mars.tensor.arange#

mars.tensor.arange(*args, **kwargs)[source]#

Return evenly spaced values within a given interval.

Values are generated within the half-open interval [start, stop) (in other words, the interval including start but excluding stop). For integer arguments the function is equivalent to the Python built-in range function, but returns a tensor rather than a list.

When using a non-integer step, such as 0.1, the results will often not be consistent. It is better to use linspace for these cases.

Parameters

  • start (number, optional) – Start of interval. The interval includes this value. The default start value is 0.

  • stop (number) – End of interval. The interval does not include this value, except in some cases where step is not an integer and floating point round-off affects the length of out.

  • step (number, optional) – Spacing between values. For any output out, this is the distance between two adjacent values, out[i+1] - out[i]. The default step size is 1. If step is specified as a position argument, start must also be given.

  • dtype (dtype) – The type of the output tensor. If dtype is not given, infer the data type from the other input arguments.

  • gpu (bool, optional) – Allocate the tensor on GPU if True, False as default

Returns

arange – Tensor of evenly spaced values.

For floating point arguments, the length of the result is ceil((stop - start)/step). Because of floating point overflow, this rule may result in the last element of out being greater than stop.

Return type

Tensor

See also

linspace

Evenly spaced numbers with careful handling of endpoints.

ogrid

Tensors of evenly spaced numbers in N-dimensions.

mgrid

Grid-shaped tensors of evenly spaced numbers in N-dimensions.

Examples

>>> import mars.tensor as mt

>>> mt.arange(3).execute()
array([0, 1, 2])
>>> mt.arange(3.0).execute()
array([ 0.,  1.,  2.])
>>> mt.arange(3,7).execute()
array([3, 4, 5, 6])
>>> mt.arange(3,7,2).execute()
array([3, 5])