Struct HypothesisState 

#[repr(C, align(16))]
pub struct HypothesisState {
    pub log_e_value: LogEValue,
    pub obs_count: u32,
    pub id: u16,
    pub target: TileVertexId,
    pub threshold: TileVertexId,
    pub hyp_type: u8,
    pub flags: u8,
}

Hypothesis state for tracking

Size: 16 bytes, aligned for efficient cache access

Fields

log_e_value: LogEValue

Current accumulated log e-value (hot field, first for cache)

obs_count: u32

Number of observations processed

id: u16

Hypothesis ID

target: TileVertexId

Target vertex (for vertex-specific hypotheses)

threshold: TileVertexId

Threshold vertex (for cut hypotheses)

hyp_type: u8

Hypothesis type (0 = connectivity, 1 = cut, 2 = flow)

flags: u8

Status flags

Implementations

impl HypothesisState

pub const FLAG_ACTIVE: u8 = 1u8

Hypothesis is active

pub const FLAG_REJECTED: u8 = 2u8

Hypothesis is rejected (e-value crossed threshold)

pub const FLAG_STRONG: u8 = 4u8

Hypothesis evidence is strong (e > 20)

pub const FLAG_VERY_STRONG: u8 = 8u8

Hypothesis evidence is very strong (e > 100)

pub const TYPE_CONNECTIVITY: u8 = 0u8

Type: connectivity hypothesis

pub const TYPE_CUT: u8 = 1u8

Type: cut membership hypothesis

pub const TYPE_FLOW: u8 = 2u8

Type: flow hypothesis

pub const fn new(id: u16, hyp_type: u8) -> Self

Create a new hypothesis

pub const fn connectivity(id: u16, vertex: TileVertexId) -> Self

Create a connectivity hypothesis for a vertex

pub const fn cut_membership( id: u16, vertex: TileVertexId, threshold: TileVertexId, ) -> Self

Create a cut membership hypothesis

pub const fn is_active(&self) -> bool

Check if hypothesis is active

OPTIMIZATION: #[inline(always)] - called in every hypothesis loop

pub const fn is_rejected(&self) -> bool

Check if hypothesis is rejected

pub const fn can_update(&self) -> bool

Check if hypothesis can be updated (active and not rejected)

OPTIMIZATION: Combined check to reduce branch mispredictions

pub fn e_value_approx(&self) -> f32

Get e-value as approximate f32 (2^(log_e/65536))

pub fn update(&mut self, likelihood_ratio: f32) -> bool

Update with a new observation (f32 likelihood ratio) Returns true if the hypothesis is now rejected

OPTIMIZATION: Uses pre-computed threshold constants

pub fn update_with_log_lr(&mut self, log_lr: LogEValue) -> bool

Update with a pre-computed log likelihood ratio (fixed-point) Returns true if the hypothesis is now rejected

OPTIMIZATION: Avoids f32->log conversion when log_lr is pre-computed

pub fn reset(&mut self)

Reset the hypothesis

Trait Implementations

impl Clone for HypothesisState

fn clone(&self) -> HypothesisState

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl Debug for HypothesisState

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl Default for HypothesisState

fn default() -> Self

Returns the “default value” for a type.

impl Copy for HypothesisState