Struct MultinomialNB 

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

MultinomialNB implements the naive Bayes algorithm for multinomially distributed data.

Implementations

impl<TX: Number + Unsigned, TY: Number + Ord + Unsigned, X: Array2<TX>, Y: Array1<TY>> MultinomialNB<TX, TY, X, Y>

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

Fits MultinomialNB with given data

• x - training data of size NxM where N is the number of samples and M is the number of features.
• y - vector with target values (classes) of length N.
• parameters - additional parameters like class priors, alpha for smoothing and binarizing threshold.

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

Estimates the class labels for the provided data.

• x - data of shape NxM where N is number of data points to estimate and M is number of features.

Returns a vector of size N with class estimates.

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

Class labels known to the classifier. Returns a vector of size n_classes.

pub fn class_count(&self) -> &Vec<usize>

Number of training samples observed in each class. Returns a vector of size n_classes.

pub fn feature_log_prob(&self) -> &Vec<Vec<f64>>

Empirical log probability of features given a class, P(x_i|y). Returns a 2d vector of shape (n_classes, n_features)

pub fn n_features(&self) -> usize

Number of features of each sample

pub fn feature_count(&self) -> &Vec<Vec<usize>>

Number of samples encountered for each (class, feature) Returns a 2d vector of shape (n_classes, n_features)

Trait Implementations

impl<TX: Debug + Number + Unsigned, TY: Debug + Number + Ord + Unsigned, X: Debug + Array2<TX>, Y: Debug + Array1<TY>> Debug for MultinomialNB<TX, TY, X, Y>

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

Formats the value using the given formatter.

impl<TX: Number + Unsigned, TY: Number + Ord + Unsigned, X: Array2<TX>, Y: Array1<TY>> Display for MultinomialNB<TX, TY, X, Y>

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

Formats the value using the given formatter.

impl<TX: PartialEq + Number + Unsigned, TY: PartialEq + Number + Ord + Unsigned, X: PartialEq + Array2<TX>, Y: PartialEq + Array1<TY>> PartialEq for MultinomialNB<TX, TY, X, Y>

fn eq(&self, other: &MultinomialNB<TX, TY, X, Y>) -> bool

Equality operator ==.

fn ne(&self, other: &Rhs) -> bool

Inequality operator !=.

impl<TX: Number + Unsigned, TY: Number + Ord + Unsigned, X: Array2<TX>, Y: Array1<TY>> Predictor<X, Y> for MultinomialNB<TX, TY, X, Y>

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

Estimate target values from new data.

impl<TX: PartialEq + Number + Unsigned, TY: PartialEq + Number + Ord + Unsigned, X: PartialEq + Array2<TX>, Y: PartialEq + Array1<TY>> StructuralPartialEq for MultinomialNB<TX, TY, X, Y>

impl<TX: Number + Unsigned, TY: Number + Ord + Unsigned, X: Array2<TX>, Y: Array1<TY>> SupervisedEstimator<X, Y, MultinomialNBParameters> for MultinomialNB<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: MultinomialNBParameters, ) -> Result<Self, Failed>

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