self.p_out = self.dist_types[parameter](*self.dist_args[parameter])
if type(self.p_out) == 'numpy.ndarray':
self.outputs += p_out.tolist()
else:
self.outputs.append(self.p_out)
else:
self.p_out = self.dist_types['Default'](*self.dist_args['Default'])
self.outputs.append(self.p_out)
else: #parameters is none: default to num_parameters and utilize defaults
self._args = self.dist_args['Default'][:]
self._args.append(self.num_parameters)
self.outputs = self.dist_types['Default'](*self._args)
self._outputs = array(self.outputs)
return self._outputs
else:
raise StopIteration()
if __name__ == "__main__":
from numpy import random
from montecarlo import MonteCarlo
_test = MonteCarlo()
_test.num_parameters = 7
_test.parameters = ['x','y','z','u']
_test.num_samples = 10
_test.dist_types = {'Default':random.uniform,'y':random.standard_normal}
_test.dist_args = {'Default':[0,1],'y':[]}
for iters in _test:
A = iters
print(A)
self.outputs += p_out.tolist()
else:
self.outputs.append(self.p_out)
else:
self.p_out = self.dist_types["Default"](*self.dist_args["Default"])
self.outputs.append(self.p_out)
else: # parameters is none: default to num_parameters and utilize defaults
self._args = self.dist_args["Default"][:]
self._args.append(self.num_parameters)
self.outputs = self.dist_types["Default"](*self._args)
self._outputs = array(self.outputs)
return self._outputs
else:
raise StopIteration()
if __name__ == "__main__":
from numpy import random
from montecarlo import MonteCarlo
_test = MonteCarlo()
_test.num_parameters = 7
_test.parameters = ["x", "y", "z", "u"]
_test.num_samples = 10
_test.dist_types = {"Default": random.uniform, "y": random.standard_normal}
_test.dist_args = {"Default": [0, 1], "y": []}
for iters in _test:
A = iters
print(A)
