10000 API: Add outer to numpy.linalg · numpy/numpy@66f8518 · GitHub
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API: Add outer to numpy.linalg
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‎doc/release/upcoming_changes/25101.new_feature.rst

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``outer`` for `numpy.linalg`
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----------------------------
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`numpy.linalg.outer` has been added. It computes the outer product of two vectors.
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It differs from `numpy.outer` by accepting one-dimensional arrays only.
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This function is compatible with Array API.

‎doc/source/reference/array_api.rst

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@@ -134,11 +134,8 @@ Function instead of method
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These functions are in the ``linalg`` sub-namespace in the array API, but are
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only in the top-level namespace in NumPy:
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- ``diagonal``
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- ``matmul`` (*)
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- ``outer``
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- ``tensordot`` (*)
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- ``trace``
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(*): These functions are also in the top-level namespace in the array API.
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‎doc/source/reference/routines.linalg.rst

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@@ -72,6 +72,7 @@ Decompositions
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:toctree: generated/
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linalg.cholesky
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linalg.outer
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linalg.qr
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linalg.svd
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‎numpy/_core/numeric.py

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@@ -873,6 +873,8 @@ def outer(a, b, out=None):
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ufunc.outer : A generalization to dimensions other than 1D and other
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operations. ``np.multiply.outer(a.ravel(), b.ravel())``
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is the equivalent.
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linalg.outer : An Array API compatible variation of ``np.outer``,
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which accepts 1-dimensional inputs only.
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tensordot : ``np.tensordot(a.ravel(), b.ravel(), axes=((), ()))``
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is the equivalent.
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‎numpy/linalg/__init__.py

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@@ -34,6 +34,7 @@
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--------------
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cholesky
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outer
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qr
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svd
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‎numpy/linalg/__init__.pyi

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@@ -5,6 +5,7 @@ from numpy.linalg._linalg import (
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tensorinv as tensorinv,
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inv as inv,
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cholesky as cholesky,
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outer as outer,
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eigvals as eigvals,
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eigvalsh as eigvalsh,
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pinv as pinv,

‎numpy/linalg/_linalg.py

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@@ -12,7 +12,7 @@
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__all__ = ['matrix_power', 'solve', 'tensorsolve', 'tensorinv', 'inv',
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'cholesky', 'eigvals', 'eigvalsh', 'pinv', 'slogdet', 'det',
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'svd', 'eig', 'eigh', 'lstsq', 'norm', 'qr', 'cond', 'matrix_rank',
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'LinAlgError', 'multi_dot', 'trace', 'diagonal', 'cross']
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'LinAlgError', 'multi_dot', 'trace', 'diagonal', 'cross', 'outer']
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import functools
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import operator
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amax, prod, abs, atleast_2d, intp, asanyarray, object_, matmul,
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swapaxes, divide, count_nonzero, isnan, sign, argsort, sort,
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reciprocal, overrides, diagonal as _core_diagonal, trace as _core_trace,
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cross as _core_cross,
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cross as _core_cross, outer as _core_outer
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)
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from numpy.lib._twodim_base_impl import triu, eye
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from numpy.lib.array_utils import normalize_axis_index
@@ -814,8 +814,53 @@ def cholesky(a):
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return wrap(r.astype(result_t, copy=False))
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# outer product
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def _outer_dispatcher(x1, x2):
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return (x1, x2)
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@array_function_dispatch(_outer_dispatcher)
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def outer(x1, x2, /):
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"""
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Compute the outer product of two vectors.
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This function is Array API compatible. Compared to ``np.outer``
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it accepts 1-dimensional inputs only.
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Parameters
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----------
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x1 : (M,) array_like
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One-dimensional input array of size ``N``.
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Must have a numeric data type.
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x2 : (N,) array_like
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One-dimensional input array of size ``M``.
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Must have a numeric data type.
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Returns
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-------
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out : (M, N) ndarray
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``out[i, j] = a[i] * b[j]``
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See also
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--------
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outer
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"""
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x1 = asarray(x1)
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x2 = asarray(x2)
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if x1.ndim != 1 or x2.ndim != 1:
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raise ValueError(
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"Input arrays must be one-dimensional, but they are "
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f"{x1.ndim=} and {x2.ndim=}."
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)
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return _core_outer(x1, x2, out=None)
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# QR decomposition
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def _qr_dispatcher(a, mode=None):
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return (a,)
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‎numpy/linalg/_linalg.pyi

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@@ -10,12 +10,15 @@ from typing import (
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Generic,
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)
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import numpy as np
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from numpy import (
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generic,
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floating,
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complexfloating,
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signedinteger,
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unsignedinteger,
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timedelta64,
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object_,
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int32,
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float64,
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complex128,
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from numpy._typing import (
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NDArray,
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ArrayLike,
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_ArrayLikeUnknown,
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_ArrayLikeBool_co,
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_ArrayLikeInt_co,
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_ArrayLikeUInt_co,
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_ArrayLikeFloat_co,
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@overload
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def cholesky(a: _ArrayLikeComplex_co) -> NDArray[complexfloating[Any, Any]]: ...
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@overload
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def outer(x1: _ArrayLikeUnknown, x2: _ArrayLikeUnknown) -> NDArray[Any]: ...
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@overload
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def outer(x1: _ArrayLikeBool_co, x2: _ArrayLikeBool_co) -> NDArray[np.bool]: ...
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@overload
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def outer(x1: _ArrayLikeUInt_co, x2: _ArrayLikeUInt_co) -> NDArray[unsignedinteger[Any]]: ...
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@overload
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def outer(x1: _ArrayLikeInt_co, x2: _ArrayLikeInt_co) -> NDArray[signedinteger[Any]]: ...
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@overload
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def outer(x1: _ArrayLikeFloat_co, x2: _ArrayLikeFloat_co) -> NDArray[floating[Any]]: ...
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@overload
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def outer(
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x1: _ArrayLikeComplex_co,
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x2: _ArrayLikeComplex_co,
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) -> NDArray[complexfloating[Any, Any]]: ...
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@overload
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def outer(
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x1: _ArrayLikeTD64_co,
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x2: _ArrayLikeTD64_co,
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out: None = ...,
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) -> NDArray[timedelta64]: ...
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@overload
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def outer(x1: _ArrayLikeObject_co, x2: _ArrayLikeObject_co) -> NDArray[object_]: ...
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@overload
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def outer(
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x1: _ArrayLikeComplex_co | _ArrayLikeTD64_co | _ArrayLikeObject_co,
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x2: _ArrayLikeComplex_co | _ArrayLikeTD64_co | _ArrayLikeObject_co,
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) -> _ArrayType: ...
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@overload
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def qr(a: _ArrayLikeInt_co, mode: _ModeKind = ...) -> QRResult: ...
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@overload

‎numpy/linalg/tests/test_linalg.py

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@@ -1842,6 +1842,23 @@ class ArraySubclass(np.ndarray):
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assert_(isinstance(res, np.ndarray))
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class TestOuter:
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arr1 = np.arange(3)
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arr2 = np.arange(3)
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expected = np.array(
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[[0, 0, 0],
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[0, 1, 2],
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[0, 2, 4]]
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)
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assert_array_equal(np.linalg.outer(arr1, arr2), expected)
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with assert_raises_regex(
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ValueError, "Input arrays must be one-dimensional"
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):
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np.linalg.outer(arr1[:, np.newaxis], arr2)
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def test_byteorder_check():
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# Byte order check should pass for native order
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if sys.byteorder == 'little':

‎numpy/typing/tests/data/reveal/linalg.pyi

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@@ -18,6 +18,7 @@ AR_c16: npt.NDArray[np.complex128]
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AR_O: npt.NDArray[np.object_]
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AR_m: npt.NDArray[np.timedelta64]
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AR_S: npt.NDArray[np.str_]
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AR_b: npt.NDArray[np.bool]
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assert_type(np.linalg.tensorsolve(AR_i8, AR_i8), npt.NDArray[np.float64])
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assert_type(np.linalg.tensorsolve(AR_i8, AR_f8), npt.NDArray[np.floating[Any]])
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assert_type(np.linalg.cholesky(AR_f8), npt.NDArray[np.floating[Any]])
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assert_type(np.linalg.cholesky(AR_c16), npt.NDArray[np.complexfloating[Any, Any]])
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assert_type(np.linalg.outer(AR_i8, AR_i8), npt.NDArray[np.signedinteger[Any]])
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assert_type(np.linalg.outer(AR_f8, AR_f8), npt.NDArray[np.floating[Any]])
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assert_type(np.linalg.outer(AR_c16, AR_c16), npt.NDArray[np.complexfloating[Any, Any]])
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assert_type(np.linalg.outer(AR_b, AR_b), npt.NDArray[np.bool])
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assert_type(np.linalg.outer(AR_O, AR_O), npt.NDArray[np.object_])
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assert_type(np.linalg.outer(AR_i8, AR_m), npt.NDArray[np.timedelta64])
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assert_type(np.linalg.qr(AR_i8), QRResult)
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assert_type(np.linalg.qr(AR_f8), QRResult)
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assert_type(np.linalg.qr(AR_c16), QRResult)

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