import networkx as nx import matplotlib.pyplot as plt from typing import List, Dict

GraphManager Class for handling graph-based word relationships

class GraphManager: def init(self): """ Initializes a GraphManager instance. """ # Initialize the graph instance using NetworkX self.graph = nx.Graph()

def add_word(self, word: str, relationships: List[str] = [], encoded_vector: List[int] = []):
    """
    Adds a word to the graph with encoded vector and relationships.
    """
    self.graph.add_node(word, encoded=encoded_vector)
    for related_word in relationships:
        self.graph.add_edge(word, related_word)

def visualize(self):
    """
    Visualizes the graph using matplotlib to render nodes with labels.
    """
    # Generate the visualization of the graph
    plt.figure(figsize=(12, 12))
    pos = nx.spring_layout(self.graph)
    nx.draw(self.graph, pos, with_labels=True, node_color='skyblue', font_size=12, node_size=5000, font_weight='bold', edge_color='gray')
    plt.title('Word Relationship Graph')
    plt.show()

def get_neighbors(self, word: str) -> List[str]:
    """
    Returns a list of words directly connected to the specified word.
    
    Parameters:
    word (str): The word for which we want to find neighbors.
    
    Returns:
    list: List of neighboring words.
    """
    return list(self.graph.neighbors(word)) if word in self.graph else []

TransformationSystem Class to handle dynamic transformations and state management

class TransformationSystem: def init(self, context_influence: Dict[str, Dict[str, int]] = None): """ Initializes the TransformationSystem with optional context influence. Parameters: context_influence (dict, optional): A dictionary defining how context should influence the system. Defaults to None. """ self.context_influence = context_influence self.system_state = {} self.state_change = {}

def execute(self, action: str, vowel: str = None, parameters: Dict[str, int] = None):
    """
    Executes the action based on vowel-based flow control, context influence, and core logic.
    
    Parameters:
    action (str): The action to execute (e.g., "Start", "Branch", "Merge").
    vowel (str, optional): The vowel that influences the flow (e.g., 'a', 'e', etc.). Defaults to None.
    parameters (dict, optional): Additional parameters influencing the action. Defaults to None.
    
    Returns:
    tuple: Contains state_change, output_effect, and the updated system_state.
    """
    try:
        # Handle vowel-based flow control and action execution
        if vowel:
            self._apply_vowel_flow(vowel, parameters)
        
        # Apply contextual influence if defined
        if self.context_influence:
            self._apply_contextual_influence(action)
        
        # Perform the action
        output_effect = self._perform_action(action, parameters)
        self.state_change = self.system_state.copy()  # Capture state change
        return self.state_change, output_effect, self.system_state
    
    except KeyError as e:
        # Handle missing parameter or state key errors
        print(f"Error: Missing key {e}")
        return {}, "Error: Missing key", self.system_state
    
    except Exception as e:
        # Handle any other unexpected errors
        print(f"Unexpected error: {e}")
        return {}, "Unexpected error", self.system_state

def _apply_vowel_flow(self, vowel: str, parameters: Dict[str, int]):
    """
    Applies vowel-based flow control to modify the system’s state.
    
    Parameters:
    vowel (str): The vowel that influences the transformation.
    parameters (dict): Parameters that determine how the vowel flow is applied.
    """
    # Example: Influence system state based on the vowel
    flow_intensity = parameters.get('intensity', 1)
    self.system_state['vowel'] = vowel
    self.system_state['flow_intensity'] = flow_intensity
    # Add more flow logic depending on vowel (for example, 'a' could increase speed, 'e' could increase scope)

def _apply_contextual_influence(self, action: str):
    """
    Modifies the system state based on the defined context (e.g., Start, Branch, etc.).
    
    Parameters:
    action (str): The action being executed that could influence the context.
    """
    if action in self.context_influence:
        context_effect = self.context_influence[action]
        self.system_state.update(context_effect)

def _perform_action(self, action: str, parameters: Dict[str, int]):
    """
    Executes core actions like initiation, branching, merging, and filtering.
    
    Parameters:
    action (str): The action to perform.
    parameters (dict): Parameters that may influence the action.
    
    Returns:
    str: A message describing the outcome of the action.
    """
    if action == "Start":
        self.system_state['status'] = "Started"
        return "System Started"
    elif action == "Branch":
        self.system_state['status'] = "Branching"
        return "System Branching"
    elif action == "Merge":
        self.system_state['status'] = "Merging"
        return "System Merging"
    elif action == "Filter":
        self.system_state['status'] = "Filtering"
        return "System Filtering"
    else:
        self.system_state['status'] = "Unknown Action"
        return "Unknown Action"

Example Usage

GraphManager Example

graph_manager = GraphManager() graph_manager.add_word("apple", relationships=["banana"], encoded_vector=[1, 2, 3]) graph_manager.add_word("banana", relationships=["apple"], encoded_vector=[4, 5, 6]) graph_manager.visualize()

Setting up transformation system with contextual influence

context_influence = { "Start": {"threshold": 10}, "Branch": {"threshold": 20}, "Combine": {"threshold": 30} } transformation_system = TransformationSystem(context_influence) state_change, output_effect, system_state = transformation_system.execute("Start", vowel="a", parameters={'intensity': 5})

print(state_change, output_effect, system_state)