- mars.tensor.compress(condition, a, axis=None, out=None)#
Return selected slices of a tensor along given axis.
When working along a given axis, a slice along that axis is returned in output for each index where condition evaluates to True. When working on a 1-D array, compress is equivalent to extract.
condition (1-D tensor of bools) – Tensor that selects which entries to return. If len(condition) is less than the size of a along the given axis, then output is truncated to the length of the condition tensor.
a (array_like) – Tensor from which to extract a part.
axis (int, optional) – Axis along which to take slices. If None (default), work on the flattened tensor.
out (Tensor, optional) – Output tensor. Its type is preserved and it must be of the right shape to hold the output.
compressed_array – A copy of a without the slices along axis for which condition is false.
- Return type
Equivalent method in ndarray
Equivalent method when working on 1-D arrays
>>> import mars.tensor as mt
>>> a = mt.array([[1, 2], [3, 4], [5, 6]]) >>> a.execute() array([[1, 2], [3, 4], [5, 6]]) >>> mt.compress([0, 1], a, axis=0).execute() array([[3, 4]]) >>> mt.compress([False, True, True], a, axis=0).execute() array([[3, 4], [5, 6]]) >>> mt.compress([False, True], a, axis=1).execute() array([, , ])
Working on the flattened tensor does not return slices along an axis but selects elements.
>>> mt.compress([False, True], a).execute() array()