| import numpy as np | |
| import pickle | |
| import embedding | |
| from tqdm import tqdm | |
| import random | |
| import train | |
| top_p = 1 | |
| class Neuron: | |
| def __init__(self, name, inp): | |
| self.name = name | |
| self.inp = embedding.getvec(name) | |
| with open("net.pckl", "rb") as f: net = pickle.load(f) | |
| def top_closest_vectors(input_vector, top_p=1): | |
| distances = [(np.linalg.norm((neuron.inp - input_vector)), ind) for ind, neuron in enumerate(net)] | |
| closest_indices = sorted(distances, reverse=False, key=lambda x:x[0])[:top_p] | |
| return closest_indices | |
| def generate(text): | |
| vec = embedding.getvec(text.replace("\n", "")) | |
| return net[random.choice(top_closest_vectors(vec))[1]].name | |