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use crate::{errors::Error, svm::problem::Problem};
/// Implemented by [`DenseSVM`](crate::DenseSVM) and [`SparseSVM`](crate::SparseSVM) to predict a [`Problem`].
///
/// # Predicting a label
///
/// To predict a label, first make sure the [`Problem`](crate::Problem) has all features set. Then calling
/// ```
/// use ffsvm::*;
///
/// fn set_features(svm: &DenseSVM, problem: &mut DenseProblem) {
/// // Predicts the value.
/// svm.predict_value(problem);
/// }
/// ```
/// will update the [`Problem::solution`] to correspond to the class label with the highest likelihood.
///
/// # Predicting a label and obtaining probability estimates.
///
/// If the libSVM model was trained with probability estimates FFSVM can not only predict the
/// label, but it can also give information about the likelihood distribution of all classes.
/// This can be helpful if you want to consider alternatives.
///
/// Probabilities are estimated like this:
///
/// ```
/// use ffsvm::*;
///
/// fn set_features(svm: &DenseSVM, problem: &mut DenseProblem) {
/// // Predicts the value.
/// svm.predict_probability(problem);
/// }
/// ```
///
/// Predicting probabilities automatically predicts the best label. In addition [`Problem::probabilities`]
/// will be updated accordingly. The class labels for each probablity entry can be obtained
/// by the SVM's `class_label_for_index` and `class_index_for_label` methods.
pub trait Predict<V32, V64>
where
Self: Sync,
{
/// Predict a single value for a [`Problem`].
///
/// The problem needs to have all features set. Once this method returns,
/// the [`Problem::solution`] will be set.
fn predict_value(&self, problem: &mut Problem<V32>) -> Result<(), Error>;
/// Predict a probability value for a problem.
///
/// The problem needs to have all features set. Once this method returns,
/// both [`Problem::solution`] will be set, and all [`Problem::probabilities`] will
/// be available accordingly.
fn predict_probability(&self, problem: &mut Problem<V32>) -> Result<(), Error>;
}