# mars.tensor.floor#

mars.tensor.floor(x, out=None, where=None, **kwargs)[source]#

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 $$\lfloor x \rfloor$$.

Parameters
• 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.

• **kwargs

Returns

y – The floor of each element in x.

Return type

Tensor or scalar

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.
>>> import mars.tensor as mt

>>> a = mt.array([-1.7, -1.5, -0.2, 0.2, 1.5, 1.7, 2.0])