AVL implementation

requirements.txt

@@ -1,3 +1,4 @@
 matplotlib
 numpy
 pygame
+graphviz

vorlesung/L06_b_baeume/analyze_b_tree.py

@@ -0,0 +1,58 @@
+from utils.memory_manager import MemoryManager
+from utils.memory_array import MemoryArray
+from utils.literal import Literal
+from b_tree import BTree
+from b_tree_node import BTreeNode
+
+class MemoryManagerBTree(MemoryManager):
+    """
+    Diese Klasse erweitert den MemoryManager, um spezifische Statistiken für B-Bäume zu speichern.
+    """
+
+    @staticmethod
+    def count_loads():
+        return sum([cell.loaded_count for cell in MemoryManager().cells if isinstance(cell, BTreeNode)])
+
+    @staticmethod
+    def count_saves():
+        return sum([cell.saved_count for cell in MemoryManager().cells if isinstance(cell, BTreeNode)])
+
+    @staticmethod
+    def save_stats(count):
+        data = {    "cells": MemoryManager.count_cells(),
+                    "reads": MemoryManager.count_reads(),
+                    "writes": MemoryManager.count_writes(),
+                    "compares": MemoryManager.count_compares(),
+                    "adds": MemoryManager.count_adds(),
+                    "subs": MemoryManager.count_subs(),
+                    "muls": MemoryManager.count_muls(),
+                    "divs": MemoryManager.count_divs(),
+                    "bitops": MemoryManager.count_bitops(),
+                    "loads": MemoryManagerBTree.count_loads(),
+                    "saves": MemoryManagerBTree.count_saves()  }
+        MemoryManager.stats[count] = data
+
+
+
+
+def analyze_complexity(sizes):
+    """
+    Analysiert die Komplexität
+
+    :param sizes: Eine Liste von Eingabegrößen für die Analyse.
+    """
+    for size in sizes:
+        MemoryManager.purge()  # Speicher zurücksetzen
+        tree = BTree(5)
+        random_array = MemoryArray.create_random_array(size, -100, 100)
+        for i in range(size-1):
+            tree.insert(int(random_array[Literal(i)]))
+        MemoryManager.reset()
+        tree.insert(int(random_array[Literal(size-1)]))
+        MemoryManagerBTree.save_stats(size)
+
+    MemoryManager.plot_stats(["cells", "compares", "loads", "saves"])
+
+if __name__ == "__main__":
+    sizes = range(1, 1001, 2)
+    analyze_complexity(sizes)