The analogy between hierarchical clustering using Word2Vec and a heuristic system like the one used in the A* (pronounced "A-star") algorithm is based on the way both approaches use prior knowledge or assumptions to optimize a process. Word2Vec provides a representation of words in a feature

clusters.py

import os
import numpy as np

# Assuming 'word2vec_function' is the Python equivalent of your 'word2vec' function
# and 'gpt_encode' is a function that encodes a string similarly to 'gpt.encode'

def word2vec_function(input_path, output_path, options, callback):
    # This function should perform the word2vec operation and write the output to 'output_path'
    # Then call 'callback' with the result
    pass

def gpt_encode(text):
    # Replace this with the actual GPT encoding function
    return text

def word2cluster(vec, n=0):
    temp = []
    if n == 0:
        for s in vec:
            temp.extend(s)
    else:
        # Assuming 'vec' is a list of strings at this point
        vec_chunked = [vec[i:i+2] for i in range(0, len(vec), 2)]
        vec_filtered = [chunk for chunk in vec_chunked if len(chunk) == 2]
        temp = [':'.join(pair) for pair in vec_filtered]

    # Write to temporary file
    with open('/tmp/w2v', 'w') as f:
        f.write(' '.join(temp))

    # Here you would call your word2vec function and wait for it to complete
    word2vec_function('/tmp/w2v', f'./data/class{n}', {'classes': 256}, callback=lambda x: process_result(x, n, temp))

def process_result(x, n, temp):
    with open(f'./data/class{n}') as f:
        lines = f.read().split('\n')

    y = {line.split(' ')[0]: int(line.split(' ')[1]) for line in lines if line}

    j = [y.get(s, s) for s in temp]  # Replace string with number if available
    n += 1
    if n < 8:
        word2cluster(j, n)

# Read data from file and start the clustering process
with open('./data/f.txt') as f:
    d = f.read().split('\n')

word2cluster([gpt_encode(line) for line in d])