Mayan Hieroglyph Translator on the GPT Store

GPT Description

GPT Prompt Starters

• # Maya Glyph Learning System for LLM # Incorporating reference files and examples from typing import Dict, List, Tuple import xml.etree.ElementTree as ET class MayaGlyphLearningSystem: def __init__(self): # Load reference dictionaries and datasets self.vocabulary = self.load_vocabulary("Vocabulary.pdf") self.glyph_translation = self.load_translations("GlyLib.py") self.data_segments = self.load_segments("DataSegments.py") # Core knowledge structures self.glyph_patterns = { "block_structure": { "superfix": [], "prefix": [], "main_sign": [], "postfix": [], "subfix": [] }, "combination_types": { "sequence": "side-by-side", "overlapping": "shared space", "infixing": "insertion", "conflation": "merged" }, "reading_order": { "direction": "left-to-right", "pattern": "double columns", "progression": "top-to-bottom" } } self.number_system = { "notation": { "dots": range(1,4), "bars": [5], "head_variants": {} }, "vigesimal": { "base": 20, "place_values": ["ones", "twenties", "four_hundreds"] } } self.grammatical_system = { "sentence_types": { "transitive": { "structure": "subject-verb-object", "verb_patterns": ["CVC", "non_CVC"] }, "intransitive": { "structure": "subject-verb", "verb_patterns": ["basic", "derived"] }, "positional": { "structure": "location-state", "components": ["temporal", "spatial"] }, "equative": { "structure": "X-is-Y", "patterns": ["nominal", "adjectival"] } } } # Visual pattern recognition self.visual_features = { "basic_shapes": [], "common_elements": [], "distinguishing_marks": [] } def learn_from_example(self, glyph_image: bytes, transcription: str = None): """Process and learn from new glyph examples""" # Decompose glyph block components = self.analyze_block_structure(glyph_image) # Identify patterns patterns = self.detect_visual_patterns(components) # Cross-reference with known vocabulary meanings = self.lookup_meanings(patterns) # Update knowledge base self.update_patterns(patterns) self.update_meanings(meanings) if transcription: self.validate_learning(meanings, transcription) def analyze_block_structure(self, glyph_image: bytes) -> Dict: """Analyze glyph block composition""" return { "main_sign": self.identify_main_sign(glyph_image), "affixes": self.identify_affixes(glyph_image), "arrangement": self.analyze_arrangement(glyph_image) } def detect_visual_patterns(self, components: Dict) -> List: """Identify recurring visual elements and patterns""" patterns = [] for component in components.values(): # Look for known patterns basic_shapes = self.match_basic_shapes(component) elements = self.identify_elements(component) arrangements = self.analyze_arrangements(component) patterns.extend([basic_shapes, elements, arrangements]) return patterns def process_sentence(self, glyph_sequence: List) -> Dict: """Process complete Maya sentences""" # Determine sentence type sentence_type = self.identify_sentence_type(glyph_sequence) # Parse grammatical structure grammar = { "verb": self.identify_verb(glyph_sequence), "arguments": self.identify_arguments(glyph_sequence), "modifiers": self.identify_modifiers(glyph_sequence) } # Build meaning translation = self.construct_translation(grammar) return { "type": sentence_type, "grammar": grammar, "translation": translation } def learn_temporal_variations(self, glyph: Dict, period: str): """Track changes in glyph forms over time""" # Record period-specific features features = { "period": period, "form": glyph["form"], "distinctive_elements": self.analyze_features(glyph) } # Update historical knowledge self.update_temporal_knowledge(features) def feedback_loop(self): """Continuous learning and improvement process""" while True: # Analyze new examples new_examples = self.get_new_examples() for example in new_examples: self.learn_from_example(example) # Compare against known patterns self.validate_patterns() # Update recognition models self.update_models() # Refine understanding self.optimize_learning() def load_vocabulary(self, file_path: str) -> Dict: """Load Maya vocabulary from reference file""" # Implementation for loading vocabulary pass def load_translations(self, file_path: str) -> Dict: """Load glyph translations from reference file""" # Implementation for loading translations pass def load_segments(self, file_path: str) -> Dict: """Load data segments from reference file""" # Implementation for loading segments pass def update_knowledge_base(self, new_data: Dict): """Update internal knowledge representation""" # Implementation for knowledge update pass if __name__ == "__main__": maya_learning = MayaGlyphLearningSystem() # Example learning session maya_learning.learn_from_example(glyph_image_bytes) maya_learning.process_sentence(glyph_sequence) maya_learning.learn_temporal_variations(glyph_data, "Classic") # Start continuous learning maya_learning.feedback_loop()