When is a Numeric Result Not a Number?

Quick answer: when the result is a NaN.

Contents

NaNs in Arithmetic

MATLAB has followed the IEEE 754: Standard for Binary Floating-Point Arithmetic for doing arithmetic from the early days, and included tools and functionality to achieve similar results on machines that did not comform to the IEEE standards, e.g., Digital Equipment Corporations VAX computers. In addition, we made choices in MATLAB to allow division by 0 rather than causing an error on machine architectures that could handle this behavior (allowed in the IEEE standard). In allowing division by 0, however, we not only encounter cases in which we divide nonzero positive numbers and negative numbers by 0 (yielding Inf and -Inf respectively, but we also encounter computations that result in undefined mathematical outcomes such as

inf-inf

ans =
   NaN

0/0

Warning: Divide by zero.
ans =
   NaN

NaNs as Placeholders

In addition to allowing NaN as the result of numeric operations, we've also encouraged their use for marking information such as missing data. If we were gathering some data for some specific times, and missed collecting one of the values, we might end up with data that look like this:

ts = 0:5;
vals = [0.2 4.3 -1.6 NaN -2.0 1.2]
plot(ts,vals,'m*-')

vals =
  Columns 1 through 5 
    0.2000    4.3000   -1.6000       NaN   -2.0000
  Column 6 
    1.2000

How to Find or Compare to NaN

According to the IEEE standard, NaN is not a number and not equal to anything, including itself. So when looking for NaN values in an array, you can't do the "obvious"

vals == NaN

ans =
     0     0     0     0     0     0

As you can see, the comparison is always false. So how do we look for them? Depending on what you are doing, you will find these functions helpful:

Though a mouthful to say or write, isequalwithequalnans allows you to carry out comparisons between arrays matching values and shape, without allowing scalar expansion.

Another way to make look for NaN is frequently cited on the MATLAB newsgroup. See this thread and comments by Paul Bodin for an example. The idea here is that since

NaN == NaN

ans =
     0

always produces a false result, we can find NaNs simply by looking for places in which the value doesn't equal itself. These are NaNs, and other values are not.

ourNaNs = vals(vals~=vals)

ourNaNs =
   NaN

nonNaNs = vals(vals==vals)

nonNaNs =
    0.2000    4.3000   -1.6000   -2.0000    1.2000

My preference is to use isnan instead because I find the code much more readable when I revisit it later. However, in some cases, depending on the amount of NaN values (and perhaps other considerations), the constructs just above are sometimes faster. Here are a couple of additional links to newsgroup threads talking more about this.

NaN issues

Some users find themselves experiencing collisions between NaN for missing data vs. NaN as a result of undefined numerical operations.

Let's hear your thoughts.

Published with MATLAB® 7.2