HMath.MachineLearning¶
Module index · Numerical conventions
Description
Reproducible dense CPU estimators, preprocessing, clustering, and manifold methods. Features are finite rows; fitted state is immutable and independent of future transforms/predictions. Fit preprocessing separately within every training fold. Iterative fits expose residual, iterations, and convergence; no successful return alone implies an optimizer reached its optimum.
On this page¶
| API | Purpose |
|---|---|
| Dataset | Finite observations as rows with a fixed, positive feature count. |
| MLConfig | Positive iteration limittolerancerate and an explicit reproducible seed. |
| MLConfig | Declaration and type contract below. |
| mlIterations | Maximum outer iterations per fitted algorithm. |
| mlTolerance | Absolute algorithm-specific stopping residual. |
| mlRate | Positive gradient step size, where used. |
| mlSeed | Seed for initialization, projection, or stochastic updates. |
| defaultMLConfig | 500 iterations, absolute residual tolerance 1e-8, rate 0.05, PCG seed 42. |
| Fit | A finite budget is a valid stopping point, distinct from convergence. |
| Fit | Declaration and type contract below. |
| fitted | Best fitted model retained when fitting stops. |
| fitIterations | Completed fitting iterations. |
| fitResidual | Algorithm-specific finite stopping residual at the returned model. |
| fitConverged | Whether the selected stopping residual met tolerance; a budget stop may return False. |
Documentation
Dataset
type Dataset = [[Double]] Source #
Finite observations as rows with a fixed, positive feature count.
MLConfig
Positive iteration limittolerancerate and an explicit reproducible seed.
Constructors
| MLConfig | |
Fields
| |
defaultMLConfig
defaultMLConfig :: MLConfig Source #
500 iterations, absolute residual tolerance 1e-8, rate 0.05, PCG seed 42.
Fit
A finite budget is a valid stopping point, distinct from convergence.
Constructors
| Fit | |
Fields
| |