Add Reservoir Sampling algorithm for streaming data

Author: cureprotocols

Diff for: searches/reservoir_sampling.py

@@ -0,0 +1,48 @@
+"""
+Reservoir Sampling Algorithm
+
+Use Case:
+Efficient for selecting k random items from a data stream of unknown size,
+or when the entire dataset cannot fit into memory.
+
+Time Complexity:
+- O(n), where n is the total number of items
+- Space Complexity: O(k)
+
+Author: Michael Alexander Montoya
+"""
+
+import random
+
+def reservoir_sampling(stream, k):
+    """
+    Performs reservoir sampling on a stream of items.
+
+    Args:
+        stream: An iterable data stream.
+        k: Number of items to sample.
+
+    Returns:
+        A list containing k randomly sampled items from the stream.
+    """
+
+    reservoir = []
+
+    for i, item in enumerate(stream):
+        if i < k:
+            reservoir.append(item)
+        else:
+            j = random.randint(0, i)
+            if j < k:
+                reservoir[j] = item
+
+    return reservoir
+
+
+# Example usage
+if __name__ == "__main__":
+    stream_data = range(1, 1001)  # Simulate a stream of numbers from 1 to 1000
+    sample_size = 10
+
+    sample = reservoir_sampling(stream_data, sample_size)
+    print(f"Random sample of {sample_size} items from stream: {sample}")