def setUp(self):
t = Numeric.arange(self._getn())
y = testfunc(t, self.A, self.lambda_, self.b)
sigma = Numeric.ones(self._getn()) * 0.1
self.data = Numeric.array((t, y, sigma), Float)
self.sys = multifit_nlin.gsl_multifit_function_fdf(
exp_f, exp_df, exp_fdf, self.data, self._getn(), self._getp())
def setup(self):
"""
Setup for GSL
"""
data = numx.array([self.data_x, self.data_y, self.data_e])
n = len(self.data_x)
p = len(self.initial_params)
pinit = numx.array(self.initial_params)
self._residual_methods = ['lmsder', 'lmder']
self._function_methods_no_jac = ['nmsimplex', 'nmsimplex2']
self._function_methods_with_jac = [
'conjugate_pr', 'conjugate_fr', 'vector_bfgs', 'vector_bfgs2',
'steepest_descent'
]
# set up the system
if self.minimizer in self._residual_methods:
mysys = multifit_nlin.gsl_multifit_function_fdf(
self._prediction_error, self._jac, self._fdf, data, n, p)
self._solver = getattr(multifit_nlin, self.minimizer)(mysys, n, p)
elif self.minimizer in self._function_methods_no_jac:
mysys = multiminimize.gsl_multimin_function(
self._chi_squared, data, p)
self._solver = getattr(multiminimize, self.minimizer)(mysys, p)
elif self.minimizer in self._function_methods_with_jac:
mysys = multiminimize.gsl_multimin_function_fdf(
self._chi_squared, self._jac_chi_squared,
self._chi_squared_fdf, data, p)
self._solver = getattr(multiminimize, self.minimizer)(mysys, p)
else:
raise UnknownMinimizerError("No {} minimizer for GSL".format(
self.minimizer))
# Set up initialization parameters
#
# These have been chosen to be consistent with the parameters
# used in Mantid.
initial_steps = 1.0 * numx.array(np.ones(p))
step_size = 0.1
tol = 1e-4
self._gradient_tol = 1e-3
self._abserror = 1e-4
self._relerror = 1e-4
self._maxits = 500
if self.minimizer in self._residual_methods:
self._solver.set(pinit)
elif self.minimizer in self._function_methods_no_jac:
self._solver.set(pinit, initial_steps)
else:
self._solver.set(pinit, step_size, tol)
def run_fdfsolver():
A = 1.
lambda_ = .1
b = .5
n = 40
p = 3
t = numx.arange(n)
y = testfunc(t, A, lambda_, b)
sigma = numx.ones(n) * 0.1
data = numx.array((t, y, sigma), )
mysys = multifit_nlin.gsl_multifit_function_fdf(exp_f, exp_df, exp_fdf,
data, n, p)
solver = multifit_nlin.lmsder(mysys, n, p)
#solver = multifit_nlin.lmder(mysys, n, p)
x = numx.array((1.0, 0.0, 0.0))
solver.set(x)
print("# Testing solver ", solver.name())
print("# %5s %9s %9s %9s %10s" % ("iter", "A", "lambda", "b", "|f(x)|"))
for iter in range(20):
status = solver.iterate()
x = solver.getx()
dx = solver.getdx()
f = solver.getf()
J = solver.getJ()
tdx = multifit_nlin.gradient(J, f)
status = multifit_nlin.test_delta(dx, x, 1e-8, 1e-8)
fn = numx.sqrt(numx.sum(f * f))
if status == errno.GSL_SUCCESS:
print("# Convereged :")
if status == errno.GSL_SUCCESS:
break
print(" %5d % .7f % .7f % .7f % .7f" % (iter, x[0], x[1], x[2], fn))
else:
raise ValueError("Number of Iterations exceeded!")
J = solver.getJ()
covar = multifit_nlin.covar(solver.getJ(), 0.0)
print("# A = % .5f +/- % .5f" % (x[0], covar[0, 0]))
print("# lambda = % .5f +/- % .5f" % (x[1], covar[1, 1]))
print("# b = % .5f +/- % .5f" % (x[2], covar[2, 2]))
