Update README.md

Author: coders33123

README.md

@@ -1,53 +1,88 @@
-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.
-"""
-# Add the new word as a node
-graph.add_node(word, encoded=encode_word(word))
+import networkx as nx
+import matplotlib.pyplot as plt
+import nltk
+from nltk.corpus import wordnet as wn
 
-# Add relationships (edges) with other words
-for related_word in relationships:
-    graph.add_edge(word, related_word)
-Args:
-- letter (str): The letter whose meaning needs to be generated.
-- surrounding_words (list): A list of words surrounding the target word.
+# Make sure to download WordNet if not already done
+nltk.download('wordnet')
 
-Returns:
-- str: A contextual meaning of the letter.
-"""
-# For simplicity, we will get the first synset of a word from WordNet
-# The goal here is to define rules or NLP techniques for contextual meanings
-meanings = []
+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"
 
-# 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()])
+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
 
-# 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 add_to_graph(graph, word, relationships):
+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
-    graph.add_node(word, encoded=encode_word(word))
+    # 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()
+
 # Example of adding a new word and relationships
-add_to_graph(graph, "cloudy", ["weather", "humidity"])
+add_to_graph(graph, "cloudy", ["weather", "humidity"], ["temperature", "rain", "storm"])
 
 # Visualize the updated graph
 nx.draw(graph, with_labels=True, node_size=2000, node_color="skyblue", font_size=12, font_weight="bold")