jax.numpy.hstack¶
-
jax.numpy.
hstack
(tup)[source]¶ Stack arrays in sequence horizontally (column wise).
LAX-backend implementation of
hstack()
. Original docstring below.This is equivalent to concatenation along the second axis, except for 1-D arrays where it concatenates along the first axis. Rebuilds arrays divided by hsplit.
This function makes most sense for arrays 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 ndarrays) – The arrays must have the same shape along all but the second axis, except 1-D arrays which can be any length.
- Returns
stacked – The array formed by stacking the given arrays.
- Return type
See also
concatenate()
Join a sequence of arrays along an existing axis.
stack()
Join a sequence of arrays along a new axis.
block()
Assemble an nd-array from nested lists of blocks.
vstack()
Stack arrays in sequence vertically (row wise).
dstack()
Stack arrays in sequence depth wise (along third axis).
column_stack()
Stack 1-D arrays as columns into a 2-D array.
hsplit()
Split an array into multiple sub-arrays horizontally (column-wise).
Examples
>>> a = np.array((1,2,3)) >>> b = np.array((2,3,4)) >>> np.hstack((a,b)) array([1, 2, 3, 2, 3, 4]) >>> a = np.array([[1],[2],[3]]) >>> b = np.array([[2],[3],[4]]) >>> np.hstack((a,b)) array([[1, 2], [2, 3], [3, 4]])