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), numx.Float)
#mysys = multifit_nlin.gsl_multifit_function_fdf(exp_f, exp_df, exp_fdf,
# data, n,p)
pygsl.set_debug_level(0)
solver = multifit_nlin.lmsder(n, p)
pygsl.set_debug_level(0)
#solver = multifit_nlin.lmder(mysys, n, p)
x = numx.array((1.0, 0.0, 0.0))
pygsl.set_debug_level(0)
solver.set(exp_f, exp_df, exp_fdf, x, data)
pygsl.set_debug_level(0)
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.x()
dx = solver.dx()
f = solver.f()
J = solver.J()
#tdx = multifit_nlin.gradient(J, f)
#status = multifit_nlin.test_delta(dx, x, 1e-8, 1e-8)
status = solver.test_delta(1e-8, 1e-8)
fn = numx.sqrt(numx.sum(f*f))
if status == 0:
print "# Convereged :"
if status == 0:
break
print " %5d % .7f % .7f % .7f % .7f" %(iter, x[0], x[1], x[2], fn)
else:
raise ValueError, "Number of Iterations exceeded!"
J = solver.J()
covar = multifit_nlin.covar(solver.J(), 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])
def _run(self, solver):
#x = Numeric.array((1.0, .4, .1))
x = Numeric.array((1.0, 0.0, 0.0))
solver.set(x)
#g.title('Start')
#g.plot(Gnuplot.Data(self.data[0], self.data[1]),
# Gnuplot.Data(self.data[0], testfunc(self.data[0]),
# with = 'line'),
# )
#raw_input()
#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()
assert (status == 0 or status == -2)
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)
#status = multifit_nlin.test_gradient(dx, 1e-4)
fn = Numeric.sqrt(Numeric.sum(f * f))
#g.title('Iteration')
if status == 0:
break
#print "%5d % .7f % .7f % .7f % .7f" %(iter, x[0], x[1], x[2], fn)
#g.plot(Gnuplot.Data(self.data[0], self.data[1]),
# Gnuplot.Data(self.data[0],
# testfunc(self.data[0], x[0], x[1], x[2]),
# with = 'line', title='iteration ' + str(iter)),
# )
#raw_input()
else:
raise ValueError, "Number of Iterations exceeded!"
#print "Convereged :"
#print "%5d % .7f % .7f %.7f % .7f" %(iter, x[0], x[1], x[2], fn)
assert (Numeric.absolute(x[0] - self.A) < _eps)
assert (Numeric.absolute(x[1] - self.lambda_) < _eps)
assert (Numeric.absolute(x[2] - self.b) < _eps)
#J = solver.getJ()
#print "shape = ", J.shape
covar = multifit_nlin.covar(solver.getJ(), 0.0)
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]))
def _run(self, solver):
#x = Numeric.array((1.0, .4, .1))
x = Numeric.array((1.0, 0.0, 0.0))
solver.set(x)
#g.title('Start')
#g.plot(Gnuplot.Data(self.data[0], self.data[1]),
# Gnuplot.Data(self.data[0], testfunc(self.data[0]),
# with = 'line'),
# )
#raw_input()
#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()
assert(status == 0 or status == -2)
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)
#status = multifit_nlin.test_gradient(dx, 1e-4)
fn = Numeric.sqrt(Numeric.sum(f*f))
#g.title('Iteration')
if status == 0:
break
#print "%5d % .7f % .7f % .7f % .7f" %(iter, x[0], x[1], x[2], fn)
#g.plot(Gnuplot.Data(self.data[0], self.data[1]),
# Gnuplot.Data(self.data[0],
# testfunc(self.data[0], x[0], x[1], x[2]),
# with = 'line', title='iteration ' + str(iter)),
# )
#raw_input()
else:
raise ValueError, "Number of Iterations exceeded!"
#print "Convereged :"
#print "%5d % .7f % .7f %.7f % .7f" %(iter, x[0], x[1], x[2], fn)
assert(Numeric.absolute(x[0] - self.A) < _eps)
assert(Numeric.absolute(x[1] - self.lambda_) < _eps)
assert(Numeric.absolute(x[2] - self.b) < _eps)
#J = solver.getJ()
#print "shape = ", J.shape
covar = multifit_nlin.covar(solver.getJ(), 0.0)
def _run(self, solver):
x = Numeric.array((1.0, 0.0, 0.0))
solver.set(x)
for iter in range(20):
status = solver.iterate()
assert (status == 0 or status == -2)
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 = Numeric.sqrt(Numeric.sum(f * f))
if status == 0:
break
else:
raise ValueError("Number of Iterations exceeded!")
assert (Numeric.absolute(x[0] - self.A) < _eps)
assert (Numeric.absolute(x[1] - self.lambda_) < _eps)
assert (Numeric.absolute(x[2] - self.b) < _eps)
covar = multifit_nlin.covar(solver.getJ(), 0.0)