'''
Created on Mar 15, 2012
.. codeauthor:: jhkwakkel <j.h.kwakkel (at) tudelft (dot) nl>
'''
from analysis import clusterer
from util import ema_logging
from core import ModelEnsemble
from test.scarcity_example import ScarcityModel
if __name__ == "__main__":
ema_logging.log_to_stderr(ema_logging.INFO)
model = ScarcityModel(r'..\..\src\test', "fluCase")
ensemble = ModelEnsemble()
ensemble.set_model_structure(model)
ensemble.parallel = True
results = ensemble.perform_experiments(200)
clusterer.cluster(data=results,
outcome='relative market price',
distance='gonenc',
cMethod='maxclust',
cValue=5,
plotDendrogram=False)
'''
This class represents a simple example of how one can extent the basic
ModelStructureInterface in order to do EMA on a simple model coded in
Python directly
'''
#specify uncertainties
uncertainties = [
ParameterUncertainty((0.1, 10), "x1"),
ParameterUncertainty((-0.01, 0.01), "x2"),
ParameterUncertainty((-0.01, 0.01), "x3")
]
#specify outcomes
outcomes = [Outcome('y')]
def model_init(self, policy, kwargs):
pass
def run_model(self, case):
"""Method for running an instantiated model structure """
self.output[
self.outcomes[0].name] = case['x1'] * case['x2'] + case['x3']
if __name__ == '__main__':
model = SimplePythonModel(None, 'simpleModel') #instantiate the model
ensemble = ModelEnsemble() #instantiate an ensemble
# ensemble.parallel = True
ensemble.set_model_structure(model) #set the model on the ensemble
results = ensemble.perform_experiments(1000) #run 1000 experiments
