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Jan 30, 2021
Merged

Bump ndarray version to 0.14 #258

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9 changes: 5 additions & 4 deletions ndarray-linalg/Cargo.toml
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Original file line number Diff line number Diff line change
Expand Up @@ -36,7 +36,7 @@ rand = "0.5"
thiserror = "1.0.20"

[dependencies.ndarray]
version = "0.13.0"
version = "0.14"
features = ["blas", "approx"]
default-features = false

Expand All @@ -46,9 +46,10 @@ path = "../lax"
default-features = false

[dev-dependencies]
paste = "0.1.9"
criterion = "0.3.1"
approx = { version = "0.3.2", features = ["num-complex"] }
paste = "1.0"
criterion = "0.3"
# Keep the same version as ndarray's dependency!
approx = { version = "0.4", features = ["num-complex"] }

[[bench]]
name = "truncated_eig"
Expand Down
20 changes: 4 additions & 16 deletions ndarray-linalg/src/generate.rs
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Expand Up @@ -110,14 +110,8 @@ where
A: Scalar,
S: Data<Elem = A>,
{
let views: Vec<_> = xs
.iter()
.map(|x| {
let n = x.len();
x.view().into_shape((n, 1)).unwrap()
})
.collect();
stack(Axis(1), &views).map_err(|e| e.into())
let views: Vec<_> = xs.iter().map(|x| x.view()).collect();
stack(Axis(1), &views).map_err(Into::into)
}

/// stack vectors into matrix vertically
Expand All @@ -126,12 +120,6 @@ where
A: Scalar,
S: Data<Elem = A>,
{
let views: Vec<_> = xs
.iter()
.map(|x| {
let n = x.len();
x.view().into_shape((1, n)).unwrap()
})
.collect();
stack(Axis(0), &views).map_err(|e| e.into())
let views: Vec<_> = xs.iter().map(|x| x.view()).collect();
stack(Axis(0), &views).map_err(Into::into)
}
5 changes: 2 additions & 3 deletions ndarray-linalg/src/lobpcg/eig.rs
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Expand Up @@ -138,12 +138,11 @@ impl<A: Float + Scalar + ScalarOperand + Lapack + PartialOrd + Default> Iterator

// add the new eigenvector to the internal constrain matrix
let new_constraints = if let Some(ref constraints) = self.eig.constraints {
let eigvecs_arr = constraints
let eigvecs_arr: Vec<_> = constraints
.gencolumns()
.into_iter()
.chain(vecs.gencolumns().into_iter())
.map(|x| x.insert_axis(Axis(1)))
.collect::<Vec<_>>();
.collect();

stack(Axis(1), &eigvecs_arr).unwrap()
} else {
Expand Down
28 changes: 14 additions & 14 deletions ndarray-linalg/src/lobpcg/lobpcg.rs
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Expand Up @@ -354,17 +354,17 @@ pub fn lobpcg<
};

sorted_eig(
stack![
concatenate![
Axis(0),
stack![Axis(1), xax, xar, xap],
stack![Axis(1), xar.t(), rar, rap],
stack![Axis(1), xap.t(), rap.t(), pap]
concatenate![Axis(1), xax, xar, xap],
concatenate![Axis(1), xar.t(), rar, rap],
concatenate![Axis(1), xap.t(), rap.t(), pap]
],
Some(stack![
Some(concatenate![
Axis(0),
stack![Axis(1), xx, xr, xp],
stack![Axis(1), xr.t(), rr, rp],
stack![Axis(1), xp.t(), rp.t(), pp]
concatenate![Axis(1), xx, xr, xp],
concatenate![Axis(1), xr.t(), rr, rp],
concatenate![Axis(1), xp.t(), rp.t(), pp]
]),
size_x,
&order,
Expand All @@ -374,15 +374,15 @@ pub fn lobpcg<
p_ap = None;

sorted_eig(
stack![
concatenate![
Axis(0),
stack![Axis(1), xax, xar],
stack![Axis(1), xar.t(), rar]
concatenate![Axis(1), xax, xar],
concatenate![Axis(1), xar.t(), rar]
],
Some(stack![
Some(concatenate![
Axis(0),
stack![Axis(1), xx, xr],
stack![Axis(1), xr.t(), rr]
concatenate![Axis(1), xx, xr],
concatenate![Axis(1), xr.t(), rr]
]),
size_x,
&order,
Expand Down
24 changes: 7 additions & 17 deletions ndarray-linalg/src/opnorm.rs
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Original file line number Diff line number Diff line change
Expand Up @@ -3,7 +3,6 @@
use lax::Tridiagonal;
use ndarray::*;

use crate::convert::*;
use crate::error::*;
use crate::layout::*;
use crate::types::*;
Expand Down Expand Up @@ -71,10 +70,9 @@ where
NormType::One => {
let zl: Array1<A> = Array::zeros(1);
let zu: Array1<A> = Array::zeros(1);
let dl = stack![Axis(0), self.dl.to_owned(), zl];
let du = stack![Axis(0), zu, self.du.to_owned()];
let arr = stack![Axis(0), into_row(du), into_row(arr1(&self.d)), into_row(dl)];
arr
let dl = concatenate![Axis(0), &self.dl, zl]; // n
let du = concatenate![Axis(0), zu, &self.du]; // n
stack![Axis(0), du, &self.d, dl] // 3 x n
}
// opnorm_inf() calculates muximum row sum.
// Therefore, This part align the rows and make a (n x 3) matrix like,
Expand All @@ -86,26 +84,18 @@ where
NormType::Infinity => {
let zl: Array1<A> = Array::zeros(1);
let zu: Array1<A> = Array::zeros(1);
let dl = stack![Axis(0), zl, self.dl.to_owned()];
let du = stack![Axis(0), self.du.to_owned(), zu];
let arr = stack![Axis(1), into_col(dl), into_col(arr1(&self.d)), into_col(du)];
arr
let dl = concatenate![Axis(0), zl, &self.dl]; // n
let du = concatenate![Axis(0), &self.du, zu]; // n
stack![Axis(1), dl, &self.d, du] // n x 3
}
// opnorm_fro() calculates square root of sum of squares.
// Because it is independent of the shape of matrix,
// this part make a (1 x (3n-2)) matrix like,
// [l1, ..., l{n-1}, d0, ..., d{n-1}, u1, ..., u{n-1}]
NormType::Frobenius => {
let arr = stack![
Axis(1),
into_row(arr1(&self.dl)),
into_row(arr1(&self.d)),
into_row(arr1(&self.du))
];
arr
concatenate![Axis(0), &self.dl, &self.d, &self.du].insert_axis(Axis(0))
}
};

let l = arr.layout()?;
let a = arr.as_allocated()?;
Ok(A::opnorm(t, l, a))
Expand Down