Power fitting objective models
Deprecated compatibility route
This v0.7 historical module will be removed in v0.8. Import
objective models from pyvoro2.inverse.separator instead.
pyvoro2.powerfit.model
Compatibility exports for separator objective models.
ExponentialBoundaryPenalty
dataclass
Bases: ScalarPenalty
Repulsive penalty near the boundaries of an interval.
The penalty is based on exponentials measured from an inner interval
[lower + margin, upper - margin].
FitModel
dataclass
Complete objective definition for inverse power-weight fitting.
The objective consists of
- one required mismatch term,
- an optional hard feasibility set,
- zero or more extra penalties,
- optional L2 regularization on the weights.
FixedValue
dataclass
HardConstraint
Base class for hard feasibility restrictions.
HuberLoss
dataclass
Bases: ScalarMismatch
Huber mismatch penalty in the chosen measurement space.
The penalty is quadratic near zero and linear for large residuals.
Interval
dataclass
L2Regularization
dataclass
Optional L2 regularization on the weight vector.
ReciprocalBoundaryPenalty
dataclass
Bases: ScalarPenalty
Reciprocal repulsion near interval boundaries.
This penalty is intended to be used together with a hard interval or a
strong outside penalty. It penalizes separator positions that enter the
boundary layers [lower, lower + margin] and [upper - margin, upper].
ScalarMismatch
Base class for mismatch terms applied to predicted separator positions.
ScalarPenalty
Base class for additional scalar penalties.
SoftIntervalPenalty
dataclass
Bases: ScalarPenalty
Quadratic penalty for leaving a preferred interval.
The penalty is zero within [lower, upper] and quadratic outside.
SquaredLoss
dataclass
:::