Update README.md

Author: coders33123

README.md

@@ -1,158 +1,36 @@
-if not graph.has_node(word):
-    graph.add_node(word, encoded=encode_word_with_context(word, surrounding_words))
- 
- import networkx as nx
-nltk.download('wordnet')
-def add_acronym_node_to_graph(graph, acronym, possible_meanings, context=None, relevance=0.5, related_nodes_weights=None):
-    """
-    Adds an ambiguous acronym node to the graph with attributes and probabilistic edges.
-
-    Args:
-    - graph (networkx.Graph): The graph to modify.
-    - acronym (str): The ambiguous acronym (e.g., 'OSA').
-    - possible_meanings (list[str]): List of potential interpretations.
-    - context (str): Contextual clue or location where the acronym appeared.
-    - relevance (float): Overall relevance score of this acronym to the graph's domain.
-    - related_nodes_weights (dict): Dictionary of related nodes and their edge weights.
-    """
-    graph.add_node(acronym,
-                   possible_meanings=possible_meanings,
-                   context=context,
-                   relevance=relevance,
-                   type='acronym')
-
-    if related_nodes_weights:
-        for node, weight in related_nodes_weights.items():
-            if node in graph.nodes:
-                graph.add_edge(acronym, node, weight=weight)
-
-    return graph
-    # Initialize graph
-graph = nx.Graph()
-graph.add_nodes_from(["temperature", "humidity", "cloudy"])
+jaris/
+│
+├── graph_core.py          # GraphManager: adds nodes, edges, snapshots
+├── context_encoder.py     # ContextEncoder: semantic + numeric encoding
+├── visualizer.py          # Visualizer: Matplotlib + Pyvis graph views
+├── logger.py              # Logger: history, acronym logs, version tracking
+├── feedback_loop.py       # FeedbackManager: edge weight learning
+│
+├── jaris_notebook.ipynb   # Jupyter notebook: interactive brainspace
+├── demo_data/             # (Optional) Seed acronyms, example contexts
+└── README.md              # Documentation overview
 import networkx as nx
-import matplotlib.pyplot as plt
-import nltk
-from nltk.corpus import wordnet as wn
-
-# Make sure to download WordNet if not already done
-nltk.download('wordnet')
-
-def get_contextual_meaning(letter, surrounding_words):
-    """
-    Generate a contextual meaning for a letter based on surrounding words.
-    
-    Args:
-    - letter (str): The letter whose meaning needs to be generated.
-    - surrounding_words (list): A list of words surrounding the target word.
-    
-    Returns:
-    - str: A contextual meaning of the letter.
-    """
-    meanings = []
-    
-    # Iterate through the surrounding words to understand the context
-    for word in surrounding_words:
-        synsets = wn.synsets(word)
-        if synsets:
-            # Use the first synset's lemma names as potential meanings
-            meanings.extend([lemma.name() for lemma in synsets[0].lemmas()])
-    
-    # Here, we're simply returning the first meaning related to the letter
-    # This could be expanded with domain-specific knowledge
-    return meanings[0] if meanings else "unknown"
-
-def encode_word_with_context(word, surrounding_words):
-    """
-    Encodes a word into a numeric vector based on the alphabet-to-number mapping,
-    but modifies each letter's encoding based on surrounding context.
-    
-    Args:
-    - word (str): The word to encode.
-    - surrounding_words (list): List of surrounding words for contextual analysis.
-    
-    Returns:
-    - List[int]: A list of integers representing the word with contextual adjustments.
-    """
-    encoded_word = []
-    
-    for char in word.upper():
-        if char.isalpha():
-            # Get the contextual meaning of the letter based on surrounding words
-            meaning = get_contextual_meaning(char, surrounding_words)
-            
-            # Adjust the letter encoding based on the contextual meaning
-            letter_encoding = (ord(char) - ord('A') + 1)  # Basic encoding (1-26)
-            
-            # Here, we could adjust encoding based on the meaning, for now, we use the length of meaning
-            # As a simple proxy, we modify the encoding based on the length of the meaning
-            contextual_encoding = letter_encoding + len(meaning)
-            encoded_word.append(contextual_encoding)
-        else:
-            encoded_word.append(0)  # For non-alphabetic characters (e.g., spaces)
-    
-    return encoded_word
-
-def add_to_graph(graph, word, relationships, surrounding_words):
-    """
-    Adds a new word (acronym) to the graph and updates relationships.
-    
-    Args:
-    - graph (networkx.Graph): The existing graph.
-    - word (str): The new word (acronym) to add.
-    - relationships (list): A list of relationships (edges) with other words.
-    - surrounding_words (list): List of surrounding words for contextual analysis.
-    """
-    # Add the new word as a node with context-aware encoding
-    graph.add_node(word, encoded=encode_word_with_context(word, surrounding_words))
-    
-    # Add relationships (edges) with other words
-    for related_word in relationships:
-        graph.add_edge(word, related_word)
 
-# Create an empty graph
-graph = nx.Graph()
+class GraphManager:
+    def __init__(self):
+        self.graph = nx.Graph()
 
-# Example of adding a new word and relationships
-add_to_graph(graph, "cloudy", ["weather", "humidity"], ["temperature", "rain", "storm"])
+    def add_acronym_node(self, acronym, meanings, context="", relevance=1.0, related_nodes_weights=None):
+        if acronym not in self.graph:
+            self.graph.add_node(acronym, type='acronym', meanings=meanings, context=context, relevance=relevance)
 
-# Visualize the updated graph
-nx.draw(graph, with_labels=True, node_size=2000, node_color="skyblue", font_size=12, font_weight="bold")
-plt.show()
-# Define OSA possible meanings
-osa_meanings = [
-    "Optical Spectrum Analyzer",
-    "Outside Air",
-    "Obstructive Sleep Apnea"
-]
+        if related_nodes_weights:
+            for related_node, weight in related_nodes_weights.items():
+                self.graph.add_edge(acronym, related_node, weight=weight)
 
-# Add OSA node with example relationships
-related_weights = {
-    "temperature": 0.6,
-    "humidity": 0.7,
-    "cloudy": 0.3
-}
+    def add_edge(self, node1, node2, weight=1.0):
+        self.graph.add_edge(node1, node2, weight=weight)
 
-graph = add_acronym_node_to_graph(
-    graph,
-    acronym="OSA",
-    possible_meanings=osa_meanings,
-    context="weather app interface",
-    relevance=0.8,
-    related_nodes_weights=related_weights
-)
+    def get_neighbors(self, node):
+        return list(self.graph.neighbors(node))
 
-# Inspect
-print(graph.nodes["OSA"])
-print(list(graph.edges("OSA", data=True)))
-feature_vector = {
-    "OSA": 1,
-    "OSA_is_outside_air": 1,
-    "OSA_is_obstructive_sleep_apnea": 0,
-    "temperature": 73,
-    "humidity": 45,
-    "context_contains": "weather app"
-}colors = ['red' if graph.nodes[node].get('type') == 'acronym' else 'skyblue' for node in graph.nodes]
+    def get_node_data(self, node):
+        return self.graph.nodes.get(node, {})
 
-nx.draw(graph, with_labels=True, node_color=colors, node_size=2000, font_weight='bold')
-plt.show()
+    def snapshot_graph(self, filename="graph_snapshot.gml"):
+        nx.write_gml(self.graph, filename)