pub struct ParticleSpec {Show 28 fields
pub num_particles: usize,
pub context_window: usize,
pub unroll_steps: usize,
pub num_cells: usize,
pub cell_dim: usize,
pub num_rules: usize,
pub selector_hidden: usize,
pub rule_hidden: usize,
pub noise_dim: usize,
pub deterministic: bool,
pub enable_noise: bool,
pub noise_scale: f64,
pub noise_anneal_steps: usize,
pub learning_rate_readout: f64,
pub learning_rate_selector: f64,
pub learning_rate_rule: f64,
pub bptt_depth: usize,
pub optimizer_momentum: f64,
pub grad_clip: f64,
pub state_clip: f64,
pub forget_lambda: f64,
pub resample_threshold: f64,
pub mutate_fraction: f64,
pub mutate_scale: f64,
pub mutate_model_params: bool,
pub diagnostics_interval: usize,
pub min_prob: f64,
pub seed: u64,
}Expand description
Configuration for a particle-latent filter ensemble rate backend.
Fields§
§num_particles: usizeNumber of particles in the ensemble.
context_window: usizeContext window length for rolling byte context.
unroll_steps: usizeNumber of latent update unroll steps per byte.
num_cells: usizeNumber of latent cells per particle.
cell_dim: usizeDimensionality of each latent cell.
num_rules: usizeNumber of discrete rules for soft routing.
Hidden dimension for the selector MLP.
Hidden dimension for each rule MLP.
noise_dim: usizeDimension of per-rule noise input (ignored when deterministic).
deterministic: boolWhether to use fully deterministic execution (no RNG).
enable_noise: boolWhether to inject noise into rule inputs (ignored when deterministic).
noise_scale: f64Base scale for deterministic hash-noise injected into rule inputs.
noise_anneal_steps: usizeNumber of steps over which injected noise linearly anneals to zero.
learning_rate_readout: f64Learning rate for readout layer SGD.
learning_rate_selector: f64Learning rate for selector MLP SGD.
learning_rate_rule: f64Learning rate for rule MLP SGD.
bptt_depth: usizeTruncated backpropagation-through-time depth (number of recent steps).
optimizer_momentum: f64Momentum coefficient for selector/rule online updates (in [0, 1)).
grad_clip: f64Gradient clipping threshold (max abs value per element).
state_clip: f64Latent cell state clipping threshold (max abs value per element).
forget_lambda: f64Forgetting factor for particle log-weights (0 = no forgetting).
resample_threshold: f64Effective sample size ratio threshold for resampling (in (0, 1]).
mutate_fraction: f64Fraction of particles to mutate after resampling (in [0, 1]).
mutate_scale: f64Scale of hash-noise perturbation applied during mutation.
mutate_model_params: boolWhether mutation also perturbs model parameters (state is always mutated).
diagnostics_interval: usizeDiagnostics print interval in steps (0 disables particle diagnostics logs).
min_prob: f64Minimum probability floor for numerical stability.
seed: u64Master seed for deterministic initialization and mutation.
Implementations§
Trait Implementations§
Source§impl Clone for ParticleSpec
impl Clone for ParticleSpec
Source§fn clone(&self) -> ParticleSpec
fn clone(&self) -> ParticleSpec
Returns a duplicate of the value. Read more
1.0.0 · Source§fn clone_from(&mut self, source: &Self)
fn clone_from(&mut self, source: &Self)
Performs copy-assignment from
source. Read moreSource§impl Debug for ParticleSpec
impl Debug for ParticleSpec
Auto Trait Implementations§
impl Freeze for ParticleSpec
impl RefUnwindSafe for ParticleSpec
impl Send for ParticleSpec
impl Sync for ParticleSpec
impl Unpin for ParticleSpec
impl UnwindSafe for ParticleSpec
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