Struct bio::stats::hmm::discrete_emission::Model [−][src]
pub struct Model { /* fields omitted */ }
Implementation of a hmm::Model
with emission values from discrete distributions.
Log-scale probabilities are used for numeric stability.
In Rabiner’s tutorial, a discrete emission value HMM has N
states and M
output symbols.
The state transition matrix with dimensions NxN
is A
, the observation probability
distribution is the matrix B
with dimensions NxM
and the initial state distribution pi
has length N
.
Implementations
impl Model
[src]
impl Model
[src]pub fn new(
transition: Array2<LogProb>,
observation: Array2<LogProb>,
initial: Array1<LogProb>
) -> Result<Self>
[src]
transition: Array2<LogProb>,
observation: Array2<LogProb>,
initial: Array1<LogProb>
) -> Result<Self>
Construct new Hidden MarkovModel with the given transition, observation, and initial state matrices and vectors already in log-probability space.
pub fn with_prob(
transition: &Array2<Prob>,
observation: &Array2<Prob>,
initial: &Array1<Prob>
) -> Result<Self>
[src]
transition: &Array2<Prob>,
observation: &Array2<Prob>,
initial: &Array1<Prob>
) -> Result<Self>
Construct new Hidden MarkovModel with the given transition, observation, and initial
state matrices and vectors already as Prob
values.
pub fn with_float(
transition: &Array2<f64>,
observation: &Array2<f64>,
initial: &Array1<f64>
) -> Result<Self>
[src]
transition: &Array2<f64>,
observation: &Array2<f64>,
initial: &Array1<f64>
) -> Result<Self>
Construct new Hidden MarkovModel with the given transition, observation, and initial
state matrices and vectors with probabilities as f64
values.
Trait Implementations
impl Model<usize> for Model
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impl Model<usize> for Model
[src]fn num_states(&self) -> usize
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fn states(&self) -> StateIterⓘ
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fn transitions(&self) -> StateTransitionIterⓘNotable traits for StateTransitionIter
impl Iterator for StateTransitionIter type Item = StateTransition;
[src]
Notable traits for StateTransitionIter
impl Iterator for StateTransitionIter type Item = StateTransition;
fn transition_prob(&self, from: State, to: State) -> LogProb
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fn initial_prob(&self, state: State) -> LogProb
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fn observation_prob(&self, state: State, observation: &usize) -> LogProb
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fn transition_prob_idx(&self, from: State, to: State, _to_idx: usize) -> LogProb
[src]
impl StructuralPartialEq for Model
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impl StructuralPartialEq for Model
[src]Auto Trait Implementations
impl RefUnwindSafe for Model
impl RefUnwindSafe for Model
impl UnwindSafe for Model
impl UnwindSafe for Model
Blanket Implementations
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>,