def test_prediction_linearn(self):
model = LaplaceGLMM_N1K3('logistic')
model.setG(self._G0, self._G1)
model.setX(self._X)
model.sety(self._y)
model.sig02 = 1.5
model.sig12 = 0.5
model.sign2 = 0.8
model.beta = NP.array([2.0, -1.0])
ps = NP.array([0.30483881,0.2252297,0.78056257,0.44734442,0.58824122,0.05238813,\
0.60337305,0.22889891,0.54169378,0.71885939])
p = model.predict(self._X, self._G0, self._G1)
for i in range(ps.shape[0]):
self.assertAlmostEqual(ps[i], p[i])
model = EPGLMM_N1K3('erf')
model.setG(self._G0, self._G1)
model.setX(self._X)
model.sety(self._y)
model.sig02 = 1.5
model.sig12 = 0.5
model.sign2 = 0.8
model.beta = NP.array([2.0, -1.0])
ps = NP.array([0.35487175, 0.20959901, 0.77285232, 0.48305102, 0.59542164,\
0.02770879, 0.6205743 , 0.14239838, 0.45330375, 0.77123692])
p = model.predict(self._X, self._G0, self._G1)
for i in range(ps.shape[0]):
self.assertAlmostEqual(ps[i], p[i])
def test_rmargll_gradient_linearn(self):
def func(x, model):
model.sig02 = x[0]
model.sig12 = x[1]
model.sign2 = x[2]
model.beta = x[3:]
return model._regular_marginal_loglikelihood()
def grad(x, model):
model.sig02 = x[0]
model.sig12 = x[1]
model.sign2 = x[2]
model.beta = x[3:]
return model._rmll_gradient()
model = LaplaceGLMM_N1K3('logistic')
model.setG(self._G0, self._G1)
model.setX(self._X)
model.sety(self._y)
self.assertAlmostEqual( 0.0, SP.optimize.check_grad(func, grad, NP.array([1.0,1.2,1.5,-3.0,0.4]), model), places=5 )
model = EPGLMM_N1K3('erf')
model.setG(self._G0, self._G1)
model.setX(self._X)
model.sety(self._y)
self.assertAlmostEqual( 0.0, SP.optimize.check_grad(func, grad, NP.array([1.0,1.2,1.5,-3.0,0.4]), model), places=5 )
def testr_rmargll_after_optimization_linearn(self):
model = LaplaceGLMM_N1K3('logistic')
model.setG(self._G0, self._G1)
model.setX(self._X)
model.sety(self._y)
model.sig02 = 1.5
model.sig12 = 0.5
model.sign2 = 0.8
model.beta = NP.array([2.0, -1.0])
model.optimize()
self.assertAlmostEqual(-6.75677866924,
model._regular_marginal_loglikelihood())
model = EPGLMM_N1K3('erf')
model.setG(self._G0, self._G1)
model.setX(self._X)
model.sety(self._y)
model.sig02 = 1.5
model.sig12 = 0.5
model.sign2 = 0.8
model.beta = NP.array([2.0, -1.0])
model.optimize()
self.assertAlmostEqual(-6.75424440734,
model._regular_marginal_loglikelihood())
def test_rmargll_linearn(self):
model = LaplaceGLMM_N1K3('logistic')
model.setG(self._G0, self._G1)
model.setX(self._X)
model.sety(self._y)
model.sig02 = 1.5
model.sig12 = 0.5
model.sign2 = 0.8
model.beta = NP.array([2.0, -1.0])
self.assertAlmostEqual(-13.821329282675068, model._regular_marginal_loglikelihood())
model = EPGLMM_N1K3('erf')
model.setG(self._G0, self._G1)
model.setX(self._X)
model.sety(self._y)
model.sig02 = 1.5
model.sig12 = 0.5
model.sign2 = 0.8
model.beta = NP.array([2.0, -1.0])
self.assertAlmostEqual(-16.5997198627, model._regular_marginal_loglikelihood())