[−][src]Trait ffsvm::Predict

pub trait Predict<V32, V64> where
    Self: Sync, {
    fn predict_value(&self, problem: &mut Problem<V32>) -> Result<(), Error>;

    fn predict_probability(
        &self, 
        problem: &mut Problem<V32>
    ) -> Result<(), Error>;
}

Implemented by DenseSVM and SparseSVM to predict a Problem.

Predicting a label

To predict a label, first make sure the 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.

Required methods

fn predict_value(&self, problem: &mut Problem<V32>) -> Result<(), Error>

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_probability(&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.

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Implementors

impl Predict<SimdVector<Simd<[f32; 8]>>, SimdVector<Simd<[f64; 4]>>> for DenseSVM[src]

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