Trait nuts_rs::Math

pub trait Math {
    type Vector: Debug;
    type EigVectors: Debug;
    type EigValues: Debug;
    type LogpErr: Debug + Send + Sync + LogpError + 'static;
    type Err: Debug + Send + Sync + Error + 'static;

    // Required methods
    fn new_array(&self) -> Self::Vector;
    fn new_eig_vectors<'a>(
        &'a mut self,
        vals: impl ExactSizeIterator<Item = &'a [f64]>,
    ) -> Self::EigVectors;
    fn new_eig_values(&mut self, vals: &[f64]) -> Self::EigValues;
    fn logp_array(
        &mut self,
        position: &Self::Vector,
        gradient: &mut Self::Vector,
    ) -> Result<f64, Self::LogpErr>;
    fn logp(
        &mut self,
        position: &[f64],
        gradient: &mut [f64],
    ) -> Result<f64, Self::LogpErr>;
    fn dim(&self) -> usize;
    fn scalar_prods3(
        &mut self,
        positive1: &Self::Vector,
        negative1: &Self::Vector,
        positive2: &Self::Vector,
        x: &Self::Vector,
        y: &Self::Vector,
    ) -> (f64, f64);
    fn scalar_prods2(
        &mut self,
        positive1: &Self::Vector,
        positive2: &Self::Vector,
        x: &Self::Vector,
        y: &Self::Vector,
    ) -> (f64, f64);
    fn read_from_slice(&mut self, dest: &mut Self::Vector, source: &[f64]);
    fn write_to_slice(&mut self, source: &Self::Vector, dest: &mut [f64]);
    fn eigs_as_array(&mut self, source: &Self::EigValues) -> Box<[f64]>;
    fn copy_into(&mut self, array: &Self::Vector, dest: &mut Self::Vector);
    fn axpy_out(
        &mut self,
        x: &Self::Vector,
        y: &Self::Vector,
        a: f64,
        out: &mut Self::Vector,
    );
    fn axpy(&mut self, x: &Self::Vector, y: &mut Self::Vector, a: f64);
    fn fill_array(&mut self, array: &mut Self::Vector, val: f64);
    fn array_all_finite(&mut self, array: &Self::Vector) -> bool;
    fn array_all_finite_and_nonzero(&mut self, array: &Self::Vector) -> bool;
    fn array_mult(
        &mut self,
        array1: &Self::Vector,
        array2: &Self::Vector,
        dest: &mut Self::Vector,
    );
    fn array_mult_eigs(
        &mut self,
        stds: &Self::Vector,
        rhs: &Self::Vector,
        dest: &mut Self::Vector,
        vecs: &Self::EigVectors,
        vals: &Self::EigValues,
    );
    fn array_vector_dot(
        &mut self,
        array1: &Self::Vector,
        array2: &Self::Vector,
    ) -> f64;
    fn array_gaussian<R: Rng + ?Sized>(
        &mut self,
        rng: &mut R,
        dest: &mut Self::Vector,
        stds: &Self::Vector,
    );
    fn array_gaussian_eigs<R: Rng + ?Sized>(
        &mut self,
        rng: &mut R,
        dest: &mut Self::Vector,
        scale: &Self::Vector,
        vals: &Self::EigValues,
        vecs: &Self::EigVectors,
    );
    fn array_update_variance(
        &mut self,
        mean: &mut Self::Vector,
        variance: &mut Self::Vector,
        value: &Self::Vector,
        diff_scale: f64,
    );
    fn array_update_var_inv_std_draw(
        &mut self,
        variance_out: &mut Self::Vector,
        inv_std: &mut Self::Vector,
        draw_var: &Self::Vector,
        scale: f64,
        fill_invalid: Option<f64>,
        clamp: (f64, f64),
    );
    fn array_update_var_inv_std_draw_grad(
        &mut self,
        variance_out: &mut Self::Vector,
        inv_std: &mut Self::Vector,
        draw_var: &Self::Vector,
        grad_var: &Self::Vector,
        fill_invalid: Option<f64>,
        clamp: (f64, f64),
    );
    fn array_update_var_inv_std_grad(
        &mut self,
        variance_out: &mut Self::Vector,
        inv_std: &mut Self::Vector,
        gradient: &Self::Vector,
        fill_invalid: f64,
        clamp: (f64, f64),
    );

    // Provided method
    fn box_array(&mut self, array: &Self::Vector) -> Box<[f64]> { ... }
}

Required Associated Types

type Vector: Debug

type EigVectors: Debug

type EigValues: Debug

type LogpErr: Debug + Send + Sync + LogpError + 'static

type Err: Debug + Send + Sync + Error + 'static

Required Methods

fn new_array(&self) -> Self::Vector

fn new_eig_vectors<'a>( &'a mut self, vals: impl ExactSizeIterator<Item = &'a [f64]>, ) -> Self::EigVectors

fn new_eig_values(&mut self, vals: &[f64]) -> Self::EigValues

fn logp_array( &mut self, position: &Self::Vector, gradient: &mut Self::Vector, ) -> Result<f64, Self::LogpErr>

Compute the unnormalized log probability density of the posterior

This needs to be implemnted by users of the library to define what distribution the users wants to sample from.

Errors during that computation can be recoverable or non-recoverable. If a non-recoverable error occurs during sampling, the sampler will stop and return an error.

fn logp( &mut self, position: &[f64], gradient: &mut [f64], ) -> Result<f64, Self::LogpErr>

fn dim(&self) -> usize

fn scalar_prods3( &mut self, positive1: &Self::Vector, negative1: &Self::Vector, positive2: &Self::Vector, x: &Self::Vector, y: &Self::Vector, ) -> (f64, f64)

fn scalar_prods2( &mut self, positive1: &Self::Vector, positive2: &Self::Vector, x: &Self::Vector, y: &Self::Vector, ) -> (f64, f64)

fn read_from_slice(&mut self, dest: &mut Self::Vector, source: &[f64])

fn write_to_slice(&mut self, source: &Self::Vector, dest: &mut [f64])

fn eigs_as_array(&mut self, source: &Self::EigValues) -> Box<[f64]>

fn copy_into(&mut self, array: &Self::Vector, dest: &mut Self::Vector)

fn axpy_out( &mut self, x: &Self::Vector, y: &Self::Vector, a: f64, out: &mut Self::Vector, )

fn axpy(&mut self, x: &Self::Vector, y: &mut Self::Vector, a: f64)

fn fill_array(&mut self, array: &mut Self::Vector, val: f64)

fn array_all_finite(&mut self, array: &Self::Vector) -> bool

fn array_all_finite_and_nonzero(&mut self, array: &Self::Vector) -> bool

fn array_mult( &mut self, array1: &Self::Vector, array2: &Self::Vector, dest: &mut Self::Vector, )

fn array_mult_eigs( &mut self, stds: &Self::Vector, rhs: &Self::Vector, dest: &mut Self::Vector, vecs: &Self::EigVectors, vals: &Self::EigValues, )

fn array_vector_dot( &mut self, array1: &Self::Vector, array2: &Self::Vector, ) -> f64

fn array_gaussian<R: Rng + ?Sized>( &mut self, rng: &mut R, dest: &mut Self::Vector, stds: &Self::Vector, )

fn array_gaussian_eigs<R: Rng + ?Sized>( &mut self, rng: &mut R, dest: &mut Self::Vector, scale: &Self::Vector, vals: &Self::EigValues, vecs: &Self::EigVectors, )

fn array_update_variance( &mut self, mean: &mut Self::Vector, variance: &mut Self::Vector, value: &Self::Vector, diff_scale: f64, )

fn array_update_var_inv_std_draw( &mut self, variance_out: &mut Self::Vector, inv_std: &mut Self::Vector, draw_var: &Self::Vector, scale: f64, fill_invalid: Option<f64>, clamp: (f64, f64), )

fn array_update_var_inv_std_draw_grad( &mut self, variance_out: &mut Self::Vector, inv_std: &mut Self::Vector, draw_var: &Self::Vector, grad_var: &Self::Vector, fill_invalid: Option<f64>, clamp: (f64, f64), )

fn array_update_var_inv_std_grad( &mut self, variance_out: &mut Self::Vector, inv_std: &mut Self::Vector, gradient: &Self::Vector, fill_invalid: f64, clamp: (f64, f64), )

Provided Methods

fn box_array(&mut self, array: &Self::Vector) -> Box<[f64]>

Object Safety

This trait is not object safe.

Implementors

impl<F: CpuLogpFunc> Math for CpuMath<F>

type Vector = Col<f64>

type EigVectors = Mat<f64>

type EigValues = Col<f64>

type LogpErr = <F as CpuLogpFunc>::LogpError

type Err = CpuMathError