pub fn moving_average<T>(
    inp: &ndarray::NdArray<T>,
    window: usize,
) -> Result<ndarray::NdArray<T>, NdArrayError>
where
    T: Copy + Default + std::iter::Sum + std::ops::Div<f32, Output = T> + std::fmt::Debug,
{
    use crate::prelude::*;
    if window == 0 {
        return Err(NdArrayError::BadInput(
            "window must be non-negative".to_string(),
        ));
    }
    let winf = f64::from(window as u32) as f32;
    let arr = match inp.shape() {
        Shape::Scalar(_) => return Err(NdArrayError::UnsupportedShape(inp.shape().clone())),
        Shape::Vector(_) => {
            let data = inp
                .as_slice()
                .windows(window)
                .map(|window| {
                    let sum: T = window.iter().cloned().sum();
                    sum / winf
                })
                .fold(prelude::Data::default(), |mut res, mean| {
                    res.push(mean);
                    res
                });

            ndarray::NdArray::new_vector(data)
        }
        _ => {
            let n: u32 = inp.shape().last();
            let win_sum = inp
                .as_slice()
                .windows(window * n as usize)
                .step_by(n as usize)
                .fold(Data::default(), |mut res, x| {
                    for i in 0..n {
                        let s: T = x.iter().skip(i as usize).step_by(n as usize).cloned().sum();
                        let mean = s / winf;
                        res.push(mean);
                    }
                    res
                });

            ndarray::NdArray::new_with_values([(win_sum.len() / n as usize) as u32, n], win_sum)
                .unwrap()
        }
    };

    Ok(arr)
}
/// Calculate moving averages of the innermost dimension.
///
/// For matrices and tensors return a matrix where each columns will contain the moving averages of
/// their corresponding columns.
///
/// ```
/// use facet_core::prelude::*;
///
/// let values = NdArray::new_vector( vec![9.0, 8.0,9.0,12.0,9.,12.,11.,7.,13.,9.,11.,10.] );
/// let ma = facet_core::moving_average(&values, 3).unwrap();
///
/// assert_eq!(ma.as_slice(),
/// &[  8.666666666666666, 9.666666666666666, 10.0, 11.0,
///     10.666666666666666, 10.0, 10.333333333333334,
///     9.666666666666666, 11.0, 10.0]);
/// ```
Calculate moving averages of the innermost dimension.
For matrices and tensors return a matrix where each columns will contain the moving averages of
their corresponding columns.






Calculate moving averages of the innermost dimension . For matrices and tensors return a matrix where each columns will contain the moving averages of their corresponding columns .
pub fn moving_average<T>(
    inp: &ndarray::NdArray<T>,
    window: usize,
) -> Result<ndarray::NdArray<T>, NdArrayError>
where
    T: Copy + Default + std::iter::Sum + std::ops::Div<f32, Output = T> + std::fmt::Debug,
{
    use crate::prelude::*;
    if window == 0 {
        return Err(NdArrayError::BadInput(
            "window must be non-negative".to_string(),
        ));
    }
    let winf = f64::from(window as u32) as f32;
    let arr = match inp.shape() {
        Shape::Scalar(_) => return Err(NdArrayError::UnsupportedShape(inp.shape().clone())),
        Shape::Vector(_) => {
            let data = inp
                .as_slice()
                .windows(window)
                .map(|window| {
                    let sum: T = window.iter().cloned().sum();
                    sum / winf
                })
                .fold(prelude::Data::default(), |mut res, mean| {
                    res.push(mean);
                    res
                });
            ndarray::NdArray::new_vector(data)
        }
        _ => {
            let n: u32 = inp.shape().last();
            let win_sum = inp
                .as_slice()
                .windows(window * n as usize)
                .step_by(n as usize)
                .fold(Data::default(), |mut res, x| {
                    for i in 0..n {
                        let s: T = x.iter().skip(i as usize).step_by(n as usize).cloned().sum();
                        let mean = s / winf;
                        res.push(mean);
                    }
                    res
                });
            ndarray::NdArray::new_with_values([(win_sum.len() / n as usize) as u32, n], win_sum)
                .unwrap()
        }
    };
    Ok(arr)
}
pub fn moving_average < T > ( inp : & ndarray :: NdArray < T > , window : usize , ) -> Result < ndarray :: NdArray < T > , NdArrayError > where T : Copy + Default + std :: iter :: Sum + std :: ops :: Div < f32 , Output = T > + std :: fmt :: Debug , { use crate :: prelude :: * ; if window == 0 { return Err ( NdArrayError :: BadInput ( " " . to_string ( ) , ) ) ; } let winf = f64 :: from ( window as u32 ) as f32 ; let arr = match inp . shape ( ) { Shape :: Scalar ( _ ) => return Err ( NdArrayError :: UnsupportedShape ( inp . shape ( ) . clone ( ) ) ) , Shape :: Vector ( _ ) => { let data = inp . as_slice ( ) . windows ( window ) . map ( | window | { let sum : T = window . iter ( ) . cloned ( ) . sum ( ) ; sum / winf } ) . fold ( prelude :: Data :: default ( ) , | mut res , mean | { res . push ( mean ) ; res } ) ; ndarray :: NdArray :: new_vector ( data ) } _ => { let n : u32 = inp . shape ( ) . last ( ) ; let win_sum = inp . as_slice ( ) . windows ( window * n as usize ) . step_by ( n as usize ) . fold ( Data :: default ( ) , | mut res , x | { for i in 0 .. n { let s : T = x . iter ( ) . skip ( i as usize ) . step_by ( n as usize ) . cloned ( ) . sum ( ) ; let mean = s / winf ; res . push ( mean ) ; } res } ) ; ndarray :: NdArray :: new_with_values ( [ ( win_sum . len ( ) / n as usize ) as u32 , n ] , win_sum ) . unwrap ( ) } } ; Ok ( arr ) }
Calculate moving averages of the innermost dimension.
Calculate moving averages of the innermost dimension .
inp
&ndarray::NdArray<T>
window
usize