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Python Parameterization.get_nodes方法代码示例

本文整理汇总了Python中parametering.Parameterization.Parameterization.get_nodes方法的典型用法代码示例。如果您正苦于以下问题：Python Parameterization.get_nodes方法的具体用法？Python Parameterization.get_nodes怎么用？Python Parameterization.get_nodes使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类parametering.Parameterization.Parameterization的用法示例。


在下文中一共展示了Parameterization.get_nodes方法的9个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。

示例1: step08

# 需要导入模块: from parametering.Parameterization import Parameterization [as 别名]
# 或者: from parametering.Parameterization.Parameterization import get_nodes [as 别名]
def step08(paramFile):
    #util = ParameterUtil(parameter_file = 'data/formatado/arxiv/nowell_example_1994_1999.txt')
    util = ParameterUtil(parameter_file = paramFile)

    myparams = Parameterization(util.keyword_decay, util.lengthVertex, util.t0, util.t0_, util.t1, util.t1_, util.FeaturesChoiced, util.graph_file, util.trainnig_graph_file, util.test_graph_file, util.decay)
    myparams.generating_Training_Graph()
    myparams.generating_Test_Graph()
    print "Trainning Period:", myparams.t0, " - ", myparams.t0_
    print "Test Period:", myparams.t1, " - ", myparams.t1_
    
    print "# Papers in Trainning: ",  myparams.get_edges(myparams.trainnigGraph)
    print "# Authors in Training: ", myparams.get_nodes(myparams.trainnigGraph)
    print "# Papers in Test: ",  myparams.get_edges(myparams.testGraph)
    print "# Authors in Test", myparams.get_nodes(myparams.testGraph)
    
    calc = Calculate(myparams, util.nodes_notlinked_file, util.calculated_file, util.ordered_file, util.maxmincalculated_file)
    calc.reading_Max_min_file()
    print "# pair of Authors with at least 3 articles Calculated: ", calc.qtyDataCalculated  #FormatingDataSets.getTotalLineNumbers(FormatingDataSets.get_abs_file_path(util.calculated_file))
    topRank = Analyse.getTopRank(util.analysed_file+ '.random.analised.txt')
    print "# pair of Authors with at least 3 articles that is connected in Test Graph in a random way: ", topRank
    print "Max values found in calculations: ", str(calc.maxValueCalculated)
    print "Min Values found in calculations: ", str(calc.minValueCalculated)
    for pathFile in calc.getfilePathOrdered_separeted():
        print "File Analised: ", pathFile +  '.analised.txt'
        number_connected =  Analyse.getTopRankABSPathFiles(pathFile + '.analised.txt')
        print "# pair of Authors that is connected in Test Graph: ", number_connected
        print "%: ", Analyse.getLastInfosofResultsABSPathFiles(pathFile + '.analised.txt', topRank)
        print "---------------------------------"
开发者ID:AndersonChaves，项目名称:Predicao-de-Links，代码行数:30，代码来源:Step08.py

示例2: execution

# 需要导入模块: from parametering.Parameterization import Parameterization [as 别名]
# 或者: from parametering.Parameterization.Parameterization import get_nodes [as 别名]
def execution(configFile):
    #DEFINE THE FILE THAT WILL KEEP THE RESULT DATA
    resultFile = open(FormatingDataSets.get_abs_file_path(configFile + 'T.EXPERIMENTO_ATUAL_CORE03.txt'), 'w')
    
    resultFile.write("Inicio da operacao\n")
    resultFile.write(str(datetime.datetime.now()))
    resultFile.write("\n")

    
    #READING THE CONFIG FILE
    util = ParameterUtil(parameter_file = configFile)
    #CREATING PARAMETRIZATION OBJECT WITH THE INFORMATIONS OF THE CONFIG FILE.
    myparams = Parameterization(t0 = util.t0, t0_ = util.t0_, t1 = util.t1, t1_ = util.t1_, linear_combination=util.linear_combination,
                                filePathGraph = util.graph_file, filePathTrainingGraph = util.trainnig_graph_file, filePathTestGraph = util.test_graph_file, decay = util.decay, domain_decay = util.domain_decay, min_edges = util.min_edges, scoreChoiced = util.ScoresChoiced, weightsChoiced = util.WeightsChoiced, weightedScoresChoiced = util.WeightedScoresChoiced, FullGraph = None, result_random_file=util.result_random_file)

    #GENERATING TRAINNING GRAPH BASED ON CONFIG FILE T0 AND T0_
    myparams.generating_Training_Graph()
      
    #GENERATING TEST GRAPH BASED ON CONcvb FIG FILE T1 AND T1_
    myparams.generating_Test_Graph()
    nodesSelection = NodeSelection(myparams.trainnigGraph, myparams.testGraph, util)
    #GET THE AUTHORS THAT PUBLISH AT TRAINNING AND TEST 
    #A NUMBER OF PAPERS DEFINED AT MIN_EDGES IN CONFIG FILE
    nodes = nodesSelection.get_NowellAuthorsCore()
    #GET A PAIR OF AUTHORS THAT PUBLISH AT LEAST ONE ARTICLE AT TRAINNING AND TEST.
    #DID NOT SEE ANY NEED
    collaborations = nodesSelection.get_NowellColaboration()
    #GET THE FIRST EDGES MADE BY THE COMBINATION OF NODES IN TRAINNING GRAPH
    eOld = nodesSelection.get_NowellE(nodes,myparams.trainnigGraph)
    #GET THE FIRST EDGES MADE BY THE COMBINATION OF NODES IN TEST GRAPH THAT DO NOT HAVE EDGES IN TRAINNING
    eNew = nodesSelection.get_NowellE2(nodes, eOld, myparams.testGraph)
    #GET THE NODES NOT LINKED OVER THE COMBINATION NODES.
    nodesNotLinked = nodesSelection.get_PairsofNodesNotinEold(nodes)
    #CREATING CALCULATION OBJECT
    calc = CalculateInMemory(myparams,nodesNotLinked)
    #CALCULATING THE SCORES.
    resultsofCalculation = calc.executingCalculate()
    #ORDERNING THE RESULTS RETURNING THE TOP N 
    orderingResults = calc.ordering(len(eNew), resultsofCalculation)
    #SAVING THE ORDERED RESULTS.
    calc.saving_orderedResult(util.ordered_file, orderingResults)
    #ANALISE THE ORDERED RESULTS AND CHECK THE FUTURE.
    ScoresResults = Analyse.AnalyseNodesWithScoresInFuture(orderingResults, myparams.testGraph)
    #SAVING THE RESULTS.  
    for index in range(len(ScoresResults)):
        Analyse.saving_analyseResult(ScoresResults[index], util.analysed_file + str(myparams.ScoresChoiced[index][0] ) + '.txt')
        resultFile.write("TOTAL OF SUCESSS USING METRIC "  + str(myparams.ScoresChoiced[index][0])  + " = " +  str(Analyse.get_TotalSucess(ScoresResults[index]) ))
        resultFile.write("\n")
        resultFile.write("\n")
         
    resultFile.write("Authors\tArticles\tCollaborations\tAuthors\tEold\tEnew\n")
    resultFile.write( str(myparams.get_nodes(myparams.trainnigGraph))+ "\t" + str(myparams.get_edges(myparams.trainnigGraph)) + "\t\t" + str(len(collaborations)*2)+ "\t\t" + str(len(nodes)) + "\t" + str(len(eOld))+"\t" + str(len(eNew)))
     
    resultFile.write("\n")

    resultFile.write("Fim da Operacao\n")
    resultFile.write(str(datetime.datetime.now()))
    
    resultFile.close()
开发者ID:AndersonChaves，项目名称:Predicao-de-Links，代码行数:61，代码来源:ExecutionNowellFull.py

示例3: execution

# 需要导入模块: from parametering.Parameterization import Parameterization [as 别名]
# 或者: from parametering.Parameterization.Parameterization import get_nodes [as 别名]
def execution(configFile):
    
    #DEFINE THE FILE THAT WILL KEEP THE RESULT DATA
    resultFile = open(FormatingDataSets.get_abs_file_path(configFile + 'core03.txt'), 'w')
    
    resultFile.write("Inicio da operacao\n")
    resultFile.write(str(datetime.datetime.now()))
    resultFile.write("\n")

    
    #READING THE CONFIG FILE
    util = ParameterUtil(parameter_file = configFile)
    #CREATING PARAMETRIZATION OBJECT WITH THE INFORMATIONS OF THE CONFIG FILE.
    myparams = Parameterization(t0 = util.t0, t0_ = util.t0_, t1 = util.t1, t1_ = util.t1_, linear_combination=util.linear_combination,
                                filePathGraph = util.graph_file, filePathTrainingGraph = util.trainnig_graph_file, filePathTestGraph = util.test_graph_file, decay = util.decay, domain_decay = util.domain_decay, min_edges = util.min_edges, scoreChoiced = util.ScoresChoiced, weightsChoiced = util.WeightsChoiced, weightedScoresChoiced = util.WeightedScoresChoiced, FullGraph = None, result_random_file=util.result_random_file)

    #GENERATING TRAINNING GRAPH BASED ON CONFIG FILE T0 AND T0_
    myparams.generating_Training_Graph()
      
    #GENERATING TEST GRAPH BASED ON CONcvb FIG FILE T1 AND T1_
    myparams.generating_Test_Graph()
    
    nodeSelection = NodeSelection(myparams.trainnigGraph, myparams.testGraph, util)
    #if not os.path.exists(FormatingDataSets.get_abs_file_path(util.trainnig_graph_file + '.fuzzyinputy.txt')):
    data = calculatingInputToFuzzy(myparams.trainnigGraph,nodeSelection.nodesNotLinked,  myparams)
    dataSorted = sorted(data, key=lambda value: value['result'], reverse=True)
    
    topRank = len(nodeSelection.eNeW)
    totalCalculated = len(dataSorted)
    dataToAnalysed = []
    if (topRank >= totalCalculated):
        for item in range(totalCalculated):
            dataToAnalysed.append({'no1':  dataSorted[item]['no1'], 'no2': dataSorted[item]['no2'], 'result':  dataSorted[item]['result'] })
    else:
        for item in range(topRank):
            dataToAnalysed.append({'no1':  dataSorted[item]['no1'], 'no2': dataSorted[item]['no2'], 'result':  dataSorted[item]['result'] })
            
    
    analise = AnalyseNodesInFuture(dataToAnalysed, myparams.testGraph)
    
    resultFile.write( repr(get_TotalSucess(analise)) )   
    
    resultFile.write("\n")
#        
    resultFile.write("Authors\tArticles\tCollaborations\tAuthors\tEold\tEnew\n")
    resultFile.write( str(myparams.get_nodes(myparams.trainnigGraph))+ "\t" + str(myparams.get_edges(myparams.trainnigGraph)) + "\t\t" + str(len(nodeSelection.get_NowellColaboration())*2)+ "\t\t" + str(len(nodeSelection.nodes)) + "\t" + str(len(nodeSelection.eOld))+"\t" + str(len(nodeSelection.eNeW)))
     
 
    resultFile.write("\n")

    resultFile.write("Fim da Operacao\n")
    resultFile.write(str(datetime.datetime.now()))
    
    resultFile.close()
开发者ID:cptullio，项目名称:Predicao-de-Links，代码行数:56，代码来源:FullExecutionGraphNowell_NoCN.py

示例4: execution

# 需要导入模块: from parametering.Parameterization import Parameterization [as 别名]
# 或者: from parametering.Parameterization.Parameterization import get_nodes [as 别名]
def execution(configFile):
   
    
    #DEFINE THE FILE THAT WILL KEEP THE RESULT DATA
    resultFile = open(FormatingDataSets.get_abs_file_path(configFile + 'wTScore03_010304.txt'), 'w')
    
    resultFile.write("Inicio da operacao\n")
    resultFile.write(str(datetime.datetime.now()))
    resultFile.write("\n")

    
    #READING THE CONFIG FILE
    util = ParameterUtil(parameter_file = configFile)
    #CREATING PARAMETRIZATION OBJECT WITH THE INFORMATIONS OF THE CONFIG FILE.
    myparams = Parameterization(t0 = util.t0, t0_ = util.t0_, t1 = util.t1, t1_ = util.t1_, linear_combination=util.linear_combination,
                                filePathGraph = util.graph_file, filePathTrainingGraph = util.trainnig_graph_file, filePathTestGraph = util.test_graph_file, decay = util.decay, domain_decay = util.domain_decay, min_edges = util.min_edges, scoreChoiced = util.ScoresChoiced, weightsChoiced = util.WeightsChoiced, weightedScoresChoiced = util.WeightedScoresChoiced, FullGraph = None, result_random_file=util.result_random_file)

    #GENERATING TRAINNING GRAPH BASED ON CONFIG FILE T0 AND T0_
    myparams.generating_Training_Graph()
      
    #GENERATING TEST GRAPH BASED ON CONcvb FIG FILE T1 AND T1_
    myparams.generating_Test_Graph()
    
    nodeSelection = NodeSelection(myparams.trainnigGraph, myparams.testGraph, util)
    db = None
    if not os.path.exists(FormatingDataSets.get_abs_file_path(util.trainnig_graph_file + '.base.pdl')):
        db = generateWeights(myparams.trainnigGraph, FormatingDataSets.get_abs_file_path(util.trainnig_graph_file + '.base.pdl') , myparams)
    else:
        db = reading_Database(FormatingDataSets.get_abs_file_path(util.trainnig_graph_file + '.base.pdl'))
    calcDb = None
    if not os.path.exists(FormatingDataSets.get_abs_file_path(util.calculated_file + '.base.pdl')):
        calcDb = calculatingWeights(myparams.trainnigGraph, nodeSelection.nodesNotLinked, db, FormatingDataSets.get_abs_file_path(util.calculated_file) + '.base.pdl')
    else:
        calcDb = reading_Database(FormatingDataSets.get_abs_file_path(util.calculated_file + '.base.pdl'))
        
    ordering = get_ordering(calcDb, len(nodeSelection.eNeW))
    
    result = get_analyseNodesInFuture(ordering, myparams.testGraph)
    
    resultFile.write(repr(result))
    
    resultFile.write("\n")
#        
    resultFile.write("Authors\tArticles\tCollaborations\tAuthors\tEold\tEnew\n")
    resultFile.write( str(myparams.get_nodes(myparams.trainnigGraph))+ "\t" + str(myparams.get_edges(myparams.trainnigGraph)) + "\t\t" + str(len(nodeSelection.get_NowellColaboration())*2)+ "\t\t" + str(len(nodeSelection.nodes)) + "\t" + str(len(nodeSelection.eOld))+"\t" + str(len(nodeSelection.eNeW)))
     
 
    resultFile.write("\n")

    resultFile.write("Fim da Operacao\n")
    resultFile.write(str(datetime.datetime.now()))
    
    resultFile.close()
开发者ID:cptullio，项目名称:Predicao-de-Links，代码行数:55，代码来源:FullExecutionGraphRich_versaoTempo.py

示例5: execution

# 需要导入模块: from parametering.Parameterization import Parameterization [as 别名]
# 或者: from parametering.Parameterization.Parameterization import get_nodes [as 别名]
def execution(configFile):
    
    #DEFINE THE FILE THAT WILL KEEP THE RESULT DATA
    resultFile = open(FormatingDataSets.get_abs_file_path(configFile + 'core03.txt'), 'w')
    
    resultFile.write("Inicio da operacao\n")
    resultFile.write(str(datetime.datetime.now()))
    resultFile.write("\n")

    
    #READING THE CONFIG FILE
    util = ParameterUtil(parameter_file = configFile)
    #CREATING PARAMETRIZATION OBJECT WITH THE INFORMATIONS OF THE CONFIG FILE.
    myparams = Parameterization(t0 = util.t0, t0_ = util.t0_, t1 = util.t1, t1_ = util.t1_, linear_combination=util.linear_combination,
                                filePathGraph = util.graph_file, filePathTrainingGraph = util.trainnig_graph_file, filePathTestGraph = util.test_graph_file, decay = util.decay, domain_decay = util.domain_decay, min_edges = util.min_edges, scoreChoiced = util.ScoresChoiced, weightsChoiced = util.WeightsChoiced, weightedScoresChoiced = util.WeightedScoresChoiced, FullGraph = None, result_random_file=util.result_random_file)

    #GENERATING TRAINNING GRAPH BASED ON CONFIG FILE T0 AND T0_
    myparams.generating_Training_Graph()
      
    #GENERATING TEST GRAPH BASED ON CONcvb FIG FILE T1 AND T1_
    myparams.generating_Test_Graph()
    
    nodeSelection = NodeSelection(myparams.trainnigGraph, myparams.testGraph, util)
    #if not os.path.exists(FormatingDataSets.get_abs_file_path(util.trainnig_graph_file + '.fuzzyinputy.txt')):
    data = calculatingInputToFuzzy(myparams.trainnigGraph,nodeSelection.nodesNotLinked,  myparams)
    saving_files_calculting_input(FormatingDataSets.get_abs_file_path(util.trainnig_graph_file + '.inputFuzzy.txt'), data)
    
    for item in data:
        calc = FuzzyCalculation(item['intensityno1'], item['intensityno2'], item['similarity'], item['ageno1'], item['ageno2'])
        print item['no1'], item['no2'], calc.potencial_ligacao, calc.grau_potencial_ligacao
        
        
       
    
    resultFile.write("\n")
#        
    resultFile.write("Authors\tArticles\tCollaborations\tAuthors\tEold\tEnew\n")
    resultFile.write( str(myparams.get_nodes(myparams.trainnigGraph))+ "\t" + str(myparams.get_edges(myparams.trainnigGraph)) + "\t\t" + str(len(nodeSelection.get_NowellColaboration())*2)+ "\t\t" + str(len(nodeSelection.nodes)) + "\t" + str(len(nodeSelection.eOld))+"\t" + str(len(nodeSelection.eNeW)))
     
 
    resultFile.write("\n")

    resultFile.write("Fim da Operacao\n")
    resultFile.write(str(datetime.datetime.now()))
    
    resultFile.close()
开发者ID:andreluizmelo，项目名称:Predicao-de-Links，代码行数:48，代码来源:FullExecutionGraphNowell.py

示例6: execution

# 需要导入模块: from parametering.Parameterization import Parameterization [as 别名]
# 或者: from parametering.Parameterization.Parameterization import get_nodes [as 别名]
def execution(configFile):
    #DEFINE THE FILE THAT WILL KEEP THE RESULT DATA
    resultFile = open(FormatingDataSets.get_abs_file_path(configFile + 'core03_execucaoFinal_cstT02.txt'), 'w')
    
    resultFile.write("Inicio da operacao\n")
    resultFile.write(str(datetime.datetime.now()))
    resultFile.write("\n")
    
    #READING THE CONFIG FILE
    util = ParameterUtil(parameter_file = configFile)
    #CREATING PARAMETRIZATION OBJECT WITH THE INFORMATIONS OF THE CONFIG FILE.
    myparams = Parameterization(t0 = util.t0, t0_ = util.t0_, t1 = util.t1, t1_ = util.t1_, linear_combination=util.linear_combination,
                                filePathGraph = util.graph_file, filePathTrainingGraph = util.trainnig_graph_file, filePathTestGraph = util.test_graph_file, decay = util.decay, domain_decay = util.domain_decay, min_edges = util.min_edges, scoreChoiced = util.ScoresChoiced, weightsChoiced = util.WeightsChoiced, weightedScoresChoiced = util.WeightedScoresChoiced, FullGraph = None, result_random_file=util.result_random_file)

    #GENERATING TRAINNING GRAPH BASED ON CONFIG FILE T0 AND T0_
    myparams.generating_Training_Graph()
      
    #GENERATING TEST GRAPH BASED ON CONcvb FIG FILE T1 AND T1_
    myparams.generating_Test_Graph()
    
    nodeSelection = NodeSelection(myparams.trainnigGraph, myparams.testGraph, util)
    #CREATING CALCULATION OBJECT
    calc = CalculatingTogether(myparams, nodeSelection.nodesNotLinked)
    
    ordering = calc.ordering(len(nodeSelection.eNeW))
    
    #calc.saving_orderedResult(util.ordered_file, ordering)
    
    calc.AnalyseNodesInFuture(ordering, myparams.testGraph)
    
    resultFile.write(repr(calc.get_TotalSucess()))
    
    resultFile.write("\n")
#        
    resultFile.write("Authors\tArticles\tCollaborations\tAuthors\tEold\tEnew\n")
    resultFile.write( str(myparams.get_nodes(myparams.trainnigGraph))+ "\t" + str(myparams.get_edges(myparams.trainnigGraph)) + "\t\t" + str(len(nodeSelection.get_NowellColaboration())*2)+ "\t\t" + str(len(nodeSelection.nodes)) + "\t" + str(len(nodeSelection.eOld))+"\t" + str(len(nodeSelection.eNeW)))
     
 
    resultFile.write("\n")

    resultFile.write("Fim da Operacao\n")
    resultFile.write(str(datetime.datetime.now()))
    
    resultFile.close()
开发者ID:cptullio，项目名称:Predicao-de-Links，代码行数:46，代码来源:FullExecutionGraphArxiv.py

示例7: execution

# 需要导入模块: from parametering.Parameterization import Parameterization [as 别名]
# 或者: from parametering.Parameterization.Parameterization import get_nodes [as 别名]
def execution(configFile, metricas):
    #DEFINE THE FILE THAT WILL KEEP THE RESULT DATA
    resultFile = open(FormatingDataSets.get_abs_file_path(configFile + 'core03.txt'), 'w')
    
    resultFile.write("Inicio da operacao\n")
    resultFile.write(str(datetime.datetime.now()))
    resultFile.write("\n")
    
    #READING THE CONFIG FILE
    util = ParameterUtil(parameter_file = configFile)
    #CREATING PARAMETRIZATION OBJECT WITH THE INFORMATIONS OF THE CONFIG FILE.
    myparams = Parameterization(t0 = util.t0, t0_ = util.t0_, t1 = util.t1, t1_ = util.t1_, linear_combination=util.linear_combination,
                                filePathGraph = util.graph_file, filePathTrainingGraph = util.trainnig_graph_file, filePathTestGraph = util.test_graph_file, decay = util.decay, domain_decay = util.domain_decay, min_edges = util.min_edges, scoreChoiced = util.ScoresChoiced, weightsChoiced = util.WeightsChoiced, weightedScoresChoiced = util.WeightedScoresChoiced, FullGraph = None, result_random_file=util.result_random_file)

    #GENERATING TRAINNING GRAPH BASED ON CONFIG FILE T0 AND T0_
    myparams.generating_Training_Graph()
      
    #GENERATING TEST GRAPH BASED ON CONcvb FIG FILE T1 AND T1_
    myparams.generating_Test_Graph()
    
    nodeSelection = NodeSelection(myparams.trainnigGraph, myparams.testGraph, util)
    
    #CREATING CALCULATION OBJECT
    weights = {'cn' : 1, 'aas': 1, 'pa':1, 'jc': 1, 'ts08':1,'ts05': 1, 'ts02':1}
    
    calc = CalculatingCombinationOnlyNowell(myparams, nodeSelection.nodesNotLinked,weights,False )

    saving_files_calculting(FormatingDataSets.get_abs_file_path(util.calculated_file), calc.results, metricas)
    
    Analise = nodeSelection.AnalyseAllNodesNotLinkedInFuture(nodeSelection.nodesNotLinked, myparams.testGraph)
    salvar_analise(FormatingDataSets.get_abs_file_path(util.analysed_file) + '.allNodes.csv', Analise)
    
    resultFile.write("Authors\tArticles\tCollaborations\tAuthors\tEold\tEnew\n")
    resultFile.write( str(myparams.get_nodes(myparams.trainnigGraph))+ "\t" + str(myparams.get_edges(myparams.trainnigGraph)) + "\t\t" + str(len(nodeSelection.get_NowellColaboration())*2)+ "\t\t" + str(len(nodeSelection.nodes)) + "\t" + str(len(nodeSelection.eOld))+"\t" + str(len(nodeSelection.eNeW)))
     
 
    resultFile.write("\n")

    resultFile.write("Fim da Operacao\n")
    resultFile.write(str(datetime.datetime.now()))
    
    resultFile.close()
开发者ID:AndersonChaves，项目名称:Predicao-de-Links，代码行数:44，代码来源:prepareToAG.py

示例8: execution

# 需要导入模块: from parametering.Parameterization import Parameterization [as 别名]
# 或者: from parametering.Parameterization.Parameterization import get_nodes [as 别名]
def execution(configFile, weights):
    #DEFINE THE FILE THAT WILL KEEP THE RESULT DATA
    resultFile = open(FormatingDataSets.get_abs_file_path(configFile + 'core03.txt'), 'w')
    
    resultFile.write("Inicio da operacao\n")
    resultFile.write(str(datetime.now()))
    resultFile.write("\n")
    #READING THE CONFIG FILE
    util = ParameterUtil(parameter_file = configFile)
    
    myparams = Parameterization(t0 = util.t0, t0_ = util.t0_, t1 = util.t1, t1_ = util.t1_, linear_combination=util.linear_combination,
                                filePathGraph = util.graph_file, filePathTrainingGraph = util.trainnig_graph_file, filePathTestGraph = util.test_graph_file, decay = util.decay, domain_decay = util.domain_decay, min_edges = util.min_edges, scoreChoiced = util.ScoresChoiced, weightsChoiced = util.WeightsChoiced, weightedScoresChoiced = util.WeightedScoresChoiced, FullGraph = None, result_random_file=util.result_random_file)
    
    myparams.generating_Test_Graph()
    myparams.generating_Training_Graph()
    
    nodeSelection = NodeSelection(myparams.trainnigGraph, myparams.testGraph, util)
    #CREATING CALCULATION OBJECT
    calc = CalculatingCombinationOnlyNowell(myparams, nodeSelection.nodesNotLinked, weights, True)
        
    ordering = calc.ordering(len(nodeSelection.eNeW))
    
    calc.AnalyseNodesInFuture(ordering, myparams.testGraph)
    
    resultFile.write(repr(calc.get_TotalSucess()))
    
    resultFile.write("\n")
#        
    resultFile.write("Authors\tArticles\tCollaborations\tAuthors\tEold\tEnew\n")
    resultFile.write( str(myparams.get_nodes(myparams.trainnigGraph))+ "\t" + str(myparams.get_edges(myparams.trainnigGraph)) + "\t\t" + str(len(nodeSelection.get_NowellColaboration())*2)+ "\t\t" + str(len(nodeSelection.nodes)) + "\t" + str(len(nodeSelection.eOld))+"\t" + str(len(nodeSelection.eNeW)))
     
 
    resultFile.write("\n")

    resultFile.write("Fim da Operacao\n")
    resultFile.write(str(datetime.now()))
    
    resultFile.close()
开发者ID:AndersonChaves，项目名称:Predicao-de-Links，代码行数:40，代码来源:AfterAG.py

示例9: Analyse

# 需要导入模块: from parametering.Parameterization import Parameterization [as 别名]
# 或者: from parametering.Parameterization.Parameterization import get_nodes [as 别名]
 resultsCalculate = calc.executingCalculate()
 
 
 calc.Separating_calculateFile()
 analise = Analyse(myparams, FormatingDataSets.get_abs_file_path(util.calculated_file), FormatingDataSets.get_abs_file_path(util.analysed_file) + '.random.analised.txt', calc.qtyDataCalculated)
 topRank = Analyse.getTopRank(util.analysed_file + '.random.analised.txt')
 calc.Ordering_separating_File(topRank)
 for OrderingFilePath in calc.getfilePathOrdered_separeted():
     analise = Analyse(myparams, OrderingFilePath, OrderingFilePath + '.analised.txt', topRank )
 
 
 print "Trainning Period:", myparams.t0, " - ", myparams.t0_
 print "Test Period:", myparams.t1, " - ", myparams.t1_
 
 print "# Papers in Trainning: ",  myparams.get_edges(myparams.trainnigGraph)
 print "# Authors in Training: ", myparams.get_nodes(myparams.trainnigGraph)
 print "# Papers in Test: ",  myparams.get_edges(myparams.testGraph)
 print "# Authors in Test", myparams.get_nodes(myparams.testGraph)
 
 print "# pair of Authors with at least 3 articles Calculated: ", calc.qtyDataCalculated  #FormatingDataSets.getTotalLineNumbers(FormatingDataSets.get_abs_file_path(util.calculated_file))
 print "# pair of Authors that is connected in Test Graph in a random way: ", topRank
 print "Max values found in calculations: ", str(calc.maxValueCalculated)
 print "Min Values found in calculations: ", str(calc.minValueCalculated)
 for pathFile in calc.getfilePathOrdered_separeted():
     print "File Analised: ", pathFile +  '.analised.txt'
     number_connected =  Analyse.getTopRankABSPathFiles(pathFile + '.analised.txt')
     print "# pair of Authors that is connected in Test Graph: ", number_connected
     print "%: ", Analyse.getLastInfosofResultsABSPathFiles(pathFile + '.analised.txt', topRank)
     print "---------------------------------"
开发者ID:AndersonChaves，项目名称:Predicao-de-Links，代码行数:31，代码来源:SingleExecutionInMemory.py


注：本文中的parametering.Parameterization.Parameterization.get_nodes方法示例由纯净天空整理自Github/MSDocs等开源代码及文档管理平台，相关代码片段筛选自各路编程大神贡献的开源项目，源码版权归原作者所有，传播和使用请参考对应项目的License；未经允许，请勿转载。