changes names

DIRECTORY.md

@@ -541,7 +541,7 @@
   * [Dimensionality Reduction](machine_learning/dimensionality_reduction.py)
   * Forecasting
     * [Run](machine_learning/forecasting/run.py)
-  * [FP Growth Algorithm](machine_learning/fp_growth.py)
+  * [Frequent Pattern Growth Algorithm](machine_learning/frequent_pattern_growth.py)
   * [Gradient Descent](machine_learning/gradient_descent.py)
   * [K Means Clust](machine_learning/k_means_clust.py)
   * [K Nearest Neighbours](machine_learning/k_nearest_neighbours.py)

machine_learning/frequent_pattern_growth.py

@@ -10,8 +10,10 @@ Examples: https://www.javatpoint.com/fp-growth-algorithm-in-data-mining
 """
 
 from typing import Optional
+from dataclasses import dataclass, field
 
 
+@dataclass
 class TreeNode:
     """
     Initialize a TreeNode.
@@ -30,14 +32,19 @@ class TreeNode:
     2
     """
 
-    def __init__(
-        self, name_value: str, num_occur: int, parent_node: Optional["TreeNode"] = None
-    ) -> None:
-        self.name = name_value
-        self.count = num_occur
-        self.node_link = None  # Initialize node_link to None
-        self.parent = parent_node
-        self.children: dict[str, TreeNode] = {}
+    # def __init__(
+    #     self, name_value: str, num_occur: int, parent_node: Optional["TreeNode"] = None
+    # ) -> None:
+    #     self.name = name_value
+    #     self.count = num_occur
+    #     self.node_link = TreeNode | None  # Initialize node_link to None
+    #     self.parent = parent_node
+    #     self.children: dict[str, TreeNode] = {}
+    name: str
+    count: int
+    node_link: Optional['TreeNode'] = None  # Initialize node_link to None
+    parent: Optional["TreeNode"] = None
+    children: dict[str, "TreeNode"] = field(default_factory=dict)
 
     def inc(self, num_occur: int) -> None:
         self.count += num_occur
@@ -50,7 +57,7 @@ class TreeNode:
 
 def create_tree(data_set: list, min_sup: int = 1) -> tuple[TreeNode, dict]:
     """
-    Create FP tree
+    Create Frequent Pattern tree
 
     Args:
         data_set (list): A list of transactions, where each transaction
@@ -193,10 +200,7 @@ def update_header(node_to_test: TreeNode, target_node: TreeNode) -> TreeNode:
     while node_to_test.node_link is not None:
         node_to_test = node_to_test.node_link
     if node_to_test.node_link is None:
-        node_to_test.node_link = TreeNode(
-            target_node.name, target_node.count, node_to_test
-        )
-    # Return the updated node
+        node_to_test.node_link = target_node
     return node_to_test
 
 
@@ -298,6 +302,7 @@ def mine_tree(
     >>> all(expected in frequent_itemsets for expected in expe_itm)
     True
     """
+    new_head: Optional['TreeNode'] = None
     sorted_items = sorted(header_table.items(), key=lambda item_info: item_info[1][0])
     big_l = [item[0] for item in sorted_items]
     for base_pat in big_l:
@@ -311,6 +316,7 @@ def mine_tree(
             header_table[base_pat][1] = update_header(
                 header_table[base_pat][1], my_cond_tree
             )
+            my_head = new_head
             mine_tree(my_cond_tree, my_head, min_sup, new_freq_set, freq_item_list)