nothing changed

bayespairing/src/pgmpy/sampling/Sampling.py

@@ -32,10 +32,9 @@ class BayesianModelSampling(Inference):
     def __init__(self, model):
         if not isinstance(model, BayesianModel):
             raise TypeError("Model expected type: BayesianModel, got type: ", type(model))
-
         self.topological_order = list(nx.topological_sort(model))
         super(BayesianModelSampling, self).__init__(model)
-
+		
     def forward_sample(self, size=1, return_type='dataframe'):
         """
         Generates sample(s) from joint distribution of the bayesian network.
@@ -201,12 +200,11 @@ class BayesianModelSampling(Inference):
         rec.array([(0, 0, 1, 0.6), (0, 0, 2, 0.6)], 
           dtype=[('diff', '<i8'), ('intel', '<i8'), ('grade', '<i8'), ('_weight', '<f8')])
         """
-        types = [(var_name, 'int') for var_name in self.topological_order]
+        types = [(var_name, np.int8) for var_name in self.topological_order]
         types.append(('_weight', 'float'))
         sampled = np.zeros(size, dtype=types).view(np.recarray)
         sampled['_weight'] = np.ones(size)
         evidence_dict = {var: st for var, st in evidence}
-
         for node in self.topological_order:
             cpd = self.model.get_cpds(node)
             states = range(self.cardinality[node])
@@ -228,6 +226,7 @@ class BayesianModelSampling(Inference):
                     for i in range(size):
                         sampled['_weight'][i] *= cpd.values[evidence_dict[node]]
                 else:
+                    print('CALLING SAMPLE_DISCRETE',states,cpd.values,size)
                     sampled[node] = sample_discrete(states, cpd.values, size)
 
         return _return_samples(return_type, sampled)