Lecture 5
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@ -2,6 +2,7 @@ from SoSe24.algodat.foundation import AlgoDatArray, AlgoDatValue, read_int_seque
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from SoSe24.lec04_trees.bin_tree import BinTree, BinTreeNode
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from time import perf_counter as pfc
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class AVLTreeNode(BinTreeNode):
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def __init__(self, value: AlgoDatValue):
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super().__init__(value)
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@ -103,9 +104,9 @@ if __name__ == "__main__":
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tree.walk()
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tree.tree_walk()
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tree.levelwalk()
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#tree.graph_walk()
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tree.graph_walk()
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tree.delete(AlgoDatValue(46))
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tree.delete(AlgoDatValue(48))
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tree.graph_walk()
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#tree.graph_walk()
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print(f"Dauer: {pfc() - start:.4f}s")
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AlgoDatValue.summary()
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134
SoSe24/lec04_trees/b_tree.py
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134
SoSe24/lec04_trees/b_tree.py
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@ -0,0 +1,134 @@
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from SoSe24.algodat.foundation import AlgoDatArray, AlgoDatValue, read_int_sequence
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from time import perf_counter as pfc
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class BTreeNode:
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def __init__(self, m: int):
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self.n = 0
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self.leaf = True
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self.keys = AlgoDatArray(2 * m - 1)
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self.children = [None] * (2 * m)
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def __str__(self):
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return "(" + " ".join([str(self.keys[i]) for i in range(self.n)]) + ")"
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class BTree:
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def __init__(self, m: int):
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self.m = m
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self.root = BTreeNode(m)
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def search(self, key: AlgoDatValue, start: BTreeNode = None) -> BTreeNode:
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if not start:
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start = self.root
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i = 0
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while i < start.n and key > start.keys[i]:
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i += 1
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if i < start.n and key == start.keys[i]:
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return start
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if start.leaf:
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return None
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return self.search(key, start.children[i])
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def split_child(self, parent: BTreeNode, i: int):
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child = parent.children[i]
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h = BTreeNode(self.m)
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h.leaf = child.leaf
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h.n = self.m - 1
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for j in range(self.m - 1):
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h.keys[j] = child.keys[j + self.m]
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if not h.leaf:
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for j in range(self.m):
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h.children[j] = child.children[j + self.m]
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for j in range(self.m, child.n + 1):
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child.children[j] = None
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child.n = self.m - 1
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for j in range(parent.n, i, -1):
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parent.children[j + 1] = parent.children[j]
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parent.keys[j] = parent.keys[j - 1]
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parent.children[i + 1] = h
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parent.keys[i] = child.keys[self.m - 1]
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parent.n += 1
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def insert(self, k: AlgoDatValue):
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r = self.root
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if r.n == 2 * self.m - 1:
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h = BTreeNode(self.m)
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self.root = h
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h.leaf = False
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h.n = 0
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h.children[0] = r
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self.split_child(h, 0)
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self.insert_in_node(h, k)
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else:
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self.insert_in_node(r, k)
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def insert_in_node(self, start: BTreeNode, k: AlgoDatValue):
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i = start.n
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if start.leaf:
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while i >= 1 and k < start.keys[i-1]:
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start.keys[i] = start.keys[i-1]
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i -= 1
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start.keys[i] = k
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start.n += 1
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else:
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j = 0
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while j < start.n and k > start.keys[j]:
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j += 1
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if start.children[j].n == 2 * self.m - 1:
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self.split_child(start, j)
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if k > start.keys[j]:
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j += 1
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self.insert_in_node(start.children[j], k)
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def walk(self, start: BTreeNode = None):
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if not start:
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start = self.root
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i = 0
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while i < start.n:
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if not start.leaf:
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self.walk(start.children[i])
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print(start.keys[i], end=" ")
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i += 1
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if not start.leaf:
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self.walk(start.children[i])
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def height(self, start: BTreeNode = None):
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if not start:
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start = self.root
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if start.leaf:
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return 0
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return 1 + self.height(start.children[0])
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def graph_walk(self):
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queue = [ self.root ]
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with open("../../btree.gv", "w") as file:
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file.write("digraph BTree {\n")
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file.write(" node [fontname=\"Arial\"];\n")
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while queue:
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current = queue.pop(0)
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p = str(current)
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file.write(f'"{p}"; \n')
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i = 0
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while i <= current.n:
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if not current.leaf:
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queue.append(current.children[i])
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c = str(current.children[i])
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file.write(f'"{p}" -> "{c}";\n')
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i += 1
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file.write("}")
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if __name__ == "__main__":
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z = read_int_sequence("../../seq2.txt")
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start = pfc()
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tree = BTree(3)
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for i in z:
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tree.insert(i)
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print(f"Height: {tree.height()}")
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tree.walk()
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tree.graph_walk()
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s = tree.search(AlgoDatValue(0))
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print(f"\nKnoten mit 0: {str(s)}")
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print(f"Dauer: {pfc() - start:.4f}s")
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AlgoDatValue.summary()
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@ -64,13 +64,13 @@ class BinTree:
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if value < current.value:
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parent = current
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current = current.left
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elif value > current.value:
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elif value >= current.value:
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parent = current
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current = current.right
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else:
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break
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else:
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return None, None
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return
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if current.left and current.right:
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parent = current
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@ -22,6 +22,7 @@ if __name__ == "__main__":
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tree.insert(i)
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memory_values.append(AlgoDatValue.memory)
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compare_values.append(AlgoDatValue.compare)
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print(right_end, AlgoDatValue.compare)
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plt.plot(range(1, n, step), memory_values, 'b', label='Memory')
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plt.plot(range(1, n, step), compare_values, 'r', label='Compare')
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