# Source code for mars.tensor.merge.hstack

#!/usr/bin/env python
# -*- coding: utf-8 -*-
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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
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from .concatenate import concatenate
[docs]def hstack(tup):
"""
Stack tensors in sequence horizontally (column wise).
This is equivalent to concatenation along the second axis, except for 1-D
tensors where it concatenates along the first axis. Rebuilds tensors divided
by `hsplit`.
This function makes most sense for tensors with up to 3 dimensions. For
instance, for pixel-data with a height (first axis), width (second axis),
and r/g/b channels (third axis). The functions `concatenate`, `stack` and
`block` provide more general stacking and concatenation operations.
Parameters
----------
tup : sequence of tensors
The tensors must have the same shape along all but the second axis,
except 1-D tensors which can be any length.
Returns
-------
stacked : Tensor
The tensor formed by stacking the given tensors.
See Also
--------
stack : Join a sequence of tensors along a new axis.
vstack : Stack tensors in sequence vertically (row wise).
dstack : Stack tensors in sequence depth wise (along third axis).
concatenate : Join a sequence of tensors along an existing axis.
hsplit : Split tensor along second axis.
block : Assemble tensors from blocks.
Examples
--------
>>> import mars.tensor as mt
>>> a = mt.array((1,2,3))
>>> b = mt.array((2,3,4))
>>> mt.hstack((a,b)).execute()
array([1, 2, 3, 2, 3, 4])
>>> a = mt.array([[1],[2],[3]])
>>> b = mt.array([[2],[3],[4]])
>>> mt.hstack((a,b)).execute()
array([[1, 2],
[2, 3],
[3, 4]])
"""
if all(x.ndim == 1 for x in tup):
return concatenate(tup, axis=0)
else:
return concatenate(tup, axis=1)