Source code for mars.tensor.merge.vstack

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright 1999-2021 Alibaba Group Holding Ltd.
# 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
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# See the License for the specific language governing permissions and
# limitations under the License.

from ..base import atleast_2d
from .concatenate import concatenate

[docs]def vstack(tup): """ Stack tensors in sequence vertically (row wise). This is equivalent to concatenation along the first axis after 1-D tensors of shape `(N,)` have been reshaped to `(1,N)`. Rebuilds tensors divided by `vsplit`. 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 first axis. 1-D tensors must have the same length. Returns ------- stacked : Tensor The tensor formed by stacking the given tensors, will be at least 2-D. See Also -------- stack : Join a sequence of tensors along a new axis. hstack : Stack tensors in sequence horizontally (column wise). dstack : Stack tensors in sequence depth wise (along third dimension). concatenate : Join a sequence of tensors along an existing axis. vsplit : Split tensor into a list of multiple sub-arrays vertically. block : Assemble tensors from blocks. Examples -------- >>> import mars.tensor as mt >>> a = mt.array([1, 2, 3]) >>> b = mt.array([2, 3, 4]) >>> mt.vstack((a,b)).execute() array([[1, 2, 3], [2, 3, 4]]) >>> a = mt.array([[1], [2], [3]]) >>> b = mt.array([[2], [3], [4]]) >>> mt.vstack((a,b)).execute() array([[1], [2], [3], [2], [3], [4]]) """ return concatenate([atleast_2d(t) for t in tup], axis=0)