Coding

Coding snippets and minimal examples to demonstrate use of classes and functions.

ALSSMs & Cost Functions

Autonomous Linear State Space Models (ALSSMs) and squared error cost functions using ALSSM models, see also: ALSSM Classes and Cost Function Classes in module Module statespace

Basic ALSSM Coding [ex101.0]

Superposition (Stacking) of ALSSMs [ex101.1]

ALSSM Trajectory [ex102.0]

Single-Segment (CostSegment) Models: Trajectories and Windows [ex103.0]

Multi-Segment (Composite) Models: Windows and Trajectories [ex104.0]

Local Signal Approximation and Trajectories [ex105.0]

Composed ALSSM Windows (Weighting Window) [ex106.0]

Backend Selection (Plain Python vs. JIT)

The module lmlib.statespace.backend provides methods to enable Just-in-Time (JIT) compilation for time-critical routines in the package lmlib.statespace. Turning on JIT significantly increases execution speed of filtering applications. To activate JIT, the Python numba package must be installed. The following examples demonstrate the usage of JIT.

Benchmarking of Python vs. JIT Backend [ex130.0]

Polynomials Basics

Examples on handling polynomials in vector exponent notation, see module Module polynomial

Structure and Evaluation of Univariate Polynomials [ex201.1]

Structure and Evaluation of Multivariate Polynomials [ex201.1]

Surface Plot of Multivariate Polynomials [ex202.0]

Calculus with Polynomials [ex203.0]

previous

lmlib.utils.beta.zero_cross_ind

next

Basic ALSSM Coding [ex101.0]