Architecture

The v2 architecture is a thin composition:

Data -> FeatureMap stack -> Solver -> Predictions

Models do not duplicate hidden-layer math. Feature computation lives in FeatureMap; learning lives in Solver; backend selection is passed uniformly through the pipeline.

Pipeline

Feature maps

FeatureMap<FloatT> is the single home for feature computation.

Map	Learns parameters?	Use
IdentityMap	No	Linear baseline and ablations
RandomAdditiveMap	No	Standard additive ELM hidden layer
RbfMap	Centers during fit	Center-based radial basis features
ElmAutoEncoderLayer	Yes	Learned encoder for feature extraction
StackedFeatureMap	Yes, layer by layer	ML-ELM and H-OS-ELM feature stacks

Solver strategies

Solver	Training mode	Main options
BatchRidgeSolver	Batch	ridgeAlpha, primal/dual path, Cholesky/QR
RlsSolver	Online	regularization, forgetting, class-distance constraint

Backend

Backend::kCpu is the correctness reference. Backend::kGpu routes heavy primitives through the CUDA primitive layer. The public model API stays backend-neutral.