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In computing, a roundoff error, [1] also called rounding error, [2] is the difference between the result produced by a given algorithm using exact arithmetic and the result produced by the same algorithm using finite-precision, rounded arithmetic. [3]
Rounding to a specified power is very different from rounding to a specified multiple; for example, it is common in computing to need to round a number to a whole power of 2. The steps, in general, to round a positive number x to a power of some positive number b other than 1, are:
For tie-breaking, Python 3 uses round to even: round(1.5) and round(2.5) both produce 2. [124] Versions before 3 used round-away-from-zero: round(0.5) is 1.0, round(-0.5) is −1.0. [125] Python allows Boolean expressions with multiple equality relations in a manner that is consistent with general use in mathematics.
This alternative definition is significantly more widespread: machine epsilon is the difference between 1 and the next larger floating point number.This definition is used in language constants in Ada, C, C++, Fortran, MATLAB, Mathematica, Octave, Pascal, Python and Rust etc., and defined in textbooks like «Numerical Recipes» by Press et al.
In the floating-point case, a variable exponent would represent the power of ten to which the mantissa of the number is multiplied. Languages that support a rational data type usually allow the construction of such a value from two integers, instead of a base-2 floating-point number, due to the loss of exactness the latter would cause.
⎕CR 'PrimeNumbers' ⍝ Show APL user-function PrimeNumbers Primes ← PrimeNumbers N ⍝ Function takes one right arg N (e.g., show prime numbers for 1 ... int N) Primes ← (2 =+ ⌿ 0 = (⍳ N) ∘. |⍳ N) / ⍳ N ⍝ The Ken Iverson one-liner PrimeNumbers 100 ⍝ Show all prime numbers from 1 to 100 2 3 5 7 11 13 17 19 23 29 31 37 41 43 ...
Variable length arithmetic represents numbers as a string of digits of a variable's length limited only by the memory available. Variable-length arithmetic operations are considerably slower than fixed-length format floating-point instructions.
In Python, a generator can be thought of as an iterator that contains a frozen stack frame. Whenever next() is called on the iterator, Python resumes the frozen frame, which executes normally until the next yield statement is reached. The generator's frame is then frozen again, and the yielded value is returned to the caller.