Module ssm 

Streaming Mamba (selective state space model) for temporal ML pipelines.

This module provides StreamingMamba, a streaming machine learning model that uses a Mamba-style selective SSM as its temporal feature extractor, feeding into a Recursive Least Squares (RLS) readout layer. It integrates with irithyll’s StreamingLearner trait and StreamingPreprocessor trait.

Architecture

input features ──→ [SelectiveSSM] ──→ temporal features ──→ [RLS] ──→ prediction
  (d_in)              (d_in state)        (d_in)            (1)

The SSM processes each feature vector as a timestep, maintaining per-channel hidden state that captures temporal dynamics. The RLS readout learns a linear mapping from the SSM’s output to the target variable.

Components

• StreamingMamba – full model implementing StreamingLearner
• MambaPreprocessor – SSM-only preprocessor implementing StreamingPreprocessor
• MambaConfig / MambaConfigBuilder – validated configuration

Example

use irithyll::ssm::{StreamingMamba, MambaConfig};
use irithyll::learner::StreamingLearner;

let config = MambaConfig::builder()
    .d_in(4)
    .n_state(16)
    .build()
    .unwrap();

let mut model = StreamingMamba::new(config);
model.train(&[1.0, 2.0, 3.0, 4.0], 5.0);
let pred = model.predict(&[1.0, 2.0, 3.0, 4.0]);
assert!(pred.is_finite());

Re-exports

pub use mamba::StreamingMamba;

pub use mamba_config::MambaConfig;

pub use mamba_config::MambaConfigBuilder;

pub use mamba_preprocessor::MambaPreprocessor;

Modules

mamba

Streaming Mamba model: selective SSM + RLS readout.

mamba_config

Configuration and builder for StreamingMamba.

mamba_preprocessor

SSM-based streaming preprocessor for pipeline composition.