def setUp(self):
data_ = zeros((5,8))
random.seed(1)
data_[:,:4] = random.rand(5,4)
data_ = DataArray(data_, ['Const', 'Var1', 'Var2', 'Var3', 'choice2_ind',
'choice1', 'choice2', 'choice3'])
var_list = [('table1', 'Var1'),
('table1', 'Var2'),
('table1', 'Var3'),
('table1', 'Const')]
variance = array([[1]])
choice1 = ['choice1']
choice2 = ['choice2']
choice3 = ['SOV', 'HOV']
coefficients1 = [{'const':2, 'Var1':2.11}]
coefficients2 = [{'const':1.5, 'var2':-.2, 'var3':16.4}]
coefficients3 = [{'Const':2, 'Var1':2.11}, {'Const':1.2}]
specification1 = Specification(choice1, coefficients1)
specification2 = Specification(choice2, coefficients2)
specification3 = Specification(choice3, coefficients3)
errorspecification = LinearRegErrorSpecification(variance)
model1 = LinearRegressionModel(specification1, errorspecification)
model2 = LinearRegressionModel(specification2, errorspecification)
model3 = LogitChoiceModel(specification3)
data_filter2 = DataFilter('choice2_ind', 'less than', 25,
{'choice2_ind':1, 'choice2':1}) #Run Until Condition
#Subset for the model condition
data_filter1 = DataFilter('Const', 'less than', 0.3)
model_seq1 = SubModel(model1, 'regression', 'choice1')
model_seq2 = SubModel(model2, 'regression', 'choice2',
data_filter=data_filter1, run_until_condition=data_filter2)
model_seq3 = SubModel(model3, 'choice', 'choice3',
run_until_condition=data_filter2)
model_list = [model_seq1, model_seq2, model_seq3]
# SPECIFY SEED TO REPLICATE RESULTS, DATA FILTER AND
# RUN UNTIL CONDITION
component = AbstractComponent('DummyComponent', model_list, var_list)
component.run(data_)
print component.data.data
def __init__(self, specification): error_specification = LinearRegErrorSpecification(array([[0]])) LinearRegressionModel.__init__(self, specification, error_specification)
def __init__(self, specification):
error_specification = LinearRegErrorSpecification(array([[0]]))
LinearRegressionModel.__init__(self, specification,
error_specification)
def __init__(self, specification, error_specification):
LinearRegressionModel.__init__(self, specification, error_specification)
def __init__(self, specification, error_specification):
LinearRegressionModel.__init__(self, specification, error_specification)
