Melvin Alvarez
17 lines
369 B
Markdown
17 lines
369 B
Markdown
# API: HMM
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```python
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from optimizr import HMM
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model = HMM(n_states=2)
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model.fit(X, n_iterations=100, tolerance=1e-6)
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states = model.predict(X)
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logp = model.score(X)
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```
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Parameters
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- `n_states`: number of hidden regimes
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- `fit(X, n_iterations=100, tolerance=1e-6)`: train with Baum-Welch
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- `predict(X)`: Viterbi decoding → `np.ndarray`
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- `score(X)`: log-likelihood
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