Struct LogisticRegression

pub struct LogisticRegression<TX: Number + FloatNumber + RealNumber, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>> { /* private fields */ }

Logistic Regression

Implementations

impl<TX: Number + FloatNumber + RealNumber, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>> LogisticRegression<TX, TY, X, Y>

pub fn fit( x: &X, y: &Y, parameters: LogisticRegressionParameters<TX>, ) -> Result<LogisticRegression<TX, TY, X, Y>, Failed>

Fits Logistic Regression to your data.

• x - NxM matrix with N observations and M features in each observation.
• y - target class values
• parameters - other parameters, use Default::default() to set parameters to default values.

pub fn predict(&self, x: &X) -> Result<Y, Failed>

Predict class labels for samples in x.

• x - KxM data where K is number of observations and M is number of features.

pub fn coefficients(&self) -> &X

Get estimates regression coefficients, this create a sharable reference

pub fn intercept(&self) -> &X

Get estimate of intercept, this create a sharable reference

pub fn classes(&self) -> &Vec<TY>

Get classes, this create a sharable reference

Trait Implementations

impl<TX: Debug + Number + FloatNumber + RealNumber, TY: Debug + Number + Ord, X: Debug + Array2<TX>, Y: Debug + Array1<TY>> Debug for LogisticRegression<TX, TY, X, Y>

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

Formats the value using the given formatter.

impl<TX: Number + FloatNumber + RealNumber, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>> PartialEq for LogisticRegression<TX, TY, X, Y>

fn eq(&self, other: &Self) -> 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<TX: Number + FloatNumber + RealNumber, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>> Predictor<X, Y> for LogisticRegression<TX, TY, X, Y>

fn predict(&self, x: &X) -> Result<Y, Failed>

Estimate target values from new data.

impl<TX: Number + FloatNumber + RealNumber, TY: Number + Ord, X: Array2<TX>, Y: Array1<TY>> SupervisedEstimator<X, Y, LogisticRegressionParameters<TX>> for LogisticRegression<TX, TY, X, Y>

fn new() -> Self

Empty constructor, instantiate an empty estimator. Object is dropped as soon as fit() is called. used to pass around the correct fit() implementation. by calling ::fit(). mostly used to be used with model_selection::cross_validate(...)

fn fit( x: &X, y: &Y, parameters: LogisticRegressionParameters<TX>, ) -> Result<Self, Failed>

Fit a model to a training dataset, estimate model’s parameters.