mars.tensor.isclose#

mars.tensor.isclose(a, b, rtol=1e-05, atol=1e-08, equal_nan=False)[source]#

Returns a boolean tensor where two tensors are element-wise equal within a tolerance.

The tolerance values are positive, typically very small numbers. The relative difference (rtol * abs(b)) and the absolute difference atol are added together to compare against the absolute difference between a and b.

Parameters

  • a (array_like) – Input tensors to compare.

  • b (array_like) – Input tensors to compare.

  • rtol (float) – The relative tolerance parameter (see Notes).

  • atol (float) – The absolute tolerance parameter (see Notes).

  • equal_nan (bool) – Whether to compare NaN’s as equal. If True, NaN’s in a will be considered equal to NaN’s in b in the output tensor.

Returns

y – Returns a boolean tensor of where a and b are equal within the given tolerance. If both a and b are scalars, returns a single boolean value.

Return type

array_like

See also

allclose

Notes

For finite values, isclose uses the following equation to test whether two floating point values are equivalent.

absolute(a - b) <= (atol + rtol * absolute(b))

The above equation is not symmetric in a and b, so that isclose(a, b) might be different from isclose(b, a) in some rare cases.

Examples

>>> import mars.tensor as mt

>>> mt.isclose([1e10,1e-7], [1.00001e10,1e-8]).execute()
array([True, False])
>>> mt.isclose([1e10,1e-8], [1.00001e10,1e-9]).execute()
array([True, True])
>>> mt.isclose([1e10,1e-8], [1.0001e10,1e-9]).execute()
array([False, True])
>>> mt.isclose([1.0, mt.nan], [1.0, mt.nan]).execute()
array([True, False])
>>> mt.isclose([1.0, mt.nan], [1.0, mt.nan], equal_nan=True).execute()
array([True, True])