def test(self):
def prior(p):
return 1.0
bounds = [[-1, 1], [-1, 1], [-1, 1]]
estimator = fitter.BayesianMCMCFitter(
likelihood=loss.likelihood,
prior=prior,
bounds=bounds,
model=physics_model.BeamSpinAsymmetryModel())
x, y = estimator.samples.shape
self.assertTrue(x == estimator.n_iterations)
self.assertTrue(y == estimator.model.n_pars)
def test(self):
estimator = fitter.SingleFitter(
loss_function=loss.chi2,
model=physics_model.BeamSpinAsymmetryModel())
# generate a test set
phi = np.linspace(-175.0, 175.0, 17)
value = estimator.model.evaluate(phi) + np.random.normal(
0, 0.01, len(phi))
error = np.repeat(0.05, len(phi))
# do fitting
true_pars = estimator.model.pars
result = estimator.fit(phi, value, error)
self.assertTrue(len(result.fit_parameters) is estimator.model.n_pars)
def test(self):
def prior(p):
return 1.0
bounds = [[-1, 1], [-1, 1], [-1, 1]]
estimator = fitter.BayesianVegasFitter(
likelihood=loss.likelihood,
prior=prior,
bounds=bounds,
model=physics_model.BeamSpinAsymmetryModel(),
n_iterations=10,
n_evaluations=15000)
# generate a test set
phi = np.linspace(-175.0, 175.0, 17)
value = estimator.model.evaluate(phi) + np.random.normal(
0, 0.05, len(phi))
error = np.repeat(0.05, len(phi))
# do fitting
true_pars = estimator.model.pars
result = estimator.fit(phi, value, error)
self.assertTrue(len(result.fit_parameters) is estimator.model.n_pars)
def test(self):
# cores = multiprocessing.cpu_count()
# if not cores:
# just test single core for now
cores = 1
estimator = fitter.ReplicaFitter(
loss_function=loss.chi2,
model=physics_model.BeamSpinAsymmetryModel(),
n_cores=cores,
n_replicas=20)
# generate a test set
phi = np.linspace(-175.0, 175.0, 17)
value = estimator.model.evaluate(phi) + np.random.normal(
0, 0.05, len(phi))
error = np.repeat(0.05, len(phi))
# do fitting
true_pars = estimator.model.pars
result = estimator.fit(phi, value, error)
self.assertTrue(len(result.fit_parameters) is estimator.model.n_pars)