nlsq.precision.bound_inference.BoundsInference

class nlsq.precision.bound_inference.BoundsInference(xdata, ydata, p0, safety_factor=10.0, enforce_positivity=True)

Bases: object

Infer reasonable parameter bounds from data characteristics.

This class implements heuristics to estimate parameter bounds that help constrain optimization without being overly restrictive. The bounds are based on data ranges, parameter scales, and common patterns.

Parameters:

• xdata (array_like) – Independent variable data

• ydata (array_like) – Dependent variable data

• p0 (array_like) – Initial parameter guess

• safety_factor (float, optional) – Multiplier for bound ranges (larger = more conservative). Default: 10.0

• enforce_positivity (bool, optional) – Force all bounds to be non-negative if p0 is positive. Default: True

x_min, x_max

Range of independent variable

Type:

float

y_min, y_max

Range of dependent variable

Type:

float

x_range, y_range

Span of data

Type:

float

Examples

>>> x = np.linspace(0, 10, 100)
>>> y = 2.5 * np.exp(-0.5 * x) + 1.0
>>> p0 = [2.0, 0.5, 1.0]
>>>
>>> inference = BoundsInference(x, y, p0)
>>> bounds = inference.infer()
>>> print(bounds)
([0.0, 0.0, 0.0], [25.0, 5.0, 10.0])

__init__(xdata, ydata, p0, safety_factor=10.0, enforce_positivity=True)

Initialize bounds inference.

infer()

Infer bounds for all parameters.

Returns:

• lower_bounds (ndarray) – Lower bounds for each parameter

• upper_bounds (ndarray) – Upper bounds for each parameter

Return type:

tuple[ndarray, ndarray]