divide(x1, x2, out=None, where=None, **kwargs)¶
Divide arguments element-wise.
- x1 : array_like
- Dividend tensor.
- x2 : array_like
- Divisor tensor.
- 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 array 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.
- out : Tensor
- The quotient x1/x2, element-wise. Returns a scalar if both x1 and x2 are scalars.
Equivalent to x1 / x2 in terms of array-broadcasting.
Behavior on division by zero can be changed using seterr.
In Python 2, when both x1 and x2 are of an integer type, divide will behave like floor_divide. In Python 3, it behaves like true_divide.
>>> import mars.tensor as mt
>>> mt.divide(2.0, 4.0).execute() 0.5 >>> x1 = mt.arange(9.0).reshape((3, 3)) >>> x2 = mt.arange(3.0) >>> mt.divide(x1, x2).execute() array([[ NaN, 1. , 1. ], [ Inf, 4. , 2.5], [ Inf, 7. , 4. ]]) Note the behavior with integer types (Python 2 only): >>> mt.divide(2, 4).execute() 0 >>> mt.divide(2, 4.).execute() 0.5 Division by zero always yields zero in integer arithmetic (again, Python 2 only), and does not raise an exception or a warning: >>> mt.divide(mt.array([0, 1], dtype=int), mt.array([0, 0], dtype=int)).execute() array([0, 0]) Division by zero can, however, be caught using seterr: >>> old_err_state = mt.seterr(divide='raise') >>> mt.divide(1, 0).execute() Traceback (most recent call last): ... FloatingPointError: divide by zero encountered in divide >>> ignored_states = mt.seterr(**old_err_state) >>> mt.divide(1, 0).execute() 0