27 lines
564 B
Markdown
27 lines
564 B
Markdown
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# Hidden Markov Models
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Gaussian HMM for regime detection and sequence modelling.
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## Usage
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```python
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import numpy as np
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from optimizr import HMM
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returns = np.concatenate([
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np.random.normal(0.01, 0.02, 500),
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np.random.normal(-0.015, 0.03, 500),
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])
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model = HMM(n_states=2)
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model.fit(returns, n_iterations=100)
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states = model.predict(returns)
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print(np.unique(states, return_counts=True))
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```
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## Notes
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- Uses Rust backend when available; falls back to Python.
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- `fit` runs Baum-Welch; `predict` runs Viterbi.
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- Call `score(X)` to compute log-likelihood.
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