Enum MLModelType 

pub enum MLModelType {
    LinearRegression,
    PolynomialRegression {
        degree: usize,
    },
    RandomForest {
        n_estimators: usize,
    },
    GradientBoosting {
        n_estimators: usize,
        learning_rate: f64,
    },
    NeuralNetwork {
        hidden_layers: Vec<usize>,
    },
    SupportVectorMachine {
        kernel: String,
    },
    GaussianProcess,
}

ML model types for benchmarking

Variants

LinearRegression

PolynomialRegression

Fields

degree: usize

RandomForest

Fields

n_estimators: usize

GradientBoosting

Fields

n_estimators: usize

learning_rate: f64

NeuralNetwork

Fields

hidden_layers: Vec<usize>

SupportVectorMachine

Fields

kernel: String

GaussianProcess

Trait Implementations

impl Clone for MLModelType

fn clone(&self) -> MLModelType

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl Debug for MLModelType

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

Formats the value using the given formatter.

impl<'de> Deserialize<'de> for MLModelType

fn deserialize<__D>(__deserializer: __D) -> Result<Self, __D::Error>

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl PartialEq for MLModelType

fn eq(&self, other: &MLModelType) -> 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 Serialize for MLModelType

fn serialize<__S>(&self, __serializer: __S) -> Result<__S::Ok, __S::Error>

where __S: Serializer,

Serialize this value into the given Serde serializer.

impl StructuralPartialEq for MLModelType