Ausgabe DieHarder auf 11,6 GiByte Zufallsdaten der Rev. K
Diese Seite zeigt die Ausgabe der DieHarder Testsuite für Zufallszahlen:
#=============================================================================#
# dieharder version 3.31.1 Copyright 2003 Robert G. Brown #
#=============================================================================#
rng_name |rands/second| Seed |
stdin_input_raw| 3.78e+02 |1911919715|
#==================================================================
# Diehard "Birthdays" test (modified).
# Each test determines the number of matching intervals from 512
# "birthdays" (by default) drawn on a 24-bit "year" (by
# default). This is repeated 100 times (by default) and the
# results cumulated in a histogram. Repeated intervals should be
# distributed in a Poisson distribution if the underlying generator
# is random enough, and a a chisq and p-value for the test are
# evaluated relative to this null hypothesis.
#
# It is recommended that you run this at or near the original
# 100 test samples per p-value with -t 100.
#
# Two additional parameters have been added. In diehard, nms=512
# but this CAN be varied and all Marsaglia's formulae still work. It
# can be reset to different values with -x nmsvalue.
# Similarly, nbits "should" 24, but we can really make it anything
# we want that's less than or equal to rmax_bits = 32. It can be
# reset to a new value with -y nbits. Both default to diehard's
# values if no -x or -y options are used.
#==================================================================
#=============================================================================#
# Histogram of test p-values #
#=============================================================================#
# Bin scale = 0.100000
# 20| | | | | | | | | | |
# | | | | | | | | | | |
# 18| | | | | | | | | | |
# | | | | | | | | | | |
# 16| | | | | | | | | | |
# | | | | | | | | | | |
# 14| | | | | | | | | | |
# | | | | | | | | | | |
# 12| | | | | |****| | | | |
# | |****| |****| |****|****| |****|****|
# 10|****|****| |****|****|****|****|****|****|****|
# |****|****| |****|****|****|****|****|****|****|
# 8|****|****| |****|****|****|****|****|****|****|
# |****|****| |****|****|****|****|****|****|****|
# 6|****|****| |****|****|****|****|****|****|****|
# |****|****| |****|****|****|****|****|****|****|
# 4|****|****| |****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# 2|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# |--------------------------------------------------
# | 0.1| 0.2| 0.3| 0.4| 0.5| 0.6| 0.7| 0.8| 0.9| 1.0|
#=============================================================================#
#=============================================================================#
test_name |ntup| tsamples |psamples| p-value |Assessment
#=============================================================================#
diehard_birthdays| 0| 100| 100|0.68060996| PASSED
#==================================================================
# Diehard Overlapping 5-Permutations Test.
# This is the OPERM5 test. It looks at a sequence of one mill-
# ion 32-bit random integers. Each set of five consecutive
# integers can be in one of 120 states, for the 5! possible or-
# derings of five numbers. Thus the 5th, 6th, 7th,...numbers
# each provide a state. As many thousands of state transitions
# are observed, cumulative counts are made of the number of
# occurences of each state. Then the quadratic form in the
# weak inverse of the 120x120 covariance matrix yields a test
# equivalent to the likelihood ratio test that the 120 cell
# counts came from the specified (asymptotically) normal dis-
# tribution with the specified 120x120 covariance matrix (with
# rank 99). This version uses 1,000,000 integers, twice.
#
# Note that Dieharder runs the test 100 times, not twice, by
# default.
#==================================================================
#=============================================================================#
# Histogram of test p-values #
#=============================================================================#
# Bin scale = 0.100000
# 20| | | | | | | | | | |
# | | | | | | | | | | |
# 18| | | | | | | | | | |
# | | | | | | | | | | |
# 16| | | | | | | | | | |
# | | | | | | | | | | |
# 14| | | | | | | | | | |
# | | | | | | | |****| | |
# 12|****| | | | | | |****| | |
# |****|****| | | | | |****| | |
# 10|****|****|****| | |****| |****|****|****|
# |****|****|****| | |****| |****|****|****|
# 8|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# 6|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# 4|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# 2|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# |--------------------------------------------------
# | 0.1| 0.2| 0.3| 0.4| 0.5| 0.6| 0.7| 0.8| 0.9| 1.0|
#=============================================================================#
#=============================================================================#
test_name |ntup| tsamples |psamples| p-value |Assessment
#=============================================================================#
diehard_operm5| 0| 1000000| 100|0.96288413| PASSED
#==================================================================
# Diehard 32x32 Binary Rank Test
# This is the BINARY RANK TEST for 32x32 matrices. A random 32x
# 32 binary matrix is formed, each row a 32-bit random integer.
# The rank is determined. That rank can be from 0 to 32, ranks
# less than 29 are rare, and their counts are pooled with those
# for rank 29. Ranks are found for 40,000 such random matrices
# and a chisquare test is performed on counts for ranks 32,31,
# 30 and <=29.
#
# As always, the test is repeated and a KS test applied to the
# resulting p-values to verify that they are approximately uniform.
#==================================================================
#=============================================================================#
# Histogram of test p-values #
#=============================================================================#
# Bin scale = 0.100000
# 20| | | | | | | | | | |
# | | | | | | | | | | |
# 18| | | | | | | | | | |
# | | | | | | | | | | |
# 16| | | | | | | | | | |
# | | | | | | | | | |****|
# 14| | | | | | | | | |****|
# |****| | | | | | | | |****|
# 12|****| | | | | | | | |****|
# |****| |****|****| |****|****| | |****|
# 10|****| |****|****| |****|****| | |****|
# |****| |****|****| |****|****| | |****|
# 8|****| |****|****|****|****|****| | |****|
# |****|****|****|****|****|****|****|****| |****|
# 6|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# 4|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# 2|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# |--------------------------------------------------
# | 0.1| 0.2| 0.3| 0.4| 0.5| 0.6| 0.7| 0.8| 0.9| 1.0|
#=============================================================================#
#=============================================================================#
test_name |ntup| tsamples |psamples| p-value |Assessment
#=============================================================================#
diehard_rank_32x32| 0| 40000| 100|0.72396526| PASSED
#==================================================================
# Diehard 6x8 Binary Rank Test
# This is the BINARY RANK TEST for 6x8 matrices. From each of
# six random 32-bit integers from the generator under test, a
# specified byte is chosen, and the resulting six bytes form a
# 6x8 binary matrix whose rank is determined. That rank can be
# from 0 to 6, but ranks 0,1,2,3 are rare; their counts are
# pooled with those for rank 4. Ranks are found for 100,000
# random matrices, and a chi-square test is performed on
# counts for ranks 6,5 and <=4.
#
# As always, the test is repeated and a KS test applied to the
# resulting p-values to verify that they are approximately uniform.
#==================================================================
#=============================================================================#
# Histogram of test p-values #
#=============================================================================#
# Bin scale = 0.100000
# 20| | | | | | | | | | |
# | | | | | | | | | | |
# 18| | | | | | | | | | |
# | | | | | | | | | | |
# 16| | | | | | | | | |****|
# | | | | | | | | | |****|
# 14| | | | | | | | | |****|
# | |****| |****| | | | | |****|
# 12| |****| |****| | | | | |****|
# | |****| |****| | |****| | |****|
# 10| |****|****|****| | |****| | |****|
# | |****|****|****| |****|****| |****|****|
# 8|****|****|****|****| |****|****| |****|****|
# |****|****|****|****| |****|****|****|****|****|
# 6|****|****|****|****| |****|****|****|****|****|
# |****|****|****|****| |****|****|****|****|****|
# 4|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# 2|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# |--------------------------------------------------
# | 0.1| 0.2| 0.3| 0.4| 0.5| 0.6| 0.7| 0.8| 0.9| 1.0|
#=============================================================================#
#=============================================================================#
test_name |ntup| tsamples |psamples| p-value |Assessment
#=============================================================================#
diehard_rank_6x8| 0| 100000| 100|0.72737480| PASSED
#==================================================================
# Diehard Bitstream Test.
# The file under test is viewed as a stream of bits. Call them
# b1,b2,... . Consider an alphabet with two "letters", 0 and 1
# and think of the stream of bits as a succession of 20-letter
# "words", overlapping. Thus the first word is b1b2...b20, the
# second is b2b3...b21, and so on. The bitstream test counts
# the number of missing 20-letter (20-bit) words in a string of
# 2^21 overlapping 20-letter words. There are 2^20 possible 20
# letter words. For a truly random string of 2^21+19 bits, the
# number of missing words j should be (very close to) normally
# distributed with mean 141,909 and sigma 428. Thus
# (j-141909)/428 should be a standard normal variate (z score)
# that leads to a uniform [0,1) p value. The test is repeated
# twenty times.
#
# NOTE WELL!
#
# The test is repeated 100 times by default in dieharder, but the
# size of the sample is fixed (tsamples cannot/should not be
# varied from the default). The sigma of this test REQUIRES the
# use of overlapping samples, and overlapping samples are not
# independent. If one uses the non-overlapping version of this
# test, sigma = 290 is used instead, smaller because now there
# are 2^21 INDEPENDENT samples.
#==================================================================
#=============================================================================#
# Histogram of test p-values #
#=============================================================================#
# Bin scale = 0.100000
# 20| | | | | | | | | | |
# | | | | | | | | | | |
# 18| | | | | | | | | | |
# | | | | | | | | |****| |
# 16| | | | | | | | |****| |
# | | | | | | | | |****| |
# 14| | | | | | | |****|****| |
# |****|****| | | | | |****|****| |
# 12|****|****| | | | | |****|****| |
# |****|****| | | |****| |****|****| |
# 10|****|****| | | |****| |****|****| |
# |****|****| |****| |****| |****|****| |
# 8|****|****|****|****| |****| |****|****| |
# |****|****|****|****| |****| |****|****| |
# 6|****|****|****|****|****|****| |****|****| |
# |****|****|****|****|****|****|****|****|****| |
# 4|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# 2|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# |--------------------------------------------------
# | 0.1| 0.2| 0.3| 0.4| 0.5| 0.6| 0.7| 0.8| 0.9| 1.0|
#=============================================================================#
#=============================================================================#
test_name |ntup| tsamples |psamples| p-value |Assessment
#=============================================================================#
diehard_bitstream| 0| 2097152| 100|0.74238785| PASSED
#==================================================================
# Diehard Overlapping Pairs Sparse Occupance (OPSO)
# The OPSO test considers 2-letter words from an alphabet of
# 1024 letters. Each letter is determined by a specified ten
# bits from a 32-bit integer in the sequence to be tested. OPSO
# generates 2^21 (overlapping) 2-letter words (from 2^21+1
# "keystrokes") and counts the number of missing words---that
# is 2-letter words which do not appear in the entire sequence.
# That count should be very close to normally distributed with
# mean 141,909, sigma 290. Thus (missingwrds-141909)/290 should
# be a standard normal variable. The OPSO test takes 32 bits at
# a time from the test file and uses a designated set of ten
# consecutive bits. It then restarts the file for the next de-
# signated 10 bits, and so on.
#
# Note 2^21 = 2097152, tsamples cannot be varied.
#==================================================================
#=============================================================================#
# Histogram of test p-values #
#=============================================================================#
# Bin scale = 0.100000
# 20| | | | | | | | | | |
# | | | | | | | | | | |
# 18| | | | | | | | | | |
# | | | | | | | | | | |
# 16| | | | | | | | | | |
# | | | | | | | | |****| |
# 14| | | | | | | | |****| |
# | | |****| |****| | | |****| |
# 12| | |****| |****| | | |****| |
# | | |****| |****| |****| |****| |
# 10| | |****|****|****| |****| |****| |
# | |****|****|****|****|****|****| |****| |
# 8| |****|****|****|****|****|****| |****| |
# | |****|****|****|****|****|****|****|****|****|
# 6|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# 4|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# 2|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# |--------------------------------------------------
# | 0.1| 0.2| 0.3| 0.4| 0.5| 0.6| 0.7| 0.8| 0.9| 1.0|
#=============================================================================#
#=============================================================================#
test_name |ntup| tsamples |psamples| p-value |Assessment
#=============================================================================#
diehard_opso| 0| 2097152| 100|0.84058352| PASSED
#==================================================================
# Diehard Overlapping Quadruples Sparce Occupancy (OQSO) Test
#
# Similar, to OPSO except that it considers 4-letter
# words from an alphabet of 32 letters, each letter determined
# by a designated string of 5 consecutive bits from the test
# file, elements of which are assumed 32-bit random integers.
# The mean number of missing words in a sequence of 2^21 four-
# letter words, (2^21+3 "keystrokes"), is again 141909, with
# sigma = 295. The mean is based on theory; sigma comes from
# extensive simulation.
#
# Note 2^21 = 2097152, tsamples cannot be varied.
#==================================================================
#=============================================================================#
# Histogram of test p-values #
#=============================================================================#
# Bin scale = 0.100000
# 20| | | | | | | | | | |
# | | | | | | | | | | |
# 18| | | | | | | | | | |
# | | | | | | | | | | |
# 16| | | | | | | | | | |
# | | | | | | | | | | |
# 14| | | | | | | | | | |
# | | |****| | |****| | | | |
# 12| | |****| | |****| | | | |
# |****| |****|****| |****| | | | |
# 10|****| |****|****| |****| |****|****| |
# |****| |****|****|****|****| |****|****|****|
# 8|****|****|****|****|****|****| |****|****|****|
# |****|****|****|****|****|****| |****|****|****|
# 6|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# 4|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# 2|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# |--------------------------------------------------
# | 0.1| 0.2| 0.3| 0.4| 0.5| 0.6| 0.7| 0.8| 0.9| 1.0|
#=============================================================================#
#=============================================================================#
test_name |ntup| tsamples |psamples| p-value |Assessment
#=============================================================================#
diehard_oqso| 0| 2097152| 100|0.91553991| PASSED
#==================================================================
# Diehard DNA Test.
#
# The DNA test considers an alphabet of 4 letters:: C,G,A,T,
# determined by two designated bits in the sequence of random
# integers being tested. It considers 10-letter words, so that
# as in OPSO and OQSO, there are 2^20 possible words, and the
# mean number of missing words from a string of 2^21 (over-
# lapping) 10-letter words (2^21+9 "keystrokes") is 141909.
# The standard deviation sigma=339 was determined as for OQSO
# by simulation. (Sigma for OPSO, 290, is the true value (to
# three places), not determined by simulation.
#
# Note 2^21 = 2097152
# Note also that we don't bother with overlapping keystrokes
# (and sample more rands -- rands are now cheap).
#==================================================================
#=============================================================================#
# Histogram of test p-values #
#=============================================================================#
# Bin scale = 0.100000
# 20| | | | | | | | | | |
# | | | | | | | | | | |
# 18| | | | | | | | | | |
# | | | | | | |****| | | |
# 16| | | | | | |****| | | |
# | | | | | | |****|****| | |
# 14| | | | | |****|****|****| | |
# | | | | | |****|****|****| | |
# 12| | | | | |****|****|****| | |
# | | | | | |****|****|****| | |
# 10| | |****| |****|****|****|****| | |
# | | |****|****|****|****|****|****| |****|
# 8| |****|****|****|****|****|****|****| |****|
# | |****|****|****|****|****|****|****| |****|
# 6| |****|****|****|****|****|****|****| |****|
# | |****|****|****|****|****|****|****| |****|
# 4|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# 2|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# |--------------------------------------------------
# | 0.1| 0.2| 0.3| 0.4| 0.5| 0.6| 0.7| 0.8| 0.9| 1.0|
#=============================================================================#
#=============================================================================#
test_name |ntup| tsamples |psamples| p-value |Assessment
#=============================================================================#
diehard_dna| 0| 2097152| 100|0.21516088| PASSED
#==================================================================
# Diehard Count the 1s (stream) (modified) Test.
# Consider the file under test as a stream of bytes (four per
# 32 bit integer). Each byte can contain from 0 to 8 1's,
# with probabilities 1,8,28,56,70,56,28,8,1 over 256. Now let
# the stream of bytes provide a string of overlapping 5-letter
# words, each "letter" taking values A,B,C,D,E. The letters are
# determined by the number of 1's in a byte:: 0,1,or 2 yield A,
# 3 yields B, 4 yields C, 5 yields D and 6,7 or 8 yield E. Thus
# we have a monkey at a typewriter hitting five keys with vari-
# ous probabilities (37,56,70,56,37 over 256). There are 5^5
# possible 5-letter words, and from a string of 256,000 (over-
# lapping) 5-letter words, counts are made on the frequencies
# for each word. The quadratic form in the weak inverse of
# the covariance matrix of the cell counts provides a chisquare
# test:: Q5-Q4, the difference of the naive Pearson sums of
# (OBS-EXP)^2/EXP on counts for 5- and 4-letter cell counts.
#==================================================================
#=============================================================================#
# Histogram of test p-values #
#=============================================================================#
# Bin scale = 0.100000
# 20| | | | | | | | | | |
# | | | | | | | | | | |
# 18| | | | | | | | | | |
# | | | | | | | | | | |
# 16| | | | | | | | | | |
# | | | | | | | | | | |
# 14| | | | | |****| | |****| |
# | | | | | |****| | |****|****|
# 12| | | | | |****| | |****|****|
# | |****| | | |****| | |****|****|
# 10| |****| | |****|****| | |****|****|
# | |****|****|****|****|****| |****|****|****|
# 8| |****|****|****|****|****| |****|****|****|
# | |****|****|****|****|****|****|****|****|****|
# 6| |****|****|****|****|****|****|****|****|****|
# | |****|****|****|****|****|****|****|****|****|
# 4|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# 2|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# |--------------------------------------------------
# | 0.1| 0.2| 0.3| 0.4| 0.5| 0.6| 0.7| 0.8| 0.9| 1.0|
#=============================================================================#
#=============================================================================#
test_name |ntup| tsamples |psamples| p-value |Assessment
#=============================================================================#
diehard_count_1s_str| 0| 256000| 100|0.51753616| PASSED
#==================================================================
# Diehard Count the 1s Test (byte) (modified).
# This is the COUNT-THE-1's TEST for specific bytes.
# Consider the file under test as a stream of 32-bit integers.
# From each integer, a specific byte is chosen , say the left-
# most:: bits 1 to 8. Each byte can contain from 0 to 8 1's,
# with probabilitie 1,8,28,56,70,56,28,8,1 over 256. Now let
# the specified bytes from successive integers provide a string
# of (overlapping) 5-letter words, each "letter" taking values
# A,B,C,D,E. The letters are determined by the number of 1's,
# in that byte:: 0,1,or 2 ---> A, 3 ---> B, 4 ---> C, 5 ---> D,
# and 6,7 or 8 ---> E. Thus we have a monkey at a typewriter
# hitting five keys with with various probabilities:: 37,56,70,
# 56,37 over 256. There are 5^5 possible 5-letter words, and
# from a string of 256,000 (overlapping) 5-letter words, counts
# are made on the frequencies for each word. The quadratic form
# in the weak inverse of the covariance matrix of the cell
# counts provides a chisquare test:: Q5-Q4, the difference of
# the naive Pearson sums of (OBS-EXP)^2/EXP on counts for 5-
# and 4-letter cell counts.
#
# Note: We actually cycle samples over all 0-31 bit offsets, so
# that if there is a problem with any particular offset it has
# a chance of being observed. One can imagine problems with odd
# offsets but not even, for example, or only with the offset 7.
# tsamples and psamples can be freely varied, but you'll likely
# need tsamples >> 100,000 to have enough to get a reliable kstest
# result.
#==================================================================
#=============================================================================#
# Histogram of test p-values #
#=============================================================================#
# Bin scale = 0.100000
# 20| | | | | | | | | | |
# | | | | | | | | | | |
# 18| | | | | | | | | | |
# | | | | | | | | | | |
# 16| | | | | | | | | | |
# | | | | | | | | | | |
# 14| | |****| | | | | | | |
# | |****|****| | | | | | | |
# 12|****|****|****| | | |****| | | |
# |****|****|****|****| | |****| | |****|
# 10|****|****|****|****| | |****|****| |****|
# |****|****|****|****| | |****|****| |****|
# 8|****|****|****|****| | |****|****| |****|
# |****|****|****|****| |****|****|****|****|****|
# 6|****|****|****|****| |****|****|****|****|****|
# |****|****|****|****| |****|****|****|****|****|
# 4|****|****|****|****| |****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# 2|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# |--------------------------------------------------
# | 0.1| 0.2| 0.3| 0.4| 0.5| 0.6| 0.7| 0.8| 0.9| 1.0|
#=============================================================================#
#=============================================================================#
test_name |ntup| tsamples |psamples| p-value |Assessment
#=============================================================================#
diehard_count_1s_byt| 0| 256000| 100|0.07538205| PASSED
#==================================================================
# Diehard Parking Lot Test (modified).
# This tests the distribution of attempts to randomly park a
# square car of length 1 on a 100x100 parking lot without
# crashing. We plot n (number of attempts) versus k (number of
# attempts that didn't "crash" because the car squares
# overlapped and compare to the expected result from a perfectly
# random set of parking coordinates. This is, alas, not really
# known on theoretical grounds so instead we compare to n=12,000
# where k should average 3523 with sigma 21.9 and is very close
# to normally distributed. Thus (k-3523)/21.9 is a standard
# normal variable, which converted to a uniform p-value, provides
# input to a KS test with a default 100 samples.
#==================================================================
#=============================================================================#
# Histogram of test p-values #
#=============================================================================#
# Bin scale = 0.100000
# 20| | | | | | | | | | |
# | | | | | | | | | | |
# 18| | | | | | | | | | |
# | | | | | | | | | | |
# 16| | | | | | | | | | |
# | | | | | | | | | | |
# 14| | | | | | | | | | |
# |****|****| | | | |****| | | |
# 12|****|****| | | | |****| |****| |
# |****|****| | | |****|****|****|****| |
# 10|****|****| | | |****|****|****|****| |
# |****|****| | | |****|****|****|****| |
# 8|****|****| | | |****|****|****|****| |
# |****|****|****|****|****|****|****|****|****| |
# 6|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# 4|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# 2|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# |--------------------------------------------------
# | 0.1| 0.2| 0.3| 0.4| 0.5| 0.6| 0.7| 0.8| 0.9| 1.0|
#=============================================================================#
#=============================================================================#
test_name |ntup| tsamples |psamples| p-value |Assessment
#=============================================================================#
diehard_parking_lot| 0| 12000| 100|0.65655550| PASSED
#==================================================================
# Diehard Minimum Distance (2d Circle) Test
# It does this 100 times:: choose n=8000 random points in a
# square of side 10000. Find d, the minimum distance between
# the (n^2-n)/2 pairs of points. If the points are truly inde-
# pendent uniform, then d^2, the square of the minimum distance
# should be (very close to) exponentially distributed with mean
# .995 . Thus 1-exp(-d^2/.995) should be uniform on [0,1) and
# a KSTEST on the resulting 100 values serves as a test of uni-
# formity for random points in the square. Test numbers=0 mod 5
# are printed but the KSTEST is based on the full set of 100
# random choices of 8000 points in the 10000x10000 square.
#
# This test uses a fixed number of samples -- tsamples is ignored.
# It also uses the default value of 100 psamples in the final
# KS test, for once agreeing precisely with Diehard.
#==================================================================
#=============================================================================#
# Histogram of test p-values #
#=============================================================================#
# Bin scale = 0.100000
# 20| | | | | | | | | | |
# | | | | | | | | | | |
# 18| | | | | | | | | | |
# | | | | | | | | | | |
# 16| | | | | | | | | | |
# | | | | | | | | | | |
# 14| | | | | | |****| | | |
# | | |****| | | |****| | | |
# 12| | |****| | | |****| | | |
# |****| |****| | |****|****|****| | |
# 10|****| |****|****| |****|****|****| | |
# |****| |****|****| |****|****|****| | |
# 8|****|****|****|****| |****|****|****|****| |
# |****|****|****|****|****|****|****|****|****|****|
# 6|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# 4|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# 2|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# |--------------------------------------------------
# | 0.1| 0.2| 0.3| 0.4| 0.5| 0.6| 0.7| 0.8| 0.9| 1.0|
#=============================================================================#
#=============================================================================#
test_name |ntup| tsamples |psamples| p-value |Assessment
#=============================================================================#
diehard_2dsphere| 2| 8000| 100|0.76442050| PASSED
#==================================================================
# Diehard 3d Sphere (Minimum Distance) Test
# Choose 4000 random points in a cube of edge 1000. At each
# point, center a sphere large enough to reach the next closest
# point. Then the volume of the smallest such sphere is (very
# close to) exponentially distributed with mean 120pi/3. Thus
# the radius cubed is exponential with mean 30. (The mean is
# obtained by extensive simulation). The 3DSPHERES test gener-
# ates 4000 such spheres 20 times. Each min radius cubed leads
# to a uniform variable by means of 1-exp(-r^3/30.), then a
# KSTEST is done on the 20 p-values.
#
# This test ignores tsamples, and runs the usual default 100
# psamples to use in the final KS test.
#==================================================================
#=============================================================================#
# Histogram of test p-values #
#=============================================================================#
# Bin scale = 0.100000
# 20| | | | | | | | | | |
# | | | | | | | | | | |
# 18| | | | | | | | | | |
# | | | | | | | | | | |
# 16| | | | |****| | | | | |
# | | | | |****| | | | | |
# 14| | | | |****|****| | | | |
# | | | | |****|****| | | | |
# 12| | |****| |****|****| | | | |
# | |****|****| |****|****| | | | |
# 10| |****|****|****|****|****| | | | |
# | |****|****|****|****|****| | | | |
# 8|****|****|****|****|****|****| | | |****|
# |****|****|****|****|****|****|****|****|****|****|
# 6|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# 4|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# 2|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# |--------------------------------------------------
# | 0.1| 0.2| 0.3| 0.4| 0.5| 0.6| 0.7| 0.8| 0.9| 1.0|
#=============================================================================#
#=============================================================================#
test_name |ntup| tsamples |psamples| p-value |Assessment
#=============================================================================#
diehard_3dsphere| 3| 4000| 100|0.10045300| PASSED
#==================================================================
# Diehard Squeeze Test.
# Random integers are floated to get uniforms on [0,1). Start-
# ing with k=2^31=2147483647, the test finds j, the number of
# iterations necessary to reduce k to 1, using the reduction
# k=ceiling(k*U), with U provided by floating integers from
# the file being tested. Such j's are found 100,000 times,
# then counts for the number of times j was <=6,7,...,47,>=48
# are used to provide a chi-square test for cell frequencies.
#==================================================================
#=============================================================================#
# Histogram of test p-values #
#=============================================================================#
# Bin scale = 0.100000
# 20| | | | | | | | | | |
# | | | | | | | | | | |
# 18| | | | | | | | | | |
# | | | | | | | | | | |
# 16| | | | | | | | | | |
# | | | | | |****| | |****| |
# 14| | | | | |****| | |****|****|
# | | | | | |****| | |****|****|
# 12| | | | | |****| | |****|****|
# | | | | | |****| | |****|****|
# 10| | | | |****|****|****|****|****|****|
# | | | | |****|****|****|****|****|****|
# 8| | | |****|****|****|****|****|****|****|
# | |****|****|****|****|****|****|****|****|****|
# 6| |****|****|****|****|****|****|****|****|****|
# | |****|****|****|****|****|****|****|****|****|
# 4|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# 2|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# |--------------------------------------------------
# | 0.1| 0.2| 0.3| 0.4| 0.5| 0.6| 0.7| 0.8| 0.9| 1.0|
#=============================================================================#
#=============================================================================#
test_name |ntup| tsamples |psamples| p-value |Assessment
#=============================================================================#
diehard_squeeze| 0| 100000| 100|0.00586033| PASSED
#==================================================================
# Diehard Sums Test
# Integers are floated to get a sequence U(1),U(2),... of uni-
# form [0,1) variables. Then overlapping sums,
# S(1)=U(1)+...+U(100), S2=U(2)+...+U(101),... are formed.
# The S's are virtually normal with a certain covariance mat-
# rix. A linear transformation of the S's converts them to a
# sequence of independent standard normals, which are converted
# to uniform variables for a KSTEST. The p-values from ten
# KSTESTs are given still another KSTEST.
#
# Comments
#
# At this point I think there is rock solid evidence that this test
# is completely useless in every sense of the word. It is broken,
# and it is so broken that there is no point in trying to fix it.
# The problem is that the transformation above is not linear, and
# doesn't work. Don't use it.
#
# For what it is worth, rgb_lagged_sums with ntuple 0 tests for
# exactly the same thing, but scalably and reliably without the
# complication of overlapping samples and covariance. Use it
# instead.
#==================================================================
#=============================================================================#
# Histogram of test p-values #
#=============================================================================#
# Bin scale = 0.100000
# 40| | | | | | | | | | |
# | | | | | | | | | | |
# 36| | | | | | | | | | |
# | | | | | | | | | | |
# 32|****| | | | | | | | | |
# |****| | | | | | | | | |
# 28|****| | | | | | | | | |
# |****| | | | | | | | | |
# 24|****| | | | | | | | | |
# |****| | | | | | | | | |
# 20|****| | | | | | | | | |
# |****| | | | | | | | | |
# 16|****| | | | | | | | | |
# |****| | | | | | | | | |
# 12|****| | | |****| | | | | |
# |****| | | |****| | | | | |
# 8|****|****| | |****|****| | |****| |
# |****|****|****|****|****|****|****|****|****| |
# 4|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# |--------------------------------------------------
# | 0.1| 0.2| 0.3| 0.4| 0.5| 0.6| 0.7| 0.8| 0.9| 1.0|
#=============================================================================#
#=============================================================================#
test_name |ntup| tsamples |psamples| p-value |Assessment
#=============================================================================#
diehard_sums| 0| 100| 100|0.00004253| WEAK
#==================================================================
# Diehard Sums Test
# Integers are floated to get a sequence U(1),U(2),... of uni-
# form [0,1) variables. Then overlapping sums,
# S(1)=U(1)+...+U(100), S2=U(2)+...+U(101),... are formed.
# The S's are virtually normal with a certain covariance mat-
# rix. A linear transformation of the S's converts them to a
# sequence of independent standard normals, which are converted
# to uniform variables for a KSTEST. The p-values from ten
# KSTESTs are given still another KSTEST.
#
# Comments
#
# At this point I think there is rock solid evidence that this test
# is completely useless in every sense of the word. It is broken,
# and it is so broken that there is no point in trying to fix it.
# The problem is that the transformation above is not linear, and
# doesn't work. Don't use it.
#
# For what it is worth, rgb_lagged_sums with ntuple 0 tests for
# exactly the same thing, but scalably and reliably without the
# complication of overlapping samples and covariance. Use it
# instead.
#==================================================================
#=============================================================================#
# Histogram of test p-values #
#=============================================================================#
# Bin scale = 0.100000
# 60| | | | | | | | | | |
# | | | | | | | | | | |
# 54| | | | | | | | | | |
# | | | | | | | | | | |
# 48| | | | | | | | | | |
# |****| | | | | | | | | |
# 42|****| | | | | | | | | |
# |****| | | | | | | | | |
# 36|****| | | | | | | | | |
# |****| | | | | | | | | |
# 30|****| | | | | | | | | |
# |****| | | | | | | | | |
# 24|****| | | | |****| | | | |
# |****| | | |****|****| | | | |
# 18|****| | |****|****|****| | | | |
# |****|****| |****|****|****| |****| |****|
# 12|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# 6|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# |--------------------------------------------------
# | 0.1| 0.2| 0.3| 0.4| 0.5| 0.6| 0.7| 0.8| 0.9| 1.0|
#=============================================================================#
#=============================================================================#
test_name |ntup| tsamples |psamples| p-value |Assessment
#=============================================================================#
diehard_sums| 0| 100| 200|0.00033963| WEAK
#==================================================================
# Diehard Sums Test
# Integers are floated to get a sequence U(1),U(2),... of uni-
# form [0,1) variables. Then overlapping sums,
# S(1)=U(1)+...+U(100), S2=U(2)+...+U(101),... are formed.
# The S's are virtually normal with a certain covariance mat-
# rix. A linear transformation of the S's converts them to a
# sequence of independent standard normals, which are converted
# to uniform variables for a KSTEST. The p-values from ten
# KSTESTs are given still another KSTEST.
#
# Comments
#
# At this point I think there is rock solid evidence that this test
# is completely useless in every sense of the word. It is broken,
# and it is so broken that there is no point in trying to fix it.
# The problem is that the transformation above is not linear, and
# doesn't work. Don't use it.
#
# For what it is worth, rgb_lagged_sums with ntuple 0 tests for
# exactly the same thing, but scalably and reliably without the
# complication of overlapping samples and covariance. Use it
# instead.
#==================================================================
#=============================================================================#
# Histogram of test p-values #
#=============================================================================#
# Bin scale = 0.100000
# 80| | | | | | | | | | |
# | | | | | | | | | | |
# 72| | | | | | | | | | |
# | | | | | | | | | | |
# 64|****| | | | | | | | | |
# |****| | | | | | | | | |
# 56|****| | | | | | | | | |
# |****| | | | | | | | | |
# 48|****| | | | | | | | | |
# |****| | | | | | | | | |
# 40|****| | | | | | | | | |
# |****| | | | | | | | | |
# 32|****| | | |****|****| | | | |
# |****| | | |****|****| | | |****|
# 24|****|****| |****|****|****|****| | |****|
# |****|****| |****|****|****|****|****|****|****|
# 16|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# 8|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# |--------------------------------------------------
# | 0.1| 0.2| 0.3| 0.4| 0.5| 0.6| 0.7| 0.8| 0.9| 1.0|
#=============================================================================#
#=============================================================================#
test_name |ntup| tsamples |psamples| p-value |Assessment
#=============================================================================#
diehard_sums| 0| 100| 300|0.00003129| WEAK
#==================================================================
# Diehard Sums Test
# Integers are floated to get a sequence U(1),U(2),... of uni-
# form [0,1) variables. Then overlapping sums,
# S(1)=U(1)+...+U(100), S2=U(2)+...+U(101),... are formed.
# The S's are virtually normal with a certain covariance mat-
# rix. A linear transformation of the S's converts them to a
# sequence of independent standard normals, which are converted
# to uniform variables for a KSTEST. The p-values from ten
# KSTESTs are given still another KSTEST.
#
# Comments
#
# At this point I think there is rock solid evidence that this test
# is completely useless in every sense of the word. It is broken,
# and it is so broken that there is no point in trying to fix it.
# The problem is that the transformation above is not linear, and
# doesn't work. Don't use it.
#
# For what it is worth, rgb_lagged_sums with ntuple 0 tests for
# exactly the same thing, but scalably and reliably without the
# complication of overlapping samples and covariance. Use it
# instead.
#==================================================================
#=============================================================================#
# Histogram of test p-values #
#=============================================================================#
# Bin scale = 0.100000
# 80| | | | | | | | | | |
# |****| | | | | | | | | |
# 72|****| | | | | | | | | |
# |****| | | | | | | | | |
# 64|****| | | | | | | | | |
# |****| | | | | | | | | |
# 56|****| | | | | | | | | |
# |****| | | | | | | | | |
# 48|****| | | | | | | | | |
# |****| | | | | | | | | |
# 40|****|****| | |****|****| | | | |
# |****|****| |****|****|****|****| | |****|
# 32|****|****| |****|****|****|****| |****|****|
# |****|****| |****|****|****|****|****|****|****|
# 24|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# 16|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# 8|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# |--------------------------------------------------
# | 0.1| 0.2| 0.3| 0.4| 0.5| 0.6| 0.7| 0.8| 0.9| 1.0|
#=============================================================================#
#=============================================================================#
test_name |ntup| tsamples |psamples| p-value |Assessment
#=============================================================================#
diehard_sums| 0| 100| 400|0.00025429| WEAK
#==================================================================
# Diehard Sums Test
# Integers are floated to get a sequence U(1),U(2),... of uni-
# form [0,1) variables. Then overlapping sums,
# S(1)=U(1)+...+U(100), S2=U(2)+...+U(101),... are formed.
# The S's are virtually normal with a certain covariance mat-
# rix. A linear transformation of the S's converts them to a
# sequence of independent standard normals, which are converted
# to uniform variables for a KSTEST. The p-values from ten
# KSTESTs are given still another KSTEST.
#
# Comments
#
# At this point I think there is rock solid evidence that this test
# is completely useless in every sense of the word. It is broken,
# and it is so broken that there is no point in trying to fix it.
# The problem is that the transformation above is not linear, and
# doesn't work. Don't use it.
#
# For what it is worth, rgb_lagged_sums with ntuple 0 tests for
# exactly the same thing, but scalably and reliably without the
# complication of overlapping samples and covariance. Use it
# instead.
#==================================================================
#=============================================================================#
# Histogram of test p-values #
#=============================================================================#
# Bin scale = 0.100000
# 100| | | | | | | | | | |
# | | | | | | | | | | |
# 90|****| | | | | | | | | |
# |****| | | | | | | | | |
# 80|****| | | | | | | | | |
# |****| | | | | | | | | |
# 70|****| | | | | | | | | |
# |****| | | | | | | | | |
# 60|****| | | | | | | | | |
# |****| | | | | | | | |****|
# 50|****|****| | | | | | | |****|
# |****|****| |****|****|****| | | |****|
# 40|****|****|****|****|****|****|****| | |****|
# |****|****|****|****|****|****|****|****|****|****|
# 30|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# 20|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# 10|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# |--------------------------------------------------
# | 0.1| 0.2| 0.3| 0.4| 0.5| 0.6| 0.7| 0.8| 0.9| 1.0|
#=============================================================================#
#=============================================================================#
test_name |ntup| tsamples |psamples| p-value |Assessment
#=============================================================================#
diehard_sums| 0| 100| 500|0.00013855| WEAK
#==================================================================
# Diehard Sums Test
# Integers are floated to get a sequence U(1),U(2),... of uni-
# form [0,1) variables. Then overlapping sums,
# S(1)=U(1)+...+U(100), S2=U(2)+...+U(101),... are formed.
# The S's are virtually normal with a certain covariance mat-
# rix. A linear transformation of the S's converts them to a
# sequence of independent standard normals, which are converted
# to uniform variables for a KSTEST. The p-values from ten
# KSTESTs are given still another KSTEST.
#
# Comments
#
# At this point I think there is rock solid evidence that this test
# is completely useless in every sense of the word. It is broken,
# and it is so broken that there is no point in trying to fix it.
# The problem is that the transformation above is not linear, and
# doesn't work. Don't use it.
#
# For what it is worth, rgb_lagged_sums with ntuple 0 tests for
# exactly the same thing, but scalably and reliably without the
# complication of overlapping samples and covariance. Use it
# instead.
#==================================================================
#=============================================================================#
# Histogram of test p-values #
#=============================================================================#
# Bin scale = 0.100000
# 120| | | | | | | | | | |
# | | | | | | | | | | |
# 108| | | | | | | | | | |
# |****| | | | | | | | | |
# 96|****| | | | | | | | | |
# |****| | | | | | | | | |
# 84|****| | | | | | | | | |
# |****| | | | | | | | | |
# 72|****| | | | | | | | | |
# |****| | | | | | | | | |
# 60|****|****| | | | | | | |****|
# |****|****| | |****|****|****| | |****|
# 48|****|****|****|****|****|****|****| |****|****|
# |****|****|****|****|****|****|****|****|****|****|
# 36|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# 24|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# 12|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# |--------------------------------------------------
# | 0.1| 0.2| 0.3| 0.4| 0.5| 0.6| 0.7| 0.8| 0.9| 1.0|
#=============================================================================#
#=============================================================================#
test_name |ntup| tsamples |psamples| p-value |Assessment
#=============================================================================#
diehard_sums| 0| 100| 600|0.00035295| WEAK
#==================================================================
# Diehard Sums Test
# Integers are floated to get a sequence U(1),U(2),... of uni-
# form [0,1) variables. Then overlapping sums,
# S(1)=U(1)+...+U(100), S2=U(2)+...+U(101),... are formed.
# The S's are virtually normal with a certain covariance mat-
# rix. A linear transformation of the S's converts them to a
# sequence of independent standard normals, which are converted
# to uniform variables for a KSTEST. The p-values from ten
# KSTESTs are given still another KSTEST.
#
# Comments
#
# At this point I think there is rock solid evidence that this test
# is completely useless in every sense of the word. It is broken,
# and it is so broken that there is no point in trying to fix it.
# The problem is that the transformation above is not linear, and
# doesn't work. Don't use it.
#
# For what it is worth, rgb_lagged_sums with ntuple 0 tests for
# exactly the same thing, but scalably and reliably without the
# complication of overlapping samples and covariance. Use it
# instead.
#==================================================================
#=============================================================================#
# Histogram of test p-values #
#=============================================================================#
# Bin scale = 0.100000
# 140| | | | | | | | | | |
# | | | | | | | | | | |
# 126| | | | | | | | | | |
# |****| | | | | | | | | |
# 112|****| | | | | | | | | |
# |****| | | | | | | | | |
# 98|****| | | | | | | | | |
# |****| | | | | | | | | |
# 84|****| | | | | | | | | |
# |****| | | | | | | | | |
# 70|****|****| | | | | | | |****|
# |****|****| | |****|****|****| | |****|
# 56|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# 42|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# 28|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# 14|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# |--------------------------------------------------
# | 0.1| 0.2| 0.3| 0.4| 0.5| 0.6| 0.7| 0.8| 0.9| 1.0|
#=============================================================================#
#=============================================================================#
test_name |ntup| tsamples |psamples| p-value |Assessment
#=============================================================================#
diehard_sums| 0| 100| 700|0.00009854| WEAK
#==================================================================
# Diehard Sums Test
# Integers are floated to get a sequence U(1),U(2),... of uni-
# form [0,1) variables. Then overlapping sums,
# S(1)=U(1)+...+U(100), S2=U(2)+...+U(101),... are formed.
# The S's are virtually normal with a certain covariance mat-
# rix. A linear transformation of the S's converts them to a
# sequence of independent standard normals, which are converted
# to uniform variables for a KSTEST. The p-values from ten
# KSTESTs are given still another KSTEST.
#
# Comments
#
# At this point I think there is rock solid evidence that this test
# is completely useless in every sense of the word. It is broken,
# and it is so broken that there is no point in trying to fix it.
# The problem is that the transformation above is not linear, and
# doesn't work. Don't use it.
#
# For what it is worth, rgb_lagged_sums with ntuple 0 tests for
# exactly the same thing, but scalably and reliably without the
# complication of overlapping samples and covariance. Use it
# instead.
#==================================================================
#=============================================================================#
# Histogram of test p-values #
#=============================================================================#
# Bin scale = 0.100000
# 140| | | | | | | | | | |
# |****| | | | | | | | | |
# 126|****| | | | | | | | | |
# |****| | | | | | | | | |
# 112|****| | | | | | | | | |
# |****| | | | | | | | | |
# 98|****| | | | | | | | | |
# |****| | | | | | | | |****|
# 84|****|****| | | | | | | |****|
# |****|****| | | | |****| | |****|
# 70|****|****| | |****|****|****| | |****|
# |****|****|****| |****|****|****|****|****|****|
# 56|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# 42|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# 28|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# 14|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# |--------------------------------------------------
# | 0.1| 0.2| 0.3| 0.4| 0.5| 0.6| 0.7| 0.8| 0.9| 1.0|
#=============================================================================#
#=============================================================================#
test_name |ntup| tsamples |psamples| p-value |Assessment
#=============================================================================#
diehard_sums| 0| 100| 800|0.00004818| WEAK
#==================================================================
# Diehard Sums Test
# Integers are floated to get a sequence U(1),U(2),... of uni-
# form [0,1) variables. Then overlapping sums,
# S(1)=U(1)+...+U(100), S2=U(2)+...+U(101),... are formed.
# The S's are virtually normal with a certain covariance mat-
# rix. A linear transformation of the S's converts them to a
# sequence of independent standard normals, which are converted
# to uniform variables for a KSTEST. The p-values from ten
# KSTESTs are given still another KSTEST.
#
# Comments
#
# At this point I think there is rock solid evidence that this test
# is completely useless in every sense of the word. It is broken,
# and it is so broken that there is no point in trying to fix it.
# The problem is that the transformation above is not linear, and
# doesn't work. Don't use it.
#
# For what it is worth, rgb_lagged_sums with ntuple 0 tests for
# exactly the same thing, but scalably and reliably without the
# complication of overlapping samples and covariance. Use it
# instead.
#==================================================================
#=============================================================================#
# Histogram of test p-values #
#=============================================================================#
# Bin scale = 0.100000
# 160| | | | | | | | | | |
# | | | | | | | | | | |
# 144|****| | | | | | | | | |
# |****| | | | | | | | | |
# 128|****| | | | | | | | | |
# |****| | | | | | | | | |
# 112|****| | | | | | | | | |
# |****| | | | | | | | | |
# 96|****|****| | | | | | | |****|
# |****|****| | | | |****| | |****|
# 80|****|****| | |****|****|****| | |****|
# |****|****| | |****|****|****|****|****|****|
# 64|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# 48|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# 32|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# 16|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# |--------------------------------------------------
# | 0.1| 0.2| 0.3| 0.4| 0.5| 0.6| 0.7| 0.8| 0.9| 1.0|
#=============================================================================#
#=============================================================================#
test_name |ntup| tsamples |psamples| p-value |Assessment
#=============================================================================#
diehard_sums| 0| 100| 900|0.00006352| WEAK
#==================================================================
# Diehard Sums Test
# Integers are floated to get a sequence U(1),U(2),... of uni-
# form [0,1) variables. Then overlapping sums,
# S(1)=U(1)+...+U(100), S2=U(2)+...+U(101),... are formed.
# The S's are virtually normal with a certain covariance mat-
# rix. A linear transformation of the S's converts them to a
# sequence of independent standard normals, which are converted
# to uniform variables for a KSTEST. The p-values from ten
# KSTESTs are given still another KSTEST.
#
# Comments
#
# At this point I think there is rock solid evidence that this test
# is completely useless in every sense of the word. It is broken,
# and it is so broken that there is no point in trying to fix it.
# The problem is that the transformation above is not linear, and
# doesn't work. Don't use it.
#
# For what it is worth, rgb_lagged_sums with ntuple 0 tests for
# exactly the same thing, but scalably and reliably without the
# complication of overlapping samples and covariance. Use it
# instead.
#==================================================================
#=============================================================================#
# Histogram of test p-values #
#=============================================================================#
# Bin scale = 0.100000
# 160| | | | | | | | | | |
# |****| | | | | | | | | |
# 144|****| | | | | | | | | |
# |****| | | | | | | | | |
# 128|****| | | | | | | | | |
# |****| | | | | | | | | |
# 112|****|****| | | | | | | |****|
# |****|****| | | | | | | |****|
# 96|****|****| | | | | | | |****|
# |****|****| | |****|****|****|****| |****|
# 80|****|****| |****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# 64|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# 48|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# 32|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# 16|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# |--------------------------------------------------
# | 0.1| 0.2| 0.3| 0.4| 0.5| 0.6| 0.7| 0.8| 0.9| 1.0|
#=============================================================================#
#=============================================================================#
test_name |ntup| tsamples |psamples| p-value |Assessment
#=============================================================================#
diehard_sums| 0| 100| 1000|0.00002446| WEAK
#==================================================================
# Diehard Sums Test
# Integers are floated to get a sequence U(1),U(2),... of uni-
# form [0,1) variables. Then overlapping sums,
# S(1)=U(1)+...+U(100), S2=U(2)+...+U(101),... are formed.
# The S's are virtually normal with a certain covariance mat-
# rix. A linear transformation of the S's converts them to a
# sequence of independent standard normals, which are converted
# to uniform variables for a KSTEST. The p-values from ten
# KSTESTs are given still another KSTEST.
#
# Comments
#
# At this point I think there is rock solid evidence that this test
# is completely useless in every sense of the word. It is broken,
# and it is so broken that there is no point in trying to fix it.
# The problem is that the transformation above is not linear, and
# doesn't work. Don't use it.
#
# For what it is worth, rgb_lagged_sums with ntuple 0 tests for
# exactly the same thing, but scalably and reliably without the
# complication of overlapping samples and covariance. Use it
# instead.
#==================================================================
#=============================================================================#
# Histogram of test p-values #
#=============================================================================#
# Bin scale = 0.100000
# 180| | | | | | | | | | |
# | | | | | | | | | | |
# 162|****| | | | | | | | | |
# |****| | | | | | | | | |
# 144|****| | | | | | | | | |
# |****| | | | | | | | | |
# 126|****|****| | | | | | | | |
# |****|****| | | | | | | |****|
# 108|****|****| | | | | | | |****|
# |****|****| | |****|****|****|****| |****|
# 90|****|****| |****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# 72|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# 54|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# 36|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# 18|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# |--------------------------------------------------
# | 0.1| 0.2| 0.3| 0.4| 0.5| 0.6| 0.7| 0.8| 0.9| 1.0|
#=============================================================================#
#=============================================================================#
test_name |ntup| tsamples |psamples| p-value |Assessment
#=============================================================================#
diehard_sums| 0| 100| 1100|0.00001315| WEAK
#==================================================================
# Diehard Sums Test
# Integers are floated to get a sequence U(1),U(2),... of uni-
# form [0,1) variables. Then overlapping sums,
# S(1)=U(1)+...+U(100), S2=U(2)+...+U(101),... are formed.
# The S's are virtually normal with a certain covariance mat-
# rix. A linear transformation of the S's converts them to a
# sequence of independent standard normals, which are converted
# to uniform variables for a KSTEST. The p-values from ten
# KSTESTs are given still another KSTEST.
#
# Comments
#
# At this point I think there is rock solid evidence that this test
# is completely useless in every sense of the word. It is broken,
# and it is so broken that there is no point in trying to fix it.
# The problem is that the transformation above is not linear, and
# doesn't work. Don't use it.
#
# For what it is worth, rgb_lagged_sums with ntuple 0 tests for
# exactly the same thing, but scalably and reliably without the
# complication of overlapping samples and covariance. Use it
# instead.
#==================================================================
#=============================================================================#
# Histogram of test p-values #
#=============================================================================#
# Bin scale = 0.100000
# 200| | | | | | | | | | |
# | | | | | | | | | | |
# 180|****| | | | | | | | | |
# |****| | | | | | | | | |
# 160|****| | | | | | | | | |
# |****| | | | | | | | | |
# 140|****|****| | | | | | | | |
# |****|****| | | | | | | |****|
# 120|****|****| | | | | | | |****|
# |****|****| | |****| |****|****| |****|
# 100|****|****| | |****|****|****|****| |****|
# |****|****|****|****|****|****|****|****|****|****|
# 80|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# 60|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# 40|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# 20|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# |--------------------------------------------------
# | 0.1| 0.2| 0.3| 0.4| 0.5| 0.6| 0.7| 0.8| 0.9| 1.0|
#=============================================================================#
#=============================================================================#
test_name |ntup| tsamples |psamples| p-value |Assessment
#=============================================================================#
diehard_sums| 0| 100| 1200|0.00000115| WEAK
#==================================================================
# Diehard Sums Test
# Integers are floated to get a sequence U(1),U(2),... of uni-
# form [0,1) variables. Then overlapping sums,
# S(1)=U(1)+...+U(100), S2=U(2)+...+U(101),... are formed.
# The S's are virtually normal with a certain covariance mat-
# rix. A linear transformation of the S's converts them to a
# sequence of independent standard normals, which are converted
# to uniform variables for a KSTEST. The p-values from ten
# KSTESTs are given still another KSTEST.
#
# Comments
#
# At this point I think there is rock solid evidence that this test
# is completely useless in every sense of the word. It is broken,
# and it is so broken that there is no point in trying to fix it.
# The problem is that the transformation above is not linear, and
# doesn't work. Don't use it.
#
# For what it is worth, rgb_lagged_sums with ntuple 0 tests for
# exactly the same thing, but scalably and reliably without the
# complication of overlapping samples and covariance. Use it
# instead.
#==================================================================
#=============================================================================#
# Histogram of test p-values #
#=============================================================================#
# Bin scale = 0.100000
# 220| | | | | | | | | | |
# | | | | | | | | | | |
# 198|****| | | | | | | | | |
# |****| | | | | | | | | |
# 176|****| | | | | | | | | |
# |****| | | | | | | | | |
# 154|****| | | | | | | | | |
# |****|****| | | | | | | |****|
# 132|****|****| | | | | | | |****|
# |****|****| | |****| |****|****| |****|
# 110|****|****| | |****|****|****|****| |****|
# |****|****|****|****|****|****|****|****|****|****|
# 88|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# 66|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# 44|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# 22|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# |--------------------------------------------------
# | 0.1| 0.2| 0.3| 0.4| 0.5| 0.6| 0.7| 0.8| 0.9| 1.0|
#=============================================================================#
#=============================================================================#
test_name |ntup| tsamples |psamples| p-value |Assessment
#=============================================================================#
diehard_sums| 0| 100| 1300|0.00000205| WEAK
#==================================================================
# Diehard Sums Test
# Integers are floated to get a sequence U(1),U(2),... of uni-
# form [0,1) variables. Then overlapping sums,
# S(1)=U(1)+...+U(100), S2=U(2)+...+U(101),... are formed.
# The S's are virtually normal with a certain covariance mat-
# rix. A linear transformation of the S's converts them to a
# sequence of independent standard normals, which are converted
# to uniform variables for a KSTEST. The p-values from ten
# KSTESTs are given still another KSTEST.
#
# Comments
#
# At this point I think there is rock solid evidence that this test
# is completely useless in every sense of the word. It is broken,
# and it is so broken that there is no point in trying to fix it.
# The problem is that the transformation above is not linear, and
# doesn't work. Don't use it.
#
# For what it is worth, rgb_lagged_sums with ntuple 0 tests for
# exactly the same thing, but scalably and reliably without the
# complication of overlapping samples and covariance. Use it
# instead.
#==================================================================
#=============================================================================#
# Histogram of test p-values #
#=============================================================================#
# Bin scale = 0.100000
# 240| | | | | | | | | | |
# | | | | | | | | | | |
# 216|****| | | | | | | | | |
# |****| | | | | | | | | |
# 192|****| | | | | | | | | |
# |****| | | | | | | | | |
# 168|****| | | | | | | | | |
# |****|****| | | | | | | |****|
# 144|****|****| | | | | | | |****|
# |****|****| | | | |****| | |****|
# 120|****|****| | |****|****|****|****| |****|
# |****|****|****|****|****|****|****|****|****|****|
# 96|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# 72|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# 48|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# 24|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# |--------------------------------------------------
# | 0.1| 0.2| 0.3| 0.4| 0.5| 0.6| 0.7| 0.8| 0.9| 1.0|
#=============================================================================#
#=============================================================================#
test_name |ntup| tsamples |psamples| p-value |Assessment
#=============================================================================#
diehard_sums| 0| 100| 1400|0.00000061| FAILED
#==================================================================
# Diehard Runs Test
# This is the RUNS test. It counts runs up, and runs down,
# in a sequence of uniform [0,1) variables, obtained by float-
# ing the 32-bit integers in the specified file. This example
# shows how runs are counted: .123,.357,.789,.425,.224,.416,.95
# contains an up-run of length 3, a down-run of length 2 and an
# up-run of (at least) 2, depending on the next values. The
# covariance matrices for the runs-up and runs-down are well
# known, leading to chisquare tests for quadratic forms in the
# weak inverses of the covariance matrices. Runs are counted
# for sequences of length 10,000. This is done ten times. Then
# repeated.
#
# In Dieharder sequences of length tsamples = 100000 are used by
# default, and 100 p-values thus generated are used in a final
# KS test.
#==================================================================
#=============================================================================#
# Histogram of test p-values #
#=============================================================================#
# Bin scale = 0.100000
# 20| | | | | | | | | | |
# | | | | | | | | | | |
# 18| | | | | | | | | | |
# | | | | | | | | | | |
# 16| | | | | | | | | |****|
# | | | | | | | | | |****|
# 14| | | | | | | | | |****|
# | |****| | | | | | | |****|
# 12| |****|****| | | |****| | |****|
# | |****|****| | | |****| | |****|
# 10| |****|****| | | |****| |****|****|
# | |****|****|****| | |****| |****|****|
# 8|****|****|****|****| | |****|****|****|****|
# |****|****|****|****|****| |****|****|****|****|
# 6|****|****|****|****|****| |****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# 4|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# 2|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# |--------------------------------------------------
# | 0.1| 0.2| 0.3| 0.4| 0.5| 0.6| 0.7| 0.8| 0.9| 1.0|
#=============================================================================#
#=============================================================================#
test_name |ntup| tsamples |psamples| p-value |Assessment
#=============================================================================#
diehard_runs| 0| 100000| 100|0.53138878| PASSED
#=============================================================================#
# Histogram of test p-values #
#=============================================================================#
# Bin scale = 0.100000
# 20| | | | | | | | | | |
# | | | | | | | | | | |
# 18| | | | | | | | | | |
# | | | | | | | | | | |
# 16| | | | | | | | | | |
# | |****| | | | | | | | |
# 14| |****| | | | | | | | |
# | |****| | | | | | | | |
# 12|****|****| | | |****| | | | |
# |****|****| | | |****|****|****|****| |
# 10|****|****| | | |****|****|****|****| |
# |****|****| | | |****|****|****|****|****|
# 8|****|****|****| | |****|****|****|****|****|
# |****|****|****| | |****|****|****|****|****|
# 6|****|****|****|****| |****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# 4|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# 2|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# |--------------------------------------------------
# | 0.1| 0.2| 0.3| 0.4| 0.5| 0.6| 0.7| 0.8| 0.9| 1.0|
#=============================================================================#
#=============================================================================#
test_name |ntup| tsamples |psamples| p-value |Assessment
#=============================================================================#
diehard_runs| 0| 100000| 100|0.56967863| PASSED
#==================================================================
# Diehard Craps Test
# This is the CRAPS TEST. It plays 200,000 games of craps, finds
# the number of wins and the number of throws necessary to end
# each game. The number of wins should be (very close to) a
# normal with mean 200000p and variance 200000p(1-p), with
# p=244/495. Throws necessary to complete the game can vary
# from 1 to infinity, but counts for all>21 are lumped with 21.
# A chi-square test is made on the no.-of-throws cell counts.
# Each 32-bit integer from the test file provides the value for
# the throw of a die, by floating to [0,1), multiplying by 6
# and taking 1 plus the integer part of the result.
#==================================================================
#=============================================================================#
# Histogram of test p-values #
#=============================================================================#
# Bin scale = 0.100000
# 20| | | | | | | | | | |
# | | | | | | | | | | |
# 18| | | | | | | | | | |
# | | | | | | | | | | |
# 16| | | | | | | | |****| |
# | | | |****| | | | |****| |
# 14| | | |****| | | | |****| |
# |****| | |****| | | | |****| |
# 12|****| | |****| | | | |****| |
# |****| | |****| | | | |****| |
# 10|****| | |****| | | | |****| |
# |****| | |****|****| |****|****|****| |
# 8|****| | |****|****|****|****|****|****|****|
# |****|****| |****|****|****|****|****|****|****|
# 6|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# 4|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# 2|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# |--------------------------------------------------
# | 0.1| 0.2| 0.3| 0.4| 0.5| 0.6| 0.7| 0.8| 0.9| 1.0|
#=============================================================================#
#=============================================================================#
test_name |ntup| tsamples |psamples| p-value |Assessment
#=============================================================================#
diehard_craps| 0| 200000| 100|0.86443880| PASSED
#=============================================================================#
# Histogram of test p-values #
#=============================================================================#
# Bin scale = 0.100000
# 20| | | | | | | | | | |
# | | | | | | | | | | |
# 18| | | | | | | | | | |
# | | | | | | | | | | |
# 16| | | | | | | | | | |
# | | | | | | | | | | |
# 14| | | | | | | | | | |
# | | | | | | | | | |****|
# 12| |****| |****| | | | | |****|
# | |****| |****|****| | | | |****|
# 10| |****| |****|****| | |****| |****|
# |****|****|****|****|****| | |****|****|****|
# 8|****|****|****|****|****| |****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# 6|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# 4|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# 2|****|****|****|****|****|****|****|****|****|****|
# |****|****|****|****|****|****|****|****|****|****|
# |--------------------------------------------------
# | 0.1| 0.2| 0.3| 0.4| 0.5| 0.6| 0.7| 0.8| 0.9| 1.0|
#=============================================================================#
#=============================================================================#
test_name |ntup| tsamples |psamples| p-value |Assessment
#=============================================================================#
diehard_craps| 0| 200000| 100|0.90656575| PASSED