Merge pull request #114 from BrianLusina/feat/contains-duplicates

feat(data structures, arrays): contains duplicates

Author: BrianLusina

DIRECTORY.md

@@ -80,6 +80,8 @@
   * Graphs
     * Course Schedule
       * [Test Course Schedule](https://github.com/BrianLusina/PythonSnips/blob/master/algorithms/graphs/course_schedule/test_course_schedule.py)
+    * Frog Position After T Seconds
+      * [Test Frog Position After T Seconds](https://github.com/BrianLusina/PythonSnips/blob/master/algorithms/graphs/frog_position_after_t_seconds/test_frog_position_after_t_seconds.py)
   * Greedy
     * Min Arrows
       * [Test Find Min Arrows](https://github.com/BrianLusina/PythonSnips/blob/master/algorithms/greedy/min_arrows/test_find_min_arrows.py)
@@ -192,6 +194,9 @@
       * [Test Array Advance Game](https://github.com/BrianLusina/PythonSnips/blob/master/datastructures/arrays/array_advance_game/test_array_advance_game.py)
     * Array Diff
       * [Diff Elements In List](https://github.com/BrianLusina/PythonSnips/blob/master/datastructures/arrays/array_diff/diff_elements_in_list.py)
+    * Contains Duplicates
+      * [Test Contains Nearby Almost Duplicate](https://github.com/BrianLusina/PythonSnips/blob/master/datastructures/arrays/contains_duplicates/test_contains_nearby_almost_duplicate.py)
+      * [Test Contains Nearby Duplicate](https://github.com/BrianLusina/PythonSnips/blob/master/datastructures/arrays/contains_duplicates/test_contains_nearby_duplicate.py)
     * Distinct
       * [Distinct Values](https://github.com/BrianLusina/PythonSnips/blob/master/datastructures/arrays/distinct/distinct_values.py)
     * Matrix
@@ -288,8 +293,10 @@
         * [Test Binary Search Tree Search](https://github.com/BrianLusina/PythonSnips/blob/master/datastructures/trees/binary/search_tree/test_binary_search_tree_search.py)
       * [Test Utils](https://github.com/BrianLusina/PythonSnips/blob/master/datastructures/trees/binary/test_utils.py)
       * Tree
+        * [Binary Tree](https://github.com/BrianLusina/PythonSnips/blob/master/datastructures/trees/binary/tree/binary_tree.py)
         * [Test Binary Tree](https://github.com/BrianLusina/PythonSnips/blob/master/datastructures/trees/binary/tree/test_binary_tree.py)
         * [Test Binary Tree Deserialize](https://github.com/BrianLusina/PythonSnips/blob/master/datastructures/trees/binary/tree/test_binary_tree_deserialize.py)
+        * [Test Binary Tree Min Camera Cover](https://github.com/BrianLusina/PythonSnips/blob/master/datastructures/trees/binary/tree/test_binary_tree_min_camera_cover.py)
         * [Test Binary Tree Serialize](https://github.com/BrianLusina/PythonSnips/blob/master/datastructures/trees/binary/tree/test_binary_tree_serialize.py)
         * [Test Binary Tree Visible Nodes](https://github.com/BrianLusina/PythonSnips/blob/master/datastructures/trees/binary/tree/test_binary_tree_visible_nodes.py)
       * [Utils](https://github.com/BrianLusina/PythonSnips/blob/master/datastructures/trees/binary/utils.py)
@@ -857,8 +864,6 @@
       * [Test Array Pair Sum](https://github.com/BrianLusina/PythonSnips/blob/master/tests/datastructures/arrays/test_array_pair_sum.py)
       * [Test Build Tower](https://github.com/BrianLusina/PythonSnips/blob/master/tests/datastructures/arrays/test_build_tower.py)
       * [Test Cascading Subsets](https://github.com/BrianLusina/PythonSnips/blob/master/tests/datastructures/arrays/test_cascading_subsets.py)
-      * [Test Contains Nearby Almost Duplicate](https://github.com/BrianLusina/PythonSnips/blob/master/tests/datastructures/arrays/test_contains_nearby_almost_duplicate.py)
-      * [Test Contains Nearby Duplicate](https://github.com/BrianLusina/PythonSnips/blob/master/tests/datastructures/arrays/test_contains_nearby_duplicate.py)
       * [Test Dynamic Array](https://github.com/BrianLusina/PythonSnips/blob/master/tests/datastructures/arrays/test_dynamic_array.py)
       * [Test Find Unique](https://github.com/BrianLusina/PythonSnips/blob/master/tests/datastructures/arrays/test_find_unique.py)
       * [Test Highest Rank](https://github.com/BrianLusina/PythonSnips/blob/master/tests/datastructures/arrays/test_highest_rank.py)

datastructures/arrays/contains_duplicates/README.md

@@ -17,9 +17,18 @@ Example 3:
 Input: nums = [1,1,1,3,3,4,3,2,4,2]
 Output: true
 
+---
+
 ## Contains Duplicates II
 
-Given an integer array nums and an integer k, return true if there are two distinct indices i and j in the array such that nums[i] == nums[j] and abs(i - j) <= k.
+Given an integer array nums and an integer k, return true if there are two distinct indices i and j in the array such 
+that nums[i] == nums[j] and abs(i - j) <= k.
+
+Constraints:
+
+- 1 <= nums.length <= 10^3
+- -10^3 <= nums[i] <= 10^3
+- 0 <= k <= 10^4
 
 Example 1:
 
@@ -34,6 +43,53 @@ Example 3:
 Input: nums = [1,2,3,1,2,3], k = 2
 Output: false
 
+### Solution
+
+The core intuition of solving this problem is maintaining a sliding window of size k to track elements within a limited 
+range using a set. As we iterate through the array, we check if the current element already exists in the set, 
+indicating a duplicate within the range. If it exists, we return TRUE. Otherwise, the element is added to the set. 
+If the set size exceeds k, we remove the oldest element to ensure that the set only contains elements within the valid 
+range at any time.
+
+Using the above intuition, the solution can be implemented as follows:
+
+1. Create a set, `seen`, to track elements within the sliding window of size `k`.
+2. Loop through each index `i` of the array `nums`.
+   - If the current element, `nums[i]`, already exists in the set, a duplicate exists within a range of `k` indices. 
+     Therefore, we return TRUE.
+   - Add the current element to the set.
+   - If the set’s size exceeds `k`, remove the oldest element in the window (`nums[i - k]`) to maintain the window’s size. 
+     This ensures only elements within the range k are tracked.
+
+3. If the loop completes without finding duplicates, we return FALSE.
+
+Let’s look at the following illustration to get a better understanding of the solution:
+
+![Solution 1](./images/solutions/contains_duplicates_ii_solution_1.png)
+![Solution 2](./images/solutions/contains_duplicates_ii_solution_2.png)
+![Solution 3](./images/solutions/contains_duplicates_ii_solution_3.png)
+![Solution 4](./images/solutions/contains_duplicates_ii_solution_4.png)
+![Solution 5](./images/solutions/contains_duplicates_ii_solution_5.png)
+![Solution 6](./images/solutions/contains_duplicates_ii_solution_6.png)
+![Solution 7](./images/solutions/contains_duplicates_ii_solution_7.png)
+![Solution 8](./images/solutions/contains_duplicates_ii_solution_8.png)
+![Solution 9](./images/solutions/contains_duplicates_ii_solution_9.png)
+![Solution 10](./images/solutions/contains_duplicates_ii_solution_10.png)
+![Solution 11](./images/solutions/contains_duplicates_ii_solution_11.png)
+
+#### Time Complexity
+
+The time complexity of the solution is O(n), where n is the length of the input array `nums`. 
+This is because we iterate through the array once, performing constant-time operations for each element.
+
+#### Space Complexity
+
+The space complexity of the solution is O(min(n, k)), where n is the length of the input array `nums` and k is the 
+maximum number of steps between duplicate elements. This is because we use a set to store the elements within the 
+sliding window of size `k`, and the maximum size of the set is limited by the minimum of `n` and `k`.
+
+---
+
 ## Contains Duplicate III
 
 Given an integer array nums and two integers k and t, return true if there are two distinct indices i and j in the array

datastructures/arrays/contains_duplicates/__init__.py

@@ -1,13 +1,60 @@
 import sys
-from typing import List
+from typing import List, Set
 
 
 def contains_nearby_duplicate(nums: List[int], k: int) -> bool:
+    """
+    Checks if there are any duplicate elements within k steps of each other
+    in the given list of numbers.
+
+    Args:
+        nums (List[int]): The list of numbers to check.
+        k (int): The maximum number of steps between duplicate elements.
+
+    Returns:
+        bool: True if there are any duplicate elements within k steps of each other, False otherwise.
+    """
+    # Dictionary to store the indices of the numbers we have seen so far
     d = dict()
     for i, n in enumerate(nums):
+        # If we have seen this number before and it is within k steps of the current position
         if n in d and i - d[n] <= k:
             return True
+        # Store the index of the current number
         d[n] = i
+    # If we have not found any duplicate elements within k steps of each other
+    return False
+
+
+def contains_nearby_duplicates_2(nums: List[int], k: int) -> bool:
+    """
+    Checks if there are any duplicate elements within k steps of each other
+    in the given list of numbers.
+
+    Args:
+        nums (List[int]): The list of numbers to check.
+        k (int): The maximum number of steps between duplicate elements.
+
+    Returns:
+        bool: True if there are any duplicate elements within k steps of each other, False otherwise.
+    """
+    # Set to store the numbers we have seen so far
+    seen: Set[int] = set()
+    # Iterate over the list of numbers
+    for idx in range(len(nums)):
+        # If we have seen this number before
+        if nums[idx] in seen:
+            # Return True
+            return True
+        # Add the current number to the set of seen numbers
+        seen.add(nums[idx])
+
+        # If we have seen more than k numbers
+        if len(seen) > k:
+            # Remove the number that is k steps behind the current number
+            seen.remove(nums[idx - k])
+
+    # If we have not found any duplicate elements within k steps of each other
     return False