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// Copyright 2014-2016 bluss and ndarray developers.
//
// Licensed under the Apache License, Version 2.0 <LICENSE-APACHE or
// http://www.apache.org/licenses/LICENSE-2.0> or the MIT license
// <LICENSE-MIT or http://opensource.org/licenses/MIT>, at your
// option. This file may not be copied, modified, or distributed
// except according to those terms.
use std::ops::Add;
use libnum::{self, Float};
use imp_prelude::*;
use numeric_util;
use {
LinalgScalar,
aview0,
};
impl<A, S, D> ArrayBase<S, D>
where S: Data<Elem=A>,
D: Dimension,
{
/// Return sum along `axis`.
///
/// ```
/// use ndarray::{aview0, aview1, arr2, Axis};
///
/// let a = arr2(&[[1., 2.],
/// [3., 4.]]);
/// assert!(
/// a.sum(Axis(0)) == aview1(&[4., 6.]) &&
/// a.sum(Axis(1)) == aview1(&[3., 7.]) &&
///
/// a.sum(Axis(0)).sum(Axis(0)) == aview0(&10.)
/// );
/// ```
///
/// **Panics** if `axis` is out of bounds.
pub fn sum(&self, axis: Axis) -> OwnedArray<A, <D as RemoveAxis>::Smaller>
where A: Clone + Add<Output=A>,
D: RemoveAxis,
{
let n = self.shape().axis(axis);
let mut res = self.subview(axis, 0).to_owned();
for i in 1..n {
let view = self.subview(axis, i);
res.iadd(&view);
}
res
}
/// Return the sum of all elements in the array.
///
/// ```
/// use ndarray::arr2;
///
/// let a = arr2(&[[1., 2.],
/// [3., 4.]]);
/// assert_eq!(a.scalar_sum(), 10.);
/// ```
pub fn scalar_sum(&self) -> A
where A: Clone + Add<Output=A> + libnum::Zero,
{
if let Some(slc) = self.as_slice_memory_order() {
return numeric_util::unrolled_sum(slc);
}
let mut sum = A::zero();
for row in self.inner_iter() {
if let Some(slc) = row.as_slice() {
sum = sum + numeric_util::unrolled_sum(slc);
} else {
sum = sum + row.iter().fold(A::zero(), |acc, elt| acc + elt.clone());
}
}
sum
}
/// Return mean along `axis`.
///
/// **Panics** if `axis` is out of bounds.
///
/// ```
/// use ndarray::{aview1, arr2, Axis};
///
/// let a = arr2(&[[1., 2.],
/// [3., 4.]]);
/// assert!(
/// a.mean(Axis(0)) == aview1(&[2.0, 3.0]) &&
/// a.mean(Axis(1)) == aview1(&[1.5, 3.5])
/// );
/// ```
pub fn mean(&self, axis: Axis) -> OwnedArray<A, <D as RemoveAxis>::Smaller>
where A: LinalgScalar,
D: RemoveAxis,
{
let n = self.shape().axis(axis);
let sum = self.sum(axis);
let mut cnt = A::one();
for _ in 1..n {
cnt = cnt + A::one();
}
sum / &aview0(&cnt)
}
/// Return `true` if the arrays' elementwise differences are all within
/// the given absolute tolerance, `false` otherwise.
///
/// If their shapes disagree, `rhs` is broadcast to the shape of `self`.
///
/// **Panics** if broadcasting to the same shape isn’t possible.
pub fn all_close<S2, E>(&self, rhs: &ArrayBase<S2, E>, tol: A) -> bool
where A: Float,
S2: Data<Elem=A>,
E: Dimension,
{
let rhs_broadcast = rhs.broadcast_unwrap(self.dim());
self.iter().zip(rhs_broadcast.iter()).all(|(x, y)| (*x - *y).abs() <= tol)
}
#[cfg_attr(has_deprecated, deprecated(note=
"Replaced by .all_close() which has clearer error cases"))]
/// ***Deprecated: Replaced by .all_close()***
///
/// Return `true` if the arrays' elementwise differences are all within
/// the given absolute tolerance.<br>
/// Return `false` otherwise, or if the shapes disagree.
pub fn allclose<S2>(&self, rhs: &ArrayBase<S2, D>, tol: A) -> bool
where A: Float,
S2: Data<Elem=A>,
{
self.shape() == rhs.shape() &&
self.iter().zip(rhs.iter()).all(|(x, y)| (*x - *y).abs() <= tol)
}
}