Exemple #1
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def test_experiments_to_cases():
    from examples.FLUvensimExample import FluModel
    from expWorkbench.model import SimpleModelEnsemble
    EMAlogging.log_to_stderr(EMAlogging.INFO)
    
    data = load_results(r'../analysis/1000 flu cases.cPickle')
    experiments, results = data
    cases = experiments_to_cases(experiments)
    
    model = FluModel(r'..\..\models\flu', "fluCase")
    ensemble = SimpleModelEnsemble()
    ensemble.set_model_structure(model)
    ensemble.perform_experiments(cases)
Exemple #2
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def test_save_and_load():
    import matplotlib.pyplot as plt

    from expWorkbench.EMAlogging import log_to_stderr, INFO
    from expWorkbench.model import SimpleModelEnsemble
    from examples.FLUvensimExample import FluModel
    from analysis.graphs import lines
    
    log_to_stderr(level= INFO)
        
    model = FluModel(r'..\..\models\flu', "fluCase")
    ensemble = SimpleModelEnsemble()
#    ensemble.parallel = True
    ensemble.set_model_structure(model)
    
    policies = [{'name': 'no policy',
                 'file': r'\FLUvensimV1basecase.vpm'},
                {'name': 'static policy',
                 'file': r'\FLUvensimV1static.vpm'},
                {'name': 'adaptive policy',
                 'file': r'\FLUvensimV1dynamic.vpm'}
                ]
    ensemble.add_policies(policies)
    
    results = ensemble.perform_experiments(10)
    file = r'C:\eclipse\workspace\EMA workbench\models\results.cPickle'
    save_results(results, file)
    
    results = load_results(file)
   
    lines(results)
    plt.show()
def test_feature_selection():
    from expWorkbench.model import SimpleModelEnsemble
    from examples.FLUvensimExample import FluModel
    
    log_to_stderr(level= INFO)
        
    model = FluModel(r'..\..\models\flu', "fluCase")
    ensemble = SimpleModelEnsemble()
    ensemble.parallel = True
    ensemble.set_model_structure(model)
    
    policies = [{'name': 'no policy',
                 'file': r'\FLUvensimV1basecase.vpm'},
                {'name': 'static policy',
                 'file': r'\FLUvensimV1static.vpm'},
                {'name': 'adaptive policy',
                 'file': r'\FLUvensimV1dynamic.vpm'}
                ]
    ensemble.add_policies(policies)
    
    results = ensemble.perform_experiments(5000)
   
    results = feature_selection(results, classify)
    for entry in results:
        print entry[0] +"\t" + str(entry[1])
    ensemble = SimpleModelEnsemble()
    ensemble.set_model_structure(model)
    
    cases, uncertainties = ensemble._generate_cases(1)
    
    valuelist = [15.467089994193 , 18.3948367845855 , 17.5216359599053 , 0.0323513175268276 , 0.0267216806566911 , 0.0252897989265933 , 0.0211748970259063 , 0.0192967619764282 , 0.0298868721235403 , 0.026846492561752 , 0.0282265728603356 , 0.0274643497911105 , 0.0206173186487346 , 0.930953610229856 , 1.05807449426449 , 58.6261672319115 , 1.0959476696141 , 48.4897275078371 , 79.8968117041453 , 2.03012275630195 , 2.33576352581696 , 2.60266175740213 , 1.24700542123355 , 3.06884098418713 , 1 , 0 , 0 , 0 , 0 , 1.45807445678444 , 3.53395235847141 , 1.75257486371618 , 2.9795030911447 , 4.00199168664975 , 1.97473349200058 , 4.74196793795403 , 4.72730891245437 , 0 , 0 , 14826.4074143275 , 1.24609526886412 , 1.18827514220571 , 1.09824115488565 , 1245886.83942348 , 6282282.69560999 , 6118827.67237203 , 9531496.10651471 , 8693813.50295679 , 32.948697875027 , 17.1705785135149 , 13.0971274404015 , 3.74255065304761 , 1.36231655867486 , 1.92101352688469 , 3.8941723138427 , 0.898745338298322 , 0.782806406356795 , 0.817631734201507 , 0.705822656618514 , 43.3820783577107]


    newcases = [] 
    case = {}
    i=0
    for uncertainty in uncertainties:
        print uncertainty.name
        case[uncertainty.name] = valuelist[i]
        i+=1
#    case['desired fraction'] = 1.0
#    uncertainties.add(ParameterUncertainty((0.7,1), "desired fraction"))    
    newcases.append(case)
    
#    uncertainties.append('desired fraction')
        
    results = ensemble.perform_experiments(newcases)
        
    print results[1]['total fraction new technologies'][0, -1]
    print results[1]['total capacity installed'][0, -1]
    
    lines(results)
    
    plt.show()
    
Exemple #5
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        deceased_population_region_1.append(deceased_population_region_1_NEXT)
        deceased_population_region_2.append(deceased_population_region_2_NEXT)
        
        #End of main code
    return (deceased_population_region_1, runTime, Max_infected, Max_time)

        
if __name__ == "__main__":
    import expWorkbench.EMAlogging as logging
    logging.log_to_stderr(logging.INFO)
    
    fluModel = MexicanFlu(None, "mexican flu example")
    ensemble = SimpleModelEnsemble()
    ensemble.parallel = True
    ensemble.set_model_structure(fluModel)
    result = ensemble.perform_experiments(10)

#    import matplotlib.pyplot as plt
#    figure = plt.figure()
#    ax = figure.add_subplot(111)
#    for entry in result:
#        a = entry[1][0]
#        b = entry[1][1]
#        
#        ax.plot(b,a)
#    plt.show()
    
# Base case parameters
# main(0.4,0.2,0.001,0.002,0.45,0.333,0.1,0.3,0.1,0.5,0.5,1,1,1.5,1,0.1,0.15,50,100,xc)