Here's a short example that illustrates this.
x = 2 - 1/3 - 1/3 - 1/3
x = 1.0000
But if we compare the result to its theoretical answer, we get that they aren't equal.
tf = isequal(x, 1)
tf = 0
If we display x in hexadecimal format to inspect the full precision,
format hex x
x = 3ff0000000000001
On the other hand, the value "1" has a hexadecimal representation of
ans = 3ff0000000000000
Note the difference in the last bit.
Because of this, usually it is not a good practice to do a straight up comparison of floating point arithmetics. Instead you may do things like this.
format short tf = abs(x - 1) <= eps(1)
tf = 1
Carlos's numcmp allows you to compare two values within a specific tolerance. You can choose from a set of various comparisons, such as '==', '~=', '<', '>', etc. You can also select the tolerance, specified by a positive integer TOL which represents 10^(-TOL).
tf = numcmp(x,'==',1,10) % tolerance of 1e-10
tf = 1
Thanks for the entry, Carlos. One comment. I like that it is vectorized, but you use repmat to match the size of the inputs. You even have a note saying that you "avoided using bsxfun". I actually recommend using bsxfun. It is much more efficient in terms of speed and memory, especially for larger data.
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