Rename numpy._array_api to numpy.array_api

Instead of the leading underscore, the experimentalness of the module will be
indicated by omitting a warning on import. That we, we do not have to change
the API from underscore to no underscore when the module is no longer
experimental.

1 files changed, 58 insertions, 0 deletions

diff --git a/numpy/array_api/_linear_algebra_functions.py b/numpy/array_api/_linear_algebra_functions.py
new file mode 100644
index 000000000..f13f9c541
--- /dev/null
+++ b/numpy/array_api/_linear_algebra_functions.py
@@ -0,0 +1,58 @@
+from __future__ import annotations
+
+from ._array_object import Array
+from ._dtypes import _numeric_dtypes, _result_type
+
+from typing import Optional, Sequence, Tuple, Union
+
+import numpy as np
+
+# einsum is not yet implemented in the array API spec.
+
+# def einsum():
+#     """
+#     Array API compatible wrapper for :py:func:`np.einsum <numpy.einsum>`.
+#
+#     See its docstring for more information.
+#     """
+#     return np.einsum()
+
+def matmul(x1: Array, x2: Array, /) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.matmul <numpy.matmul>`.
+
+    See its docstring for more information.
+    """
+    # Note: the restriction to numeric dtypes only is different from
+    # np.matmul.
+    if x1.dtype not in _numeric_dtypes or x2.dtype not in _numeric_dtypes:
+        raise TypeError('Only numeric dtypes are allowed in matmul')
+    # Call result type here just to raise on disallowed type combinations
+    _result_type(x1.dtype, x2.dtype)
+
+    return Array._new(np.matmul(x1._array, x2._array))
+
+# Note: axes must be a tuple, unlike np.tensordot where it can be an array or array-like.
+def tensordot(x1: Array, x2: Array, /, *, axes: Union[int, Tuple[Sequence[int], Sequence[int]]] = 2) -> Array:
+    # Note: the restriction to numeric dtypes only is different from
+    # np.tensordot.
+    if x1.dtype not in _numeric_dtypes or x2.dtype not in _numeric_dtypes:
+        raise TypeError('Only numeric dtypes are allowed in tensordot')
+    # Call result type here just to raise on disallowed type combinations
+    _result_type(x1.dtype, x2.dtype)
+
+    return Array._new(np.tensordot(x1._array, x2._array, axes=axes))
+
+def transpose(x: Array, /, *, axes: Optional[Tuple[int, ...]] = None) -> Array:
+    """
+    Array API compatible wrapper for :py:func:`np.transpose <numpy.transpose>`.
+
+    See its docstring for more information.
+    """
+    return Array._new(np.transpose(x._array, axes=axes))
+
+# Note: vecdot is not in NumPy
+def vecdot(x1: Array, x2: Array, /, *, axis: Optional[int] = None) -> Array:
+    if axis is None:
+        axis = -1
+    return tensordot(x1, x2, axes=((axis,), (axis,)))