Struct SumBlock 

pub struct SumBlock<Terms> {
    pub terms: Terms,
}

Sum of several predictor blocks sharing the same observations.

The local beta slice is split between terms in tuple order. This keeps nonlinear or sparse user-defined terms composable without dynamic dispatch.

Fields

terms: Terms

Predictor terms summed into one parameter predictor.

Implementations

impl<Terms> SumBlock<Terms>

pub fn new(terms: Terms) -> Self

Creates a summed predictor from tuple terms.

Trait Implementations

impl<Terms: Clone> Clone for SumBlock<Terms>

fn clone(&self) -> SumBlock<Terms>

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<Terms: Copy> Copy for SumBlock<Terms>

impl<Terms: Debug> Debug for SumBlock<Terms>

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

Formats the value using the given formatter.

impl<Terms: Default> Default for SumBlock<Terms>

fn default() -> SumBlock<Terms>

Returns the “default value” for a type.

impl<Terms: Eq> Eq for SumBlock<Terms>

impl<Terms: PartialEq> PartialEq for SumBlock<Terms>

fn eq(&self, other: &SumBlock<Terms>) -> bool

Tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl<T1> PredictorBlock for SumBlock<(T1,)>

where T1: PredictorBlock,

fn nrows(&self) -> usize

Number of observations.

fn nparams(&self) -> usize

Number of local coefficients consumed by this block.

fn eta_row(&self, row: usize, beta: &[f64]) -> f64

Predictor contribution for one row.

fn add_gradient(&self, scores: &[f64], beta: &[f64], grad: &mut [f64])

Adds the gradient contribution implied by scores into grad.

fn validate(&self) -> Result<(), ModelError>

Validates internal block consistency.

impl<T1, T2> PredictorBlock for SumBlock<(T1, T2)>

where T1: PredictorBlock, T2: PredictorBlock,

fn nrows(&self) -> usize

Number of observations.

fn nparams(&self) -> usize

Number of local coefficients consumed by this block.

fn eta_row(&self, row: usize, beta: &[f64]) -> f64

Predictor contribution for one row.

fn add_gradient(&self, scores: &[f64], beta: &[f64], grad: &mut [f64])

Adds the gradient contribution implied by scores into grad.

fn validate(&self) -> Result<(), ModelError>

Validates internal block consistency.

impl<T1, T2, T3> PredictorBlock for SumBlock<(T1, T2, T3)>

where T1: PredictorBlock, T2: PredictorBlock, T3: PredictorBlock,

fn nrows(&self) -> usize

Number of observations.

fn nparams(&self) -> usize

Number of local coefficients consumed by this block.

fn eta_row(&self, row: usize, beta: &[f64]) -> f64

Predictor contribution for one row.

fn add_gradient(&self, scores: &[f64], beta: &[f64], grad: &mut [f64])

Adds the gradient contribution implied by scores into grad.

fn validate(&self) -> Result<(), ModelError>

Validates internal block consistency.

impl<T1, T2, T3, T4> PredictorBlock for SumBlock<(T1, T2, T3, T4)>

where T1: PredictorBlock, T2: PredictorBlock, T3: PredictorBlock, T4: PredictorBlock,

fn nrows(&self) -> usize

Number of observations.

fn nparams(&self) -> usize

Number of local coefficients consumed by this block.

fn eta_row(&self, row: usize, beta: &[f64]) -> f64

Predictor contribution for one row.

fn add_gradient(&self, scores: &[f64], beta: &[f64], grad: &mut [f64])

Adds the gradient contribution implied by scores into grad.

fn validate(&self) -> Result<(), ModelError>

Validates internal block consistency.

impl<T1, T2, T3, T4, T5> PredictorBlock for SumBlock<(T1, T2, T3, T4, T5)>

where T1: PredictorBlock, T2: PredictorBlock, T3: PredictorBlock, T4: PredictorBlock, T5: PredictorBlock,

fn nrows(&self) -> usize

Number of observations.

fn nparams(&self) -> usize

Number of local coefficients consumed by this block.

fn eta_row(&self, row: usize, beta: &[f64]) -> f64

Predictor contribution for one row.

fn add_gradient(&self, scores: &[f64], beta: &[f64], grad: &mut [f64])

Adds the gradient contribution implied by scores into grad.

fn validate(&self) -> Result<(), ModelError>

Validates internal block consistency.

impl<T1, T2, T3, T4, T5, T6> PredictorBlock for SumBlock<(T1, T2, T3, T4, T5, T6)>

where T1: PredictorBlock, T2: PredictorBlock, T3: PredictorBlock, T4: PredictorBlock, T5: PredictorBlock, T6: PredictorBlock,

fn nrows(&self) -> usize

Number of observations.

fn nparams(&self) -> usize

Number of local coefficients consumed by this block.

fn eta_row(&self, row: usize, beta: &[f64]) -> f64

Predictor contribution for one row.

fn add_gradient(&self, scores: &[f64], beta: &[f64], grad: &mut [f64])

Adds the gradient contribution implied by scores into grad.

fn validate(&self) -> Result<(), ModelError>

Validates internal block consistency.

impl<Terms> StructuralPartialEq for SumBlock<Terms>