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from ..datasource import tensor as astensor
from .split import split
[docs]def hsplit(a, indices_or_sections):
"""
Split a tensor into multiple sub-tensors horizontally (column-wise).
Please refer to the `split` documentation. `hsplit` is equivalent
to `split` with ``axis=1``, the tensor is always split along the second
axis regardless of the tensor dimension.
See Also
--------
split : Split an array into multiple sub-arrays of equal size.
Examples
--------
>>> import mars.tensor as mt
>>> x = mt.arange(16.0).reshape(4, 4)
>>> x.execute()
array([[ 0., 1., 2., 3.],
[ 4., 5., 6., 7.],
[ 8., 9., 10., 11.],
[ 12., 13., 14., 15.]])
>>> mt.hsplit(x, 2).execute()
[array([[ 0., 1.],
[ 4., 5.],
[ 8., 9.],
[ 12., 13.]]),
array([[ 2., 3.],
[ 6., 7.],
[ 10., 11.],
[ 14., 15.]])]
>>> mt.hsplit(x, mt.array([3, 6])).execute()
[array([[ 0., 1., 2.],
[ 4., 5., 6.],
[ 8., 9., 10.],
[ 12., 13., 14.]]),
array([[ 3.],
[ 7.],
[ 11.],
[ 15.]]),
array([], dtype=float64)]
With a higher dimensional array the split is still along the second axis.
>>> x = mt.arange(8.0).reshape(2, 2, 2)
>>> x.execute()
array([[[ 0., 1.],
[ 2., 3.]],
[[ 4., 5.],
[ 6., 7.]]])
>>> mt.hsplit(x, 2)
[array([[[ 0., 1.]],
[[ 4., 5.]]]),
array([[[ 2., 3.]],
[[ 6., 7.]]])]
"""
ary = a
a = astensor(a)
if a.ndim == 0:
raise ValueError("hsplit only works on tensors of 1 or more dimensions")
if a.ndim > 1:
return split(ary, indices_or_sections, 1)
else:
return split(ary, indices_or_sections, 0)