Merge pull request #117 from termoshtt/release-0.10

Author: mergecat[bot]

README.md

@@ -1,42 +1,65 @@
 ndarray-linalg
 ===============
+[![CircleCI](https://circleci.com/gh/termoshtt/ndarray-linalg.svg?style=shield)](https://circleci.com/gh/termoshtt/ndarray-linalg)
 [![Crate](http://meritbadge.herokuapp.com/ndarray-linalg)](https://crates.io/crates/ndarray-linalg)
 [![docs.rs](https://docs.rs/ndarray-linalg/badge.svg)](https://docs.rs/ndarray-linalg)
-[![CircleCI](https://circleci.com/gh/termoshtt/ndarray-linalg.svg?style=shield)](https://circleci.com/gh/termoshtt/ndarray-linalg)
-[![Gitter chat](https://badges.gitter.im/termoshtt-scirust/ndarray-linalg.png)](https://gitter.im/termoshtt-scirust/ndarray-linalg)
 
-Linear algebra package for Rust with [rust-ndarray](https://github.com/bluss/rust-ndarray).
+Linear algebra package for Rust with [ndarray](https://github.com/bluss/ndarray) based on external LAPACK implementations.
+
+Examples
+---------
+See [examples](https://github.com/termoshtt/ndarray-linalg/tree/master/examples) directory.
+
+**Note**: To run examples, you must specify which backend will be used (as described below).
+For example, you can execute the [solve](examples/solve.rs) example with the OpenBLAS backend like this:
 
-LAPACKE Backend
-----------------
+```sh
+cargo run --example solve --features=openblas
+```
 
-Currently three LAPACKE implementations are supported and tested:
+and run all tests of ndarray-linalg with OpenBLAS
+
+```sh
+cargo test --features=openblas
+```
+
+BLAS/LAPACK Backend
+-------------------
+
+Three BLAS/LAPACK implementations are supported:
 
 - [OpenBLAS](https://github.com/cmr/openblas-src)
   - needs `gfortran` (or other Fortran compiler)
 - [Netlib](https://github.com/cmr/netlib-src)
   - needs `cmake` and `gfortran`
 - [Intel MKL](https://github.com/termoshtt/rust-intel-mkl) (non-free license, see the linked page)
   - needs `curl`
-There are two ways to link LAPACKE backend:
 
-### backend features (recommended)
 There are three features corresponding to the backend implementations (`openblas` / `netlib` / `intel-mkl`):
 
 ```toml
 [dependencies]
 ndarray = "0.12"
-ndarray-linalg = { version = "0.9", features = ["openblas"] }
+ndarray-linalg = { version = "0.10", features = ["openblas"] }
+```
+
+### For librarian
+If you creating a library depending on this crate, we encourage you not to link any backend:
+
+```toml
+[dependencies]
+ndarray = "0.12"
+ndarray-linalg = "0.10"
 ```
 
-### link backend crate manually
+### Link backend crate manually
 For the sake of linking flexibility, you can provide LAPACKE implementation (as an `extern crate`) yourself.
 You should link a LAPACKE implementation to a final crate (like binary executable or dylib) only, not to a Rust library.
 
 ```toml
 [dependencies]
 ndarray = "0.12"
-ndarray-linalg = "0.9"
+ndarray-linalg = "0.10"
 openblas-src = "0.5" # or another backend of your choice
 
 ```
@@ -48,35 +71,3 @@ extern crate ndarray;
 extern crate ndarray_linalg;
 extern crate openblas_src; // or another backend of your choice
 ```
-
-### For librarian
-If you creating a library depending on this crate, we encourage you not to link any backend for flexibility:
-
-```toml
-[dependencies]
-ndarray = "0.12"
-ndarray-linalg = { version = "0.9", default-features = false }
-```
-
-However, if you hope simplicity instead of the flexibility, you can link your favorite backend in the way described above.
-
-### Tests and Examples
-
-To run tests or examples for `ndarray-linalg`, you must specify the desired
-backend. For example, you can run the tests with the OpenBLAS backend like
-this:
-
-```sh
-cargo test --features=openblas
-```
-
-Examples
----------
-See [examples](https://github.com/termoshtt/ndarray-linalg/tree/master/examples) directory.
-
-Note that to run an example, you must specify the desired backend. For example,
-you can run the the `solve` example with the OpenBLAS backend like this:
-
-```sh
-cargo run --example solve --features=openblas
-```

examples/eigh.rs

@@ -6,7 +6,7 @@ use ndarray_linalg::*;
 
 fn main() {
     let a = arr2(&[[3.0, 1.0, 1.0], [1.0, 3.0, 1.0], [1.0, 1.0, 3.0]]);
-    let (e, vecs): (Array1<_>, Array2<_>) = a.clone().eigh(UPLO::Upper).unwrap();
+    let (e, vecs) = a.clone().eigh(UPLO::Upper).unwrap();
     println!("eigenvalues = \n{:?}", e);
     println!("V = \n{:?}", vecs);
     let av = a.dot(&vecs);