def outer(a, b, out=None):
"""Returns the outer product of two vectors.
The input arrays are flattened into 1-D vectors and then it performs outer
product of these vectors.
Args:
a (cupy.ndarray): The first argument.
b (cupy.ndarray): The second argument.
out (cupy.ndarray): Output array.
Returns:
cupy.ndarray: 2-D array of the outer product of ``a`` and ``b``.
.. seealso:: :func:`numpy.outer`
"""
n = a.size
m = b.size
ret_shape = (n, m)
if out is None:
return core.tensordot_core(a, b, None, n, m, 1, ret_shape)
if out.size != n * m:
raise ValueError('Output array has an invalid size')
if out.flags.c_contiguous:
return core.tensordot_core(a, b, out, n, m, 1, ret_shape)
else:
out[:] = core.tensordot_core(a, b, None, n, m, 1, ret_shape)
return out
def outer(a, b, out=None):
"""Returns the outer product of two vectors.
The input arrays are flattened into 1-D vectors and then it performs outer
product of these vectors.
Args:
a (cupy.ndarray): The first argument.
b (cupy.ndarray): The second argument.
out (cupy.ndarray): Output array.
Returns:
cupy.ndarray: 2-D array of the outer product of ``a`` and ``b``.
.. seealso:: :func:`numpy.outer`
"""
n = a.size
m = b.size
ret_shape = (n, m)
if out is None:
return core.tensordot_core(a, b, None, n, m, 1, ret_shape)
if out.size != n * m:
raise ValueError('Output array has an invalid size')
if out.flags.c_contiguous:
return core.tensordot_core(a, b, out, n, m, 1, ret_shape)
else:
out[:] = core.tensordot_core(a, b, None, n, m, 1, ret_shape)
return out
def kron(a, b):
"""Returns the kronecker product of two arrays.
Args:
a (~cupy.ndarray): The first argument.
b (~cupy.ndarray): The second argument.
Returns:
~cupy.ndarray: Output array.
.. seealso:: :func:`numpy.kron`
"""
a_ndim = a.ndim
b_ndim = b.ndim
if a_ndim == 0 or b_ndim == 0:
return cupy.multiply(a, b)
ndim = b_ndim
a_shape = a.shape
b_shape = b.shape
if a_ndim != b_ndim:
if b_ndim > a_ndim:
a_shape = (1,) * (b_ndim - a_ndim) + a_shape
else:
b_shape = (1,) * (a_ndim - b_ndim) + b_shape
ndim = a_ndim
axis = ndim - 1
out = core.tensordot_core(a, b, None, a.size, b.size, 1, a_shape + b_shape)
for _ in six.moves.range(ndim):
out = core.concatenate_method(out, axis=axis)
return out
def vdot(a, b):
"""Returns the dot product of two vectors.
The input arrays are flattened into 1-D vectors and then it performs inner
product of these vectors.
Args:
a (cupy.ndarray): The first argument.
b (cupy.ndarray): The second argument.
Returns:
cupy.ndarray: Zero-dimensional array of the dot product result.
.. seealso:: :func:`numpy.vdot`
"""
if a.size != b.size:
raise ValueError('Axis dimension mismatch')
return core.tensordot_core(a, b, None, 1, 1, a.size, ())
def vdot(a, b):
"""Returns the dot product of two vectors.
The input arrays are flattened into 1-D vectors and then it performs inner
product of these vectors.
Args:
a (cupy.ndarray): The first argument.
b (cupy.ndarray): The second argument.
Returns:
cupy.ndarray: Zero-dimensional array of the dot product result.
.. seealso:: :func:`numpy.vdot`
"""
if a.size != b.size:
raise ValueError('Axis dimension mismatch')
return core.tensordot_core(a, b, None, 1, 1, a.size, ())
def inner(a, b):
"""Returns the inner product of two arrays.
It uses the last axis of each argument to take sum product.
Args:
a (cupy.ndarray): The first argument.
b (cupy.ndarray): The second argument.
Returns:
cupy.ndarray: The inner product of ``a`` and ``b``.
.. seealso:: :func:`numpy.inner`
"""
a_ndim = a.ndim
b_ndim = b.ndim
if a_ndim == 0 or b_ndim == 0:
return cupy.multiply(a, b)
a_axis = a_ndim - 1
b_axis = b_ndim - 1
if a.shape[-1] != b.shape[-1]:
raise ValueError('Axis dimension mismatch')
if a_axis:
a = cupy.rollaxis(a, a_axis, 0)
if b_axis:
b = cupy.rollaxis(b, b_axis, 0)
ret_shape = a.shape[1:] + b.shape[1:]
k = a.shape[0]
n = a.size // k
m = b.size // k
return core.tensordot_core(a, b, None, n, m, k, ret_shape)
def inner(a, b):
"""Returns the inner product of two arrays.
It uses the last axis of each argument to take sum product.
Args:
a (cupy.ndarray): The first argument.
b (cupy.ndarray): The second argument.
Returns:
cupy.ndarray: The inner product of ``a`` and ``b``.
.. seealso:: :func:`numpy.inner`
"""
a_ndim = a.ndim
b_ndim = b.ndim
if a_ndim == 0 or b_ndim == 0:
return cupy.multiply(a, b)
a_axis = a_ndim - 1
b_axis = b_ndim - 1
if a.shape[-1] != b.shape[-1]:
raise ValueError('Axis dimension mismatch')
if a_axis:
a = cupy.rollaxis(a, a_axis, 0)
if b_axis:
b = cupy.rollaxis(b, b_axis, 0)
ret_shape = a.shape[1:] + b.shape[1:]
k = a.shape[0]
n = a.size // k
m = b.size // k
return core.tensordot_core(a, b, None, n, m, k, ret_shape)
def tensordot(a, b, axes=2):
"""Returns the tensor dot product of two arrays along specified axes.
This is equivalent to compute dot product along the specified axes which
are treated as one axis by reshaping.
Args:
a (cupy.ndarray): The first argument.
b (cupy.ndarray): The second argument.
axes:
- If it is an integer, then ``axes`` axes at the last of ``a`` and
the first of ``b`` are used.
- If it is a pair of sequences of integers, then these two
sequences specify the list of axes for ``a`` and ``b``. The
corresponding axes are paired for sum-product.
Returns:
cupy.ndarray: The tensor dot product of ``a`` and ``b`` along the
axes specified by ``axes``.
.. seealso:: :func:`numpy.tensordot`
"""
a_ndim = a.ndim
b_ndim = b.ndim
if a_ndim == 0 or b_ndim == 0:
if axes != 0 and axes != ((), ()):
raise ValueError('An input is zero-dim while axes has dimensions')
return cupy.multiply(a, b)
if isinstance(axes, collections.Sequence):
if len(axes) != 2:
raise ValueError('Axes must consist of two arrays.')
a_axes, b_axes = axes
if numpy.isscalar(a_axes):
a_axes = a_axes,
if numpy.isscalar(b_axes):
b_axes = b_axes,
else:
a_axes = tuple(six.moves.range(a_ndim - axes, a_ndim))
b_axes = tuple(six.moves.range(axes))
sum_ndim = len(a_axes)
if sum_ndim != len(b_axes):
raise ValueError('Axes length mismatch')
for a_axis, b_axis in zip(a_axes, b_axes):
if a.shape[a_axis] != b.shape[b_axis]:
raise ValueError('Axis dimension mismatch')
# Make the axes non-negative
a = _move_axes_to_head(a, [axis % a_ndim for axis in a_axes])
b = _move_axes_to_head(b, [axis % b_ndim for axis in b_axes])
ret_shape = a.shape[sum_ndim:] + b.shape[sum_ndim:]
k = internal.prod(a.shape[:sum_ndim])
n = a.size // k
m = b.size // k
return core.tensordot_core(a, b, None, n, m, k, ret_shape)
def tensordot(a, b, axes=2):
"""Returns the tensor dot product of two arrays along specified axes.
This is equivalent to compute dot product along the specified axes which
are treated as one axis by reshaping.
Args:
a (cupy.ndarray): The first argument.
b (cupy.ndarray): The second argument.
axes:
- If it is an integer, then ``axes`` axes at the last of ``a`` and
the first of ``b`` are used.
- If it is a pair of sequences of integers, then these two
sequences specify the list of axes for ``a`` and ``b``. The
corresponding axes are paired for sum-product.
out (cupy.ndarray): Output array.
Returns:
cupy.ndarray: The tensor dot product of ``a`` and ``b`` along the
axes specified by ``axes``.
.. seealso:: :func:`numpy.tensordot`
"""
a_ndim = a.ndim
b_ndim = b.ndim
if a_ndim == 0 or b_ndim == 0:
if axes != 0 and axes != ((), ()):
raise ValueError('An input is zero-dim while axes has dimensions')
return cupy.multiply(a, b)
if isinstance(axes, collections.Sequence):
if len(axes) != 2:
raise ValueError('Axes must consist of two arrays.')
a_axes, b_axes = axes
if numpy.isscalar(a_axes):
a_axes = a_axes,
if numpy.isscalar(b_axes):
b_axes = b_axes,
else:
a_axes = tuple(six.moves.range(a_ndim - axes, a_ndim))
b_axes = tuple(six.moves.range(axes))
sum_ndim = len(a_axes)
if sum_ndim != len(b_axes):
raise ValueError('Axes length mismatch')
for a_axis, b_axis in zip(a_axes, b_axes):
if a.shape[a_axis] != b.shape[b_axis]:
raise ValueError('Axis dimension mismatch')
# Make the axes non-negative
a = _move_axes_to_head(a, [axis % a_ndim for axis in a_axes])
b = _move_axes_to_head(b, [axis % b_ndim for axis in b_axes])
ret_shape = a.shape[sum_ndim:] + b.shape[sum_ndim:]
k = internal.prod(a.shape[:sum_ndim])
n = a.size // k
m = b.size // k
return core.tensordot_core(a, b, None, n, m, k, ret_shape)
