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divide_and_conquer/strassen_matrix_multiplication.py

@@ -67,6 +67,10 @@ def matrix_dimensions(matrix: list) -> tuple[int, int]:
     return len(matrix), len(matrix[0])
 
 
+def print_matrix(matrix: list) -> None:
+    print("\n".join(str(line) for line in matrix))
+
+
 def actual_strassen(matrix_a: list, matrix_b: list) -> list:
     """
     Recursive function to calculate the product of two matrices, using the Strassen

dynamic_programming/subset_generation.py

@@ -38,7 +38,7 @@ def print_combination(arr, n, r):
 
 
 if __name__ == "__main__":
-    # Driver function to check for above function
+    # Driver code to check the function above
     arr = [10, 20, 30, 40, 50]
     print_combination(arr, len(arr), 3)
     # This code is contributed by Ambuj sahu

dynamic_programming/sum_of_subset.py

@@ -8,7 +8,7 @@ def is_sum_subset(arr: list[int], required_sum: int) -> bool:
     # a subset value says 1 if that subset sum can be formed else 0
     # initially no subsets can be formed hence False/0
     arr_len = len(arr)
-    subset = [[False for _ in range(required_sum + 1)] for _ in range(arr_len + 1)]
+    subset = [[False] * (required_sum + 1) for _ in range(arr_len + 1)]
 
     # for each arr value, a sum of zero(0) can be formed by not taking any element
     # hence True/1

machine_learning/forecasting/run.py

@@ -147,15 +147,12 @@ def data_safety_checker(list_vote: list, actual_result: float) -> bool:
     tst_match = total_match[len(total_match) - 1 :]
 
     # voting system with forecasting
-    res_vote = []
-    res_vote.append(
-        linear_regression_prediction(trn_date, trn_user, trn_match, tst_date, tst_match)
-    )
-    res_vote.append(sarimax_predictor(trn_user, trn_match, tst_match))
-    res_vote.append(support_vector_regressor(x_train, x_test, trn_user))
+    res_vote = [
+        linear_regression_prediction(trn_date, trn_user, trn_match, tst_date, tst_match),
+        sarimax_predictor(trn_user, trn_match, tst_match),
+        support_vector_regressor(x_train, x_test, trn_user),
+    ]
 
     # check the safety of today's data
-    if data_safety_checker(res_vote, tst_user):
-        print("Today's data is safe.")
-    else:
-        print("Today's data is not safe.")
+    not_str = "" if data_safety_checker(res_vote, tst_user) else "not "
+    print("Today's data is {not_str}safe.")