feat: implement the experiment

lomakinae/docs/data/01/src/bench.py

@@ -0,0 +1,72 @@
+import time
+from ll import ll_insert, ll_find, ll_delete
+from ht import ht_new, ht_insert, ht_find, ht_delete
+from bst import bst_insert, bst_find, bst_delete
+
+
+def _build_ll(records):
+    head = None
+    for name, phone in records:
+        head = ll_insert(head, name, phone)
+    return head
+
+
+def _build_ht(records):
+    buckets = ht_new()
+    for name, phone in records:
+        ht_insert(buckets, name, phone)
+    return buckets
+
+
+def _build_bst(records):
+    root = None
+    for name, phone in records:
+        root = bst_insert(root, name, phone)
+    return root
+
+
+def _time_insert(build_fn, records):
+    start = time.perf_counter()
+    structure = build_fn(records)
+    end = time.perf_counter()
+    elapsed = end - start
+    return elapsed, structure
+
+
+def _time_find(find_fn, structure, names):
+    start = time.perf_counter()
+    for name in names:
+        find_fn(structure, name)
+    end = time.perf_counter()
+    elapsed = end - start
+    return elapsed
+
+
+def _time_delete(delete_fn, structure, names):
+    start = time.perf_counter()
+    for name in names:
+        result = delete_fn(structure, name)
+        if result is not None:
+            structure = result
+    end = time.perf_counter()
+    elapsed = end - start
+    return elapsed, structure
+
+
+def run_once(records, search_names, delete_names):
+    results = []
+
+    structures = {
+        'LinkedList': (_build_ll, ll_find, ll_delete),
+        'HashTable':  (_build_ht, ht_find, ht_delete),
+        'BST':        (_build_bst, bst_find, bst_delete),
+    }
+
+    for label, (build_fn, find_fn, delete_fn) in structures.items():
+        t_insert, structure = _time_insert(build_fn, records)
+        t_find = _time_find(find_fn, structure, search_names)
+        t_delete, structure = _time_delete(delete_fn, structure, delete_names)
+
+        results.append((label, t_insert, t_find, t_delete))
+
+    return results

lomakinae/docs/data/01/src/experiment.py

@@ -0,0 +1,53 @@
+import csv
+import random
+import sys
+
+from generator import generate_records, shuffle_records, sort_records, sample_existing, sample_nonexistent
+from bench import run_once 
+
+N = 10000
+RUNS = 5
+SEARCH_K = 100
+SEARCH_MISSING_K = 10
+DELETE_K = 50
+sys.setrecursionlimit(15000)
+
+
+def run_experiment(records, mode):
+    search_names = sample_existing(records, SEARCH_K) + sample_nonexistent(SEARCH_MISSING_K)
+    delete_names = sample_existing(records, DELETE_K)
+
+    all_rows = []
+
+    for run_i in range(1, RUNS + 1):
+        print(f"  [{mode}] run {run_i}/{RUNS} ...")
+        run_results = run_once(records, search_names, delete_names)
+        for label, t_insert, t_find, t_delete in run_results:
+            all_rows.append([label, mode, 'insert', run_i, round(t_insert, 6)])
+            all_rows.append([label, mode, 'find',   run_i, round(t_find,   6)])
+            all_rows.append([label, mode, 'delete', run_i, round(t_delete, 6)])
+
+    return all_rows
+
+
+if __name__ == '__main__':
+    random.seed(52)
+
+    records_base = generate_records(N)
+    records_shuffled = shuffle_records(records_base)
+    records_sorted = sort_records(records_base)
+
+    rows = []
+    rows += run_experiment(records_shuffled, 'shuffled')
+    rows += run_experiment(records_sorted,   'sorted')
+
+    header = ['structure', 'mode', 'operation', 'run', 'time_sec']
+    output_path = 'results.csv'
+
+    with open(output_path, 'w', newline='') as f:
+        writer = csv.writer(f)
+        writer.writerow(header)
+        writer.writerows(rows)
+
+    print(f"Done. Results saved to {output_path}")
+    print(f"Total rows: {len(rows)}")

lomakinae/docs/data/01/src/results.csv

@@ -0,0 +1,91 @@
+structure,mode,operation,run,time_sec
+LinkedList,shuffled,insert,1,3.146608
+LinkedList,shuffled,find,1,0.038634
+LinkedList,shuffled,delete,1,0.02447
+HashTable,shuffled,insert,1,0.032404
+HashTable,shuffled,find,1,0.000543
+HashTable,shuffled,delete,1,0.000314
+BST,shuffled,insert,1,0.024271
+BST,shuffled,find,1,0.000238
+BST,shuffled,delete,1,0.00014
+LinkedList,shuffled,insert,2,3.360195
+LinkedList,shuffled,find,2,0.038226
+LinkedList,shuffled,delete,2,0.026174
+HashTable,shuffled,insert,2,0.031514
+HashTable,shuffled,find,2,0.000531
+HashTable,shuffled,delete,2,0.000297
+BST,shuffled,insert,2,0.025051
+BST,shuffled,find,2,0.000227
+BST,shuffled,delete,2,0.000134
+LinkedList,shuffled,insert,3,3.298482
+LinkedList,shuffled,find,3,0.038049
+LinkedList,shuffled,delete,3,0.026032
+HashTable,shuffled,insert,3,0.030625
+HashTable,shuffled,find,3,0.000472
+HashTable,shuffled,delete,3,0.000286
+BST,shuffled,insert,3,0.023956
+BST,shuffled,find,3,0.000218
+BST,shuffled,delete,3,0.000128
+LinkedList,shuffled,insert,4,3.342319
+LinkedList,shuffled,find,4,0.038768
+LinkedList,shuffled,delete,4,0.02615
+HashTable,shuffled,insert,4,0.03061
+HashTable,shuffled,find,4,0.000462
+HashTable,shuffled,delete,4,0.000286
+BST,shuffled,insert,4,0.023686
+BST,shuffled,find,4,0.000213
+BST,shuffled,delete,4,0.000132
+LinkedList,shuffled,insert,5,3.376158
+LinkedList,shuffled,find,5,0.039137
+LinkedList,shuffled,delete,5,0.026008
+HashTable,shuffled,insert,5,0.030944
+HashTable,shuffled,find,5,0.000488
+HashTable,shuffled,delete,5,0.000291
+BST,shuffled,insert,5,0.022971
+BST,shuffled,find,5,0.000214
+BST,shuffled,delete,5,0.000135
+LinkedList,sorted,insert,1,3.226524
+LinkedList,sorted,find,1,0.03718
+LinkedList,sorted,delete,1,0.0253
+HashTable,sorted,insert,1,0.030623
+HashTable,sorted,find,1,0.000491
+HashTable,sorted,delete,1,0.000274
+BST,sorted,insert,1,11.323304
+BST,sorted,find,1,0.096899
+BST,sorted,delete,1,0.053995
+LinkedList,sorted,insert,2,3.3209
+LinkedList,sorted,find,2,0.037479
+LinkedList,sorted,delete,2,0.024458
+HashTable,sorted,insert,2,0.030579
+HashTable,sorted,find,2,0.000491
+HashTable,sorted,delete,2,0.000274
+BST,sorted,insert,2,11.633176
+BST,sorted,find,2,0.098893
+BST,sorted,delete,2,0.055018
+LinkedList,sorted,insert,3,3.248168
+LinkedList,sorted,find,3,0.036984
+LinkedList,sorted,delete,3,0.025185
+HashTable,sorted,insert,3,0.03095
+HashTable,sorted,find,3,0.000482
+HashTable,sorted,delete,3,0.000276
+BST,sorted,insert,3,11.457209
+BST,sorted,find,3,0.098241
+BST,sorted,delete,3,0.054005
+LinkedList,sorted,insert,4,3.175529
+LinkedList,sorted,find,4,0.036891
+LinkedList,sorted,delete,4,0.025014
+HashTable,sorted,insert,4,0.030979
+HashTable,sorted,find,4,0.000505
+HashTable,sorted,delete,4,0.000284
+BST,sorted,insert,4,11.49292
+BST,sorted,find,4,0.099439
+BST,sorted,delete,4,0.055095
+LinkedList,sorted,insert,5,3.228069
+LinkedList,sorted,find,5,0.03687
+LinkedList,sorted,delete,5,0.025142
+HashTable,sorted,insert,5,0.031015
+HashTable,sorted,find,5,0.000505
+HashTable,sorted,delete,5,0.00028
+BST,sorted,insert,5,11.420234
+BST,sorted,find,5,0.09862
+BST,sorted,delete,5,0.054981