Update ndarray-rand to rand 0.8

ndarray-rand/Cargo.toml

@@ -1,9 +1,9 @@
 [package]
 name = "ndarray-rand"
-version = "0.12.0"
+version = "0.13.0"
 edition = "2018"
 authors = ["bluss"]
-license = "MIT/Apache-2.0"
+license = "MIT OR Apache-2.0"
 
 repository = "https://github.com/rust-ndarray/ndarray"
 documentation = "https://docs.rs/ndarray-rand/"
@@ -15,15 +15,15 @@ keywords = ["multidimensional", "matrix", "rand", "ndarray"]
 
 [dependencies]
 ndarray = { version = "0.14", path = ".." }
-rand_distr = "0.3.0"
+rand_distr = "0.4.0"
 quickcheck = { version = "0.9", default-features = false, optional = true }
 
 [dependencies.rand]
-version = "0.7.0"
+version = "0.8.0"
 features = ["small_rng"]
 
 [dev-dependencies]
-rand_isaac = "0.2.0"
+rand_isaac = "0.3.0"
 quickcheck = { version = "0.9", default-features = false }
 
 [package.metadata.release]

ndarray-rand/README.md

@@ -26,9 +26,9 @@ fn main() {
 Dependencies
 ============
 
-``ndarray-rand`` depends on ``rand`` 0.7.
+``ndarray-rand`` depends on ``rand``.
 
-[`rand`](https://docs.rs/rand/0.7.0/rand/) and [`rand-distr`](https://docs.rs/rand_distr/0.3/) are 
+[`rand`](https://docs.rs/rand/) and [`rand-distr`](https://docs.rs/rand_distr/) are 
 re-exported as sub-modules, `ndarray_rand::rand` and `ndarray_rand::rand_distr` respectively. 
 Please rely on these submodules for guaranteed version compatibility.
 

ndarray-rand/RELEASES.md

@@ -1,6 +1,13 @@
 Recent Changes
 --------------
 
+- 0.13.0
+
+- Require ndarray 0.14 (unchanged from previous version)
+- Require rand 0.8
+- Require rand_distr 0.4
+- Fix methods `sample_axis` and `sample_axis_using` so that they can be used on array views too.
+
 - 0.12.0
 
   - Require ndarray 0.14

ndarray-rand/src/lib.rs

@@ -12,16 +12,16 @@
 //!
 //! ## Note
 //!
-//! `ndarray-rand` depends on [`rand` 0.7][rand].
+//! `ndarray-rand` depends on [`rand` 0.8][rand].
 //!
 //! [`rand`][rand] and [`rand_distr`][rand_distr]
 //! are re-exported as sub-modules, [`ndarray_rand::rand`](rand/index.html)
 //! and [`ndarray_rand::rand_distr`](rand_distr/index.html) respectively.
 //! You can use these submodules for guaranteed version compatibility or
 //! convenience.
 //!
-//! [rand]: https://docs.rs/rand/0.7
-//! [rand_distr]: https://docs.rs/rand_distr/0.3
+//! [rand]: https://docs.rs/rand/0.8
+//! [rand_distr]: https://docs.rs/rand_distr/0.4
 //!
 //! If you want to use a random number generator or distribution from another crate
 //! with `ndarray-rand`, you need to make sure that the other crate also depends on the
@@ -35,16 +35,16 @@ use crate::rand::seq::index;
 use crate::rand::{thread_rng, Rng, SeedableRng};
 
 use ndarray::{Array, Axis, RemoveAxis, ShapeBuilder};
-use ndarray::{ArrayBase, DataOwned, Dimension};
+use ndarray::{ArrayBase, DataOwned, RawData, Data, Dimension};
 #[cfg(feature = "quickcheck")]
 use quickcheck::{Arbitrary, Gen};
 
-/// [`rand`](https://docs.rs/rand/0.7), re-exported for convenience and version-compatibility.
+/// `rand`, re-exported for convenience and version-compatibility.
 pub mod rand {
     pub use rand::*;
 }
 
-/// [`rand-distr`](https://docs.rs/rand_distr/0.3), re-exported for convenience and version-compatibility.
+/// `rand-distr`, re-exported for convenience and version-compatibility.
 pub mod rand_distr {
     pub use rand_distr::*;
 }
@@ -55,16 +55,15 @@ pub mod rand_distr {
 /// for other types.
 ///
 /// The default RNG is a fast automatically seeded rng (currently
-/// [`rand::rngs::SmallRng`](https://docs.rs/rand/0.7/rand/rngs/struct.SmallRng.html)
-/// seeded from [`rand::thread_rng`](https://docs.rs/rand/0.7/rand/fn.thread_rng.html)).
+/// [`rand::rngs::SmallRng`], seeded from [`rand::thread_rng`]).
 ///
 /// Note that `SmallRng` is cheap to initialize and fast, but it may generate
 /// low-quality random numbers, and reproducibility is not guaranteed. See its
 /// documentation for information. You can select a different RNG with
 /// [`.random_using()`](#tymethod.random_using).
 pub trait RandomExt<S, A, D>
 where
-    S: DataOwned<Elem = A>,
+    S: RawData<Elem = A>,
     D: Dimension,
 {
     /// Create an array with shape `dim` with elements drawn from
@@ -88,6 +87,7 @@ where
     fn random<Sh, IdS>(shape: Sh, distribution: IdS) -> ArrayBase<S, D>
     where
         IdS: Distribution<S::Elem>,
+        S: DataOwned<Elem = A>,
         Sh: ShapeBuilder<Dim = D>;
 
     /// Create an array with shape `dim` with elements drawn from
@@ -118,6 +118,7 @@ where
     where
         IdS: Distribution<S::Elem>,
         R: Rng + ?Sized,
+        S: DataOwned<Elem = A>,
         Sh: ShapeBuilder<Dim = D>;
 
     /// Sample `n_samples` lanes slicing along `axis` using the default RNG.
@@ -164,6 +165,7 @@ where
     fn sample_axis(&self, axis: Axis, n_samples: usize, strategy: SamplingStrategy) -> Array<A, D>
     where
         A: Copy,
+        S: Data<Elem = A>,
         D: RemoveAxis;
 
     /// Sample `n_samples` lanes slicing along `axis` using the specified RNG `rng`.
@@ -224,17 +226,19 @@ where
     where
         R: Rng + ?Sized,
         A: Copy,
+        S: Data<Elem = A>,
         D: RemoveAxis;
 }
 
 impl<S, A, D> RandomExt<S, A, D> for ArrayBase<S, D>
 where
-    S: DataOwned<Elem = A>,
+    S: RawData<Elem = A>,
     D: Dimension,
 {
     fn random<Sh, IdS>(shape: Sh, dist: IdS) -> ArrayBase<S, D>
     where
         IdS: Distribution<S::Elem>,
+        S: DataOwned<Elem = A>,
         Sh: ShapeBuilder<Dim = D>,
     {
         Self::random_using(shape, dist, &mut get_rng())
@@ -244,6 +248,7 @@ where
     where
         IdS: Distribution<S::Elem>,
         R: Rng + ?Sized,
+        S: DataOwned<Elem = A>,
         Sh: ShapeBuilder<Dim = D>,
     {
         Self::from_shape_simple_fn(shape, move || dist.sample(rng))
@@ -252,6 +257,7 @@ where
     fn sample_axis(&self, axis: Axis, n_samples: usize, strategy: SamplingStrategy) -> Array<A, D>
     where
         A: Copy,
+        S: Data<Elem = A>,
         D: RemoveAxis,
     {
         self.sample_axis_using(axis, n_samples, strategy, &mut get_rng())
@@ -267,6 +273,7 @@ where
     where
         R: Rng + ?Sized,
         A: Copy,
+        S: Data<Elem = A>,
         D: RemoveAxis,
     {
         let indices: Vec<_> = match strategy {
@@ -298,7 +305,7 @@ pub enum SamplingStrategy {
 #[cfg(feature = "quickcheck")]
 impl Arbitrary for SamplingStrategy {
     fn arbitrary<G: Gen>(g: &mut G) -> Self {
-        if g.gen_bool(0.5) {
+        if bool::arbitrary(g) {
             SamplingStrategy::WithReplacement
         } else {
             SamplingStrategy::WithoutReplacement

ndarray-rand/tests/tests.rs

@@ -35,6 +35,13 @@ fn test_dim_f() {
     }
 }
 
+#[test]
+fn sample_axis_on_view() {
+    let m = 5;
+    let a = Array::random((m, 4), Uniform::new(0., 2.));
+    let _samples = a.view().sample_axis(Axis(0), m, SamplingStrategy::WithoutReplacement);
+}
+
 #[test]
 #[should_panic]
 fn oversampling_without_replacement_should_panic() {

numeric-tests/Cargo.toml

@@ -8,10 +8,10 @@ publish = false
 approx = "0.4"
 ndarray = { path = "..", features = ["approx"] }
 ndarray-rand = { path = "../ndarray-rand/" }
-rand_distr = "0.3"
+rand_distr = "0.4"
 
 [dependencies.rand]
-version = "0.7.0"
+version = "0.8.0"
 features = ["small_rng"]
 
 [lib]

numeric-tests/tests/accuracy.rs

@@ -76,8 +76,8 @@ fn accurate_eye_f32() {
     // pick a few random sizes
     let mut rng = SmallRng::from_entropy();
     for _ in 0..10 {
-        let i = rng.gen_range(15, 512);
-        let j = rng.gen_range(15, 512);
+        let i = rng.gen_range(15..512);
+        let j = rng.gen_range(15..512);
         println!("Testing size {} by {}", i, j);
         let a = gen(Ix2(i, j));
         let eye = Array::eye(i);
@@ -104,8 +104,8 @@ fn accurate_eye_f64() {
     // pick a few random sizes
     let mut rng = SmallRng::from_entropy();
     for _ in 0..10 {
-        let i = rng.gen_range(15, 512);
-        let j = rng.gen_range(15, 512);
+        let i = rng.gen_range(15..512);
+        let j = rng.gen_range(15..512);
         println!("Testing size {} by {}", i, j);
         let a = gen_f64(Ix2(i, j));
         let eye = Array::eye(i);
@@ -121,9 +121,9 @@ fn accurate_mul_f32() {
     // pick a few random sizes
     let mut rng = SmallRng::from_entropy();
     for i in 0..20 {
-        let m = rng.gen_range(15, 512);
-        let k = rng.gen_range(15, 512);
-        let n = rng.gen_range(15, 1560);
+        let m = rng.gen_range(15..512);
+        let k = rng.gen_range(15..512);
+        let n = rng.gen_range(15..1560);
         let a = gen(Ix2(m, k));
         let b = gen(Ix2(n, k));
         let b = b.t();
@@ -145,9 +145,9 @@ fn accurate_mul_f32_general() {
     // pick a few random sizes
     let mut rng = SmallRng::from_entropy();
     for i in 0..20 {
-        let m = rng.gen_range(15, 512);
-        let k = rng.gen_range(15, 512);
-        let n = rng.gen_range(15, 1560);
+        let m = rng.gen_range(15..512);
+        let k = rng.gen_range(15..512);
+        let n = rng.gen_range(15..1560);
         let a = gen(Ix2(m, k));
         let b = gen(Ix2(n, k));
         let mut c = gen(Ix2(m, n));
@@ -171,9 +171,9 @@ fn accurate_mul_f64() {
     // pick a few random sizes
     let mut rng = SmallRng::from_entropy();
     for i in 0..20 {
-        let m = rng.gen_range(15, 512);
-        let k = rng.gen_range(15, 512);
-        let n = rng.gen_range(15, 1560);
+        let m = rng.gen_range(15..512);
+        let k = rng.gen_range(15..512);
+        let n = rng.gen_range(15..1560);
         let a = gen_f64(Ix2(m, k));
         let b = gen_f64(Ix2(n, k));
         let b = b.t();
@@ -195,9 +195,9 @@ fn accurate_mul_f64_general() {
     // pick a few random sizes
     let mut rng = SmallRng::from_entropy();
     for i in 0..20 {
-        let m = rng.gen_range(15, 512);
-        let k = rng.gen_range(15, 512);
-        let n = rng.gen_range(15, 1560);
+        let m = rng.gen_range(15..512);
+        let k = rng.gen_range(15..512);
+        let n = rng.gen_range(15..1560);
         let a = gen_f64(Ix2(m, k));
         let b = gen_f64(Ix2(n, k));
         let mut c = gen_f64(Ix2(m, n));
@@ -221,8 +221,8 @@ fn accurate_mul_with_column_f64() {
     // pick a few random sizes
     let mut rng = SmallRng::from_entropy();
     for i in 0..10 {
-        let m = rng.gen_range(1, 350);
-        let k = rng.gen_range(1, 350);
+        let m = rng.gen_range(1..350);
+        let k = rng.gen_range(1..350);
         let a = gen_f64(Ix2(m, k));
         let b_owner = gen_f64(Ix2(k, k));
         let b_row_col;