Struct BayesianProbitRegressor 

pub struct BayesianProbitRegressor {
    pub number_of_features: u32,
    pub bias: Option<Gaussian>,
    pub features: Vec<FeatureWeight>,
    pub regression_input_feature_name: String,
    pub optimism_input_feature_name: String,
    pub sampling_scale_input_feature_name: String,
    pub sampling_truncation_input_feature_name: String,
    pub mean_output_feature_name: String,
    pub variance_output_feature_name: String,
    pub pessimistic_probability_output_feature_name: String,
    pub sampled_probability_output_feature_name: String,
}

Fields

number_of_features: u32

bias: Option<Gaussian>

bias term

features: Vec<FeatureWeight>

Set of features with associated weights

feature weights

regression_input_feature_name: String

Set this name to be the same as input feature of type multi-array (1D) in the model description you want to use as the regression input

optimism_input_feature_name: String

Set this name to be the same as optional input feature of type double in the model description you want to use as the optimism input

sampling_scale_input_feature_name: String

Set this name to be the same as optional input feature of type double in the model description you want to use as the samplingScale input

sampling_truncation_input_feature_name: String

Set this name to be the same as optional input feature of type double in the model description you want to use as the samplingBounds input

mean_output_feature_name: String

name of ‘mean’ output feature

variance_output_feature_name: String

name of ‘variance’ output feature

pessimistic_probability_output_feature_name: String

name of ‘pessimistic’ output feature

sampled_probability_output_feature_name: String

name of ‘sampled’ output feature: samples from the scaled posterior probability distribuiton

Trait Implementations

impl Clone for BayesianProbitRegressor

fn clone(&self) -> BayesianProbitRegressor

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for BayesianProbitRegressor

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

Formats the value using the given formatter.

impl Default for BayesianProbitRegressor

fn default() -> Self

Returns the “default value” for a type.

impl Message for BayesianProbitRegressor

fn encoded_len(&self) -> usize

Returns the encoded length of the message without a length delimiter.

fn clear(&mut self)

Clears the message, resetting all fields to their default.

fn encode(&self, buf: &mut impl BufMut) -> Result<(), EncodeError>

where Self: Sized,

Encodes the message to a buffer.

fn encode_to_vec(&self) -> Vec<u8>

where Self: Sized,

Encodes the message to a newly allocated buffer.

fn encode_length_delimited( &self, buf: &mut impl BufMut, ) -> Result<(), EncodeError>

where Self: Sized,

Encodes the message with a length-delimiter to a buffer.

fn encode_length_delimited_to_vec(&self) -> Vec<u8>

where Self: Sized,

Encodes the message with a length-delimiter to a newly allocated buffer.

fn decode(buf: impl Buf) -> Result<Self, DecodeError>

where Self: Default,

Decodes an instance of the message from a buffer.

fn decode_length_delimited(buf: impl Buf) -> Result<Self, DecodeError>

where Self: Default,

Decodes a length-delimited instance of the message from the buffer.

fn merge(&mut self, buf: impl Buf) -> Result<(), DecodeError>

where Self: Sized,

Decodes an instance of the message from a buffer, and merges it into self.

fn merge_length_delimited(&mut self, buf: impl Buf) -> Result<(), DecodeError>

where Self: Sized,

Decodes a length-delimited instance of the message from buffer, and merges it into self.

impl PartialEq for BayesianProbitRegressor

fn eq(&self, other: &BayesianProbitRegressor) -> 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 StructuralPartialEq for BayesianProbitRegressor