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This commit is contained in:
Jeel Gajera 2023-10-21 14:48:21 +05:30
parent 71776e7802
commit c0470094d0
2 changed files with 20 additions and 14 deletions
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2
DIRECTORY.md
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@ -541,7 +541,7 @@
* [Dimensionality Reduction](machine_learning/dimensionality_reduction.py) * [Dimensionality Reduction](machine_learning/dimensionality_reduction.py)
* Forecasting * Forecasting
* [Run](machine_learning/forecasting/run.py) * [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) * [Gradient Descent](machine_learning/gradient_descent.py)
* [K Means Clust](machine_learning/k_means_clust.py) * [K Means Clust](machine_learning/k_means_clust.py)
* [K Nearest Neighbours](machine_learning/k_nearest_neighbours.py) * [K Nearest Neighbours](machine_learning/k_nearest_neighbours.py)

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