(0) = "C"; bn_Array(i).l(1) = "S";bn_Array(i).l(2) = "D"
bn_Array(i).latent(3-1) = 1
}else if(i == 1) { //创建数组中每个对象
reference.NodeNum = 5 //设置BN节点数
bn_Array(i) = new BayesianNetwork(reference.NodeNum)
bn_Array(i).r(0) = 2; bn_Array(i).r(1) = 2; bn_Array(i).r(2) = 2; bn_Array(i).r(3) = 2; bn_Array(i).r(4) = 2
bn_Array(i).l(0) = "D"; bn_Array(i).l(1) = "C"; bn_Array(i).l(2) = "D"; bn_Array(i).l(3) = "S"; bn_Array(i).l(4) = "D";
bn_Array(i).latent(5-1) = 1
}
//修改文件名
var temp_string = (i+1).toString + ".txt"
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
// inputFile.incremental_structure = "/mnt/shared/Datasets/wuxinran/SEM_logB/optimal_structure" + temp_string
// inputFile.incremental_theta = "/mnt/shared/Datasets/wuxinran/SEM_logB/optimal_theta" + temp_string
//
// outputFile.incremental_optimal_structure = "/mnt/shared/Datasets/wuxinran/SEM_logB/Incremental/incremental_optimal_structure" + temp_string
// outputFile.incremental_optimal_theta = "/mnt/shared/Datasets/wuxinran/SEM_logB/Incremental/incremental_optimal_theta" + temp_string
// outputFile.incremental_optimal_structure_S = "/mnt/shared/Datasets/wuxinran/SEM_logB/Incremental/incremental_optimal_structure_S" + temp_string
// outputFile.incremental_optimal_theta_S = "/mnt/shared/Datasets/wuxinran/SEM_logB/Incremental/incremental_optimal_theta_S" + temp_string
//
// logFile.candidateStructure = "/mnt/shared/Datasets/wuxinran/SEM_logB/Incremental/incremental_candidateStructure" + temp_string
// logFile.parameterLearning = "/mnt/shared/Datasets/wuxinran/SEM_logB/parameterLearning_log" + temp_string
// logFile.incremental_Learning = "/mnt/shared/Datasets/wuxinran/SEM_logB/Incremental/incremental_log" + temp_string
// logFile.incremental_BIC_score = "/mnt/shared/Datasets/wuxinran/SEM_logB/Incremental/incremental_BIC_score" + temp_string
// // logFile.incremental_LearningTime = "/mnt/shared/Datasets/wuxinran/SEM_logB/Incremental/incremental_LearningTime" + temp_string
// logFile.parameterLearningTime = "/mnt/shared/Datasets/wuxinran/SEM_logB/parameterLeaning_time" + temp_string
// logFile.structureLearningTime = "/mnt/shared/Datasets/wuxinran/SEM_logB/structureLeaning_time" + temp_string
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
inputFile.incremental_structure = "out/Log/incremental/optimal_structure" + temp_string
inputFile.incremental_theta = "out/Log/Incremental/optimal_theta" + temp_string
outputFile.incremental_optimal_structure = "out/Log/Incremental/incremental_optimal_structure" + temp_string
outputFile.incremental_optimal_theta = "out/Log/Incremental/incremental_optimal_theta" + temp_string
outputFile.incremental_optimal_structure_S = "out/Log/Incremental/incremental_optimal_structure_S" + temp_string
outputFile.incremental_optimal_theta_S = "out/Log/Incremental/incremental_optimal_theta_S" + temp_string
// logFile.incremental_candidateStructure = "out/Log/Incremental/incremental_candidateStructure" + temp_string
logFile.parameterLearning = "out/Log/Incremental/parameterLearning_log" + temp_string
logFile.incremental_Learning = "out/Log/Incremental/incremental_log" + temp_string
logFile.incremental_BIC_score = "out/Log/Incremental/incremental_BIC_score" + temp_string
logFile.incremental_LearningTime = "out/Log/Incremental/incremental_LearningTime" + temp_string
logFile.parameterLearningTime = "out/Log/Incremental/parameterLeaning_time" + temp_string
//////////////////////////////////////////////////////////////////////////////////////////////////////////
//截取与子图相关的数据
if(i==0){
reference.inFile_Data = reference.inFile.map {
line =>
val data_Array = line.split(separator.data)
var lines = ""
lines += data_Array(1-1) + separator.data
lines += data_Array(7-1) + separator.data
lines += data_Array(2-1)
lines
}
}else if(i==1){
reference.inFile_Data = reference.inFile.map {
line =>
val data_Array = line.split(separator.data)
var lines = ""
lines += data_Array(3-1) + separator.data
lines += data_Array(4-1) + separator.data
lines += data_Array(6-1) + separator.data
lines += data_Array(8-1) + separator.data
lines += data_Array(5-1)
lines
}
}
reference.inFile_Data.count()
// reference.inFile_Data.foreach(println(_))
// System.exit(0)
// 增量学习
incremental_change_mark(i) = new Array[Int](reference.NodeNum)
incremental_change_mark(i) = IncrementalLearning.learningProcess(bn_Array(i))
if(incremental_change_mark(i).sum > 0)
incremental_change_flag = 1
println("incremental_change_flag : " + incremental_change_flag)
for(j <- 0 until reference.NodeNum){
print(incremental_change_mark(i)(j) + " ")
}
println()
}
// System.exit(0)
if(incremental_change_flag == 1 && 2 > 1){
reference.BIC_latent = 0.26
reference.R_type = "chest_clinic_2L_3+5_merge"
for(i <- 0 until 2){
//修改文件名
var temp_string = (i+1).toString + ".txt"
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
// outputFile.optimal_structure = "/mnt/shared/Datasets/wuxinran/SEM_logB/Incremental/incremental_optimal_structure" + temp_string
// outputFile.optimal_theta = "/mnt/shared/Datasets/wuxinran/SEM_logB/Incremental/incremental_optimal_theta" + temp_string
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
outputFile.optimal_structure = "out/Log/Incremental/incremental_optimal_structure" + temp_string
outputFile.optimal_theta = "out/Log/Incremental/incremental_optimal_theta" + temp_string
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
val input = new BN_Input
//从文件中读取表示BN初始结构的邻接矩阵到bn.structure中
input.structureFromFile(outputFile.optimal_structure, separator.structure, bn_Array(i))
bn_Array(i).qi_computation()
bn_Array(i).create_CPT()
//从文件中读取参数θ到动态三维数组bn.theta
input.CPT_FromFile(outputFile.optimal_theta, separator.theta, bn_Array(i))
}
//修改文件名
var temp_string = ".txt"
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
// outputFile.initial_structure = "/mnt/shared/Datasets/wuxinran/SEM_logB/initial_structure" + temp_string
// outputFile.initial_theta = "/mnt/shared/Datasets/wuxinran/SEM_logB/initial_theta" + temp_string
// outputFile.optimal_structure = "/mnt/shared/Datasets/wuxinran/SEM_logB/optimal_structure" + temp_string
// outputFile.optimal_theta = "/mnt/shared/Datasets/wuxinran/SEM_logB/optimal_theta" + temp_string
// logFile.candidateStructure = "/mnt/shared/Datasets/wuxinran/SEM_logB/candidateStructure_file" + temp_string
// logFile.parameterLearning = "/mnt/shared/Datasets/wuxinran/SEM_logB/parameterLearning_log" + temp_string
// logFile.structureLearning = "/mnt/shared/Datasets/wuxinran/SEM_logB/structureLeaning_log" + temp_string
// logFile.BIC_score = "/mnt/shared/Datasets/wuxinran/SEM_logB/BIC_score" + temp_string
// logFile.structureLearningTime = "/mnt/shared/Datasets/wuxinran/SEM_logB/structureLeaning_time" + temp_string
// logFile.parameterLearningTime = "/mnt/shared/Datasets/wuxinran/SEM_logB/parameterLeaning_time" + temp_string
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
inputFile.incremental_structure = "out/Log/incremental/optimal_structure" + temp_string
inputFile.incremental_theta = "out/Log/incremental/optimal_theta" + temp_string
outputFile.initial_structure = "out/Log/Incremental/initial_structure" + temp_string
outputFile.initial_theta = "out/Log/Incremental/initial_theta" + temp_string
outputFile.optimal_structure = "out/Log/Incremental/incremental_optimal_structure" + temp_string
outputFile.optimal_theta = "out/Log/Incremental/incremental_optimal_theta" + temp_string
logFile.candidateStructure = "out/Log/Incremental/candidateStructure_file" + temp_string
logFile.parameterLearning = "out/Log/Incremental/parameterLearning_log" + temp_string
logFile.structureLearning = "out/Log/Incremental/structureLeaning_log" + temp_string
logFile.BIC_score = "out/Log/Incremental/BIC_score" + temp_string
logFile.structureLearningTime = "out/Log/Incremental/structureLeaning_time" + temp_string
logFile.parameterLearningTime = "out/Log/Incremental/parameterLeaning_time" + temp_string
//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////
// 结构合并
print("incremental_change_mark : ")
for(i <- 0 until 2){
print(incremental_change_mark(i) + " ")
}
println()
// System.exit(0)
StructureLearning2.learningProcess1(bn, bn_Array, 1, incremental_change_mark)
}else if (incremental_change_flag == 0){
print("无子图需要增量学习，无需合并增量学习！")
}else if (reference.LatentNum_Total == 1){
print("仅有一个隐变量，无需合并增量学习！")
}
val t_end = new Date() //getTime获取得到的数值单位：数
val runtime = (t_end.getTime - t_start.getTime)/1000.0
val outTime = new FileWriter(logFile.totalTime)
outTime.write(runtime + "\r\n")
outTime.close()
case 9 => // 各子图增量学习
reference.BIC_latent = 0.1
reference.LatentNum_Total = 2 //设置BN隐变量数