Removing code from the learn.md

Author: kartik-git

questions/162_implement-k-nearest-neighbors/learn.md

@@ -7,23 +7,4 @@ The key insight is to use numpy's vectorized operations to efficiently calculate
 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
-
-## Key Implementation Details:
-
-Vectorized distance calculation: np.sqrt(np.sum((points_array - query_array) ** 2, axis=1)) computes all distances in one operation instead of looping
-Broadcasting: numpy automatically handles the subtraction between the query point and all data points
-Efficient sorting: np.argsort() returns indices of sorted elements without actually sorting the array, allowing us to select just the k smallest
-Dimension agnostic: The solution works for any number of dimensions (2D, 3D, etc.) without modification
-
-Time Complexity: O(n log n) where n is the number of points, dominated by the sorting step
-Space Complexity: O(n) for storing the distance array
-
-## Edge Case Handling:
-
-Empty points list returns empty result
-k larger than available points returns all points
-Single point datasets work correctly
-Duplicate points at same distance are handled by numpy's stable sorting
-
-The numpy approach is much more efficient than a naive loop-based implementation, especially for large datasets, as it leverages optimized C implementations for mathematical operations.
+Return as tuples - Convert the selected points back to tuple format