ThotDjehuty
b10263b4f2
Updated Branding in ReadTheDocs: - ✅ All 'OptimizR' → 'Optimiz-rs' (17 files) - ✅ Project name in conf.py - ✅ HTML title and short title - ✅ All algorithm documentation - ✅ Getting started guide - ✅ Installation guide - ✅ Theory/mathematical foundations - ✅ Archive documentation Documentation now consistently uses the new 'optimiz-rs' branding that matches both PyPI and crates.io package names. Note: Python module name 'optimizr' in import statements intentionally unchanged (that's the actual module name).
123 lines
2.9 KiB
Markdown
123 lines
2.9 KiB
Markdown
# Examples
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Practical snippets for every Optimiz-rs component.
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## Differential Evolution (global optimization)
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```python
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import numpy as np
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from optimizr import differential_evolution
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def sphere(x):
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return np.sum(x**2)
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best_x, best_fx = differential_evolution(
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objective_fn=sphere,
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bounds=[(-10, 10)] * 5,
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strategy="rand1",
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maxiter=300,
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adaptive=True,
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)
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print(best_fx)
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```
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## Grid Search (hyper-parameter sweep)
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```python
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from optimizr import grid_search
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def objective(params):
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lr, momentum = params["lr"], params["momentum"]
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return (lr - 0.05)**2 + (momentum - 0.9)**2
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best_params, best_score = grid_search(
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objective_fn=objective,
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param_grid={"lr": [0.01, 0.05, 0.1], "momentum": [0.8, 0.9, 0.95]},
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)
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print(best_params, best_score)
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```
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## Hidden Markov Models (regime detection)
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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.random.randn(800) * 0.02 + 0.005
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returns[400:] -= 0.015 # regime shift
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model = HMM(n_states=2).fit(returns)
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states = model.predict(returns)
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print(np.bincount(states))
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```
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## MCMC (posterior sampling)
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```python
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import numpy as np
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from optimizr import mcmc_sample
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def log_likelihood(params, data):
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mu, sigma = params
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residuals = (data - mu) / sigma
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return -0.5 * np.sum(residuals**2) - len(data) * np.log(sigma)
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data = np.random.randn(500) + 1.0
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samples = mcmc_sample(
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log_likelihood_fn=log_likelihood,
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data=data,
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initial_params=np.array([0.0, 1.0]),
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param_bounds=[(-5, 5), (0.1, 5.0)],
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)
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print(samples.mean(axis=0))
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```
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## Mean Field Games (1D solver)
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```python
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from optimizr import MFGConfig, solve_mfg_1d_rust
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config = MFGConfig(nx=64, nt=32, x_min=-2.0, x_max=2.0, T=1.0, epsilon=0.1, kappa=1.0)
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solution = solve_mfg_1d_rust(config)
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print(solution.converged)
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```
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## Sparse Optimization (Sparse PCA)
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```python
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import numpy as np
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from optimizr import sparse_pca_py
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X = np.random.randn(200, 10)
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components = sparse_pca_py(X, n_components=3, l1_ratio=0.2)
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print(components.shape)
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```
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## Risk Metrics (time series)
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```python
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import numpy as np
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from optimizr import hurst_exponent_py, estimate_half_life_py
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returns = np.random.randn(1000) * 0.01
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print("Hurst:", hurst_exponent_py(returns))
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print("Half-life:", estimate_half_life_py(returns))
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```
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## Notebooks
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- Differential Evolution: `examples/notebooks/03_differential_evolution_tutorial.ipynb`
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- Mean Field Games: `examples/notebooks/mean_field_games_tutorial.ipynb`
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- HMM: `examples/notebooks/01_hmm_tutorial.ipynb`
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- MCMC: `examples/notebooks/02_mcmc_tutorial.ipynb`
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- Optimal Control & Kalman: `examples/notebooks/03_optimal_control_tutorial.ipynb`
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- Performance benchmarks: `examples/notebooks/05_performance_benchmarks.ipynb`
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## Contribute Examples
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1. Fork the repository and add notebooks under `examples/notebooks/`
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2. Keep dependencies minimal (NumPy/Matplotlib preferred)
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3. Ensure the notebook runs end-to-end before submitting a PR
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