Merge pull request #514 from kartik-git/add-knn-questions

Adding initial template for KNN question

Author: moe18

questions/173_implement-k-nearest-neighbors/description.md

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+## Problem
+
+Given a list of points in n-dimensional space represented as tuples and a query point, implement a function to find the k nearest neighbors to the query point using Euclidean distance.

questions/173_implement-k-nearest-neighbors/example.json

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+{
+  "input": "points = [(1, 2), (3, 4), (1, 1), (5, 6), (2, 3)], query_point = (2, 2), k = 3",
+  "output": "[(1, 2), (2, 3), (1, 1)]",
+  "reasoning": "The 3 closest points to (2, 2) are (1, 2), (2, 3), and (1, 1), so the output is [(1, 2), (2, 3), (1, 1)]."
+}

questions/173_implement-k-nearest-neighbors/learn.md

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+## Solution Explanation
+
+The key insight is to use numpy's vectorized operations to efficiently calculate distances between the query point and all data points simultaneously, then select the k smallest distances.
+
+## Algorithm Steps:
+
+Convert to numpy arrays - Transform the input tuples into numpy arrays for vectorized operations
+Calculate distances - Use broadcasting to compute Euclidean distance from query point to all points at once
+Find k nearest - Use np.argsort() to get indices of points sorted by distance, then take first k
+Return as tuples - Convert the selected points back to tuple format

questions/173_implement-k-nearest-neighbors/meta.json

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+{
+  "id": "173",
+  "title": "Implement K-Nearest Neighbors",
+  "difficulty": "medium",
+  "category": "Machine Learning",
+  "video": "",
+  "likes": "0",
+  "dislikes": "0",
+  "contributor": [
+      {
+        "profile_link": "https://github.com/kartik-git",
+        "name": "kartik-git"
+      }
+  ]
+}

questions/173_implement-k-nearest-neighbors/solution.py

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+def k_nearest_neighbors(points, query_point, k):
+    """
+    Find k nearest neighbors to a query point
+    
+    Args:
+        points: List of tuples representing points [(x1, y1), (x2, y2), ...]
+        query_point: Tuple representing query point (x, y)
+        k: Number of nearest neighbors to return
+    
+    Returns:
+        List of k nearest neighbor points as tuples
+    """
+    if not points or k <= 0:
+        return []
+    
+    if k > len(points):
+        k = len(points)
+    
+    # Convert to numpy arrays for vectorized operations
+    points_array = np.array(points)
+    query_array = np.array(query_point)
+    
+    # Calculate Euclidean distances using broadcasting
+    distances = np.sqrt(np.sum((points_array - query_array) ** 2, axis=1))
+    
+    # Get indices of k smallest distances
+    k_nearest_indices = np.argsort(distances)[:k]
+    
+    # Return the k nearest points as tuples
+    return [tuple(points_array[i]) for i in k_nearest_indices]

questions/173_implement-k-nearest-neighbors/starter_code.py

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+def k_nearest_neighbors(points, query_point, k):
+    """
+    Find k nearest neighbors to a query point
+    
+    Args:
+        points: List of tuples representing points [(x1, y1), (x2, y2), ...]
+        query_point: Tuple representing query point (x, y)
+        k: Number of nearest neighbors to return
+    
+    Returns:
+        List of k nearest neighbor points as tuples
+    """
+    pass

questions/173_implement-k-nearest-neighbors/tests.json

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+[
+  {
+      "test": "print(k_nearest_neighbors([(1, 2), (3, 4), (1, 1), (5, 6), (2, 3)], (2, 2), 3))",
+      "expected_output": "[(1, 2), (2, 3), (1, 1)]"
+  },
+  {
+      "test": "print(k_nearest_neighbors([(0, 0), (1, 1), (2, 2), (3, 3)], (1.5, 1.5), 2))",
+      "expected_output": "[(1, 1), (2, 2)]"
+  },
+  {
+      "test": "print(k_nearest_neighbors([(1, 1), (2, 2), (3, 3)], (0, 0), 1))",
+      "expected_output": "[(1, 1)]"
+  }
+]