Source code for xorbits._mars.tensor.merge.hstack

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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)