Bug fix

Author: yuta-nakahara

bayesml/metatree/_metatree_x_discrete.py

@@ -411,7 +411,7 @@ def _set_h_params_recursion(self,node:_Node,original_tree_node:_Node):
                 node.h_g = self.h_g
             # node.sub_model.set_h_params(**self.sub_h_params)
             node.sub_model = self.SubModel.GenModel(**self.sub_h_params)
-            for i in range(self.c_k):
+            for i in range(self.c_num_children):
                 if node.children[i] is not None:
                     self._set_h_params_recursion(node.children[i],None)
         else:
@@ -428,7 +428,7 @@ def _set_h_params_recursion(self,node:_Node,original_tree_node:_Node):
                 node.leaf = True
             else:
                 node.leaf = False
-                for i in range(self.c_k):
+                for i in range(self.c_num_children):
                     if node.children[i] is None:
                         node.children[i] = _Node(
                             node.depth+1,
@@ -611,7 +611,7 @@ def get_params(self):
         """
         return {"root":self.root}
 
-    def gen_sample(self,sample_size,x=None):
+    def gen_sample(self,sample_size=None,x=None):
         """Generate a sample from the stochastic data generative model.
 
         Parameters
@@ -630,10 +630,20 @@ def gen_sample(self,sample_size,x=None):
         y : numpy ndarray
             1 dimensional array whose size is ``sammple_size``.
         """
-        _check.pos_int(sample_size,'sample_size',DataFormatError)
-
-        if x is None:
+        if x is not None:
+            _check.int_vecs(x,'x',DataFormatError)
+            _check.shape_consistency(
+                x.shape[-1],'x.shape[-1]',
+                self.c_k,'self.c_k',
+                ParameterFormatError
+                )
+            x = x.reshape(-1,self.c_k)
+            sample_size = x.shape[0]
+        elif sample_size is not None:
+            _check.pos_int(sample_size,'sample_size',DataFormatError)
             x = self.rng.choice(self.c_num_children,(sample_size,self.c_k))
+        else:
+            raise(DataFormatError("Either of the sample_size and the x must be given as a input."))
 
         if self.SubModel in DISCRETE_MODELS:
             y = np.empty(sample_size,dtype=int)

bayesml/metatree/metatree_test.py

@@ -3,16 +3,32 @@
 import numpy as np
 import copy
 
-gen_model = metatree.GenModel(3,3,h_g=0.7)
+gen_model = metatree.GenModel(5,3,h_g=0.75,SubModel=normal)
 gen_model.gen_params()
 gen_model.visualize_model(filename='tree.pdf')
-params1 = copy.deepcopy(gen_model.get_params())
-gen_model.gen_params(feature_fix=True)
-gen_model.visualize_model(filename='tree2.pdf')
-params2 = copy.deepcopy(gen_model.get_params())
+x,y = gen_model.gen_sample(sample_size=100)
+print(x)
+print(y)
+# gen_model.gen_params(feature_fix=True,tree_fix=True)
+# gen_model.visualize_model(filename='tree2.pdf')
+# gen_model.gen_params(feature_fix=True,tree_fix=True)
+# gen_model.visualize_model(filename='tree3.pdf')
 
-# gen_model2 = metatree.GenModel(3,3)
+# gen_model2 = metatree.GenModel(3,3,h_g=0.01)
+# gen_model2.gen_params()
 # gen_model2.visualize_model(filename='tree3.pdf')
-# gen_model2.set_h_params(h_metatree_list=[params1['root'],params2['root']])
+# gen_model2.set_params(params1['root'])
+# gen_model2.visualize_model(filename='tree4.pdf')
+# gen_model2.gen_params(feature_fix=True)
+# print(gen_model2.get_h_params())
+# gen_model2.set_h_params(sub_h_params={'h_beta':100.0},h_g=0.99)
+# print(gen_model2.get_h_params())
+# gen_model2.gen_params()
+# gen_model2.visualize_model(filename='tree3.pdf')
+
+# gen_model2.set_h_params(h_g=0.99)
+# gen_model2.gen_params()
+# gen_model2.visualize_model(filename='tree4.pdf')
+
 # gen_model2.gen_params(feature_fix=True)
 # gen_model2.visualize_model(filename='tree4.pdf')