finish llm homework
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Jan-Niclas Loosen
228
llm/generation-output/claude.txt
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228
llm/generation-output/claude.txt
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Claude Antwort
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##################################################################################################################
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Let me break this down into sub-problems:
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1. Node class implementation with required attributes
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2. Red-Black Tree class with fundamental BST operations
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3. Red-Black specific operations (rotations, color adjustments)
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4. Visualization logic using Matplotlib
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Questions before proceeding:
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1. Should the visualization be static or would you like interactive features (e.g., showing steps of insertions)?
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2. Do you need specific operations beyond insert/delete/search?
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3. Would you like the visualization to display the tree horizontally or vertically?
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These details will help me provide a more focused implementation. Please let me know your preferences.
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#################################################################################################################
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I'll create a visualizable Red-Black tree with the following implementation approach:
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Key Components:
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1. Node class: value, color (RED/BLACK), parent/left/right pointers
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2. RBTree class: root node, BST operations, RB balancing
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3. Visualization: matplotlib animation showing tree growth and rebalancing
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Core Operations to Implement:
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- Standard BST insert/delete/search
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- Left/right rotations
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- Color flipping
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- RB balancing after insertions/deletions
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- Tree layout calculation for visualization
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- Animation of node positions and colors
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```python
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import matplotlib.pyplot as plt
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import matplotlib.animation as animation
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import numpy as np
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class Node:
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def __init__(self, value):
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self.value = value
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self.left = None
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self.right = None
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self.parent = None
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self.color = "RED"
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self.x = 0 # For visualization
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self.y = 0 # For visualization
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class RedBlackTree:
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def __init__(self):
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self.nil = Node(None)
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self.nil.color = "BLACK"
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self.root = self.nil
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self.fig, self.ax = plt.subplots(figsize=(10, 8))
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self.frames = []
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def search(self, value):
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return self._search_recursive(self.root, value)
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def _search_recursive(self, node, value):
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if node == self.nil or value == node.value:
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return node
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if value < node.value:
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return self._search_recursive(node.left, value)
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return self._search_recursive(node.right, value)
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def insert(self, value):
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node = Node(value)
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node.left = self.nil
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node.right = self.nil
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y = None
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x = self.root
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while x != self.nil:
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y = x
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if node.value < x.value:
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x = x.left
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else:
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x = x.right
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node.parent = y
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if y == None:
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self.root = node
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elif node.value < y.value:
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y.left = node
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else:
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y.right = node
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self._fix_insert(node)
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self._update_positions()
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self._capture_frame()
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def _fix_insert(self, k):
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while k.parent and k.parent.color == "RED":
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if k.parent == k.parent.parent.right:
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u = k.parent.parent.left
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if u.color == "RED":
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u.color = "BLACK"
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k.parent.color = "BLACK"
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k.parent.parent.color = "RED"
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k = k.parent.parent
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else:
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if k == k.parent.left:
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k = k.parent
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self._right_rotate(k)
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k.parent.color = "BLACK"
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k.parent.parent.color = "RED"
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self._left_rotate(k.parent.parent)
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else:
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u = k.parent.parent.right
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if u.color == "RED":
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u.color = "BLACK"
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k.parent.color = "BLACK"
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k.parent.parent.color = "RED"
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k = k.parent.parent
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else:
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if k == k.parent.right:
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k = k.parent
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self._left_rotate(k)
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k.parent.color = "BLACK"
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k.parent.parent.color = "RED"
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self._right_rotate(k.parent.parent)
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if k == self.root:
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break
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self.root.color = "BLACK"
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def _left_rotate(self, x):
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y = x.right
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x.right = y.left
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if y.left != self.nil:
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y.left.parent = x
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y.parent = x.parent
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if x.parent == None:
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self.root = y
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elif x == x.parent.left:
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x.parent.left = y
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else:
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x.parent.right = y
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y.left = x
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x.parent = y
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self._update_positions()
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self._capture_frame()
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def _right_rotate(self, x):
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y = x.left
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x.left = y.right
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if y.right != self.nil:
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y.right.parent = x
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y.parent = x.parent
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if x.parent == None:
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self.root = y
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elif x == x.parent.right:
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x.parent.right = y
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else:
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x.parent.left = y
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y.right = x
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x.parent = y
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self._update_positions()
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self._capture_frame()
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def _update_positions(self):
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def calculate_positions(node, level=0, offset=0, width=2000):
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if node == self.nil:
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return
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node.y = -level * 100
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node.x = offset
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next_width = width / 2
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calculate_positions(node.left, level + 1, offset - next_width, next_width)
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calculate_positions(node.right, level + 1, offset + next_width, next_width)
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calculate_positions(self.root)
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def _capture_frame(self):
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self.ax.clear()
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self._draw_tree(self.root)
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self.ax.set_title("Red-Black Tree")
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self.ax.set_xlim(-1000, 1000)
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self.ax.set_ylim(-500, 100)
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self.ax.axis('off')
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self.frames.append([plt.gcf()])
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def _draw_tree(self, node):
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if node == self.nil:
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return
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# Draw connections to children
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if node.left != self.nil:
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self.ax.plot([node.x, node.left.x], [node.y, node.left.y], 'k-')
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if node.right != self.nil:
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self.ax.plot([node.x, node.right.x], [node.y, node.right.y], 'k-')
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# Draw node
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circle = plt.Circle((node.x, node.y), 30, color=node.color.lower())
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self.ax.add_patch(circle)
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self.ax.text(node.x, node.y, str(node.value),
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horizontalalignment='center',
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verticalalignment='center',
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color='white' if node.color == "BLACK" else 'black')
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def visualize(self):
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ani = animation.ArtistAnimation(self.fig, self.frames, interval=1000, blit=True)
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plt.show()
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# Example usage
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tree = RedBlackTree()
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test_values = [7, 3, 18, 10, 22, 8, 11, 26, 2, 6, 13]
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for value in test_values:
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tree.insert(value)
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tree.visualize()
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```
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This implementation:
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- Uses matplotlib for dynamic visualization
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- Handles all core RB tree operations
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- Shows tree growth and rebalancing with animation
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- Maintains RB tree properties
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- Includes test data for demonstration
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The tree will animate each insertion and rebalancing operation, showing nodes in their appropriate colors and positions.
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Would you like me to explain any specific part in more detail?
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