Modify h_params_recursion

Author: yuta-nakahara

bayesml/contexttree/_contexttree.py

@@ -190,17 +190,13 @@ def _set_h_params_recursion(self,node:_Node,original_tree_node:_Node):
                 a object from _Node class
         """
         if original_tree_node is None:
-            node.h_g = self.h_g
-            node.h_beta_vec[:] = self.h_beta_vec
-            if node.depth == self.c_d_max:  # 葉ノード
-                node.leaf = True
-                if node.depth == self.c_d_max:
-                    node.h_g = 0
+            if node.depth == self.c_d_max:
+                node.h_g = 0
             else:
-                node.leaf = False
-                for i in range(self.c_k):
-                    if node.children[i] is None:
-                        node.children[i] = _Node(node.depth+1,self.c_k)
+                node.h_g = self.h_g
+            node.h_beta_vec[:] = self.h_beta_vec
+            for i in range(self.c_k):
+                if node.children[i] is not None:
                     self._set_h_params_recursion(node.children[i],None)
         else:
             node.h_g = original_tree_node.h_g
@@ -525,17 +521,13 @@ def _set_h0_params_recursion(self,node:_Node,original_tree_node:_Node):
                 a object from _Node class
         """
         if original_tree_node is None:
-            node.h_g = self.h0_g
-            node.h_beta_vec[:] = self.h0_beta_vec
-            if node.depth == self.c_d_max:  # 葉ノード
-                node.leaf = True
-                if node.depth == self.c_d_max:
-                    node.h_g = 0
+            if node.depth == self.c_d_max:
+                node.h_g = 0
             else:
-                node.leaf = False
-                for i in range(self.c_k):
-                    if node.children[i] is None:
-                        node.children[i] = _Node(node.depth+1,self.c_k)
+                node.h_g = self.h0_g
+            node.h_beta_vec[:] = self.h0_beta_vec
+            for i in range(self.c_k):
+                if node.children[i] is not None:
                     self._set_h0_params_recursion(node.children[i],None)
         else:
             node.h_g = original_tree_node.h_g
@@ -562,17 +554,13 @@ def _set_hn_params_recursion(self,node:_Node,original_tree_node:_Node):
                 a object from _Node class
         """
         if original_tree_node is None:
-            node.h_g = self.hn_g
-            node.h_beta_vec[:] = self.hn_beta_vec
-            if node.depth == self.c_d_max:  # 葉ノード
-                node.leaf = True
-                if node.depth == self.c_d_max:
-                    node.h_g = 0
+            if node.depth == self.c_d_max:
+                node.h_g = 0
             else:
-                node.leaf = False
-                for i in range(self.c_k):
-                    if node.children[i] is None:
-                        node.children[i] = _Node(node.depth+1,self.c_k)
+                node.h_g = self.hn_g
+            node.h_beta_vec[:] = self.hn_beta_vec
+            for i in range(self.c_k):
+                if node.children[i] is not None:
                     self._set_hn_params_recursion(node.children[i],None)
         else:
             node.h_g = original_tree_node.h_g

bayesml/contexttree/contexttree_test.py

@@ -0,0 +1,17 @@
+from bayesml.contexttree import GenModel
+from bayesml.contexttree import LearnModel
+import numpy as np
+
+gen_model = GenModel(2,3,h_g=0.7,h_beta_vec=1.0)
+gen_model.gen_params()
+gen_model.visualize_model(filename='tree1.pdf')
+
+x = gen_model.gen_sample(1000)
+
+learn_model = LearnModel(2,4)
+learn_model.visualize_posterior(filename='tree2.pdf')
+learn_model.reset_hn_params()
+learn_model.visualize_posterior(filename='tree3.pdf')
+learn_model.update_posterior(x)
+learn_model.visualize_posterior(filename='tree4.pdf')
+learn_model.estimate_params()