diff --git a/llm/description-output/chatgpt.txt b/llm/description-output/chatgpt.txt new file mode 100644 index 0000000..7691f8c --- /dev/null +++ b/llm/description-output/chatgpt.txt @@ -0,0 +1,219 @@ +ChatGPT Antwort +################################################################################################################## + +### **Breaking Down the WordPress Cron Job Script** + +This PHP script is a **pseudo-cron daemon** for scheduling and executing WordPress tasks. Normally, WordPress cron jobs are triggered when someone visits the website. However, if a site does not get frequent visits, scheduled tasks might be delayed. This script ensures that WordPress cron jobs run as expected by allowing them to be triggered externally (e.g., via a server's actual cron job). + +I will break the script into **logical sections**, explain each one, and then describe how they interact. + +--- + +## **1. Setting Up the Environment** + +### **Ignoring User Abort** +```php +ignore_user_abort( true ); +``` +This line ensures that the script continues to execute even if the user (or browser) disconnects before it completes. This is important for cron jobs that may take longer to execute. + +### **Preventing Caching** +```php +if ( ! headers_sent() ) { + header( 'Expires: Wed, 11 Jan 1984 05:00:00 GMT' ); + header( 'Cache-Control: no-cache, must-revalidate, max-age=0' ); +} +``` +These headers prevent browsers and proxy servers from caching the response. Since this is a background task, caching would be unnecessary and could cause unexpected behavior. + +--- + +## **2. Attempting to Finish Request Early** +```php +if ( function_exists( 'fastcgi_finish_request' ) ) { + fastcgi_finish_request(); +} elseif ( function_exists( 'litespeed_finish_request' ) ) { + litespeed_finish_request(); +} +``` +This part tells the web server that it can finish responding to the request while the script continues running in the background. This helps reduce the load time for any user that triggered the cron. + +--- + +## **3. Checking If the Request Should Be Processed** +```php +if ( ! empty( $_POST ) || defined( 'DOING_AJAX' ) || defined( 'DOING_CRON' ) ) { + die(); +} +``` +- If the request is coming from an **AJAX request** or **another cron job is already running**, the script stops (`die();`). +- This prevents multiple cron jobs from running simultaneously and causing conflicts. + +--- + +## **4. Defining Cron Execution Mode** +```php +define( 'DOING_CRON', true ); +``` +This constant tells WordPress that a cron job is currently running. + +### **Loading WordPress Core** +```php +if ( ! defined( 'ABSPATH' ) ) { + require_once __DIR__ . '/wp-load.php'; +} +``` +This ensures that the script loads the WordPress environment, which is necessary for executing scheduled tasks. + +### **Increasing PHP Memory for Cron Jobs** +```php +wp_raise_memory_limit( 'cron' ); +``` +- Increases the PHP memory limit so that large cron jobs don’t fail due to memory exhaustion. + +--- + +## **5. Checking and Acquiring Cron Lock** +A "cron lock" ensures that only **one** cron process runs at a time. If multiple processes start, they could interfere with each other. + +### **Fetching the Cron Lock** +```php +function _get_cron_lock() { + global $wpdb; + + $value = 0; + if ( wp_using_ext_object_cache() ) { + $value = wp_cache_get( 'doing_cron', 'transient', true ); + } else { + $row = $wpdb->get_row( $wpdb->prepare( "SELECT option_value FROM $wpdb->options WHERE option_name = %s LIMIT 1", '_transient_doing_cron' ) ); + if ( is_object( $row ) ) { + $value = $row->option_value; + } + } + + return $value; +} +``` +- This function retrieves the current cron lock stored in the WordPress database. +- If caching is enabled, it tries to fetch the lock from the cache instead. + +### **Checking if Cron Tasks Exist** +```php +$crons = wp_get_ready_cron_jobs(); +if ( empty( $crons ) ) { + die(); +} +``` +- If no scheduled tasks are ready, the script stops. + +### **Handling the Cron Lock** +```php +$gmt_time = microtime( true ); +$doing_cron_transient = get_transient( 'doing_cron' ); + +if ( empty( $doing_wp_cron ) ) { + if ( empty( $_GET['doing_wp_cron'] ) ) { + if ( $doing_cron_transient && ( $doing_cron_transient + WP_CRON_LOCK_TIMEOUT > $gmt_time ) ) { + return; + } + $doing_wp_cron = sprintf( '%.22F', microtime( true ) ); + set_transient( 'doing_cron', $doing_wp_cron ); + } else { + $doing_wp_cron = $_GET['doing_wp_cron']; + } +} +``` +- If another process is already running (`doing_cron` transient exists and is recent), the script stops. +- Otherwise, it sets a new lock using the current timestamp. + +--- + +## **6. Executing the Cron Jobs** +```php +foreach ( $crons as $timestamp => $cronhooks ) { + if ( $timestamp > $gmt_time ) { + break; + } + + foreach ( $cronhooks as $hook => $keys ) { + foreach ( $keys as $k => $v ) { +``` +- This loops through all scheduled cron jobs and processes only those whose execution time has arrived. + +### **Rescheduling Recurring Tasks** +```php +$schedule = $v['schedule']; + +if ( $schedule ) { + $result = wp_reschedule_event( $timestamp, $schedule, $hook, $v['args'], true ); + + if ( is_wp_error( $result ) ) { + error_log( sprintf( + __( 'Cron reschedule event error for hook: %1$s, Error code: %2$s, Error message: %3$s, Data: %4$s' ), + $hook, + $result->get_error_code(), + $result->get_error_message(), + wp_json_encode( $v ) + ) ); + do_action( 'cron_reschedule_event_error', $result, $hook, $v ); + } +} +``` +- If the cron job is a **recurring** event, it is rescheduled after execution. + +### **Removing Completed Tasks** +```php +$result = wp_unschedule_event( $timestamp, $hook, $v['args'], true ); + +if ( is_wp_error( $result ) ) { + error_log( sprintf( + __( 'Cron unschedule event error for hook: %1$s, Error code: %2$s, Error message: %3$s, Data: %4$s' ), + $hook, + $result->get_error_code(), + $result->get_error_message(), + wp_json_encode( $v ) + ) ); + do_action( 'cron_unschedule_event_error', $result, $hook, $v ); +} +``` +- After running, non-recurring cron jobs are removed from the schedule. + +### **Executing the Cron Job** +```php +do_action_ref_array( $hook, $v['args'] ); +``` +- This runs the function associated with the scheduled hook. + +### **Checking If Another Process Took Over** +```php +if ( _get_cron_lock() !== $doing_wp_cron ) { + return; +} +``` +- If another cron process has taken over, the script stops. + +--- + +## **7. Cleaning Up** +```php +if ( _get_cron_lock() === $doing_wp_cron ) { + delete_transient( 'doing_cron' ); +} +die(); +``` +- If this process still holds the cron lock, it removes the lock to allow new cron jobs to run in the future. +- Finally, the script terminates. + +--- + +## **How Everything Works Together** +1. The script sets up a background execution environment. +2. It checks whether another cron job is already running. +3. It fetches and locks the cron job list. +4. It loops through the scheduled cron jobs: + - If a job is due, it runs it. + - If it's a recurring job, it reschedules it. + - If it's a one-time job, it removes it. +5. It releases the cron lock when finished. + +This ensures that WordPress cron jobs run efficiently without requiring visitor traffic. diff --git a/llm/description-output/claude.txt b/llm/description-output/claude.txt new file mode 100644 index 0000000..2b0ee8b --- /dev/null +++ b/llm/description-output/claude.txt @@ -0,0 +1,62 @@ +Claude Antwort +################################################################################################################## + +I'll help explain this WordPress cron implementation code. Let me first make sure I understand what you're looking for - would you like me to break down how WordPress's pseudo-cron system works, explaining the key components and their interactions in a way that would make sense to coding beginners? + +This is a fairly complex piece of code that handles WordPress's scheduled task system, so I want to make sure I explain it in an accessible way that builds understanding from the ground up. Would you like me to start with explaining what a cron system is in general, before diving into how WordPress implements it? + +################################################################################################################### + +I'll explain WordPress's cron system step by step, starting with the basics and building up to how everything works together. + +First, let's understand what a cron system is: +A cron system is like an alarm clock for websites - it helps run tasks automatically at specific times. For example, it might check for updates every hour or clean up old data every day. + +Now, let's break down the main parts of this WordPress cron code: + +1. Initial Setup +```php +ignore_user_abort(true); +``` +This line tells PHP to keep running even if the user closes their browser. Think of it like telling a delivery person "please finish delivering all packages even if the person who ordered them isn't home." + +2. Header Management +```php +if ( ! headers_sent() ) { + header('Expires: Wed, 11 Jan 1984 05:00:00 GMT'); + header('Cache-Control: no-cache, must-revalidate, max-age=0'); +} +``` +This part tells browsers not to cache (save) the results. It's like putting a "Do Not Save" stamp on the response. + +3. Lock System +The code includes a locking mechanism to prevent multiple cron jobs from running at the same time. It's similar to a "Busy" sign on a bathroom door - only one person can use it at a time: +```php +$doing_cron_transient = get_transient('doing_cron'); +``` + +4. Task Processing +```php +foreach ($crons as $timestamp => $cronhooks) { + if ($timestamp > $gmt_time) { + break; + } + // Process each task... +} +``` +This part goes through all scheduled tasks and runs the ones that are due. It's like going through a to-do list and doing tasks that are past their scheduled time. + +5. Error Handling +The code includes comprehensive error logging and handling: +```php +if (is_wp_error($result)) { + error_log(sprintf(/* ... */)); +} +``` + +What makes WordPress's cron system special is that it's a "pseudo-cron" system. Unlike regular server cron jobs that run at exact times, WordPress's cron runs when someone visits your website. When a visitor comes to the site, WordPress checks if there are any overdue tasks and runs them. + +Think of it like a store that only checks its delivery schedule when a customer walks in, rather than having someone constantly watching the clock. This approach works well for most websites but might not be perfect for tasks that need to run at very specific times. + +Would you like me to explain any of these parts in more detail? Or shall we dive deeper into how these components work together? + diff --git a/llm/description-output/deepseek.txt b/llm/description-output/deepseek.txt new file mode 100644 index 0000000..0b2a40f --- /dev/null +++ b/llm/description-output/deepseek.txt @@ -0,0 +1,94 @@ +DeepSeek Antwort +################################################################################################################## + +Let's break down this WordPress cron system into smaller, digestible parts. I'll explain each component step by step before showing how they work together. + +**1. Initial Setup and Performance Optimization** +```php +ignore_user_abort(true); +// ... header settings ... +if (function_exists('fastcgi_finish_request')) { + fastcgi_finish_request(); +} +``` +- *What it does*: Ensures the script keeps running even if the user closes their browser +- *Why it's important*: Cron jobs might take longer than a browser is willing to wait +- *Real-world analogy*: Like sending a letter and continuing to work while it's in the mail + +**2. Security Checks** +```php +if (!empty($_POST) || defined('DOING_AJAX') || defined('DOING_CRON')) { + die(); +} +``` +- *What it does*: Prevents accidental execution during form submissions or AJAX calls +- *Why it's important*: Protects against unintended behavior during normal site operations +- *Like*: A "Do Not Disturb" sign during important meetings + +**3. Environment Preparation** +```php +if (!defined('ABSPATH')) { + require_once __DIR__ . '/wp-load.php'; +} +wp_raise_memory_limit('cron'); +``` +- *What it does*: Loads WordPress core files if not already loaded +- *Why it's important*: Gives access to WordPress functions and configuration +- *Memory limit analogy*: Getting a bigger workbench before starting a big project + +**4. The Cron Lock System** +```php +function _get_cron_lock() { /* ... */ } +$doing_cron_transient = get_transient('doing_cron'); +// ... lock checking logic ... +``` +- *What it does*: Creates a "virtual lock" to prevent multiple cron runs overlapping +- *Why it's important*: Prevents duplicate task execution and server overload +- *Real-world example*: Like a "Checked Out" tag on a library book + +**5. Task Scheduling Mechanism** +```php +$crons = wp_get_ready_cron_jobs(); +foreach ($crons as $timestamp => $cronhooks) { + if ($timestamp > $gmt_time) break; + // ... processing ... +} +``` +- *What it does*: Retrieves and processes scheduled tasks that are due +- *Why it's important*: Ensures timely execution of scheduled events +- *Like*: A chef checking which orders in the kitchen are ready to cook + +**6. Event Execution** +```php +do_action_ref_array($hook, $v['args']); +``` +- *What it does*: Triggers the actual WordPress hooks/events +- *Why it's important*: This is where the scheduled tasks actually run +- *Analogy*: Pushing the "Start" button on a washing machine cycle + +**How Everything Works Together:** +1. The script starts by setting up a stable environment (Parts 1-3) +2. Checks if it's safe to run (Part 2) +3. Sets up a lock to prevent overlaps (Part 4) +4. Fetches due tasks (Part 5) +5. Executes them one by one (Part 6) +6. Cleans up after finishing (Part 4 lock removal) + +**Visual Workflow:** +``` +[Visitor Request] -> [Quick Response Sent] -> [Background Processing] + │ │ + └─▶ [Security Check]─┤ + ├─▶ [Lock System]─▶ [Task Processing] + │ + └─▶ [Memory Management] +``` + +**Key Concept to Remember:** +This system acts like a restaurant manager who: +1. Takes your order (request) +2. Immediately gives you a receipt (fast finish) +3. Works on your order in the kitchen (background processing) +4. Uses a ticket system (lock) to ensure your order isn't made twice + +Would you like me to elaborate on any of these components or discuss potential error scenarios they handle? diff --git a/llm/description-output/gemini.txt b/llm/description-output/gemini.txt new file mode 100644 index 0000000..0b4bc7b --- /dev/null +++ b/llm/description-output/gemini.txt @@ -0,0 +1,48 @@ +Gemini Antwort +################################################################################################################## + +Let's break down this PHP code, which is a crucial part of WordPress's scheduling system, WP-Cron. Think of WP-Cron as a task scheduler, like a calendar that automatically triggers actions at specific times. This code is the "engine" that makes those scheduled actions happen. + +**Sub-problems and Explanations:** + +1. **Preventing Interference:** + + * `ignore_user_abort( true );`: This line tells the server not to interrupt the script even if the user closes their browser. This is vital because cron jobs need to run to completion, regardless of user activity. + * `if ( ! headers_sent() ) { ... }`: These lines set HTTP headers to prevent caching. Caching could interfere with cron's operation, so we disable it. + * `if ( function_exists( 'fastcgi_finish_request' ) ) { ... }` and `if ( function_exists( 'litespeed_finish_request' ) ) { ... }`: These lines try to finish the HTTP request as quickly as possible. This is important to ensure that the user who triggered the cron execution doesn't experience any slowdown. The actual cron processing happens in the background. + * `if ( ! empty( $_POST ) || defined( 'DOING_AJAX' ) || defined( 'DOING_CRON' ) ) { die(); }`: This check prevents the script from running if it's accessed via a POST request, AJAX call, or if it's already running as a cron job. This avoids accidental or malicious triggering and prevents multiple cron processes from running simultaneously. + +2. **WordPress Environment Setup:** + + * `define( 'DOING_CRON', true );`: This line defines a constant to indicate that the script is running as a cron job. Other parts of WordPress might check this constant to behave differently. + * `if ( ! defined( 'ABSPATH' ) ) { require_once __DIR__ . '/wp-load.php'; }`: This is the most important part! It loads the core WordPress files. Without this, none of the WordPress functions (like scheduling events) would work. `wp-load.php` sets up the entire WordPress environment. + * `wp_raise_memory_limit( 'cron' );`: This attempts to increase the PHP memory limit, which is important because some cron jobs might require a lot of memory. + +3. **Cron Job Retrieval and Locking:** + + * `function _get_cron_lock() { ... }`: This function is responsible for retrieving the cron lock. The cron lock is a transient (a temporary piece of data stored in the database) that prevents multiple cron processes from running at the same time. This is a critical concurrency control mechanism. + * `$crons = wp_get_ready_cron_jobs();`: This function retrieves all the cron jobs that are due to run. It checks the scheduled times and returns a list of jobs ready for execution. + * `$gmt_time = microtime( true );`: Gets the current time in GMT (Greenwich Mean Time) with microseconds. This is used for comparing against scheduled times. + * The code then implements a locking mechanism using `get_transient( 'doing_cron' )`, `set_transient( 'doing_cron', $doing_wp_cron )`, and `delete_transient( 'doing_cron' )`. This ensures that only one cron process runs at a time. It uses a timestamp-based lock to prevent deadlocks. The `WP_CRON_LOCK_TIMEOUT` constant (not shown in your code) determines how long a lock is valid. If a cron process crashes, the lock will eventually expire, allowing another process to take over. + +4. **Executing Cron Jobs:** + + * The code then loops through each scheduled time (`$timestamp`) and then each hook (`$hook`) within that timestamp. A hook is essentially the name of a function that will be executed. + * `wp_reschedule_event( $timestamp, $schedule, $hook, $v['args'], true );`: If a job is scheduled to recur, this function reschedules it for the next run. + * `wp_unschedule_event( $timestamp, $hook, $v['args'], true );`: This unschedules the current event as it is about to run. + * `do_action_ref_array( $hook, $v['args'] );`: This is the core part! It executes the function associated with the hook, passing any arguments (`$v['args']`) that were scheduled with it. This is where the actual work of the cron job happens. + * The code also includes error handling for rescheduling and unscheduling events, logging errors using `error_log()` and triggering actions `cron_reschedule_event_error` and `cron_unschedule_event_error`. + +5. **Releasing the Lock:** + + * `if ( _get_cron_lock() === $doing_wp_cron ) { delete_transient( 'doing_cron' ); }`: After all the jobs are executed, the cron lock is released by deleting the `doing_cron` transient. + +**Interaction:** + +These sub-problems work together in a sequence. First, the script makes sure it's running in the correct context and sets up the WordPress environment. Then, it retrieves the cron jobs that are due. The locking mechanism ensures that only one cron process runs at a time. Finally, it loops through the jobs, rescheduling recurring ones, unscheduling the current instance, and executing them. The lock is released after all jobs are processed. + +**In summary:** This script acts as a pseudo-cron daemon for WordPress. It's called periodically (either by a real server cron or when a user visits the site) and executes scheduled WordPress tasks. The locking mechanism is crucial to prevent conflicts and ensure that jobs are executed only once. The script is designed to be robust and efficient, minimizing the impact on website visitors. + +Let me know if you have any more questions! We can delve deeper into specific parts if you'd like. + + diff --git a/llm/evaluation-output/chatgpt.txt b/llm/evaluation-output/chatgpt.txt new file mode 100644 index 0000000..d237bd3 --- /dev/null +++ b/llm/evaluation-output/chatgpt.txt @@ -0,0 +1,87 @@ +ChatGPT Antwort +################################################################################################################## + +Below is an evaluation of the executable parts of the program, along with some thoughts on alternative evaluation methodologies you might consider. + +--- + +### Alternative Evaluation Methodologies + +Before diving into the code’s specifics, in addition to using measures such as “bad smells per NCLOC,” cognitive complexity (à la SonarQube), and bugs per NCLOC, you might also consider: + +- **Static Analysis & Linting:** + Tools like PyLint, Flake8, or mypy (for type hints) can automatically flag style issues, potential bugs, and adherence to best practices (including PEP8 compliance). + +- **Unit Test Coverage:** + Evaluate how well the code is exercised by unit tests. High test coverage (with clear tests for edge cases) can serve as a proxy for code reliability and maintainability. + +- **Cyclomatic Complexity & Maintainability Index:** + While cognitive complexity is one metric, cyclomatic complexity (which counts decision points) and an overall maintainability index can help determine if functions need to be refactored. + +- **Code Review & Pair Programming:** + Having peers review the code for clarity, adherence to design patterns, and separation of concerns can reveal issues that automated tools might miss. + +- **Performance Profiling:** + For data structures like trees, ensuring that operations (insertion, deletion, search) perform as expected on larger datasets might be important, especially if the code is to be used in a performance-critical environment. + +--- + +### Evaluation of the Executable Code + +#### **1. Correctness and Algorithm Implementation** + +- **Red-Black Tree Logic:** + The implementation of the red-black tree operations (insertion with fix-up, deletion with fix-up, rotations, and transplant) follows the standard algorithm. The use of a sentinel node (`self.NIL`) is correct and helps to avoid many common pitfalls when dealing with null pointers. + +- **Edge Cases Handling:** + The code correctly handles the typical edge cases inherent in red-black trees (for example, when the tree is empty or when performing rotations at the root). + +- **Visualization:** + The dynamic visualization using Matplotlib is a nice touch for educational or debugging purposes. The `draw_tree` method computes positions via an in-order traversal and then draws nodes and edges accordingly. Although this code is intermingled with the tree logic, it works as intended. + +#### **2. Code Structure and Organization** + +- **Separation of Concerns:** + The core red-black tree operations (insertion, deletion, search, rotations) are grouped together in the `RedBlackTree` class. However, the visualization functionality (`draw_tree`) is also placed within the same class. In a production environment, you might consider separating the tree logic from its visualization (for example, by using a separate module or class for UI/graphical representation). + +- **Naming and Readability:** + The function and variable names are generally clear and descriptive. The structure of the code follows standard Python idioms, making it easy to follow the flow of operations. + +#### **3. Complexity and Maintainability** + +- **Cognitive Complexity:** + The red-black tree fix-up methods (`insert_fixup` and `delete_fixup`) contain several nested conditionals that, while standard for this algorithm, contribute to high cognitive complexity. This is acceptable given the inherent complexity of the algorithm but might benefit from refactoring into smaller helper functions if further maintenance or modifications are expected. + +- **Duplication:** + Noticeable mirror-image code appears in both the insertion and deletion fix-up routines. While this duplication is common in red-black tree implementations (because of the “mirror” cases), it could be documented or even abstracted further to aid understanding, provided it does not reduce clarity. + +#### **4. Robustness and Error Handling** + +- **Error Reporting:** + The `delete_value` method prints an error message if a value is not found. For a library intended for broader use, it might be preferable to raise a specific exception so that calling code can handle the error programmatically. + +- **Type Safety:** + The code does not use type annotations. While not strictly necessary in Python, adding them (especially in a learning context) could improve clarity and allow tools like mypy to help catch bugs. + +#### **5. Integration with External Libraries** + +- **Matplotlib Usage:** + The visualization leverages Matplotlib’s interactive mode (`plt.ion()`) and `plt.pause()`, which are appropriate for dynamic updates. However, in a non-interactive environment or for unit testing, you might want to separate or disable these visualization features to avoid side effects. + +--- + +### Summary + +- **Strengths:** + - **Algorithm Correctness:** The red-black tree operations are implemented according to standard algorithms. + - **Readable and Structured Code:** Clear naming conventions and logical organization of functions. + - **Educational Visualization:** The dynamic visualization aids in understanding the tree’s evolution after operations. + +- **Areas for Improvement:** + - **Separation of Concerns:** Consider decoupling the tree logic from the visualization code to improve maintainability and testability. + - **Error Handling:** Instead of printing errors (e.g., in `delete_value`), use exceptions or return status values. + - **Cognitive Complexity:** The fix-up routines are naturally complex; if further development is expected, refactoring into helper methods might help reduce the cognitive load. + - **Static Typing & Documentation:** Introducing type annotations and more robust docstrings (even if comments are ignored for this evaluation) can improve future maintainability and integration with static analysis tools. + - **Testing:** There is no automated unit test coverage. Integrating a suite of tests would provide additional assurance of correctness, especially as the codebase evolves. + +Overall, the executable code demonstrates a solid understanding of red-black tree algorithms and basic dynamic visualization in Python. With a few refinements aimed at decoupling, error handling, and maintainability, the code would be well-suited for production-level or educational use. diff --git a/llm/evaluation-output/claude.txt b/llm/evaluation-output/claude.txt new file mode 100644 index 0000000..2d76e87 --- /dev/null +++ b/llm/evaluation-output/claude.txt @@ -0,0 +1,211 @@ +Claude Antwort +################################################################################################################## + +I'll help evaluate this Python code that implements a Red-Black Tree with visualization capabilities. Before diving into the evaluation, let me outline the methodology I'll use: + +1. Code Quality Metrics: +- Bugs per NCLOC (Non-Comment Lines of Code) +- Cognitive Complexity per function +- Code smells per NCLOC + +2. Additional relevant metrics for this case: +- Cyclomatic Complexity (especially important for tree operations) +- SOLID Principles adherence +- DRY (Don't Repeat Yourself) principle +- Error handling and edge cases +- Object-oriented design patterns +- Testing capabilities + +Let me proceed with the evaluation: + +1. Structure and Design Issues: + +a) Positive aspects: +- Clear class hierarchy with Node and RedBlackTree classes +- Good separation of concerns between tree operations and visualization +- Logical method naming and organization + +b) Code Smells (approximately 5 per 150 NCLOC): +- The Node class mixes tree logic with visualization concerns (x, y coordinates) +- No input validation in insert method +- No exception handling +- Tight coupling between tree operations and matplotlib visualization +- Hard-coded visualization parameters + +2. Cognitive Complexity Issues: + +The highest complexity is in these methods: +- `_fix_insert`: ~15 (exceeds recommended threshold of 10) +- `insert`: ~8 +- `_left_rotate` and `_right_rotate`: ~6 each + +3. Potential Bugs (approximately 3 per 150 NCLOC): + +1. Memory leak risk in `visualize()` due to accumulating frames without cleanup +2. No null checks for parent references in rotation operations +3. Possible infinite loop in `_fix_insert` if tree structure is corrupted + +4. Improvement Recommendations: + + + +```python +from dataclasses import dataclass +from typing import Optional, Any +from enum import Enum +import abc + +class Color(Enum): + RED = "RED" + BLACK = "BLACK" + +@dataclass +class Position: + x: float = 0 + y: float = 0 + +class NodeBase(abc.ABC): + @property + @abc.abstractmethod + def is_nil(self) -> bool: + pass + +class Node(NodeBase): + def __init__(self, value: Any): + self.value = value + self.left: Optional[Node] = None + self.right: Optional[Node] = None + self.parent: Optional[Node] = None + self.color: Color = Color.RED + self.position: Position = Position() + + @property + def is_nil(self) -> bool: + return False + +class NilNode(NodeBase): + def __init__(self): + self.color: Color = Color.BLACK + self.position: Position = Position() + + @property + def is_nil(self) -> bool: + return True + +class RedBlackTree: + def __init__(self): + self.nil = NilNode() + self.root: NodeBase = self.nil + + def insert(self, value: Any) -> None: + try: + self._validate_input(value) + node = self._create_node(value) + self._insert_node(node) + self._fix_insert(node) + except ValueError as e: + raise ValueError(f"Invalid insert operation: {str(e)}") + + def _validate_input(self, value: Any) -> None: + if value is None: + raise ValueError("Cannot insert None value") + if not hasattr(value, "__lt__"): + raise ValueError("Value must be comparable") + + def _create_node(self, value: Any) -> Node: + node = Node(value) + node.left = self.nil + node.right = self.nil + return node + + def _insert_node(self, node: Node) -> None: + parent: Optional[NodeBase] = None + current = self.root + + while not current.is_nil: + parent = current + if isinstance(current, Node): # Type guard for static typing + if node.value < current.value: + current = current.left + else: + current = current.right + + node.parent = parent + if parent is None: + self.root = node + elif node.value < parent.value: + parent.left = node + else: + parent.right = node + +class RedBlackTreeVisualizer: + def __init__(self, tree: RedBlackTree): + self.tree = tree + self.frames = [] + + def update_positions(self) -> None: + def calculate_positions(node: NodeBase, level: int = 0, + offset: float = 0, width: float = 2000) -> None: + if node.is_nil: + return + + node.position.y = -level * 100 + node.position.x = offset + + next_width = width / 2 + if isinstance(node, Node): # Type guard + calculate_positions(node.left, level + 1, + offset - next_width, next_width) + calculate_positions(node.right, level + 1, + offset + next_width, next_width) + + calculate_positions(self.tree.root) + +``` + +Key improvements in the refactored code: + +1. Separation of Concerns: +- Split visualization into separate class +- Used dataclass for Position +- Created proper class hierarchy with ABC +- Removed direct matplotlib dependencies from tree logic + +2. Type Safety: +- Added type hints +- Proper null checks +- Input validation +- Exception handling + +3. Better OO Design: +- Enum for node colors +- Abstract base class for nodes +- Proper encapsulation +- Interface segregation + +4. Code Maintainability: +- Smaller, focused methods +- Reduced cognitive complexity +- Better error handling +- Cleaner class structure + +5. Performance Considerations: +- Memory management for visualization +- Proper type guards +- Optimized position calculations + +Recommendations for further improvement: + +1. Add comprehensive unit tests +2. Implement iterator pattern for tree traversal +3. Add proper logging +4. Create configuration class for visualization parameters +5. Add documentation strings +6. Implement proper cleanup in visualizer + +The original code quality metrics: +- Bugs per NCLOC: ~0.02 (3/150) +- Code smells per NCLOC: ~0.033 (5/150) +- Average cognitive complexity: ~8.75 + +The refactored version should show significant improvements in all these metrics. Would you like me to elaborate on any specific aspect of the evaluation or provide additional recommendations? diff --git a/llm/evaluation-output/deepseek.txt b/llm/evaluation-output/deepseek.txt new file mode 100644 index 0000000..8a0ae1b --- /dev/null +++ b/llm/evaluation-output/deepseek.txt @@ -0,0 +1,81 @@ +DeepSeek Antwort +################################################################################################################## + +**Code Quality Evaluation Report** + +The provided code implements a Red-Black Tree (RBT) with visualization. Below is an assessment of code quality, potential bugs, and areas for improvement. + +--- + +### **1. Code Metrics & Quality Indicators** +- **Cognitive Complexity**: + - The `fix_insert` method is highly complex (nested loops/conditionals), making it error-prone and hard to maintain. This is a critical issue per SonarQube standards. + - **Recommendation**: Refactor into helper methods (e.g., `_handle_uncle_red`, `_handle_left_case`). + +- **Code Smells**: + - **Magic Strings**: Use of `'red'`/`'black'` increases typo risk. + - **Recommendation**: Replace with `Enum` or class-level constants. + - **Side Effects**: `_assign_positions` mutates an external `pos_map` instead of returning a value. + - **Inadequate Encapsulation**: `Node` fields (e.g., `color`) are public and modifiable outside the RBT class, risking invalid states. + +- **Potential Bugs**: + - **Duplicates Allowed**: The tree permits duplicate values, which may violate RBT assumptions if uniqueness is required. + - **Visualization Overlap**: `_assign_positions` uses in-order traversal for horizontal placement, which may misrepresent parent-child relationships (e.g., parent not centered over children). + +--- + +### **2. Alternative Evaluation Methodologies** +- **Static Analysis**: Tools like Pylint/Flake8 can enforce PEP8 compliance (current code is mostly compliant). +- **Mutation Testing**: Would help validate test robustness (but no tests are included here). +- **Property-Based Testing**: Verify RBT invariants (root is black, no double reds, equal black heights). + +--- + +### **3. Critical Issues** +#### **a. Red-Black Tree Implementation** +- **Rotation Edge Cases**: + - Rotations correctly update parent/child pointers, but lack tests for edge cases (e.g., root rotation). +- **Color Enforcement**: + - The `fix_insert` logic generally maintains RBT properties, but untested scenarios (e.g., uncle is black with a red child) could hide bugs. + +#### **b. Visualization** +- **Positioning Flaw**: Using in-order traversal for `x` coordinates may misalign nodes (e.g., parent not centered between children). + - **Recommendation**: Use hierarchical coordinates (e.g., root at center, children spaced proportionally). + +--- + +### **4. Recommendations** +1. **Refactor `fix_insert`**: + - Split into helper methods to reduce cognitive complexity. + - Example: + ```python + def fix_insert(self, node): + while node.parent.color == 'red': + if self._is_parent_left_child(node): + self._fix_left_case(node) + else: + self._fix_right_case(node) + self.root.color = 'black' + ``` + +2. **Replace Magic Strings**: + ```python + class Color(Enum): + RED = 'red' + BLACK = 'black' + ``` + +3. **Improve Visualization**: + - Calculate `x` positions based on subtree width to center parents over children. + +4. **Add Validation**: + - Ensure inserted values are comparable (e.g., raise `TypeError` for invalid types). + - Optionally enforce uniqueness. + +5. **Testing**: + - Write unit tests for RBT invariants and edge cases (e.g., insertion, rotation, color changes). + +--- + +### **5. Conclusion** +The code correctly implements core RBT operations but has significant maintainability and robustness concerns. Prioritize refactoring `fix_insert`, replacing magic strings, and adding tests. The visualization works for small trees but may misrepresent structure for larger/complex cases. diff --git a/llm/evaluation-output/gemini.txt b/llm/evaluation-output/gemini.txt new file mode 100644 index 0000000..4f29d46 --- /dev/null +++ b/llm/evaluation-output/gemini.txt @@ -0,0 +1,46 @@ +Gemini Antwort +################################################################################################################## + +Let's evaluate this Python code for a Red-Black Tree implementation. I'll focus on maintainability, readability, and correctness, which are key aspects of code quality, and touch upon performance where relevant. While cyclomatic complexity and bugs/NCLOC are useful metrics, a direct, manual assessment is more insightful for a learning exercise like this, especially since we're dealing with a specific data structure implementation. I'll also suggest some alternative evaluation methodologies. + +**Alternative Evaluation Methodologies:** + +1. **Code Review Checklist:** Using a structured checklist can ensure consistent evaluation across different code submissions. This checklist could include items related to naming conventions, code style, error handling, algorithm correctness, and test coverage. + +2. **Automated Code Analysis Tools:** Tools like Pylint, Flake8, and MyPy can automatically detect potential issues like style violations, unused variables, and type errors. While not a complete evaluation, they can highlight areas needing attention. + +3. **Test-Driven Development (TDD) Evaluation:** If the code includes unit tests, evaluating the completeness and effectiveness of these tests can provide insights into the developer's understanding of the code and its edge cases. Code coverage metrics would be useful here. + +4. **Pair Programming Evaluation:** Observing the trainee while they write code or explain their code can provide valuable insights into their problem-solving approach and coding skills. + +**Code Evaluation:** + +**Strengths:** + +* **Structure:** The code is reasonably well-structured, with separate classes for `Node` and `Tree`. The methods are generally of reasonable length. +* **Red-Black Tree Logic:** The core insertion and balancing logic seems generally correct at first glance. The color flips and rotations are implemented, and the root is always black. +* **Visualization:** The `visualize` method is a great addition. It allows for easy inspection of the tree's structure and helps in understanding the effects of insertions and deletions. Using `networkx` is a good choice. +* **Deletion:** The deletion logic, including the `_transplant` and `_find_minimum` helpers, appears correct. +* **Rotations:** The rotation logic seems correct. + +**Weaknesses and Areas for Improvement:** + +1. **Clarity of `_balance_after_delete`:** The `_balance_after_delete` method is quite complex and difficult to follow. The multiple nested `if` statements and the symmetric cases make it hard to reason about the code's behavior. This part would benefit from a more structured approach, perhaps breaking it down into smaller, more manageable helper functions. The comments are also minimal, making understanding the different cases harder. +2. **Missing Comments/Documentation:** While the core logic is present, the code lacks sufficient comments, especially within the balancing and deletion methods. Explaining the different Red-Black Tree cases (e.g., Case 1, Case 2, etc.) with comments would greatly improve readability. Docstrings for the classes and methods would be very beneficial. +3. **Error Handling:** The code doesn't handle potential errors, such as inserting duplicate values. While the comment mentions assuming no duplicates, it's good practice to explicitly handle such cases, even if it's just raising an exception. +4. **Testing:** The code lacks unit tests. Thorough testing is crucial for ensuring the correctness of a complex data structure like a Red-Black Tree. Tests should cover various scenarios, including insertions, deletions, and edge cases (empty tree, single node, etc.). +5. **Naming Conventions:** While generally okay, some variable names (e.g., `w`, `x`, `y` in the rotation and balancing methods) could be more descriptive. More descriptive names would improve readability. +6. **Performance:** While the Red-Black Tree generally provides good performance (O(log n) for most operations), there's room for improvement. For example, the `_find_node` method could potentially be optimized further, though it's likely not a major bottleneck. + +**Specific Suggestions:** + +* **Refactor `_balance_after_delete`:** Break it down into smaller, well-named helper functions, each responsible for a specific case. Add detailed comments explaining each case. +* **Add Comprehensive Tests:** Use a testing framework like `unittest` or `pytest` to write unit tests for all methods, including edge cases. +* **Add Docstrings:** Add docstrings to the `Node` and `Tree` classes and all their methods, explaining their purpose, parameters, and return values. +* **Improve Variable Names:** Use more descriptive variable names within the balancing and rotation methods. +* **Handle Duplicate Insertions:** Add a check for duplicate values in the `insert` method and handle them appropriately (e.g., by raising an exception or simply ignoring them). + +**Overall Assessment:** + +The code demonstrates a reasonable understanding of Red-Black Tree concepts and implements the core logic correctly. The visualization is a strong plus. However, the lack of comments, the complexity of the `_balance_after_delete` method, the missing error handling, and the absence of unit tests detract from the code's overall quality and maintainability. Addressing these points would significantly improve the code. The trainee has a good foundation but needs to focus on writing cleaner, more robust, and testable code. + diff --git a/llm/generation-output/chatgpt.txt b/llm/generation-output/chatgpt.txt new file mode 100644 index 0000000..d033234 --- /dev/null +++ b/llm/generation-output/chatgpt.txt @@ -0,0 +1,438 @@ +ChatGPT Antwort +################################################################################################################## + +Before I begin coding, I’d like to clarify a few points to ensure that my solution meets your requirements as closely as possible: + +1. **Operations to Implement:** + - Should the red-black tree support only insertion (and the associated balancing/fix-ups) or should it also include deletion and search operations? + - Are there any additional operations you expect the tree to have? + +2. **Visualization Details:** + - Do you want a static visualization of the final tree (using Matplotlib to display nodes and edges) or an animated step-by-step visualization that shows the tree as nodes are inserted and the tree is rebalanced? + - Do you have any preferences for the layout of the tree (e.g., spacing, orientation) or styling details for the nodes and edges (e.g., displaying node values, coloring nodes as red or black)? + +3. **Input and Testing:** + - Should the program include a predefined set of insertions (e.g., a list of numbers) to demonstrate the tree, or would you prefer to have a way for the user to input values interactively? + +4. **Assumptions and Explanations:** + - Would you like detailed inline comments and a separate explanation of the reasoning behind the implementation, or is a brief summary sufficient? + +Once I have your clarifications on these points, I can proceed by breaking the problem into sub-problems and then combining everything into one complete solution. Please let me know your preferences! + +#################################################################################################################### + +Below is a summary of my approach to solving the problem: + +1. **Dividing the Problem into Sub-Problems:** + + - **Data Structures and Core Classes:** + - **Node Class:** + Each node will store a value, its color (red or black), and pointers to its left child, right child, and parent. + - **Tree Class:** + This class will maintain the root of the red-black tree and will include methods for insertion, deletion, and search. It will also implement the required rotations (left and right) and fix-up procedures to maintain the red-black tree properties after insertions or deletions. + + - **Red-Black Tree Operations:** + - **Insertion:** + We will perform a standard BST insertion and then run an insertion fix-up to restore the red-black properties (e.g., ensuring that no red node has a red child and that all paths have the same black height). + - **Deletion:** + Deletion in a red-black tree is more involved. We will remove the node as in a standard BST deletion and then execute a deletion fix-up procedure to maintain the tree’s properties. + - **Search:** + A standard BST search will be used to locate nodes. + + - **Dynamic Visualization Using Matplotlib:** + - We will create a function that computes the positions of nodes so that the tree grows vertically (with the root at the top and children below). + - Each node will be drawn as a circle with its value inside, colored red or black based on the node’s color. + - The visualization will be dynamic, updating after each operation (insert, delete, or search) using Matplotlib’s animation or interactive update capabilities. + - We plan to animate the steps (for example, after each insertion or deletion) to provide a clear view of how the tree rebalances itself. + +2. **Assumptions and Considerations:** + + - **Operations:** + The tree will support insert, delete, and search operations. + - **Visualization:** + The tree will be displayed with a vertical layout. Nodes will be clearly colored (red or black) and annotated with their values. We will implement dynamic updating of the tree visualization after each operation. + - **Testing:** + A set of arbitrary numbers will be inserted into the tree for demonstration purposes. + - **Limitations:** + - The deletion algorithm for red-black trees can be quite complex. While we’ll implement the standard approach, there might be edge cases that need more extensive testing in a production scenario. + - The dynamic visualization will depend on Matplotlib’s capabilities. In some environments, interactive animations may behave differently or require specific backends. + - **Environment:** + We assume that Python and Matplotlib (and any necessary libraries) are available and up-to-date. + +3. **Combining the Components:** + + - The final solution will consist of the Node and Tree classes with all the required operations. + - After performing operations (insertion, deletion, search), the tree’s state will be re-plotted dynamically to show the current structure. + - The complete solution will be integrated into a single Python script that can be run to see the red-black tree in action. + +If everything looks good with this plan, I’ll proceed to implement the code accordingly. + +################################################################################################################## + +Below is the complete Python script that implements a red‐black tree with insert, delete, and search operations and uses Matplotlib to dynamically visualize the tree after each operation. (The code uses a sentinel NIL node to simplify the red–black tree algorithms.) You can run the script to see a vertical tree that updates after each insertion or deletion. + +To explain briefly (outside of inline comments): + +1. We define a **Node** class that holds a value, its color ("red" or "black"), and pointers to its left child, right child, and parent. +2. The **RedBlackTree** class creates a NIL sentinel (a black node with no value) and sets the tree’s root to this NIL. It provides methods for insertion (with an insert_fixup), deletion (with transplant, minimum, and delete_fixup helper methods), and search. The standard red–black tree algorithms from textbooks (e.g. CLRS) are used. +3. For dynamic visualization, the tree has a method called **draw_tree**. It recursively computes positions for nodes (assigning x coordinates by in‐order sequence and y coordinates by depth, with the root at the top) and then uses Matplotlib to draw lines for edges and circles (colored red or black) for nodes, with the node’s value written in the circle. After each tree operation, draw_tree is called and plt.pause is used to animate the changes. +4. In the main section a set of arbitrary numbers is inserted into the tree (with updates shown), then a couple of deletions are performed, and a search is done. + +You can modify the numbers and operations as desired. + +Below is the complete code: + +--- + +```python +import matplotlib.pyplot as plt +import time + +# ------------------------------- +# Node Class +# ------------------------------- +class Node: + def __init__(self, value, color="red", left=None, right=None, parent=None): + self.value = value # Stored value (None for the NIL node) + self.color = color # "red" or "black" + self.left = left # Left child + self.right = right # Right child + self.parent = parent # Parent pointer + +# ------------------------------- +# Red-Black Tree Class +# ------------------------------- +class RedBlackTree: + def __init__(self): + # Create the NIL sentinel node. This is used in place of None. + self.NIL = Node(value=None, color="black") + self.NIL.left = self.NIL.right = self.NIL.parent = self.NIL + self.root = self.NIL + + # --------------------------- + # Left Rotate x around its right child y. + # --------------------------- + def left_rotate(self, x): + y = x.right + x.right = y.left + if y.left != self.NIL: + y.left.parent = x + y.parent = x.parent + if x.parent == self.NIL: + self.root = y + elif x == x.parent.left: + x.parent.left = y + else: + x.parent.right = y + y.left = x + x.parent = y + + # --------------------------- + # Right Rotate x around its left child y. + # --------------------------- + def right_rotate(self, x): + y = x.left + x.left = y.right + if y.right != self.NIL: + y.right.parent = x + y.parent = x.parent + if x.parent == self.NIL: + self.root = y + elif x == x.parent.right: + x.parent.right = y + else: + x.parent.left = y + y.right = x + x.parent = y + + # --------------------------- + # Insert a new value into the red-black tree. + # --------------------------- + def insert(self, value): + # Create new node with left and right children set to NIL. + z = Node(value=value, color="red", left=self.NIL, right=self.NIL, parent=self.NIL) + y = self.NIL + x = self.root + + # Standard BST insertion. + while x != self.NIL: + y = x + if z.value < x.value: + x = x.left + else: + x = x.right + + z.parent = y + if y == self.NIL: + self.root = z + elif z.value < y.value: + y.left = z + else: + y.right = z + + # Fix up any red-black violations. + self.insert_fixup(z) + + # --------------------------- + # Restore red-black properties after insertion. + # --------------------------- + def insert_fixup(self, z): + while z.parent.color == "red": + if z.parent == z.parent.parent.left: + y = z.parent.parent.right # uncle + if y.color == "red": + # Case 1: Uncle is red. + z.parent.color = "black" + y.color = "black" + z.parent.parent.color = "red" + z = z.parent.parent + else: + if z == z.parent.right: + # Case 2: z is right child. + z = z.parent + self.left_rotate(z) + # Case 3: z is left child. + z.parent.color = "black" + z.parent.parent.color = "red" + self.right_rotate(z.parent.parent) + else: + # Mirror image of the above code. + y = z.parent.parent.left + if y.color == "red": + z.parent.color = "black" + y.color = "black" + z.parent.parent.color = "red" + z = z.parent.parent + else: + if z == z.parent.left: + z = z.parent + self.right_rotate(z) + z.parent.color = "black" + z.parent.parent.color = "red" + self.left_rotate(z.parent.parent) + self.root.color = "black" + + # --------------------------- + # Transplant: Replace subtree rooted at u with subtree rooted at v. + # --------------------------- + def transplant(self, u, v): + if u.parent == self.NIL: + self.root = v + elif u == u.parent.left: + u.parent.left = v + else: + u.parent.right = v + v.parent = u.parent + + # --------------------------- + # Find the minimum node starting from a given node. + # --------------------------- + def minimum(self, node): + while node.left != self.NIL: + node = node.left + return node + + # --------------------------- + # Delete a node with a given value from the tree. + # --------------------------- + def delete_value(self, value): + node = self.search(value) + if node == self.NIL: + print(f"Value {value} not found in the tree.") + return + self.delete_node(node) + + # --------------------------- + # Delete a node z from the tree. + # --------------------------- + def delete_node(self, z): + y = z + y_original_color = y.color + if z.left == self.NIL: + x = z.right + self.transplant(z, z.right) + elif z.right == self.NIL: + x = z.left + self.transplant(z, z.left) + else: + y = self.minimum(z.right) + y_original_color = y.color + x = y.right + if y.parent == z: + x.parent = y + else: + self.transplant(y, y.right) + y.right = z.right + y.right.parent = y + self.transplant(z, y) + y.left = z.left + y.left.parent = y + y.color = z.color + if y_original_color == "black": + self.delete_fixup(x) + + # --------------------------- + # Restore red-black properties after deletion. + # --------------------------- + def delete_fixup(self, x): + while x != self.root and x.color == "black": + if x == x.parent.left: + w = x.parent.right + if w.color == "red": + w.color = "black" + x.parent.color = "red" + self.left_rotate(x.parent) + w = x.parent.right + if w.left.color == "black" and w.right.color == "black": + w.color = "red" + x = x.parent + else: + if w.right.color == "black": + w.left.color = "black" + w.color = "red" + self.right_rotate(w) + w = x.parent.right + w.color = x.parent.color + x.parent.color = "black" + w.right.color = "black" + self.left_rotate(x.parent) + x = self.root + else: + # Mirror image of above code. + w = x.parent.left + if w.color == "red": + w.color = "black" + x.parent.color = "red" + self.right_rotate(x.parent) + w = x.parent.left + if w.right.color == "black" and w.left.color == "black": + w.color = "red" + x = x.parent + else: + if w.left.color == "black": + w.right.color = "black" + w.color = "red" + self.left_rotate(w) + w = x.parent.left + w.color = x.parent.color + x.parent.color = "black" + w.left.color = "black" + self.right_rotate(x.parent) + x = self.root + x.color = "black" + + # --------------------------- + # Search for a node with a given value. + # --------------------------- + def search(self, value): + current = self.root + while current != self.NIL and current.value != value: + if value < current.value: + current = current.left + else: + current = current.right + return current + + # --------------------------- + # Dynamic visualization of the tree using Matplotlib. + # The tree is drawn vertically with the root at the top. + # --------------------------- + def draw_tree(self): + plt.clf() # Clear current figure + + positions = {} # Dictionary mapping node -> (x, y) + + # Helper function to perform an in-order traversal and assign positions. + def compute_positions(node, depth, pos_dict, x_counter): + if node == self.NIL: + return + compute_positions(node.left, depth + 1, pos_dict, x_counter) + # x_counter[0] holds the current horizontal position. + pos_dict[node] = (x_counter[0], -depth) + x_counter[0] += 1 + compute_positions(node.right, depth + 1, pos_dict, x_counter) + + compute_positions(self.root, 0, positions, [0]) + ax = plt.gca() + + # Draw the edges (lines between parent and child). + for node, (x, y) in positions.items(): + if node.left != self.NIL: + child_pos = positions[node.left] + ax.plot([x, child_pos[0]], [y, child_pos[1]], 'k-') + if node.right != self.NIL: + child_pos = positions[node.right] + ax.plot([x, child_pos[0]], [y, child_pos[1]], 'k-') + + # Draw the nodes (circles with text). + for node, (x, y) in positions.items(): + circle = plt.Circle((x, y), 0.3, color=node.color, ec="black", zorder=2) + ax.add_patch(circle) + # For readability: if the node is black, write white text; otherwise black text. + text_color = "white" if node.color == "black" else "black" + ax.text(x, y, str(node.value), ha="center", va="center", color=text_color, zorder=3) + + ax.set_aspect(1.0) + ax.axis("off") + plt.pause(0.5) # Pause to animate the update + +# ------------------------------- +# Main testing and dynamic visualization +# ------------------------------- +if __name__ == "__main__": + tree = RedBlackTree() + + # Turn on interactive mode for dynamic plotting. + plt.ion() + + # Insert some arbitrary numbers into the tree. + numbers_to_insert = [10, 20, 30, 15, 25, 5, 1] + print("Inserting values:", numbers_to_insert) + for num in numbers_to_insert: + tree.insert(num) + tree.draw_tree() # Update the visualization after each insertion + time.sleep(0.5) # Optional: add extra pause for clarity + + # Delete a couple of nodes. + print("Deleting value 20") + tree.delete_value(20) + tree.draw_tree() + time.sleep(0.5) + + print("Deleting value 10") + tree.delete_value(10) + tree.draw_tree() + time.sleep(0.5) + + # Search for a value. + search_value = 15 + result = tree.search(search_value) + if result != tree.NIL: + print(f"Search: Found node with value {result.value}") + else: + print(f"Search: Value {search_value} not found.") + + # Keep the final plot open. + plt.ioff() + plt.show() +``` + +--- + +### How to Run + +1. Make sure you have Python 3 and Matplotlib installed (e.g., via `pip install matplotlib`). +2. Save the code above into a file (for example, `red_black_tree.py`). +3. Run the script with: + `python red_black_tree.py` +4. A Matplotlib window will open and update dynamically as nodes are inserted and deleted. The console will also print the operations and search result. + +### Limitations and Considerations + +- **Deletion Complexity:** Red–black tree deletion is intricate. While this implementation follows the standard algorithm, extensive testing in production systems is recommended. +- **Visualization Layout:** The in-order traversal assigns x coordinates based solely on node order. For very unbalanced trees, the layout might be uneven. +- **Dynamic Updates:** The dynamic visualization uses Matplotlib’s interactive mode (`plt.ion()`) and `plt.pause()`, which may behave differently in some environments or IDEs. + +Feel free to adjust timing, styling, or add further error checking as needed. + + diff --git a/llm/generation-output/claude.txt b/llm/generation-output/claude.txt new file mode 100644 index 0000000..8b4a250 --- /dev/null +++ b/llm/generation-output/claude.txt @@ -0,0 +1,228 @@ +Claude Antwort +################################################################################################################## + +Let me break this down into sub-problems: + +1. Node class implementation with required attributes +2. Red-Black Tree class with fundamental BST operations +3. Red-Black specific operations (rotations, color adjustments) +4. Visualization logic using Matplotlib + +Questions before proceeding: +1. Should the visualization be static or would you like interactive features (e.g., showing steps of insertions)? +2. Do you need specific operations beyond insert/delete/search? +3. Would you like the visualization to display the tree horizontally or vertically? + +These details will help me provide a more focused implementation. Please let me know your preferences. + +################################################################################################################# + +I'll create a visualizable Red-Black tree with the following implementation approach: + +Key Components: +1. Node class: value, color (RED/BLACK), parent/left/right pointers +2. RBTree class: root node, BST operations, RB balancing +3. Visualization: matplotlib animation showing tree growth and rebalancing + +Core Operations to Implement: +- Standard BST insert/delete/search +- Left/right rotations +- Color flipping +- RB balancing after insertions/deletions +- Tree layout calculation for visualization +- Animation of node positions and colors + + + +```python +import matplotlib.pyplot as plt +import matplotlib.animation as animation +import numpy as np + +class Node: + def __init__(self, value): + self.value = value + self.left = None + self.right = None + self.parent = None + self.color = "RED" + self.x = 0 # For visualization + self.y = 0 # For visualization + +class RedBlackTree: + def __init__(self): + self.nil = Node(None) + self.nil.color = "BLACK" + self.root = self.nil + self.fig, self.ax = plt.subplots(figsize=(10, 8)) + self.frames = [] + + def search(self, value): + return self._search_recursive(self.root, value) + + def _search_recursive(self, node, value): + if node == self.nil or value == node.value: + return node + if value < node.value: + return self._search_recursive(node.left, value) + return self._search_recursive(node.right, value) + + def insert(self, value): + node = Node(value) + node.left = self.nil + node.right = self.nil + + y = None + x = self.root + + while x != self.nil: + y = x + if node.value < x.value: + x = x.left + else: + x = x.right + + node.parent = y + if y == None: + self.root = node + elif node.value < y.value: + y.left = node + else: + y.right = node + + self._fix_insert(node) + self._update_positions() + self._capture_frame() + + def _fix_insert(self, k): + while k.parent and k.parent.color == "RED": + if k.parent == k.parent.parent.right: + u = k.parent.parent.left + if u.color == "RED": + u.color = "BLACK" + k.parent.color = "BLACK" + k.parent.parent.color = "RED" + k = k.parent.parent + else: + if k == k.parent.left: + k = k.parent + self._right_rotate(k) + k.parent.color = "BLACK" + k.parent.parent.color = "RED" + self._left_rotate(k.parent.parent) + else: + u = k.parent.parent.right + if u.color == "RED": + u.color = "BLACK" + k.parent.color = "BLACK" + k.parent.parent.color = "RED" + k = k.parent.parent + else: + if k == k.parent.right: + k = k.parent + self._left_rotate(k) + k.parent.color = "BLACK" + k.parent.parent.color = "RED" + self._right_rotate(k.parent.parent) + if k == self.root: + break + self.root.color = "BLACK" + + def _left_rotate(self, x): + y = x.right + x.right = y.left + if y.left != self.nil: + y.left.parent = x + y.parent = x.parent + if x.parent == None: + self.root = y + elif x == x.parent.left: + x.parent.left = y + else: + x.parent.right = y + y.left = x + x.parent = y + self._update_positions() + self._capture_frame() + + def _right_rotate(self, x): + y = x.left + x.left = y.right + if y.right != self.nil: + y.right.parent = x + y.parent = x.parent + if x.parent == None: + self.root = y + elif x == x.parent.right: + x.parent.right = y + else: + x.parent.left = y + y.right = x + x.parent = y + self._update_positions() + self._capture_frame() + + def _update_positions(self): + def calculate_positions(node, level=0, offset=0, width=2000): + if node == self.nil: + return + + node.y = -level * 100 + node.x = offset + + next_width = width / 2 + calculate_positions(node.left, level + 1, offset - next_width, next_width) + calculate_positions(node.right, level + 1, offset + next_width, next_width) + + calculate_positions(self.root) + + def _capture_frame(self): + self.ax.clear() + self._draw_tree(self.root) + self.ax.set_title("Red-Black Tree") + self.ax.set_xlim(-1000, 1000) + self.ax.set_ylim(-500, 100) + self.ax.axis('off') + self.frames.append([plt.gcf()]) + + def _draw_tree(self, node): + if node == self.nil: + return + + # Draw connections to children + if node.left != self.nil: + self.ax.plot([node.x, node.left.x], [node.y, node.left.y], 'k-') + if node.right != self.nil: + self.ax.plot([node.x, node.right.x], [node.y, node.right.y], 'k-') + + # Draw node + circle = plt.Circle((node.x, node.y), 30, color=node.color.lower()) + self.ax.add_patch(circle) + self.ax.text(node.x, node.y, str(node.value), + horizontalalignment='center', + verticalalignment='center', + color='white' if node.color == "BLACK" else 'black') + + def visualize(self): + ani = animation.ArtistAnimation(self.fig, self.frames, interval=1000, blit=True) + plt.show() + +# Example usage +tree = RedBlackTree() +test_values = [7, 3, 18, 10, 22, 8, 11, 26, 2, 6, 13] +for value in test_values: + tree.insert(value) +tree.visualize() + +``` + +This implementation: +- Uses matplotlib for dynamic visualization +- Handles all core RB tree operations +- Shows tree growth and rebalancing with animation +- Maintains RB tree properties +- Includes test data for demonstration + +The tree will animate each insertion and rebalancing operation, showing nodes in their appropriate colors and positions. + +Would you like me to explain any specific part in more detail? diff --git a/llm/generation-output/deepseek.txt b/llm/generation-output/deepseek.txt new file mode 100644 index 0000000..0498b7c --- /dev/null +++ b/llm/generation-output/deepseek.txt @@ -0,0 +1,202 @@ +DeepSeek Antwort +################################################################################################################## + +To address the problem of implementing and visualizing a red-black tree, we divide the solution into several steps: creating the `Node` class, implementing the red-black tree (`RedBlackTree`) with insertion and balancing operations, and visualizing the tree using Matplotlib. Each step is explained in detail, followed by the complete code. + +### Step-by-Step Explanations + +1. **Node Class**: + - Represents each node in the tree with attributes for value, color, left/right children, and parent. + - New nodes are initialized as red, with children pointing to a sentinel `NIL` node. + +2. **RedBlackTree Class**: + - Manages the tree structure, including insertion and balancing. + - Uses a sentinel `NIL` node (black) to represent leaves. + - Insertion follows BST rules, followed by fixing the tree to maintain red-black properties using rotations and recoloring. + +3. **Tree Balancing (Insert Fix)**: + - After inserting a node as red, fix violations of red-black properties. + - Cases handled based on the uncle's color and node position (left/right child). + +4. **Rotations**: + - Left and right rotations adjust the tree structure to balance it after insertion. + +5. **Visualization**: + - Assigns coordinates to each node using a modified in-order traversal for horizontal placement and depth for vertical placement. + - Uses Matplotlib to draw nodes (colored circles) and edges (lines between nodes). + +### Solution Code + +```python +import matplotlib.pyplot as plt + +class Node: + def __init__(self, value, color='red'): + self.value = value + self.color = color + self.left = None + self.right = None + self.parent = None + + def __repr__(self): + return f"Node({self.value}, {self.color})" + +class RedBlackTree: + def __init__(self): + self.NIL = Node(None, 'black') + self.NIL.left = self.NIL + self.NIL.right = self.NIL + self.NIL.parent = self.NIL + self.root = self.NIL + + def insert(self, value): + new_node = Node(value) + new_node.left = self.NIL + new_node.right = self.NIL + new_node.parent = self.NIL + + current = self.root + parent = self.NIL + while current != self.NIL: + parent = current + if new_node.value < current.value: + current = current.left + else: + current = current.right + new_node.parent = parent + if parent == self.NIL: + self.root = new_node + elif new_node.value < parent.value: + parent.left = new_node + else: + parent.right = new_node + + self.fix_insert(new_node) + + def fix_insert(self, node): + while node.parent.color == 'red': + if node.parent == node.parent.parent.left: + uncle = node.parent.parent.right + if uncle.color == 'red': + node.parent.color = 'black' + uncle.color = 'black' + node.parent.parent.color = 'red' + node = node.parent.parent + else: + if node == node.parent.right: + node = node.parent + self.left_rotate(node) + node.parent.color = 'black' + node.parent.parent.color = 'red' + self.right_rotate(node.parent.parent) + else: + uncle = node.parent.parent.left + if uncle.color == 'red': + node.parent.color = 'black' + uncle.color = 'black' + node.parent.parent.color = 'red' + node = node.parent.parent + else: + if node == node.parent.left: + node = node.parent + self.right_rotate(node) + node.parent.color = 'black' + node.parent.parent.color = 'red' + self.left_rotate(node.parent.parent) + if node == self.root: + break + self.root.color = 'black' + + def left_rotate(self, x): + y = x.right + x.right = y.left + if y.left != self.NIL: + y.left.parent = x + y.parent = x.parent + if x.parent == self.NIL: + self.root = y + elif x == x.parent.left: + x.parent.left = y + else: + x.parent.right = y + y.left = x + x.parent = y + + def right_rotate(self, y): + x = y.left + y.left = x.right + if x.right != self.NIL: + x.right.parent = y + x.parent = y.parent + if y.parent == self.NIL: + self.root = x + elif y == y.parent.right: + y.parent.right = x + else: + y.parent.left = x + x.right = y + y.parent = x + + def _assign_positions(self, node, pos_map, x=0, depth=0): + if node == self.NIL: + return x + x = self._assign_positions(node.left, pos_map, x, depth + 1) + pos_map[node] = (x, depth) + x += 1 + x = self._assign_positions(node.right, pos_map, x, depth + 1) + return x + + def visualize(self): + pos_map = {} + self._assign_positions(self.root, pos_map) + if not pos_map: + print("Tree is empty.") + return + + fig, ax = plt.subplots() + max_x = max(x for x, _ in pos_map.values()) + max_depth = max(d for _, d in pos_map.values()) + + h_spacing = 1.0 + v_spacing = 1.0 + + for node in pos_map: + x, depth = pos_map[node] + scaled_x = x * h_spacing + scaled_y = -depth * v_spacing + + if node.left != self.NIL: + l_x, l_depth = pos_map[node.left] + ax.plot([scaled_x, l_x * h_spacing], [scaled_y, -l_depth * v_spacing], 'k-', linewidth=1) + if node.right != self.NIL: + r_x, r_depth = pos_map[node.right] + ax.plot([scaled_x, r_x * h_spacing], [scaled_y, -r_depth * v_spacing], 'k-', linewidth=1) + + color = 'red' if node.color == 'red' else 'black' + circle = plt.Circle((scaled_x, scaled_y), 0.3, color=color, ec='k') + ax.add_patch(circle) + text_color = 'white' if node.color == 'black' else 'black' + plt.text(scaled_x, scaled_y, str(node.value), color=text_color, ha='center', va='center', fontsize=10) + + ax.set_xlim(-h_spacing, (max_x + 1) * h_spacing) + ax.set_ylim(-(max_depth + 1) * v_spacing, v_spacing) + ax.set_aspect('equal') + ax.axis('off') + plt.show() +``` + +### Explanation and Assumptions + +- **Node Class**: Stores value, color, and references to left/right children and parent. The default color for new nodes is red. +- **RedBlackTree Class**: Manages tree operations. Uses a sentinel `NIL` node to represent leaves (all black). +- **Insertion and Balancing**: After BST insertion, the tree is rebalanced using rotations and recoloring based on the uncle node's color and position. +- **Visualization**: Nodes are positioned using in-order traversal to determine horizontal placement and depth for vertical placement. Nodes are drawn as circles with their values, colored according to red-black properties. + +### Limitations and Ambiguities + +- **Visualization**: The in-order traversal-based positioning might not perfectly center parent nodes over their children, especially in unbalanced trees. This can lead to overlapping or misaligned nodes in complex structures. +- **Functionality**: The implementation does not handle node deletion, which is more complex and requires additional balancing logic. +- **Edge Cases**: The code assumes unique values; inserting duplicates may lead to incorrect behavior. +- **Performance**: The visualization may not scale efficiently for very large trees due to the simple positioning algorithm. + +This solution provides a foundational implementation of a red-black tree with visualization, suitable for educational purposes and moderate-sized trees. diff --git a/llm/generation-output/gemini.txt b/llm/generation-output/gemini.txt new file mode 100644 index 0000000..b74d56d --- /dev/null +++ b/llm/generation-output/gemini.txt @@ -0,0 +1,377 @@ +Gemini Antwort +################################################################################################################## + +Okay, I understand the requirements. Let's break down the problem into smaller, manageable parts before coding. This will help ensure a clear and robust implementation. + +**Sub-Problems:** + +1. **Node Class:** Define the `Node` class with attributes for value, left child, right child, color (red or black), and potentially a parent pointer (useful for some operations). + +2. **Tree Class:** Define the `Tree` class, which will manage the nodes. This includes: + * **Insertion:** Implement the red-black tree insertion algorithm, including rotations and color flips to maintain balance. + * **Deletion (Optional):** While not explicitly requested, deletion is a core operation. We can include it if desired, but it adds significant complexity. Let me know if you want to include it. + * **Search:** Implement a search function to find a node with a given value. + * **Visualization:** Implement a method to visualize the tree using Matplotlib. This will likely involve traversing the tree and plotting nodes based on their positions. + +3. **Helper Functions:** Create helper functions for rotations (left and right), color flips, and potentially other utility functions to simplify the main algorithms. + +**Reasoning and Assumptions:** + +* **Color Representation:** We'll use strings "red" and "black" for node colors. Alternatively, we could use boolean values (True/False) or enums. +* **Nil Nodes:** For simplicity, we'll use `None` to represent nil nodes (leaves or empty subtrees). Some implementations use special sentinel nodes. +* **Visualization Approach:** We'll use a simple approach for visualization, positioning nodes based on their level and order. More sophisticated layouts are possible but add complexity. +* **No Duplicates:** We'll assume no duplicate values are inserted. Handling duplicates would require modification to the insertion logic. +* **Parent Pointers (Optional):** Including parent pointers in the `Node` class can simplify some operations (especially deletion) but adds a small overhead. We can include them if you'd like. + +**Potential Ambiguities and Limitations:** + +* **Deletion:** As mentioned, deletion is complex. We can choose to include it or leave it as an extension. +* **Visualization:** The visualization will be a basic representation. For very large trees, it might become cluttered. Interactive visualizations could be considered for more advanced use cases. +* **Balancing:** The core complexity of red-black trees lies in maintaining balance after insertions and deletions. The code will focus on correctly implementing the rotations and color flips required for this. + +**Questions before Coding:** + +1. Do you want to include deletion in this implementation? +2. Is a basic visualization sufficient, or do you have specific visualization requirements? +3. Are parent pointers in the `Node` class desired? + +Once we clarify these points, I can start writing the code. + +################################################################################################################## + +Okay, I understand. We'll implement insert, delete, and search, and create a dynamic, vertically growing visualization using Matplotlib. The visualization will display node values and colors. We'll also include parent pointers in the `Node` class to simplify some operations. + +Here's a summary of my approach: + +1. **Node Class:** Attributes: `value`, `left`, `right`, `color` ("red" or "black"), and `parent`. + +2. **Tree Class:** + * `insert(value)`: Standard red-black tree insertion with rotations and color flips. + * `delete(value)`: Standard red-black tree deletion (the most complex part). + * `search(value)`: Returns the node with the given value or `None` if not found. + * `visualize()`: Dynamically generates a plot of the tree. This will involve: + * Calculating node positions based on level and order. We'll need a way to traverse the tree level by level. + * Using Matplotlib to draw nodes as circles with appropriate colors and labels (values). The plot will need to adjust its size dynamically as the tree grows. + +3. **Helper Functions:** `rotate_left()`, `rotate_right()`, `flip_colors()`, and potentially others for tree traversal or node manipulation. + +4. **Visualization Details:** + * Nodes will be drawn as circles. + * Edges will be lines connecting parent and child nodes. + * Node color will represent the red-black color. + * Node labels will display the node's value. + * The plot will be updated after each insertion or deletion to reflect the changes. + +5. **Testing:** We'll insert some sample values to demonstrate the functionality and visualization. + +This approach addresses the requirements for insert, delete, search, and a dynamic visualization. The deletion operation and dynamic visualization are the most challenging parts, but breaking them down into smaller functions should make them manageable. I'll start with the `Node` and basic `Tree` structure, then move on to insertion, deletion, search, and finally the visualization. + +################################################################################################################## + +```python +import matplotlib.pyplot as plt +import networkx as nx + +class Node: + def __init__(self, value, color="red"): + self.value = value + self.left = None + self.right = None + self.parent = None + self.color = color + +class Tree: + def __init__(self): + self.root = None + + def insert(self, value): + new_node = Node(value) + if self.root is None: + self.root = new_node + self.root.color = "black" # Root is always black + return + + current = self.root + while True: + if value < current.value: + if current.left is None: + current.left = new_node + new_node.parent = current + break + current = current.left + elif value > current.value: + if current.right is None: + current.right = new_node + new_node.parent = current + break + current = current.right + else: # Duplicate value (we're assuming no duplicates) + return + + self._balance_after_insert(new_node) + self.visualize() # Visualize after each insertion + + + def _balance_after_insert(self, node): + while node.parent is not None and node.parent.color == "red": + if node.parent == node.parent.parent.left: # Parent is left child + uncle = node.parent.parent.right + if uncle is not None and uncle.color == "red": # Case 1: Red uncle + node.parent.color = "black" + uncle.color = "black" + node.parent.parent.color = "red" + node = node.parent.parent + else: # Case 2 & 3: Black uncle + if node == node.parent.right: # Case 2: Node is right child + node = node.parent + self._rotate_left(node) + node.parent.color = "black" # Case 3 + node.parent.parent.color = "red" + self._rotate_right(node.parent.parent) + else: # Parent is right child (symmetric cases) + uncle = node.parent.parent.left + if uncle is not None and uncle.color == "red": + node.parent.color = "black" + uncle.color = "black" + node.parent.parent.color = "red" + node = node.parent.parent + else: + if node == node.parent.left: + node = node.parent + self._rotate_right(node) + node.parent.color = "black" + node.parent.parent.color = "red" + self._rotate_left(node.parent.parent) + + self.root.color = "black" # Root is always black + + + + def delete(self, value): + node_to_delete = self._find_node(value) + if node_to_delete is None: + return + + # ... (Deletion logic - see next response for implementation due to length) + + def _find_node(self, value): + current = self.root + while current is not None: + if value == current.value: + return current + elif value < current.value: + current = current.left + else: + current = current.right + return None + + def _rotate_left(self, node): + # ... (Rotation logic - see next response for implementation due to length) + + def _rotate_right(self, node): + # ... (Rotation logic - see next response for implementation due to length) + + def visualize(self): + # ... (Visualization logic - see next response for implementation due to length) + +# Example Usage (will be expanded in later responses): +tree = Tree() +tree.insert(10) +tree.insert(5) +tree.insert(15) +tree.insert(3) +tree.insert(7) +tree.insert(12) +tree.insert(18) + + +plt.show() + +``` + +I've started with the `Node` and `Tree` classes, including the `insert` method and the helper function `_balance_after_insert`, which also includes the color flips. I've left placeholders for `delete`, `_rotate_left`, `_rotate_right`, and `visualize` as they are more complex and would make this response too long. I'll provide those in the next responses. Let me know if you have any questions so far. + +################################################################################################################ + +```python +import matplotlib.pyplot as plt +import networkx as nx + +# ... (Node class from previous response) + +class Tree: + # ... (insert and _balance_after_insert from previous response) + + def delete(self, value): + node_to_delete = self._find_node(value) + if node_to_delete is None: + return + + if node_to_delete.left is None: + self._transplant(node_to_delete, node_to_delete.right) + elif node_to_delete.right is None: + self._transplant(node_to_delete, node_to_delete.left) + else: + successor = self._find_minimum(node_to_delete.right) + if successor.parent != node_to_delete: + self._transplant(successor, successor.right) + successor.right = node_to_delete.right + successor.right.parent = successor + self._transplant(node_to_delete, successor) + successor.left = node_to_delete.left + successor.left.parent = successor + successor.color = node_to_delete.color + + if node_to_delete.color == "black": + self._balance_after_delete(successor) # Pass the successor if it was moved + + self.visualize() + + def _balance_after_delete(self, x): + while x != self.root and x is not None and x.color == "black": + if x == x.parent.left: + w = x.parent.right + if w is None or w.color == "black": + if w is not None and w.left is not None and w.left.color == "red": + w.left.color = "black" + w.color = "red" + self._rotate_right(w) + w = x.parent.right + elif w is not None and w.right is not None and w.right.color == "red": + w.right.color = "black" + w.color = "red" + self._rotate_left(w) + else: + if w is not None: + w.color = "red" + x = x.parent + else: + w.color = "black" + x.parent.color = "red" + self._rotate_left(x.parent) + else: # Symmetric case + w = x.parent.left + if w is None or w.color == "black": + if w is not None and w.right is not None and w.right.color == "red": + w.right.color = "black" + w.color = "red" + self._rotate_left(w) + w = x.parent.left + elif w is not None and w.left is not None and w.left.color == "red": + w.left.color = "black" + w.color = "red" + self._rotate_right(w) + else: + if w is not None: + w.color = "red" + x = x.parent + else: + w.color = "black" + x.parent.color = "red" + self._rotate_right(x.parent) + if x is not None: + x.color = "black" + self.root.color = "black" + + + + def _transplant(self, u, v): + if u.parent is None: + self.root = v + elif u == u.parent.left: + u.parent.left = v + else: + u.parent.right = v + if v is not None: + v.parent = u.parent + + def _find_minimum(self, node): + while node.left is not None: + node = node.left + return node + + # ... (_find_node from previous response) + + def _rotate_left(self, x): + y = x.right + x.right = y.left + if y.left is not None: + y.left.parent = x + y.parent = x.parent + if x.parent is None: + self.root = y + elif x == x.parent.left: + x.parent.left = y + else: + x.parent.right = y + y.left = x + x.parent = y + + def _rotate_right(self, y): + x = y.left + y.left = x.right + if x.right is not None: + x.right.parent = y + x.parent = y.parent + if y.parent is None: + self.root = x + elif y == y.parent.left: + y.parent.left = x + else: + y.parent.right = x + x.right = y + y.parent = x + + + def visualize(self): + if self.root is None: + return + + graph = nx.DiGraph() + self._build_graph(self.root, graph) + + pos = nx.drawing.hierarchy.tree.layered_pos(graph, root=self.root, orientation='TB') # Vertical layout + + colors = [node[1]['color'] for node in graph.nodes(data=True)] + labels = {node[0]: node[0] for node in graph.nodes(data=True)} # Node values as labels + + plt.figure(figsize=(10, 8)) # Adjust figure size as needed + nx.draw(graph, pos, with_labels=True, labels=labels, node_size=700, node_color=colors, font_size=10, font_color="white", arrowsize=20) #Increased node size, white font color + + plt.show() + + def _build_graph(self, node, graph): + graph.add_node(node.value, color=node.color) # Store color as node attribute + if node.left: + graph.add_edge(node.value, node.left.value) + self._build_graph(node.left, graph) + if node.right: + graph.add_edge(node.value, node.right.value) + self._build_graph(node.right, graph) + + + +# Example Usage: +tree = Tree() +tree.insert(10) +tree.insert(5) +tree.insert(15) +tree.insert(3) +tree.insert(7) +tree.insert(12) +tree.insert(18) +tree.insert(1) +tree.insert(2) + +tree.delete(10) +tree.delete(7) +tree.delete(18) +tree.delete(12) + +plt.show() + +``` + +Here's the implementation of `delete`, `_rotate_left`, `_rotate_right`, `_transplant`, `_find_minimum` and `visualize`. The `visualize` function now uses `networkx` and `matplotlib` to create a dynamic, vertically growing visualization. The `_build_graph` helper function recursively constructs the graph for visualization. I've also added some sample insertions and deletions to demonstrate the functionality. Let me know if you have any other questions. + + diff --git a/project/notes.txt b/project/notes.txt index 320808b..a49efa3 100644 --- a/project/notes.txt +++ b/project/notes.txt @@ -1 +1 @@ -Starred but Smelly? Investigating the link between GitHub Popularity and Code Quality. \ No newline at end of file +The smell of Stars. An investigation of the correlation between GitHub Popularity and Code Quality. \ No newline at end of file