epsilon

Is there a "best practice" for less-than-equal comparisons with floating point number after a series of floating-point arithmetic operations? I have the following example in R (although the question applies to any language using floating-point). I have a double x = 1 on which I apply a series of additions and subtractions. In the end x should be exactly one but is not due to floating-point arithmetic (from what I gather). Here is the example: > stop_times <- seq(0.25, 2, by = .25) > expr <- expression(replicate(100,{ x <- 1 for(i in 1:10) { tmp <- rexp(1, 1) n <- sample.int(1e2, 1) delta <-

This is a follow-up to Testing for floating-point value equality: Is there a standard name for the “precision” constant? . There is a very similar question Double.Epsilon for equality, greater than, less than, less than or equal to, greater than or equal to . It is well known that an equality test for two floating-point values x and y should look more like this (rather than a straightforward =): abs( x - y ) < epsilon , where epsilon is some very small value. How to choose a value for epsilon ? It would obviously be preferable to choose for epsilon as small a value as possible, to get the

I need to find the machine epsilon and I am doing the following: eps = 1; while 1.0 + eps > 1.0 do eps = eps /2; end However, it shows me this: Undefined function or variable 'do'. Error in epsilon (line 3) while 1.0 + eps > 1.0 do What should I do? rayryeng First and foremost, there is no such thing as a do keyword in MATLAB, so eliminate that from your code. Also, don't use eps as an actual variable. This is a pre-defined function in MATLAB that calculates machine epsilon , which is also what you are trying to calculate. By creating a variable called eps , you would shadow over the actual

I have a unit test, testing boundaries: [TestMethod] [ExpectedException(typeof(ArgumentOutOfRangeException))] public void CreateExtent_InvalidTop_ShouldThrowArgumentOutOfRangeException() { var invalidTop = 90.0 + Double.Epsilon; new Extent(invalidTop, 0.0, 0.0, 0.0); } public static readonly double MAX_LAT = 90.0; public Extent(double top, double right, double bottom, double left) { if (top > GeoConstants.MAX_LAT) throw new ArgumentOutOfRangeException("top"); // not hit } I thought I'd just tip the 90.0 over the edge by adding the minimum possible positive double to it, but now the exception

Is there a standard value for (or method for obtaining) epsilon in Python? I need to compare floating point values and want to compare against the smallest possible difference. In C++ there's a function provided numeric_limits::epsilon( ) which gives the epsilon value for any given data type. Is there an equivalent in Python? The information is available in sys.float_info , which corresponds to float.h in C99. >>> import sys >>> sys.float_info.epsilon 2.220446049250313e-16 Ergwun As strcat posted , there is sys.float_info.epsilon . But don't forget the pitfalls of using it as an absolute error

As part of a unit test, I need to test some boundary conditions. One method accepts a System.Double argument. Is there a way to get the next-smallest double value? (i.e. decrement the mantissa by 1 unit-value)? I considered using Double.Epsilon but this is unreliable as it's only the smallest delta from zero, and so doesn't work for larger values (i.e. 9999999999 - Double.Epsilon == 9999999999 ). So what is the algorithm or code needed such that: NextSmallest(Double d) < d ...is always true. If your numbers are finite, you can use a couple of convenient methods in the BitConverter class: long

I am trying to understand what is machine epsilon. According to the Wikipedia, it can be calculated as follows: def machineEpsilon(func=float): machine_epsilon = func(1) while func(1)+func(machine_epsilon) != func(1): machine_epsilon_last = machine_epsilon machine_epsilon = func(machine_epsilon) / func(2) return machine_epsilon_last However, it is suitable only for double precision numbers. I am interested in modifying it to support also single precision numbers. I read that numpy can be used, particularly numpy.float32 class. Can anybody help with modifying the function? An easier way to get