Struct MultipleLinearRegression 

pub struct MultipleLinearRegression<T = f64>
where
    T: Float + Debug + Default + Serialize,
{
    pub coefficients: Vec<T>,
    pub r_squared: T,
    pub adjusted_r_squared: T,
    pub standard_error: T,
    pub n: usize,
    pub p: usize,
}

Multiple linear regression model that fits a hyperplane to multivariate data points.

Fields

coefficients: Vec<T>

Regression coefficients, including intercept as the first element

r_squared: T

Coefficient of determination (R²) - goodness of fit

adjusted_r_squared: T

Adjusted R² which accounts for the number of predictors

standard_error: T

Standard error of the estimate

n: usize

Number of data points used for regression

p: usize

Number of predictor variables (excluding intercept)

Implementations

impl<T> MultipleLinearRegression<T>

where T: Float + Debug + Default + NumCast + Serialize + for<'de> Deserialize<'de>,

pub fn new() -> Self

Create a new multiple linear regression model without fitting any data

pub fn fit<U, V>( &mut self, x_values: &[Vec<U>], y_values: &[V], ) -> Result<(), String>

where U: NumCast + Copy, V: NumCast + Copy,

Fit a multiple linear regression model to the provided data

Arguments

• x_values - 2D array where each row is an observation and each column is a predictor
• y_values - Dependent variable values (observations)

Returns

• Result<(), String> - Ok if successful, Err with message if the inputs are invalid

pub fn predict<U>(&self, x: &[U]) -> T

where U: NumCast + Copy,

Predict y value for a given set of x values using the fitted model

Arguments

• x - Vector of x values for prediction

Returns

• The predicted y value

pub fn predict_many<U>(&self, x_values: &[Vec<U>]) -> Vec<T>

where U: NumCast + Copy,

Calculate predictions for multiple observations

Arguments

• x_values - 2D array of feature values for prediction

Returns

• Vector of predicted y values

pub fn save<P: AsRef<Path>>(&self, path: P) -> Result<(), Error>

Save the model to a file

Arguments

• path - Path where to save the model

Returns

• Result<(), io::Error> - Ok if successful, Err with IO error if saving fails

pub fn save_binary<P: AsRef<Path>>(&self, path: P) -> Result<(), Error>

Save the model in binary format

Arguments

• path - Path where to save the model

Returns

• Result<(), io::Error> - Ok if successful, Err with IO error if saving fails

pub fn load<P: AsRef<Path>>(path: P) -> Result<Self, Error>

Load a model from a file

Arguments

• path - Path to the saved model file

Returns

• Result<Self, io::Error> - Loaded model or IO error

pub fn load_binary<P: AsRef<Path>>(path: P) -> Result<Self, Error>

Load a model from a binary file

Arguments

• path - Path to the saved model file

Returns

• Result<Self, io::Error> - Loaded model or IO error

pub fn to_json(&self) -> Result<String, String>

Save the model to a string in JSON format

Returns

• Result<String, String> - JSON string representation or error message

pub fn from_json(json: &str) -> Result<Self, String>

Load a model from a JSON string

Arguments

• json - JSON string containing the model data

Returns

• Result<Self, String> - Loaded model or error message

Trait Implementations

impl<T> Clone for MultipleLinearRegression<T>

where T: Float + Debug + Default + Serialize + Clone,

fn clone(&self) -> MultipleLinearRegression<T>

Returns a duplicate of the value.

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

Performs copy-assignment from source.

impl<T> Debug for MultipleLinearRegression<T>

where T: Float + Debug + Default + Serialize + Debug,

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

Formats the value using the given formatter.

impl<T> Default for MultipleLinearRegression<T>

where T: Float + Debug + Default + NumCast + Serialize + for<'de> Deserialize<'de>,

fn default() -> Self

Returns the “default value” for a type.

impl<'de, T> Deserialize<'de> for MultipleLinearRegression<T>

where T: Float + Debug + Default + Serialize + Deserialize<'de>,

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

where __D: Deserializer<'de>,

Deserialize this value from the given Serde deserializer.

impl<T> Serialize for MultipleLinearRegression<T>

where T: Float + Debug + Default + Serialize + Serialize,

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

where __S: Serializer,

Serialize this value into the given Serde serializer.