Feature implementing aprior algorithm

main.py

@@ -1,5 +1,6 @@
 from src.apriori import apriori
 
 if __name__ == '__main__':
+    #assigning the value of apriori to the results variable
     result = apriori([[1, 2, 3], [1, 2, 3], [1, 2, 3]], 0.3, 0.7)
-    print(result)
+    print(result)

src/apriori.py

@@ -106,4 +106,4 @@ def get_subsets(itemset: List[object]) -> List[List[object]]:
 
 
 
-
+

src/confidence.py

@@ -1,9 +1,25 @@
+import support
+dataset_type = list[list[object]]
+rule_type = tuple[list[object], list[object]]
 from typing import List, Tuple
 from support import support
 
 dataset_type = List[List[object]]
 rule_type = Tuple[List[object], List[object]]
 
+def confidence(data_set: dataset_type, rule: rule_type) -> float:
+
+    support_AB = support(data_set, rule[0] + rule[1])
+    support_A = support(data_set, rule[0])
+    if support_A != 0 :
+        confidence = support_AB / support_A
+    else:
+        confidence = 0
+    return confidence
+    """
+    To measure the likelihood of occurrence of an itemset given another itemset.
+    """
+pass
 
 def confidence(dataset: dataset_type, rule: rule_type) -> float:
     """

src/support.py

@@ -4,6 +4,14 @@
 dataset_type = List[List[object]]
 
 def support(itemsets: dataset_type, data_set: itemset_type) -> float:
+    count = 0
+    for transaction in itemsets:
+        total_transactions = len(itemsets)
+        if set(data_set).issubset(transaction):
+            count += 1
+        support = count / total_transactions
+        return support
+
     """
     To find the frequency of itemsets in the dataset.
     """

test/test_support.py

@@ -1,5 +1,4 @@
 import unittest
-
 from src.support import support as calc_support