class FrenchLearner:  # ❌ Container
    def __init__(self):
        self.vocabulary = {}  # ❌ Dict storage
        self.grammar = {}     # ❌ Dict storage
        self.phonemes = []    # ❌ List storage

# French 'R' sound = node
french_r = {
    'type': 'phoneme',
    'sound': 'ʁ',  # IPA
    'language': 'french',
    'series': [],  # Usage history
    'links': []    # Words containing this sound
}

# Track usage over time
french_r['series'].append({
    't': 0,
    'heard_count': 0,
    'produced_count': 0,
    'recognized': False
})

# After practice
french_r['series'].append({
    't': 100,
    'heard_count': 47,
    'produced_count': 12,
    'recognized': True
})

# "chat" (cat) = node
chat_word = {
    'type': 'word',
    'text': 'chat',
    'meaning': 'cat',
    'language': 'french',
    'series': [],  # Learning history
    'links': []    # Phonemes, related words, sentences
}

# Learning progression
chat_word['series'].append({
    't': 0,
    'known': False,
    'encounters': 0,
    'correct_usage': 0
})

chat_word['series'].append({
    't': 50,
    'known': True,
    'encounters': 15,
    'correct_usage': 12,
    'last_used': 'le chat noir'
})

# Article agreement rule = node
article_agreement = {
    'type': 'grammar',
    'rule': 'article_gender_agreement',
    'language': 'french',
    'series': [],
    'links': []  # Words following this rule
}

# Rule learning
article_agreement['series'].append({
    't': 0,
    'understood': False,
    'examples_seen': 0,
    'correct_applications': 0
})

article_agreement['series'].append({
    't': 200,
    'understood': True,
    'examples_seen': 89,
    'correct_applications': 76,
    'accuracy': 0.85
})

# "eat" concept = node (exists across languages)
eat_concept = {
    'type': 'concept',
    'meaning': 'consume_food',
    'languages': {},  # Word in each language
    'series': [],
    'links': []
}

# Appears in multiple languages
eat_concept['languages']['french'] = 'manger'
eat_concept['languages']['spanish'] = 'comer'
eat_concept['languages']['mandarin'] = '吃'

eat_concept['series'].append({
    't': 0,
    'languages_known': 0,
    'universal': False
})

eat_concept['series'].append({
    't': 500,
    'languages_known': 3,
    'universal': True,  # Recognized across all
    'pidgin_form': 'eat'  # Simplified
})

# t=0: Create phoneme nodes
graph = []

# French phonemes
french_phonemes = ['a', 'b', 's', 'ʃ', 'ʁ', ...]  # IPA

for phoneme in french_phonemes:
    phoneme_node = {
        'type': 'phoneme',
        'sound': phoneme,
        'language': 'french',
        'series': [{'t': 0, 'heard': 0, 'produced': 0}],
        'links': []
    }
    graph.append(phoneme_node)

# No container. Just nodes.

def learn_word(graph, word_text, phonemes_used, meaning):
    """
    Learn word = create word node + link to phonemes
    
    NOT: Add to vocabulary dict
    BUT: Create new node, link to existing phoneme nodes
    """
    # Create word node
    word_node = {
        'type': 'word',
        'text': word_text,
        'meaning': meaning,
        'series': [{'t': len(graph), 'known': True, 'encounters': 1}],
        'links': []
    }
    
    graph.append(word_node)
    
    # Link to phoneme nodes
    for phoneme_sound in phonemes_used:
        # Find phoneme node
        phoneme_node = next(
            n for n in graph 
            if n['type'] == 'phoneme' and n['sound'] == phoneme_sound
        )
        
        # Create link
        link = {
            'from': phoneme_node['sound'],
            'to': word_node['text'],
            'type': 'contains_phoneme',
            'strength': 1
        }
        
        word_node['links'].append(link)
        phoneme_node['links'].append(link)
        
        # Update phoneme series
        phoneme_node['series'].append({
            't': len(graph),
            'used_in_word': word_text,
            'strength': len(phoneme_node['links'])
        })
    
    return word_node

# Learn "chat"
chat = learn_word(graph, 'chat', ['ʃ', 'a'], 'cat')

# Learn "la"
la = learn_word(graph, 'la', ['l', 'a'], 'the_feminine')

# Graph grows additively!

def discover_grammar_pattern(graph, examples):
    """
    Discover grammar by finding patterns in word usage
    
    NOT: Define grammar rules upfront
    BUT: Extract patterns from observed examples
    """
    # Example: "le chat", "la maison"
    # Pattern: article before noun, gender agrees
    
    pattern_node = {
        'type': 'grammar',
        'pattern': 'article_noun_agreement',
        'series': [],
        'links': []
    }
    
    # Track examples
    for example in examples:
        pattern_node['series'].append({
            't': len(graph),
            'example': example,
            'confidence': len(pattern_node['series']) / 10.0
        })
        
        # Link to words following this pattern
        for word in example.split():
            word_node = next(
                n for n in graph 
                if n['type'] == 'word' and n['text'] == word
            )
            
            link = {
                'from': pattern_node['pattern'],
                'to': word_node['text'],
                'type': 'follows_rule'
            }
            
            pattern_node['links'].append(link)
    
    graph.append(pattern_node)
    return pattern_node

# Grammar emerges from examples
examples = ['le chat', 'la maison', 'le pain']
grammar = discover_grammar_pattern(graph, examples)

# French graph
french_graph = []
# ... add French phonemes, words, grammar

# Spanish graph  
spanish_graph = []
# ... add Spanish phonemes, words, grammar

# Both exist independently as node collections
# No shared container

# Combined graph (for analysis)
combined_graph = french_graph + spanish_graph

# Now can query across both languages

def find_universal_concepts(graphs_by_language):
    """
    Find concepts that exist in ALL languages
    
    These become pidgin vocabulary
    """
    universal_nodes = []
    
    # Essential concepts
    concepts = ['eat', 'drink', 'sleep', 'go', 'come', 'hello', 'yes', 'no']
    
    for concept in concepts:
        # Find word for this concept in each language
        words_found = {}
        
        for lang_name, graph in graphs_by_language.items():
            # Search for word with this meaning
            word_nodes = [
                n for n in graph 
                if n['type'] == 'word' and n.get('meaning') == concept
            ]
            
            if word_nodes:
                words_found[lang_name] = word_nodes[0]['text']
        
        # If found in ALL languages, create universal concept node
        if len(words_found) == len(graphs_by_language):
            universal_node = {
                'type': 'universal_concept',
                'concept': concept,
                'words': words_found,
                'series': [{'t': 0, 'languages': len(words_found)}],
                'links': []
            }
            
            # Link to word nodes in each language
            for lang_name, word_text in words_found.items():
                graph = graphs_by_language[lang_name]
                word_node = next(
                    n for n in graph 
                    if n['text'] == word_text
                )
                
                link = {
                    'from': universal_node['concept'],
                    'to': word_node['text'],
                    'language': lang_name,
                    'type': 'translates_to'
                }
                
                universal_node['links'].append(link)
            
            universal_nodes.append(universal_node)
    
    return universal_nodes

# Find universals
languages = {
    'french': french_graph,
    'spanish': spanish_graph,
    'mandarin': mandarin_graph
}

universal_concepts = find_universal_concepts(languages)
# These nodes form the pidgin vocabulary!

def create_pidgin_graph(universal_concepts, languages):
    """
    Create pidgin as new graph
    
    NOT: Pidgin as class combining languages
    BUT: Pidgin as emergent graph from intersection
    """
    pidgin_graph = []
    
    for concept_node in universal_concepts:
        # Create simplified pidgin word from concept
        # Use shortest or most common form
        words = list(concept_node['words'].values())
        pidgin_word = min(words, key=len)  # Shortest
        
        # Create pidgin word node
        pidgin_node = {
            'type': 'pidgin_word',
            'text': pidgin_word,
            'meaning': concept_node['concept'],
            'source_words': concept_node['words'],
            'series': [{'t': 0, 'formed_from': len(words), 'usage': 0}],
            'links': []
        }
        
        # Link back to source concept
        link = {
            'from': pidgin_node['text'],
            'to': concept_node['concept'],
            'type': 'simplifies'
        }
        pidgin_node['links'].append(link)
        concept_node['links'].append(link)
        
        pidgin_graph.append(pidgin_node)
    
    # Add simplified grammar nodes
    pidgin_grammar = {
        'type': 'pidgin_grammar',
        'rules': ['SVO_order', 'no_conjugation', 'no_gender'],
        'series': [{'t': 0, 'complexity': 'minimal'}],
        'links': []
    }
    pidgin_graph.append(pidgin_grammar)
    
    return pidgin_graph

# Pidgin emerges!
pidgin = create_pidgin_graph(universal_concepts, languages)

# Start with French + Spanish pidgin
pidgin_v1 = create_pidgin_graph(
    find_universal_concepts({'french': french_graph, 'spanish': spanish_graph}),
    {'french': french_graph, 'spanish': spanish_graph}
)

# Add Mandarin later (additive!)
mandarin_graph = []  # Build Mandarin nodes
# ... add Mandarin phonemes, words, grammar

# Update universal concepts with Mandarin
languages_v2 = {
    'french': french_graph,
    'spanish': spanish_graph,
    'mandarin': mandarin_graph
}

universal_v2 = find_universal_concepts(languages_v2)

# Create expanded pidgin
pidgin_v2 = create_pidgin_graph(universal_v2, languages_v2)

# Pidgin naturally expands!
# No class modification needed

# Track "eat" across contexts

eat_french = next(n for n in french_graph if n['text'] == 'manger')
eat_spanish = next(n for n in spanish_graph if n['text'] == 'comer')
eat_pidgin = next(n for n in pidgin if n['text'] == 'eat')

# Day 1: Learn French "manger"
eat_french['series'].append({
    't': 1,
    'encounters': 1,
    'contexts': ['restaurant'],
    'known': True
})

# Day 30: Use Spanish "comer"
eat_spanish['series'].append({
    't': 30,
    'encounters': 1,
    'contexts': ['home'],
    'known': True
})

# Day 60: Pidgin "eat" emerges from interaction
eat_pidgin['series'].append({
    't': 60,
    'formed': True,
    'source_languages': ['french', 'spanish'],
    'usage_count': 0
})

# Day 90: Use pidgin in conversation
eat_pidgin['series'].append({
    't': 90,
    'usage_count': 5,
    'contexts': ['market', 'street', 'meeting'],
    'understood_by': ['french_speaker', 'spanish_speaker']
})

# Series shows complete evolution history

# Query: "How well do I know French?"
def query_language_knowledge(graph, language):
    """
    Traverse graph to assess knowledge
    """
    word_nodes = [
        n for n in graph 
        if n['type'] == 'word' and n['language'] == language
    ]
    
    known_words = [
        n for n in word_nodes 
        if n['series'][-1].get('known', False)
    ]
    
    grammar_nodes = [
        n for n in graph 
        if n['type'] == 'grammar' and n['language'] == language
    ]
    
    understood_rules = [
        n for n in grammar_nodes 
        if n['series'][-1].get('understood', False)
    ]
    
    return {
        'vocabulary_size': len(known_words),
        'grammar_rules': len(understood_rules),
        'fluency': estimate_fluency(len(known_words))
    }

# Query French knowledge
french_level = query_language_knowledge(combined_graph, 'french')
# → {'vocabulary_size': 342, 'grammar_rules': 12, 'fluency': 'A2'}

# Query: "What pidgin words exist?"
pidgin_words = [n for n in pidgin if n['type'] == 'pidgin_word']
# → List of pidgin nodes

# Query: "Show learning progression for 'chat'"
chat = next(n for n in french_graph if n['text'] == 'chat')
progression = chat['series']
# → Complete history from unknown → known

def store_language_graph(graph):
    """
    Store language graph in R³
    
    Each node = series
    Links = references
    """
    for node in graph:
        if node['type'] == 'phoneme':
            r3_store(
                key=f"phoneme:{node['language']}:{node['sound']}",
                series=node['series'],
                links=node['links']
            )
        
        elif node['type'] == 'word':
            r3_store(
                key=f"word:{node['language']}:{node['text']}",
                series=node['series'],
                links=node['links']
            )
        
        elif node['type'] == 'grammar':
            r3_store(
                key=f"grammar:{node['language']}:{node['pattern']}",
                series=node['series'],
                links=node['links']
            )
        
        elif node['type'] == 'universal_concept':
            r3_store(
                key=f"concept:{node['concept']}",
                series=node['series'],
                links=node['links']
            )
        
        elif node['type'] == 'pidgin_word':
            r3_store(
                key=f"pidgin:{node['text']}",
                series=node['series'],
                links=node['links']
            )

def query_word(language, word_text):
    """
    Query word from R³
    """
    node = r3_load(f"word:{language}:{word_text}")
    
    # Load linked phonemes
    phonemes = []
    for link in node['links']:
        if link['type'] == 'contains_phoneme':
            phoneme = r3_load(f"phoneme:{language}:{link['from']}")
            phonemes.append(phoneme)
    
    return {
        'word': node,
        'phonemes': phonemes,
        'history': node['series']
    }

# French speaker wants to communicate with Spanish speaker

# 1. Identify intent
intent = 'buy_food'

# 2. Find universal concept nodes
universal_concepts = [
    n for n in universal_concepts 
    if n['concept'] in ['buy', 'food', 'want']
]

# 3. Use pidgin words
pidgin_words = []
for concept in universal_concepts:
    pidgin_word = next(
        n for n in pidgin 
        if n['meaning'] == concept['concept']
    )
    pidgin_words.append(pidgin_word['text'])

# 4. Construct simple sentence (SVO)
pidgin_grammar = next(n for n in pidgin if n['type'] == 'pidgin_grammar')

# Simple template
pidgin_sentence = f"mi want buy food"

# 5. Both speakers understand
# French speaker: "Je veux acheter de la nourriture" → "mi want buy food"
# Spanish speaker: "Quiero comprar comida" → "mi want buy food"

# Communication achieved through intersection!