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_Training_Graph() selection = VariableSelection(myparams.trainnigGraph, util.min_edges) nodesNotLinked = selection.readingResultsFile(util.nodes_notlinked_file) calc = CalculateInMemory(myparams, nodesNotLinked) resultsofCalculation = calc.executingCalculate() #print resultsofCalculation calc.saving_calculateResult(util.calculated_file, resultsofCalculation) calc.saving_calculateResult_normalized( util.calculated_file + '_normalizated.csv', resultsofCalculation) result2 = calc.reading_calculateResult_normalized(util.calculated_file) calc.save_Max_min_file(util.maxmincalculated_file, calc.qtyDataCalculated, calc.minValueCalculated, calc.maxValueCalculated) print resultsofCalculation print result2
from parametering.ParameterUtil import ParameterUtil from parametering.Parameterization import Parameterization from calculating.CalculateInMemory import CalculateInMemory from calculating.VariableSelection import VariableSelection if __name__ == '__main__': util = ParameterUtil(parameter_file = 'data/formatado/arxiv/exemplomenor/config/config.txt') #util = ParameterUtil(parameter_file = 'data/formatado/arxiv/nowell_astroph_1994_1999/config/configuration_forAG.txt') 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_Training_Graph() selection = VariableSelection(myparams.trainnigGraph, util.min_edges) nodesNotLinked = selection.readingResultsFile(util.nodes_notlinked_file) calc = CalculateInMemory(myparams,nodesNotLinked) resultsofCalculation = calc.executingCalculate() #print resultsofCalculation calc.saving_calculateResult(util.calculated_file, resultsofCalculation) calc.saving_calculateResult_normalized(util.calculated_file + '_normalizated.csv', resultsofCalculation) result2 = calc.reading_calculateResult_normalized(util.calculated_file) calc.save_Max_min_file(util.maxmincalculated_file, calc.qtyDataCalculated, calc.minValueCalculated, calc.maxValueCalculated) print resultsofCalculation print result2