pub fn accuracy<T: PartialEq>(ground_truth: &[T], predicted: &[T]) -> f64 {
    let (correct, all) = ground_truth.iter()
        .zip(predicted.iter())
        .fold((0, 0), |(c, a), (gt, pr)| (c + (gt == pr) as i32, a + 1));
    (correct as f64) / (all as f64)
}
/// The weighted average accuracy of the prediction vector across all classes. Classification
/// problems only.
The weighted average accuracy of the prediction vector across all classes. Classification
problems only.
The weighted average accuracy of the prediction vector across all classes . Classification problems only .
pub fn accuracy<T: PartialEq>(ground_truth: &[T], predicted: &[T]) -> f64 {
    let (correct, all) = ground_truth.iter()
        .zip(predicted.iter())
        .fold((0, 0), |(c, a), (gt, pr)| (c + (gt == pr) as i32, a + 1));
    (correct as f64) / (all as f64)
}
pub fn accuracy < T : PartialEq > ( ground_truth : & [ T ] , predicted : & [ T ] ) -> f64 { let ( correct , all ) = ground_truth . iter ( ) . zip ( predicted . iter ( ) ) . fold ( ( 0 , 0 ) , | ( c , a ) , ( gt , pr ) | ( c + ( gt == pr ) as i32 , a + 1 ) ) ; ( correct as f64 ) / ( all as f64 ) }
The weighted average accuracy of the prediction vector across all classes.
The weighted average accuracy of the prediction vector across all classes .
ground_truth
&[T]
predicted
&[T]
ground_truth
&[T]
predicted
&[T]