Floating-point comparisons
The representation of floating-point numbers in computers is not exact. This leads to issues when comparing floating-point numbers. The assert_array_almost_equal_nulp() and assert_array_max_ulp() NumPy functions provide consistent floating-point comparisons. Unit of Least Precision (ULP) of floating-point numbers, according to the IEEE 754 specification, a half ULP precision is required for elementary arithmetic operations. You can compare this to a ruler. A metric system ruler usually has ticks for millimeters, but beyond that you can only estimate half millimeters.
Machine epsilon is the largest relative rounding error in floating-point arithmetic. Machine epsilon is equal to ULP relative to 1. The NumPy finfo() function ...
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