| import numpy as np | |
| # SpTn-Model-only-1P | |
| # A 1-parameter generative model | |
| np.random.seed(42) | |
| # The only trainable parameter | |
| w = np.array(0.5) | |
| # Training data: probabilities we want to approximate | |
| data = np.array([0.1, 0.2, 0.3, 0.4]) | |
| learning_rate = 0.1 | |
| for step in range(1000): | |
| prediction = w * data | |
| loss = np.mean((prediction - data) ** 2) | |
| gradient = np.mean(2 * (prediction - data) * data) | |
| w -= learning_rate * gradient | |
| print("SpTn-Model-only-1P") | |
| print("Trainable parameters: 1") | |
| print("Learned parameter:", w) | |
| print("Loss:", loss) | |
| # Generation | |
| random_value = np.random.random() | |
| generated = w * random_value | |
| print("Random input:", random_value) | |
| print("Generated value:", generated) |