- xorbits.numpy.allclose(a, b, rtol=1e-05, atol=1e-08, equal_nan=False)#
Returns True if two arrays 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.
NaNs are treated as equal if they are in the same place and if
equal_nan=True. Infs are treated as equal if they are in the same place and of the same sign in both arrays.
a (array_like) – Input arrays to compare.
b (array_like) – Input arrays 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 array.
New in version 1.10.0(numpy).
allclose – Returns True if the two arrays are equal within the given tolerance; False otherwise.
- Return type
If the following equation is element-wise True, then allclose returns True.
absolute(a - b) <= (atol + rtol * absolute(b))
The above equation is not symmetric in a and b, so that
allclose(a, b)might be different from
allclose(b, a)in some rare cases.
The comparison of a and b uses standard broadcasting, which means that a and b need not have the same shape in order for
allclose(a, b)to evaluate to True. The same is true for equal but not array_equal.
allclose is not defined for non-numeric data types. bool is considered a numeric data-type for this purpose.
>>> np.allclose([1e10,1e-7], [1.00001e10,1e-8]) False >>> np.allclose([1e10,1e-8], [1.00001e10,1e-9]) True >>> np.allclose([1e10,1e-8], [1.0001e10,1e-9]) False >>> np.allclose([1.0, np.nan], [1.0, np.nan]) False >>> np.allclose([1.0, np.nan], [1.0, np.nan], equal_nan=True) True
This docstring was copied from numpy.