Struct fetish_lib::normal_inverse_wishart::NormalInverseWishart

pub struct NormalInverseWishart {
    pub mean: Array2<f32>,
    pub precision_u: Array2<f32>,
    pub precision: Array2<f32>,
    pub sigma: Array2<f32>,
    pub big_v: Array2<f32>,
    pub little_v: f32,
    pub t: usize,
    pub s: usize,
}

Matrix-normal-inverse-wishart distribution representation for bayesian inference

Fields

mean: Array2<f32>

precision_u: Array2<f32>

This is always maintained to equal mean.dot(&precision)

precision: Array2<f32>

sigma: Array2<f32>

This is always maintained to equal pseudoinverse_h(&precision)

big_v: Array2<f32>

little_v: f32

t: usize

The output dimensionality

s: usize

The input dimensionality

Implementations

impl NormalInverseWishart

pub fn recompute_derived(&mut self)

Manually re-computes self.sigma and self.precision_u to align with their definitions based on the other fields in self. This method takes cubic time, so it’s not recommended to call this unless you observe issues with these fields accumulating numerical errors away from what they should be.

pub fn get_total_dims(&self) -> usize

Gets the total dimensionality of self.mean.

pub fn sample(&self, rng: &mut ThreadRng) -> Array2<f32>

Draws a sample from the represented MNIW distribution

pub fn sample_as_vec(&self, rng: &mut ThreadRng) -> Array1<f32>

The same as Self::sample, but the result is flattened.

pub fn get_mean_as_vec(&self) -> ArrayView1<'_, f32>

Returns the mean of the represented MNIW distribution, but flattened to be a vector.

pub fn get_mean(&self) -> Array2<f32>

Returns the mean of the represented MNIW distribution as a linear map.

pub fn get_schmear(&self) -> FuncSchmear

Gets the FuncSchmear over linear mappings given by this MNIW distribution

pub fn get_inverse_schmear(&self) -> FuncInverseSchmear

Gets the FuncInverseSchmear over linear mappings given by this MNIW distribution.

pub fn get_precision(&self) -> FuncScatterTensor

Gets the precision (inverse covariance) FuncScatterTensor of this MNIW distribution.

pub fn get_covariance(&self) -> FuncScatterTensor

Gets the covariance FuncScatterTensor of this MNIW distribution.

impl NormalInverseWishart

pub fn from_in_out_dims(
    prior_specification: &dyn PriorSpecification,
    feat_dims: usize,
    out_dims: usize
) -> NormalInverseWishart

Constructs a NormalInverseWishart distribution from the given PriorSpecification, the given feature dimensions, and the given output dimensions.

pub fn from_space_info(
    prior_specification: &dyn PriorSpecification,
    func_space_info: &FunctionSpaceInfo<'_>
) -> NormalInverseWishart

Constructs a NormalInverseWishart distribution from the given PriorSpecification and the given FunctionSpaceInfo.

pub fn new(
    mean: Array2<f32>,
    precision: Array2<f32>,
    big_v: Array2<f32>,
    little_v: f32
) -> NormalInverseWishart

Constructs a NormalInverseWishart distribution with the given mean, input precision, total output error covariance, and (pseudo-)observation count.

impl NormalInverseWishart

pub fn update_datapoints(&mut self, data_points: &DataPoints)

Updates this NormalInverseWishart distribution to reflect new data-points for linear regression.

Trait Implementations

impl AddAssign<&'_ DataPoint> for NormalInverseWishart

fn add_assign(&mut self, other: &DataPoint)

Updates this NormalInverseWishart distribution to incorporate regression information from the given DataPoint.

impl AddAssign<&'_ InputToSchmearedOutput> for NormalInverseWishart

fn add_assign(&mut self, update: &InputToSchmearedOutput)

Updates this NormalInverseWishart distribution to incorporate regression information from the given InputToSchmearedOutput.

impl AddAssign<&'_ Multiple<InputToSchmearedOutput>> for NormalInverseWishart

fn add_assign(&mut self, update: &Multiple<InputToSchmearedOutput>)

Performs the += operation.

impl AddAssign<&'_ Multiple<NormalInverseWishart>> for NormalInverseWishart

fn add_assign(&mut self, other: &Multiple<NormalInverseWishart>)

Performs the += operation.

impl AddAssign<&'_ NormalInverseWishart> for NormalInverseWishart

fn add_assign(&mut self, other: &NormalInverseWishart)

Updates this NormalInverseWishart distribution to the MNIW-sum of it and the passed in NormalInverseWishart distribution.

impl<'a> AddAssign<&'_ NormalInverseWishart> for Model<'a>

fn add_assign(&mut self, other: &NormalInverseWishart)

Updates this Model to reflect new prior information from the given NormalInverseWishart, which is assumed to come from another Model with the same input and return spaces.

impl BitXorAssign<()> for NormalInverseWishart

fn bitxor_assign(&mut self, _rhs: ())

Inverts this NormalInverseWishart distribution in place with respect to the addition operation given by the MNIW sum. This always will satisfy let mut other = self.clone(); other ^= (); other += self; self == zero_normal_inverse_wishart(self.t, self.s)

impl Clone for NormalInverseWishart

fn clone(&self) -> NormalInverseWishart

Returns a copy of the value.

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

Performs copy-assignment from source.

impl SubAssign<&'_ DataPoint> for NormalInverseWishart

fn sub_assign(&mut self, other: &DataPoint)

Updates this NormalInverseWishart distribution to remove regression information from the given DataPoint.

impl SubAssign<&'_ InputToSchmearedOutput> for NormalInverseWishart

fn sub_assign(&mut self, update: &InputToSchmearedOutput)

Updates this NormalInverseWishart distribution to remove regression information from the given InputToSchmearedOutput.

impl SubAssign<&'_ Multiple<InputToSchmearedOutput>> for NormalInverseWishart

fn sub_assign(&mut self, update: &Multiple<InputToSchmearedOutput>)

Performs the -= operation.

impl SubAssign<&'_ Multiple<NormalInverseWishart>> for NormalInverseWishart

fn sub_assign(&mut self, other: &Multiple<NormalInverseWishart>)

Performs the -= operation.

impl SubAssign<&'_ NormalInverseWishart> for NormalInverseWishart

fn sub_assign(&mut self, other: &NormalInverseWishart)

Updates this NormalInverseWishart distribution to the MNIW-sum of it and the additive inverse of the passed in NormalInverseWishart distribution.

impl<'a> SubAssign<&'_ NormalInverseWishart> for Model<'a>

fn sub_assign(&mut self, other: &NormalInverseWishart)

Undoes the action of the corresponding add_assign method.