Return the floor of the input, element-wise.
The floor of the scalar x is the largest integer i, such that
i <= x. It is often denoted as \(\lfloor x \rfloor\).
x (array_like) – Input data.
out (Tensor, None, or tuple of Tensor and None, optional) – A location into which the result is stored. If provided, it must have
a shape that the inputs broadcast to. If not provided or None,
a freshly-allocated tensor is returned. A tuple (possible only as a
keyword argument) must have length equal to the number of outputs.
where (array_like, optional) – Values of True indicate to calculate the ufunc at that position, values
of False indicate to leave the value in the output alone.
y – The floor of each element in x.
Tensor or scalar
ceil(), trunc(), rint()
Some spreadsheet programs calculate the “floor-towards-zero”, in other
words floor(-2.5) == -2. NumPy instead uses the definition of
floor where floor(-2.5) == -3.
floor(-2.5) == -2
>>> import mars.tensor as mt
>>> a = mt.array([-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0])
array([-2., -2., -1., 0., 1., 1., 2.])