Poker Testbed - Java-Compatible Hand Evaluator for Bot Testing

A comprehensive poker bot testing framework with Java-compatible hand evaluation. This library implements the Meerkat API algorithm with full Java compatibility, specifically designed for automated poker bot testing, development, and integration with existing poker tools.

Recent Updates

• Input Validation: Added comprehensive validation to Card::new() ensuring rank (0-12) and suit (0-3) are within valid ranges
• Enhanced Documentation: All public methods now include working code examples and panic condition documentation
• Error Handling: Improved error handling throughout the codebase with proper Result types

Features

• Java-Compatible: Full compatibility with Java Meerkat API for seamless tool integration
• Bot Testing Framework: Specifically designed for automated poker bot testing and development
• Blazing Fast: O(1) hand evaluation using perfect hash lookup tables
• Memory Efficient: ~128MB for complete evaluation tables
• Standards Compliant: Based on proven poker evaluation algorithms with Java compatibility
• Multi-Hand Support: Evaluate 5, 6, or 7-card poker hands with Java-compatible results
• Texas Hold'em Ready: Optimized for Texas Hold'em bot analysis and testing
• Comprehensive Testing: Extensive test suite with millions of verification cases
• Deterministic Results: Consistent hand evaluation for reproducible bot testing
• Tool Integration: Compatible with existing Java poker tools and frameworks

Performance

Hand Type	Evaluation Time	Combinations Evaluated
5-Card	~10-20ns	Direct lookup
6-Card	~50-80ns	6 combinations
7-Card	~150-250ns	21 combinations

Documentation

For detailed project information, see:

• VISION.md - Project goals and architectural vision
• ACHIEVEMENTS.md - Completed milestones and success metrics
• ROADMAP.md - Future development phases and implementation plan

Installation

Add this to your Cargo.toml:

[dependencies]
poker-api = "0.1.0"

Quick Start - Bot Testing

use holdem_core::hand_evaluator::LookupHandEvaluator;
use holdem_core::api::{hand::Hand, card::Card};

// Initialize Java-compatible evaluator for bot testing
let evaluator = LookupHandEvaluator::new().unwrap();

// Create hole cards for bot analysis
let mut hole_cards = Hand::new();
hole_cards.add_card(Card::from_string("As").unwrap()).unwrap();
hole_cards.add_card(Card::from_string("Ks").unwrap()).unwrap();

// Evaluate hand strength for bot decision making
let rank = evaluator.rank_hand(&hole_cards);
println!("Hole card rank: {}", rank); // Lower = stronger hand

// Use rank for bot strategy decisions
if rank < 1000 { // Strong hand threshold
    println!("Bot: This is a premium starting hand!");
}

Bot Testing Capabilities

This framework provides specialized capabilities for poker bot testing and development:

Automated Bot Testing

• Standardized Evaluation: Java-compatible hand evaluation for consistent bot testing
• Performance Benchmarking: Fast evaluation for large-scale bot performance testing
• Range Analysis: Efficient hand range evaluation for bot strategy analysis
• Decision Tree Testing: Quick hand strength calculation for bot decision trees

Java Tool Integration

• Cross-Platform Compatibility: Works seamlessly with existing Java poker tools
• Consistent Results: Identical hand rankings to Java Meerkat API
• Tool Chain Integration: Compatible with Java-based poker analysis frameworks
• Bot Framework Support: Integrates with popular Java poker bot frameworks

Bot Development Features

• Hole Card Analysis: Fast evaluation of starting hand strength for pre-flop bots
• Post-Flop Analysis: Efficient board texture and hand strength analysis
• Incremental Evaluation: Multi-street hand evaluation for complex bot scenarios
• Memory Efficient: Optimized for bot testing environments with limited resources

Algorithm Overview

This library implements the Java-compatible Meerkat algorithm, a perfect hash-based approach optimized for poker bot testing:

1. Java-Style Card Encoding: Each card uses Java Meerkat's 8-bit encoding (rrrr-sss format)
2. Perfect Hash: Cards are mapped to unique indices using Java-compatible mathematical functions
3. Table Lookup: Precomputed ranking tables provide instant hand strength values matching Java API
4. Best Hand Selection: For 6-7 card hands, finds optimal 5-card combination using Java logic

Table Generation

The first time an evaluator is created, it generates evaluation tables:

// Tables are generated automatically on first use
let evaluator = LookupHandEvaluator::new().unwrap();
// Output: "Generating state table..." (first run only)
// Output: "Hand evaluation tables saved to math/data/"

Tables are saved to math/data/ directory as separate files and reused for subsequent runs:

• 5_card_table.lut (~10 MB)
• 6_card_table.lut (~80 MB)
• 7_card_table.lut (~535 MB)

Hand Types

The evaluator recognizes all standard poker hand types:

Hand Type	Description	Example
Straight Flush	Five cards in sequence, same suit	A♠ K♠ Q♠ J♠ T♠
Four of a Kind	Four cards of same rank	A♠ A♥ A♦ A♣ K♦
Full House	Three of a kind + one pair	K♠ K♥ K♦ Q♠ Q♥
Flush	Five cards of same suit	A♠ J♠ 8♠ 5♠ 2♠
Straight	Five cards in sequence	A♦ K♣ Q♥ J♠ T♦
Three of a Kind	Three cards of same rank	Q♠ Q♥ Q♦ A♣ 7♦
Two Pair	Two different pairs	J♠ J♥ 7♦ 7♣ A♦
One Pair	Two cards of same rank	A♠ A♥ K♦ Q♣ 7♦
High Card	No matching cards	A♠ K♥ Q♦ J♣ 9♦

Bot Testing Examples

Texas Hold'em Bot Analysis

use holdem_core::hand_evaluator::LookupHandEvaluator;
use holdem_core::api::{card::Card, hand::Hand};

let evaluator = LookupHandEvaluator::new().unwrap();

// Bot analyzes hole cards + flop for decision making
let hole_card1 = Card::from_string("As").unwrap().index() as u32;
let hole_card2 = Card::from_string("Ks").unwrap().index() as u32;

let mut board = Hand::new();
board.add_card(Card::from_string("Td").unwrap()).unwrap();
board.add_card(Card::from_string("Jc").unwrap()).unwrap();
board.add_card(Card::from_string("Qh").unwrap()).unwrap();

// Bot evaluates complete hand strength
let hand_strength = evaluator.rank_hand_with_hole_cards(hole_card1, hole_card2, &board);
println!("Hand strength after flop: {}", hand_strength);

// Bot uses this for post-flop decision making
if hand_strength < 2000 { // Strong hand
    println!("Bot: Continuing with strong hand");
}

Bot Range Analysis

use holdem_core::hand_evaluator::LookupHandEvaluator;
use holdem_core::api::card::Card;

let evaluator = LookupHandEvaluator::new().unwrap();

// Bot analyzes range of starting hands for pre-flop strategy
let premium_hands = vec![
    (Card::from_string("AA").unwrap(), Card::from_string("KK").unwrap()),
    (Card::from_string("QQ").unwrap(), Card::from_string("JJ").unwrap()),
    (Card::from_string("AK").unwrap(), Card::from_string("AQ").unwrap()),
];

for (card1, card2) in premium_hands {
    let mut hand = Hand::new();
    hand.add_card(card1).unwrap();
    hand.add_card(card2).unwrap();

    let strength = evaluator.rank_hand(&hand);
    println!("Bot range analysis - {}: {}", hand.to_string(), strength);
}

Advanced Usage

7-Card Hand Evaluation for Bot Testing

use holdem_core::hand_evaluator::LookupHandEvaluator;
use holdem_core::api::card::Card;

let evaluator = LookupHandEvaluator::new().unwrap();

// Texas Hold'em complete hand: 2 hole cards + 5 community cards
let hole_cards = [
    Card::from_string("As").unwrap().index(),
    Card::from_string("Ks").unwrap().index(),
];

let community_cards = [
    Card::from_string("Td").unwrap().index(),
    Card::from_string("Jc").unwrap().index(),
    Card::from_string("Qh").unwrap().index(),
    Card::from_string("9s").unwrap().index(),
    Card::from_string("2d").unwrap().index(),
];

// Bot evaluates best possible hand from all cards
let all_cards: Vec<u32> = hole_cards.iter().chain(community_cards.iter()).cloned().collect();
let best_rank = evaluator.rank_hand7(&all_cards);
println!("Bot best hand rank: {}", best_rank);

Custom Table Generation

use holdem_core::evaluator_generator::state_table_generator::StateTableGenerator;

let mut generator = StateTableGenerator::new();
generator.generate_tables();        // ~1-2 seconds
generator.save_tables().unwrap();   // Save to disk

Testing

Run the comprehensive test suite:

cargo test

Run performance benchmarks:

cargo test --release hand_eval_integration_test -- --nocapture

Mathematical Foundation

Perfect Hash Algorithm

The evaluator uses a perfect hash function that maps each possible card combination to a unique index:

Hash = f(card₁, card₂, ..., cardₙ)
Index = Hash % TABLE_SIZE
Rank = TABLE[Index]

Card Encoding

Each card is encoded using multiple techniques:

• Prime Numbers: Unique primes for rank identification (2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41)
• Bit Patterns: Suit detection using bit manipulation
• Binary Representation: Compact 8-bit card encoding

Hand Strength Calculation

Hand strength is calculated using:

1. Flush Detection: Bitwise suit comparison
2. Rank Analysis: Prime product uniqueness
3. Pattern Matching: Specialized algorithms per hand type
4. Relative Ranking: Comparison against all possible hands

References

• Meerkat API: Original Java implementation by Ray Wotton (full compatibility maintained)
• C Implementation: Paul Senzee's optimized C version (algorithm foundation)
• Perfect Hash: Kevin Suffecool's hashing algorithm (core mathematical approach)
• Poker Standards: 2+2 Poker hand ranking standards (ranking reference)
• Poker Testbed: Framework designed for automated bot testing and analysis
• Java Integration: Compatible with existing Java poker tools and frameworks

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contributing

Contributions are welcome! Areas for improvement:

• Hardware acceleration (SIMD optimizations)
• Additional poker variants (Omaha, Stud, etc.)
• Memory-mapped table loading
• Distributed table generation
• Advanced benchmarking tools

Support

For questions, issues, or contributions, please:

1. Check existing documentation
2. Review test cases for usage examples
3. Open GitHub issues for bugs or feature requests
4. Submit pull requests with comprehensive tests

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Performance Note: First run includes ~1-2 second table generation. Subsequent runs load precomputed tables in ~100-200ms.