def setUp(self):
self.initial_parameters = numpy.array(
[0.1, 560, 50, 1.0, 610, 30, 0.3], float)
self.peak_model = PeakModel(self.initial_parameters)
self.wavenumbers = numpy.arange(400, 700, 3.37, dtype=float)
self.target_data = numpy.random.normal(0, 1, self.wavenumbers.shape)
self.cost_function = FitCostFunction(self.wavenumbers,
self.target_data, self.peak_model)
class FitTestCase(unittest.TestCase):
def setUp(self):
self.initial_parameters = numpy.array([0.1, 560, 50, 1.0, 610, 30, 0.3], float)
self.peak_model = PeakModel(self.initial_parameters)
self.wavenumbers = numpy.arange(400, 700, 3.37, dtype=float)
self.target_data = numpy.random.normal(0, 1, self.wavenumbers.shape)
self.cost_function = FitCostFunction(self.wavenumbers, self.target_data, self.peak_model)
def test_model_gradient(self):
self.peak_model.set_parameters(self.initial_parameters)
epsilon = 1e-5
ag = self.peak_model.gradient(self.wavenumbers) # analytical gradient
for index in xrange(len(self.initial_parameters)):
tmp = self.initial_parameters.copy()
tmp[index] -= 0.5 * epsilon
self.peak_model.set_parameters(tmp)
m = self.peak_model(self.wavenumbers)
tmp = self.initial_parameters.copy()
tmp[index] += 0.5 * epsilon
self.peak_model.set_parameters(tmp)
p = self.peak_model(self.wavenumbers)
# print "target = (p-m)/epsilon"
target = (p - m) / epsilon
# print target
# print "outcome = ag[index]"
outcome = ag[index]
# print outcome
# print "ratio = outcome/target"
# print outcome/target
error = abs((p - m) / epsilon - ag[index]).max()
oom = abs(ag[index]).max()
# print error,oom
self.assert_(error / oom < 1e-2, "model gradient incorrect, parameter %i" % index)
def test_model_hessian(self):
self.peak_model.set_parameters(self.initial_parameters)
epsilon = 1e-5
ah = self.peak_model.hessian(self.wavenumbers) # analytical hessian
for index1 in xrange(len(self.initial_parameters)):
tmp = self.initial_parameters.copy()
tmp[index1] -= 0.5 * epsilon
self.peak_model.set_parameters(tmp)
m = self.peak_model.gradient(self.wavenumbers)
tmp = self.initial_parameters.copy()
tmp[index1] += 0.5 * epsilon
self.peak_model.set_parameters(tmp)
p = self.peak_model.gradient(self.wavenumbers)
for index2 in xrange(len(self.initial_parameters)):
# print "index1 index2 =", index1, index2
# print "target = (p[index2]-m[index2])/epsilon"
target = (p[index2] - m[index2]) / epsilon
# print target
# print "outcome = ah[index1,index2]"
outcome = ah[index1, index2]
# print outcome
# print "target/outcome"
# print target/outcome
error = abs(outcome - target).max()
oom = abs(target).max()
# print error,oom
if oom == 0:
self.assert_(error < epsilon, "model hessian incorrect, parameters (%i,%i)" % (index1, index2))
else:
self.assert_(error / oom < 1e-2, "model hessian incorrect, parameters (%i,%i)" % (index1, index2))
def test_cost_gradient(self):
epsilon = 1e-5
ag = self.cost_function.gradient(self.initial_parameters) # analytical gradient
for index in xrange(len(self.initial_parameters)):
tmp = self.initial_parameters.copy()
tmp[index] -= 0.5 * epsilon
m = self.cost_function(tmp)
tmp = self.initial_parameters.copy()
tmp[index] += 0.5 * epsilon
p = self.cost_function(tmp)
# print "(p-m)/epsilon"
# print (p-m)/epsilon
# print "ag[index]"
# print ag[index]
# print "ratio"
# print ag[index]/(p-m)/epsilon
error = abs((p - m) / epsilon - ag[index]).max()
oom = abs(ag[index]).max()
# print error,oom
self.assert_(error / oom < 1e-2, "model gradient incorrect, parameter %i" % index)
def test_cost_hessian(self):
epsilon = 1e-5
ah = self.cost_function.hessian(self.initial_parameters) # analytical hessian
for index1 in xrange(len(self.initial_parameters)):
tmp = self.initial_parameters.copy()
tmp[index1] -= 0.5 * epsilon
m = self.cost_function.gradient(tmp)
tmp = self.initial_parameters.copy()
tmp[index1] += 0.5 * epsilon
p = self.cost_function.gradient(tmp)
for index2 in xrange(len(self.initial_parameters)):
# print "index1 index2 =", index1, index2
# print "target = (p[index2]-m[index2])/epsilon"
target = (p[index2] - m[index2]) / epsilon
# print target
# print "outcome = ah[index1,index2]"
outcome = ah[index1, index2]
# print outcome
# print "target/outcome"
# print target/outcome
error = abs(outcome - target).max()
oom = abs(target).max()
# print error,oom
if oom == 0:
self.assert_(error < epsilon, "cost hessian incorrect, parameters (%i,%i)" % (index1, index2))
else:
self.assert_(error / oom < 1e-2, "cost hessian incorrect, parameters (%i,%i)" % (index1, index2))