A Pseudorandom Number Sequence Test Program
This page describes a program, ent, which applies various
tests to sequences of bytes stored in files and reports the results of
those tests. The program is useful for evaluating pseudorandom
number generators for encryption and statistical sampling
applications, compression algorithms, and other applications where the
information density of a file is of interest.
ent - pseudorandom number sequence test
ent [ -b -c -f -t -u ] [ infile ]
ent performs a variety of tests on the stream of
bytes in infile (or standard input if no infile
is specified) and produces output as follows on the standard
Entropy = 7.980627 bits per character.
Optimum compression would reduce the size
of this 51768 character file by 0 percent.
Chi square distribution for 51768 samples is 1542.26, and randomly
would exceed this value less than 0.01 percent of the times.
Arithmetic mean value of data bytes is 125.93 (127.5 = random).
Monte Carlo value for Pi is 3.169834647 (error 0.90 percent).
Serial correlation coefficient is 0.004249 (totally uncorrelated = 0.0).
The values calculated are as follows:
- The information density of the contents of the file,
expressed as a number of bits per character. The
results above, which resulted from processing an image
file compressed with JPEG, indicate that the file is
extremely dense in information—essentially random.
Hence, compression of the file is unlikely to reduce
its size. By contrast, the C source code of the
program has entropy of about 4.9 bits per character,
indicating that optimal compression of the file would
reduce its size by 38%. [Hamming, pp. 104–108]
- Chi-square Test
The chi-square test is the most commonly used test for
the randomness of data, and is extremely sensitive to
errors in pseudorandom sequence generators. The
chi-square distribution is calculated for the stream
of bytes in the file and expressed as an absolute
number and a percentage which indicates how frequently
a truly random sequence would exceed the value
calculated. We interpret the percentage as the degree
to which the sequence tested is suspected of being
non-random. If the percentage is greater than 99% or
less than 1%, the sequence is almost certainly not
random. If the percentage is between 99% and 95% or
between 1% and 5%, the sequence is suspect.
Percentages between 90% and 95% and 5% and 10%
indicate the sequence is “almost suspect”. Note that
our JPEG file, while very dense in information, is far
from random as revealed by the chi-square test.
Applying this test to the output of various
pseudorandom sequence generators is interesting. The
low-order 8 bits returned by the standard Unix
rand() function, for example, yields:
Chi square distribution for 500000 samples is 0.01,
and randomly would exceed this value more than 99.99 percent
of the times.
While an improved generator [Park & Miller] reports:
Chi square distribution for 500000 samples is
212.53, and randomly would exceed this value 97.53
percent of the times.
Thus, the standard Unix generator (or at least the
low-order bytes it returns) is unacceptably
non-random, while the improved generator is much
better but still sufficiently non-random to cause
concern for demanding applications. Contrast both of
these software generators with the chi-square result
of a genuine random sequence created by
radioactive decay events.
Chi square distribution for 500000 samples is 249.51, and randomly
would exceed this value 40.98 percent of the times.
See [Knuth, pp. 35–40] for more information on the
chi-square test. An interactive
calculator is available at this site.
- Arithmetic Mean
- This is simply the result of summing the all the bytes
(bits if the -b option is specified) in the file
and dividing by the file length. If the data are
close to random, this should be about 127.5 (0.5
for -b option output). If the
mean departs from this value, the values are
consistently high or low.
- Monte Carlo Value for Pi
- Each successive sequence of six bytes is used as 24 bit X and Y
co-ordinates within a square. If the distance of the
randomly-generated point is less than the radius of a
circle inscribed within the square, the six-byte
sequence is considered a “hit”. The percentage of
hits can be used to calculate the value of Pi. For
very large streams (this approximation converges very
slowly), the value will approach the correct value of
Pi if the sequence is close to random. A 500000 byte
file created by radioactive decay yielded:
Monte Carlo value for Pi is 3.143580574 (error 0.06 percent).
- Serial Correlation Coefficient
- This quantity measures the extent to which each byte in the file
depends upon the previous byte. For random sequences,
this value (which can be positive or negative) will,
of course, be close to zero. A non-random byte stream
such as a C program will yield a serial correlation
coefficient on the order of 0.5. Wildly predictable
data such as uncompressed bitmaps will exhibit serial
correlation coefficients approaching 1. See [Knuth,
pp. 64–65] for more details.
- The input is treated as a stream of bits rather
than of 8-bit bytes. Statistics reported reflect
the properties of the bitstream.
- Print a table of the number of occurrences of
each possible byte (or bit, if the -b option
is also specified) value, and the fraction of the
overall file made up by that value. Printable
characters in the ISO 8859-1 Latin-1 character set
are shown along with their decimal byte values.
In non-terse output mode, values with zero
occurrences are not printed.
- Fold upper case letters to lower case before
computing statistics. Folding is done based on the
ISO 8859-1 Latin-1 character set, with accented
letters correctly processed.
- Terse mode: output is written in Comma
Separated Value (CSV) format, suitable for
loading into a spreadsheet and easily read
by any programming language. See
Terse Mode Output Format
below for additional details.
- Print how-to-call information.
If no infile is specified, ent obtains its input
from standard input. Output is always written to standard
Terse mode is selected by specifying the -t option
on the command line. Terse mode output is written in
Comma Separated Value (CSV) format, which can be directly
loaded into most spreadsheet programs and is easily read
by any programming language. Each record in the CSV
file begins with a record type field, which identifies
the content of the following fields. If the -c
option is not specified, the terse mode output will
consist of two records, as follows:
where the italicised values in the type 1 record are the
numerical values for the quantities named in the type 0
column title record. If the -b option is specified, the second
field of the type 0 record will be “File-bits”, and
the file_length field in type 1 record will be given
in bits instead of bytes. If the -c option is specified,
additional records are appended to the terse mode output which
contain the character counts:
. . .
If the -b option is specified, only two type 3 records will
appear for the two bit values v=0 and v=1.
Otherwise, 256 type 3 records are included, one for each
possible byte value. The second field of a type 3 record
indicates how many bytes (or bits) of value v
appear in the input, and fraction gives the decimal
fraction of the file which has value v (which is
equal to the count value of this record divided by
the file_length field in the type 1 record).
Note that the “optimal compression” shown for the file is
computed from the byte- or bit-stream entropy and thus
reflects compressibility based on a reading frame of
the chosen width (8-bit bytes or individual bits if the
-b option is specified). Algorithms which use
a larger reading frame, such as the Lempel-Ziv [Lempel & Ziv]
algorithm, may achieve greater compression if the file
contains repeated sequences of multiple bytes.
The program is provided as random.zip,
a Zipped archive
containing an ready-to-run Win32 command-line program,
ent.exe (compiled using Microsoft Visual C++ .NET,
creating a 32-bit Windows executable), and in source code form
along with a
Makefile to build the program under
to Probability and Statistics
- Hamming, Richard W.
Coding and Information Theory.
Englewood Cliffs NJ: Prentice-Hall, 1980.
- Knuth, Donald E.
The Art of Computer Programming,
Volume 2 / Seminumerical Algorithms.
Reading MA: Addison-Wesley, 1969.
- [Lempel & Ziv]
- Ziv J. and A. Lempel.
“A Universal Algorithm for Sequential Data Compression”.
IEEE Transactions on Information Theory
23, 3, pp. 337-343.
- [Park & Miller]
- Park, Stephen K. and Keith W. Miller.
“Random Number Generators: Good Ones Are Hard to Find”.
Communications of the ACM, October 1988, p. 1192.
This software is in the public domain. Permission to use, copy,
modify, and distribute this software and its documentation for
any purpose and without fee is hereby granted, without any
conditions or restrictions. This software is provided “as is”
without express or implied warranty.