Struct goko::plugins::discrete::baseline::KLDivergenceBaseline [−][src]
pub struct KLDivergenceBaseline { pub num_sequences: usize, pub sequence_len: Vec<usize>, pub stats: Vec<KLDivergenceBaselineStats>, }
Computing the KL div of each node’s prior and posterior is expensive.
Fields
num_sequences: usize
The number of sequences we’re have the stats from
sequence_len: Vec<usize>
The lenght of the sequences we have stats for. All other values are linearly interpolated between these.
stats: Vec<KLDivergenceBaselineStats>
The actual stats objects. These are stored by the moments, but are returned by (mean,var)
Implementations
impl KLDivergenceBaseline
[src]
impl KLDivergenceBaseline
[src]pub fn stats(&self, i: usize) -> KLDivergenceBaselineStats
[src]
Gets the stats object that stores an approximate mean and variance of the samples.
Auto Trait Implementations
impl RefUnwindSafe for KLDivergenceBaseline
impl RefUnwindSafe for KLDivergenceBaseline
impl Send for KLDivergenceBaseline
impl Send for KLDivergenceBaseline
impl Sync for KLDivergenceBaseline
impl Sync for KLDivergenceBaseline
impl Unpin for KLDivergenceBaseline
impl Unpin for KLDivergenceBaseline
impl UnwindSafe for KLDivergenceBaseline
impl UnwindSafe for KLDivergenceBaseline
Blanket Implementations
impl<T, U> Cast<U> for T where
U: FromCast<T>,
impl<T, U> Cast<U> for T where
U: FromCast<T>,
pub fn cast(self) -> U
impl<T> FromCast<T> for T
impl<T> FromCast<T> for T
pub fn from_cast(t: T) -> T
impl<T> Same<T> for T
impl<T> Same<T> for T
type Output = T
Should always be Self
impl<SS, SP> SupersetOf<SS> for SP where
SS: SubsetOf<SP>,
impl<SS, SP> SupersetOf<SS> for SP where
SS: SubsetOf<SP>,
pub fn to_subset(&self) -> Option<SS>
pub fn is_in_subset(&self) -> bool
pub unsafe fn to_subset_unchecked(&self) -> SS
pub fn from_subset(element: &SS) -> SP
impl<V, T> VZip<V> for T where
V: MultiLane<T>,
impl<V, T> VZip<V> for T where
V: MultiLane<T>,