Merge e850e9f into 02ef44f

pymc3/tests/test_step.py

@@ -2,11 +2,12 @@
 
 from .checks import close_to
 from .models import mv_simple, mv_simple_discrete, simple_2model
+from .helpers import SeededTest
 from pymc3.sampling import assign_step_methods, sample
 from pymc3.model import Model
 from pymc3.step_methods import (NUTS, BinaryGibbsMetropolis, Metropolis, Constant, Slice,
                                 CompoundStep, MultivariateNormalProposal, HamiltonianMC)
-from pymc3.distributions import Binomial, Normal, Bernoulli, Categorical
+from pymc3.distributions import Binomial, Normal, Bernoulli, Categorical, Uniform
 from numpy.testing import assert_almost_equal
 import numpy as np
 
@@ -113,3 +114,31 @@ def test_binomial(self):
             Binomial('x', 10, 0.5)
             steps = assign_step_methods(model, [])
         self.assertIsInstance(steps, Metropolis)
+
+class TestSampleEstimates(SeededTest):
+    def test_parameter_estimate(self):
+
+        alpha_true, sigma_true = 1, 0.5
+        beta_true = np.array([1, 2.5])
+
+        size = 100
+
+        X1 = np.random.randn(size)
+        X2 = np.random.randn(size) * 0.2
+        Y = alpha_true + beta_true[0] * X1 + beta_true[1] * X2 + np.random.randn(size) * sigma_true
+
+        for step_method in (NUTS(), Metropolis(), Slice()):
+
+            with Model() as model:
+                alpha = Normal('alpha', mu=0, sd=10)
+                beta = Normal('beta', mu=0, sd=10, shape=2)
+                sigma = Uniform('sigma', lower=0.0, upper=1.0)
+                mu = alpha + beta[0]*X1 + beta[1]*X2
+                Y_obs = Normal('Y_obs', mu=mu, sd=sigma, observed=Y)
+
+                trace = sample(1000, step=step_method)
+
+
+            assert np.isclose(np.median(trace.beta, 0), beta_true, rtol=0.1).all()
+            assert np.isclose(np.median(trace.alpha), alpha_true, rtol=0.1)
+            assert np.isclose(np.median(trace.sigma), sigma_true, rtol=0.1)