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2.9 KiB
2.9 KiB
Examples
Practical snippets for every OptimizR component.
Differential Evolution (global optimization)
import numpy as np
from optimizr import differential_evolution
def sphere(x):
return np.sum(x**2)
best_x, best_fx = differential_evolution(
objective_fn=sphere,
bounds=[(-10, 10)] * 5,
strategy="rand1",
maxiter=300,
adaptive=True,
)
print(best_fx)
Grid Search (hyper-parameter sweep)
from optimizr import grid_search
def objective(params):
lr, momentum = params["lr"], params["momentum"]
return (lr - 0.05)**2 + (momentum - 0.9)**2
best_params, best_score = grid_search(
objective_fn=objective,
param_grid={"lr": [0.01, 0.05, 0.1], "momentum": [0.8, 0.9, 0.95]},
)
print(best_params, best_score)
Hidden Markov Models (regime detection)
import numpy as np
from optimizr import HMM
returns = np.random.randn(800) * 0.02 + 0.005
returns[400:] -= 0.015 # regime shift
model = HMM(n_states=2).fit(returns)
states = model.predict(returns)
print(np.bincount(states))
MCMC (posterior sampling)
import numpy as np
from optimizr import mcmc_sample
def log_likelihood(params, data):
mu, sigma = params
residuals = (data - mu) / sigma
return -0.5 * np.sum(residuals**2) - len(data) * np.log(sigma)
data = np.random.randn(500) + 1.0
samples = mcmc_sample(
log_likelihood_fn=log_likelihood,
data=data,
initial_params=np.array([0.0, 1.0]),
param_bounds=[(-5, 5), (0.1, 5.0)],
)
print(samples.mean(axis=0))
Mean Field Games (1D solver)
from optimizr import MFGConfig, solve_mfg_1d_rust
config = MFGConfig(nx=64, nt=32, x_min=-2.0, x_max=2.0, T=1.0, epsilon=0.1, kappa=1.0)
solution = solve_mfg_1d_rust(config)
print(solution.converged)
Sparse Optimization (Sparse PCA)
import numpy as np
from optimizr import sparse_pca_py
X = np.random.randn(200, 10)
components = sparse_pca_py(X, n_components=3, l1_ratio=0.2)
print(components.shape)
Risk Metrics (time series)
import numpy as np
from optimizr import hurst_exponent_py, estimate_half_life_py
returns = np.random.randn(1000) * 0.01
print("Hurst:", hurst_exponent_py(returns))
print("Half-life:", estimate_half_life_py(returns))
Notebooks
- Differential Evolution:
examples/notebooks/03_differential_evolution_tutorial.ipynb - Mean Field Games:
examples/notebooks/mean_field_games_tutorial.ipynb - HMM:
examples/notebooks/01_hmm_tutorial.ipynb - MCMC:
examples/notebooks/02_mcmc_tutorial.ipynb - Optimal Control & Kalman:
examples/notebooks/03_optimal_control_tutorial.ipynb - Performance benchmarks:
examples/notebooks/05_performance_benchmarks.ipynb
Contribute Examples
- Fork the repository and add notebooks under
examples/notebooks/ - Keep dependencies minimal (NumPy/Matplotlib preferred)
- Ensure the notebook runs end-to-end before submitting a PR