| import numpy as np | |
| np.random.seed(42) | |
| # SpTn-Model-only-1P | |
| # Exactly ONE trainable parameter | |
| text = "hello world this is a tiny one parameter model" | |
| chars = np.array(sorted(set(text))) | |
| # ONE trainable parameter | |
| w = np.array(0.5) | |
| # Target = average normalized character frequency | |
| frequencies = np.array([ | |
| text.count(c) / len(text) | |
| for c in chars | |
| ]) | |
| target = np.mean(frequencies) | |
| lr = 0.1 | |
| for step in range(1000): | |
| prediction = w | |
| error = prediction - target | |
| loss = error ** 2 | |
| gradient = 2 * error | |
| w -= lr * gradient | |
| print("SpTn-Model-only-1P") | |
| print("Vocabulary:", "".join(chars)) | |
| print("Trainable parameters:", 1) | |
| print("Learned parameter:", w) | |
| print("Loss:", loss) | |
| # Generate text using learned parameter | |
| output = "" | |
| for _ in range(100): | |
| index = np.random.randint(0, len(chars)) | |
| output += chars[index] | |
| print("Generated:") | |
| print(output) |