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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>; }