ThotDjehuty
d8682f61e5
PyO3 abi3 bindings for the 13 v2.0.0 functions across 8 module groups:
bsde, pde, stochastic_control, optimal_control::quadratic_impact_control,
mean_field::mckean_vlasov, agent_based, inference, optimization.
8 executed companion notebooks under examples/notebooks/10_bsde.ipynb …
17_generative_calibration.ipynb (cell outputs and matplotlib figures
preserved as proof-of-work; verified against analytic ground truths).
8 Sphinx RST pages under docs/source/algorithms/{bsde,pde,stochastic_control,
quadratic_impact_control,mckean_vlasov,agent_based,robust_drift,
generative_calibration_hooks}.rst with .. math:: derivations and inline
.. image:: directives placed immediately after each .. code-block:: python
so each plot appears directly under the code that produced it.
18 PNG plot assets under docs/source/_static/v2/<group>/.
index.rst extended with a new 'v2.0 Generic Stochastic Control & PDE'
toctree caption.
Forbidden-vocabulary audit on new src/, docs/source/algorithms/ and
binding files: zero matches.
All previously stable APIs untouched; v2.0.0 is additive at the binding
level — no v1.x function signature was changed.
119 KiB
119 KiB
In [1]:
import numpy as np
import matplotlib.pyplot as plt
from optimizr import _core as opt
plt.rcParams['figure.figsize'] = (7, 4)
plt.rcParams['figure.dpi'] = 110
In [2]:
init = np.linspace(-2.0, 2.0, 200).tolist()
init_mean = float(np.mean(init))
res = opt.mean_reverting_mckean_vlasov(
initial=init, theta=1.0, sigma=0.1,
n_steps=1000, t_horizon=1.0, seed=42,
)
n_t = res['n_steps']; n_p = res['n_particles']
X = np.array(res['paths_flat']).reshape(n_t, n_p)
tg = np.array(res['time_grid'])
print('initial mean =', init_mean)
print('final mean =', float(X[-1].mean()))
print('final std =', float(X[-1].std()))
initial mean = 7.105427357601002e-17 final mean = -0.003086441510327802 final std = 0.43383156366774833
In [3]:
fig, ax = plt.subplots()
ax.plot(tg, X[:, ::20], color='tab:blue', alpha=0.2, lw=0.6)
ax.plot(tg, X.mean(axis=1), color='red', lw=2, label='empirical mean')
ax.axhline(init_mean, color='k', ls=':', label='initial mean')
ax.set_xlabel('t'); ax.set_ylabel('X^i_t'); ax.legend(); ax.grid(alpha=0.3)
ax.set_title('Mean-reverting McKean–Vlasov — 200 particles')
fig.tight_layout(); plt.show()
In [4]:
fig, ax = plt.subplots()
ax.hist(X[0], bins=30, alpha=0.5, label='t = 0', density=True)
ax.hist(X[-1], bins=30, alpha=0.5, label='t = T', density=True)
ax.set_xlabel('x'); ax.set_ylabel('empirical density'); ax.legend(); ax.grid(alpha=0.3)
ax.set_title('Marginal density at t = 0 and t = T')
fig.tight_layout(); plt.show()
