def fdf_solver():
params = numx.array((1., 2.), )
sys = multiminimize.gsl_multimin_function_fdf(my_f, my_df, my_fdf, params, 2)
#solver = multiminimize.conjugate_pr(sys, 2)
#solver = multiminimize.conjugate_fr(sys, 2)
#solver = multiminimize.vector_bfgs(sys, 2)
solver = multiminimize.vector_bfgs2(sys, 2)
#solver = multiminimize.steepest_descent(sys, 2)
start = numx.array((5., 7.), )
solver.set(start, 0.01, 1e-4)
print( "Using solver ", solver.name() )
print( "%5s %9s %9s %9s %9s %9s" % ("iter", "x", "y", "f", "dx", "dy"))
for iter in range(200):
status = solver.iterate()
gradient = solver.gradient()
x = solver.getx()
f = solver.getf()
status = multiminimize.test_gradient(gradient, 1e-3)
if status == errno.GSL_SUCCESS:
print( "Converged ")
print( "%5d % .7f % .7f % .7f % .7f % .7f" %(iter, x[0], x[1], f, gradient[0], gradient[1]))
if status == errno.GSL_SUCCESS:
break
else:
raise ValueError("Number of Iterations exceeded!")
def fit(self):
"""
Run problem with GSL
"""
for _ in range(self._maxits):
status = self._solver.iterate()
# check if the method has converged
if self.minimizer in self._residual_methods:
x = self._solver.getx()
dx = self._solver.getdx()
status = multifit_nlin.test_delta(dx, x, self._abserror,
self._relerror)
elif self.minimizer in self._function_methods_no_jac:
simplex_size = self._solver.size()
status = multiminimize.test_size(simplex_size, self._abserror)
else: # must be in function_methods_with_jac
gradient = self._solver.gradient()
status = multiminimize.test_gradient(gradient,
self._gradient_tol)
if status == errno.GSL_SUCCESS:
self.flag = 0
break
elif status != errno.GSL_CONTINUE:
self.flag = 2
else:
self.flag = 1
def fdf_solver():
params = numx.array((1., 2.), )
sys = multiminimize.gsl_multimin_function_fdf(my_f, my_df, my_fdf, params, 2)
#solver = multiminimize.conjugate_pr(sys, 2)
#solver = multiminimize.conjugate_fr(sys, 2)
solver = multiminimize.vector_bfgs(sys, 2)
#solver = multiminimize.steepest_descent(sys, 2)
start = numx.array((5., 7.), )
solver.set(start, 0.01, 1e-4)
print "Using solver ", solver.name()
print "%5s %9s %9s %9s %9s %9s" % ("iter", "x", "y", "f", "dx", "dy")
for iter in range(200):
status = solver.iterate()
gradient = solver.gradient()
x = solver.getx()
f = solver.getf()
status = multiminimize.test_gradient(gradient, 1e-3)
if status == errno.GSL_SUCCESS:
print "Converged "
print "%5d % .7f % .7f % .7f % .7f % .7f" %(iter, x[0], x[1], f, gradient[0], gradient[1])
if status == errno.GSL_SUCCESS:
break
else:
raise ValueError, "Number of Iterations exceeded!"
def test20Run(self):
maxiter = self._getMaxIter()
self._setStartPoint()
for cnt in range(maxiter):
status = self._solver.iterate()
gradient = self._solver.gradient()
status = multiminimize.test_gradient(gradient, 1e-3)
if status == errno.GSL_SUCCESS:
break
self.assertNotEqual(maxiter - 1, cnt)
x = self._solver.getx()
fval = self._solver.getf()
self._checkPos(x)
self._checkF(fval)
def _run(self, solver):
tmp = Numeric.array((5., 7.), Float)
solver.set(tmp, 0.01, 1e-4)
#print "Testing solver ", solver.name()
#print "%5s %9s %9s %9s %9s %9s" % ("iter", "x", "y", "f", "dx", "dy")
for iter in range(200):
status = solver.iterate()
gradient = solver.gradient()
x = solver.getx()
f = solver.getf()
status = multiminimize.test_gradient(gradient, 1e-3)
if status == 0:
break
#print "%5d % .7f % .7f % .7f % .7f % .7f" %(iter, x[0], x[1], f, gradient[0], gradient[1])
else:
raise ValueError, "Number of Iterations exceeded!"
assert(Numeric.absolute(x[0] - 1)<1e-3)
assert(Numeric.absolute(x[1] - 2)<1e-3)
assert(Numeric.absolute(f - 30)<1e-3)
assert(Numeric.absolute(gradient[0])<1e-3)
assert(Numeric.absolute(gradient[1])<1e-3)
mi = 10000.0
preMi = 10000.0
for itera in range(MAXITNS):
try:
status1 = solver.iterate()
except errors.gsl_NoProgressError, msg:
print "No progress error"
print msg
break
except:
print "Unexpected error:", sys.exc_info()[0]
raise
gradient = solver.gradient()
waAs = solver.getx()
mi = solver.getf()
status2 = multiminimize.test_gradient(gradient, STOPTOLERANCE)
if fbinX == 0 or fbinX == 100 or fbinX == 200:
print 'MI(%s) %d %d %f' % (pdfKind, fbinX, itera, mi)
print '||w_a|| ', abs(waAs)
if status2 == 0:
print 'MI(%s) Converged %d %d %f' % (pdfKind, fbinX, itera, mi)
break
diff = abs(preMi - mi)
if diff < DIFFSTOPTOLERANCE:
print 'MI(%s) Converged %d %d %f (%f)' % (pdfKind, fbinX, itera,
mi, diff)
break
preMi = mi
# Unpack current weights