Update README.md

Author: coders33123

README.md

@@ -1,70 +1,54 @@
-import nltk
-from nltk.corpus import wordnet as wn
-from collections import defaultdict
+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))
 
-# Make sure to download WordNet if not already downloaded
-nltk.download('wordnet')
+# 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.
 
-def get_contextual_meaning(letter, surrounding_words):
-    """
-    Generate a contextual meaning for a letter based on surrounding words.
-    This could use WordNet or a rule-based system to find the meaning of each letter.
-    
-    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.
-    """
-    # 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 = []
-    
-    # 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"
+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 = []
+
+# 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):
+# 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):
     """
-    Encodes a word into a numeric vector based on the alphabet-to-number mapping,
-    but modifies each letter's encoding based on surrounding context.
+    Adds a new word (acronym) to the graph and updates relationships.
 
     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.
+    - graph (networkx.Graph): The existing graph.
+    - word (str): The new word (acronym) to add.
+    - relationships (list): A list of relationships (edges) with other words.
     """
-    encoded_word = []
+    # Add the new word as a node
+    graph.add_node(word, encoded=encode_word(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
+    # Add relationships (edges) with other words
+    for related_word in relationships:
+        graph.add_edge(word, related_word)
+
+# Example of adding a new word and relationships
+add_to_graph(graph, "cloudy", ["weather", "humidity"])
 
-# Example usage
-word = "weather"
-surrounding_words = ["temperature", "humidity", "rain"]
-encoded_word_with_context = encode_word_with_context(word, surrounding_words)
-print(f"Contextually encoded '{word}': {encoded_word_with_context}")
+# Visualize the updated graph
+nx.draw(graph, with_labels=True, node_size=2000, node_color="skyblue", font_size=12, font_weight="bold")
+plt.show()