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"""
Fitting scalar and residual based objective functions
"""
import unittest
import numpy as np
import bornagain as ba
class Rosenbrock:
def __init__(self):
self.m_expected_minimum = 0.0
self.m_expected_params = [1, 1.0]
def objective_function(self, params):
x = params["x"].value
y = params["y"].value
return (1 - x)**2 + 100 * (y - x**2)**2
def decaying_sin(params, x):
amp = params['amp'].value
phaseshift = params['phase'].value
freq = params['frequency'].value
decay = params['decay'].value
return amp * np.sin(x * freq + phaseshift) * np.exp(-x * x * decay)
class DecayingSin:
def __init__(self):
self.m_x = np.linspace(0, 10, 100)
self.m_params = ba.Parameters()
self.m_params.add(ba.Parameter('amp', 10))
self.m_params.add(ba.Parameter('decay', 0.05))
self.m_params.add(ba.Parameter('phase', 1))
self.m_params.add(ba.Parameter('frequency', 4))
self.m_eps_data = np.linspace(0, 10, 100)
self.m_eps_data.fill(0.01)
self.m_data = decaying_sin(self.m_params, self.m_x)
def objective_function(self, params):
model = decaying_sin(params, self.m_x)
return (self.m_data - model) / self.m_eps_data
class StandaloneFitTest(unittest.TestCase):
# Rosenbrock fit test disabled while waiting for ways to set tolerance etc
# (https://jugit.fz-juelich.de/mlz/bornagain/-/issues/693)
#
# def test_RosenbrockFit(self):
# print("\nStandalone fit test: Rosenbrock function")
#
# params = ba.Parameters()
# params.add(ba.Parameter("x", -1.2, ba.AttLimits.limited(-5, 5), 0.01))
# params.add(ba.Parameter("y", 1, ba.AttLimits.limited(-5, 5), 0.01))
#
# model = Rosenbrock()
# minimizer = ba.Minimizer()
# options = ba.MinimizerOptions()
# options.addOption("Strategy", 2);
# options.addOption("Tolerance", 1e-10);
# minimizer.setOptions(options)
#
# result = minimizer.minimize(model.objective_function, params)
#
# print(result.toString())
#
# # check found parameter values
# np.testing.assert_almost_equal(result.parameters().values(),
# model.m_expected_params, 2)
#
# # check found minimum
# np.testing.assert_almost_equal(result.minValue(),
# model.m_expected_minimum, 2)
#
# print("Done with Rosenbrock function\n")
def test_DecayingSinFit(self):
print("\nStandalone fit test: Decaying sin function")
params = ba.Parameters()
params.add(ba.Parameter('amp', 1, ba.AttLimits.positive()))
params.add(ba.Parameter('decay', 0.1, ba.AttLimits.positive()))
params.add(ba.Parameter('phase', 0.1, ba.AttLimits.limited(0, 3.1)))
params.add(ba.Parameter('frequency', 1, ba.AttLimits.positive()))
model = DecayingSin()
minimizer = ba.Minimizer()
result = minimizer.minimize(model.objective_function, params)
print(result.toString())
# check found parameter values
np.testing.assert_almost_equal(result.parameters().values(),
model.m_params.values(), 3)
print("Done with decaying sin function\n")
if __name__ == '__main__':
unittest.main()
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