70 lines
2.6 KiB
Rust
70 lines
2.6 KiB
Rust
use crate::error::Failed;
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use crate::linalg::BaseVector;
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use crate::linalg::Matrix;
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use crate::math::num::RealNumber;
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use std::marker::PhantomData;
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/// Distribution used in the Naive Bayes classifier.
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pub(crate) trait NBDistribution<T: RealNumber, M: Matrix<T>> {
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/// Prior of class at the given index.
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fn prior(&self, class_index: usize) -> T;
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/// Conditional probability of sample j given class in the specified index.
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fn conditional_probability(&self, class_index: usize, j: &M::RowVector) -> T;
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/// Possible classes of the distribution.
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fn classes(&self) -> &Vec<T>;
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}
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/// Base struct for the Naive Bayes classifier.
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pub(crate) struct BaseNaiveBayes<T: RealNumber, M: Matrix<T>, D: NBDistribution<T, M>> {
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distribution: D,
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_phantom_t: PhantomData<T>,
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_phantom_m: PhantomData<M>,
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}
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impl<T: RealNumber, M: Matrix<T>, D: NBDistribution<T, M>> BaseNaiveBayes<T, M, D> {
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/// Fits NB classifier to a given NBdistribution.
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/// * `distribution` - NBDistribution of the training data
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pub fn fit(distribution: D) -> Result<Self, Failed> {
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Ok(Self {
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distribution,
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_phantom_t: PhantomData,
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_phantom_m: PhantomData,
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})
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}
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/// Estimates the class labels for the provided data.
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/// * `x` - data of shape NxM where N is number of data points to estimate and M is number of features.
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/// Returns a vector of size N with class estimates.
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pub fn predict(&self, x: &M) -> Result<M::RowVector, Failed> {
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let y_classes = self.distribution.classes();
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let (rows, _) = x.shape();
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let predictions = (0..rows)
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.map(|row_index| {
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let row = x.get_row(row_index);
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let (prediction, _probability) = y_classes
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.iter()
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.enumerate()
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.map(|(class_index, class)| {
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(
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class,
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self.distribution.conditional_probability(class_index, &row)
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* self.distribution.prior(class_index),
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)
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})
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.max_by(|(_, p1), (_, p2)| p1.partial_cmp(p2).unwrap())
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.unwrap();
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*prediction
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})
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.collect::<Vec<T>>();
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let mut y_hat = M::RowVector::zeros(rows);
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for (i, prediction) in predictions.iter().enumerate().take(rows) {
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y_hat.set(i, *prediction);
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}
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Ok(y_hat)
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}
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}
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mod categorical;
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pub use categorical::{CategoricalNB, CategoricalNBParameters};
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