xorbits.numpy.isclose#

xorbits.numpy.isclose(a, b, rtol=1e-05, atol=1e-08, equal_nan=False)[源代码]#

Returns a boolean array where 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.

警告

The default atol is not appropriate for comparing numbers that are much smaller than one (see Notes).

参数

  • 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.

返回

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

返回类型

array_like

参见

allclose, math.isclose

提示

1.7.0(numpy) 新版功能.

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

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

Unlike the built-in math.isclose, the above equation is not symmetric in a and b – it assumes b is the reference value – so that isclose(a, b) might be different from isclose(b, a). Furthermore, the default value of atol is not zero, and is used to determine what small values should be considered close to zero. The default value is appropriate for expected values of order unity: if the expected values are significantly smaller than one, it can result in false positives. atol should be carefully selected for the use case at hand. A zero value for atol will result in False if either a or b is zero.

isclose is not defined for non-numeric data types. bool is considered a numeric data-type for this purpose.

实际案例

>>> np.isclose([1e10,1e-7], [1.00001e10,1e-8])  
array([ True, False])
>>> np.isclose([1e10,1e-8], [1.00001e10,1e-9])  
array([ True, True])
>>> np.isclose([1e10,1e-8], [1.0001e10,1e-9])  
array([False,  True])
>>> np.isclose([1.0, np.nan], [1.0, np.nan])  
array([ True, False])
>>> np.isclose([1.0, np.nan], [1.0, np.nan], equal_nan=True)  
array([ True, True])
>>> np.isclose([1e-8, 1e-7], [0.0, 0.0])  
array([ True, False])
>>> np.isclose([1e-100, 1e-7], [0.0, 0.0], atol=0.0)  
array([False, False])
>>> np.isclose([1e-10, 1e-10], [1e-20, 0.0])  
array([ True,  True])
>>> np.isclose([1e-10, 1e-10], [1e-20, 0.999999e-10], atol=0.0)  
array([False,  True])

This docstring was copied from numpy.