superstats.transition.deterministic.polynomial#

Polynomial deterministic transition.

Classes

Polynomial([bounds, intercept, betas, ...])

Deterministic polynomial transition with an intercept and beta weights.
class superstats.transition.deterministic.polynomial.Polynomial(bounds=None, intercept=None, betas=None, degree=2, normalize_steps=True)[source]#

Bases: DeterministicTransition

Deterministic polynomial transition with an intercept and beta weights.

Parameters:
boundssequence of two floats or None, optional, default: None

Lower and upper bounds for the deterministic trajectory. Tuples and lists are accepted.

interceptfloat, Prior, or None, optional, default: None

Constant term of the polynomial. A Prior samples one intercept per trajectory; None uses the deterministic default prior.

betasfloat, Prior, sequence of float/Prior/None, or None

Polynomial coefficients for the non-constant terms. If a single scalar or Prior is provided, the same specification is used for every beta. If a sequence is provided, it must have length degree and each element is used for the corresponding beta weight.

degreeint, optional, default: 2

Number of polynomial terms beyond the intercept. For example, degree=1 reproduces a linear model, while the default degree=2 gives a quadratic model.

normalize_stepsbool, optional, default: True

If True, use a time axis from 0 to 1. If False, use integer step indices, so higher-order terms are evaluated on raw step numbers.

Parameters:

Notes

The sample method returns a dict with keys deterministic_params, hyper_params, and fixed_params. Trajectory values are clipped to bounds.

sample(batch_size, num_steps)[source]#

Draw batch_size polynomial trajectories of length num_steps.

Parameters:

  • batch_size (int)

  • num_steps (int)

Return type:

Dict[str, Any]

sample_from_parameters(params, batch_size, num_steps)[source]#

Generate trajectories from resolved intercept and beta terms.

Parameters:
Return type:

ndarray