Generative Modeling ແມ່ນຫຍັງ?

import numpy as np

# A generative model learns p(x) from data, then samples new x from it.
rng = np.random.default_rng(0)
heights = rng.normal(170, 7, size=1000)          # "real" data we observe

mu, sigma = heights.mean(), heights.std()        # learn p(x) = N(mu, sigma^2)
new_people = rng.normal(mu, sigma, size=5)       # generate new samples

print(f"learned  mu={mu:.1f}  sigma={sigma:.1f}")
print("generated:", np.round(new_people, 1))
# Likelihood tells how plausible a value is under the model
for x in (170, 200):
    p = np.exp(-(x - mu) ** 2 / (2 * sigma ** 2)) / (sigma * np.sqrt(2 * np.pi))
    print(f"p({x}) = {p:.5f}")