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//!
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//! * [Similarities](#similarities)
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//! * [Some key differences](#some-key-differences)
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+ //! * [The ndarray ecosystem](#the-ndarray-ecosystem)
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//! * [Other Rust array/matrix crates](#other-rust-arraymatrix-crates)
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//! * [Rough `ndarray`–NumPy equivalents](#rough-ndarraynumpy-equivalents)
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//!
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//! </tr>
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//! </table>
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//!
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+ //! # The ndarray ecosystem
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+ //!
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+ //! `ndarray` does not provide advanced linear algebra routines out of the box (e.g. SVD decomposition).
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+ //! Most of the routines that you can find in `SciPy.linalg`/`NumPy.linalg` are provided by another crate,
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+ //! [`ndarray-linalg`](https://crates.io/crates/ndarray-linalg).
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+ //!
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+ //! The same holds for statistics: `ndarray` provides some basic functionalities (e.g. `mean`)
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+ //! but more advanced routines can be found in [`ndarray-stats`](https://crates.io/crates/ndarray-stats).
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+ //!
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+ //! If you are looking to generate random arrays instead, check out [`ndarray-rand`](https://crates.io/crates/ndarray-rand).
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+ //!
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//! # Other Rust array/matrix crates
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//!
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//! Of the array/matrix types in Rust crates, the `ndarray` array type is probably
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//! elementwise multiplication instead of matrix multiplication. (You have to
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//! specifically call `.dot()` if you want matrix multiplication.)
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//!
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- //! Linear algebra with `ndarray` is provided by another crates,
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- //! [`ndarray-linalg`](https://crates.io/crates/ndarray-linalg).
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- //!
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//! # Rough `ndarray`–NumPy equivalents
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//!
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//! These tables provide some rough equivalents of NumPy operations in `ndarray`.
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