def testdx(self):
"Test GaussianRestraint(3) x derivative"
gr = GaussianRestraint(*self.all)
for i in range(100):
map(self.change_value, self.all)
map(self.change_sign, self.locations)
gr.evaluate(True)
self.assertAlmostEqual(Nuisance(self.x).get_nuisance_derivative(),
self.deriv_x(*self.all))
def testdmu(self):
"Test GaussianRestraint(3) mu derivative"
gr = GaussianRestraint(*self.all)
for i in range(100):
map(self.change_value, self.all)
map(self.change_sign, self.locations)
gr.evaluate(True)
self.assertAlmostEqual(
Nuisance(self.mu).get_nuisance_derivative(),
self.deriv_mu(*self.all))
def testE(self):
"Test GaussianRestraint(3) score"
gr = GaussianRestraint(*self.all)
for i in range(100):
map(self.change_value, self.all)
map(self.change_sign, self.locations)
e = gr.evaluate(False)
self.assertAlmostEqual(e, self.normal_e(*self.all))
def testSanityPE(self):
"Test if GaussianRestraint(13) prob is exp(-score)"
gr = GaussianRestraint(*self.all)
for i in range(100):
map(self.change_value, self.all)
map(self.change_sign, self.locations)
self.assertAlmostEqual(gr.get_probability(),
exp(-gr.evaluate(False)), delta=0.001)
def testE(self):
"Test GaussianRestraint(3) score"
gr = GaussianRestraint(*self.all)
for i in range(100):
map(self.change_value, self.all)
map(self.change_sign, self.locations)
e = gr.evaluate(False)
self.assertAlmostEqual(e, self.normal_e(*self.all))
def testSanityPE(self):
"Test if GaussianRestraint(13) prob is exp(-score)"
gr = GaussianRestraint(*self.all)
for i in range(100):
map(self.change_value, self.all)
map(self.change_sign, self.locations)
self.assertAlmostEqual(gr.get_probability(),
exp(-gr.evaluate(False)),
delta=0.001)
def testSanityEP(self):
"Test if GaussianRestraint(13) score is -log(prob)"
gr = GaussianRestraint(*self.all)
for i in range(100):
map(self.change_value, self.all)
map(self.change_sign, self.locations)
expected = gr.get_probability()
if expected == 0:
continue
self.assertAlmostEqual(-log(expected),
gr.evaluate(False), delta=0.7)
def testSanityEP(self):
"Test if GaussianRestraint(13) score is -log(prob)"
gr = GaussianRestraint(*self.all)
for i in range(100):
map(self.change_value, self.all)
map(self.change_sign, self.locations)
expected = gr.get_probability()
if expected == 0:
continue
self.assertAlmostEqual(-log(expected),
gr.evaluate(False),
delta=0.7)