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require 'test_helper'
class RngTest < GSL::TestCase
N = 1000
N2 = 20000
BINS = 17
EXTRA = 10
{
'rand' => [[1, 10000, 1910041713]],
'randu' => [[1, 10000, 1623524161]],
'cmrg' => [[1, 10000, 719452880]],
'minstd' => [[1, 10000, 1043618065]],
'mrg' => [[1, 10000, 2064828650]],
'taus' => [[1, 10000, 2733957125]],
'taus113' => [[1, 1000, 1925420673]],
'transputer' => [[1, 10000, 1244127297]],
'vax' => [[1, 10000, 3051034865]],
'borosh13' => [[1, 10000, 2513433025]],
'fishman18' => [[1, 10000, 330402013]],
'fishman2x' => [[1, 10000, 540133597]],
'knuthran2' => [[1, 10000, 1084477620]],
'knuthran' => [[310952, 1009 * 2009 + 1, 461390032]],
'lecuyer21' => [[1, 10000, 2006618587]],
'waterman14' => [[1, 10000, 3776680385]],
'coveyou' => [[6, 10000, 1416754246]],
'fishman20' => [[6, 10000, 248127575]],
'ranlux' => [[314159265, 10000, 12077992]],
'ranlux389' => [[314159265, 10000, 165942]],
'ranlxs0' => [[1, 10000, 11904320]],
'ranlxs1' => [[1, 10000, 8734328]],
'ranlxs2' => [[1, 10000, 6843140]],
'ranlxd1' => [[1, 10000, 1998227290]],
'ranlxd2' => [[1, 10000, 3949287736]],
'slatec' => [[1, 10000, 45776]],
'uni' => [[1, 10000, 9214]],
'uni32' => [[1, 10000, 1155229825]],
'zuf' => [[1, 10000, 3970]],
'r250' => [[1, 10000, 1100653588]],
'mt19937' => [[4357, 1000, 1186927261]],
'mt19937_1999' => [[4357, 1000, 1030650439]],
'mt19937_1998' => [[4357, 1000, 1309179303]],
'tt800' => [[0, 10000, 2856609219]],
'ran0' => [[0, 10000, 1115320064]],
'ran1' => [[0, 10000, 1491066076]],
'ran2' => [[0, 10000, 1701364455]],
'ran3' => [[0, 10000, 186340785]],
'ranmar' => [[1, 10000, 14428370]],
'rand48' => [[0, 10000, 0xDE095043],
[1, 10000, 0xEDA54977]],
'random_glibc2' => [[0, 10000, 1908609430]],
'random8_glibc2' => [[0, 10000, 1910041713]],
'random32_glibc2' => [[0, 10000, 1587395585]],
'random64_glibc2' => [[0, 10000, 52848624]],
'random128_glibc2' => [[0, 10000, 1908609430]],
'random256_glibc2' => [[0, 10000, 179943260]],
'random_bsd' => [[0, 10000, 1457025928]],
'random8_bsd' => [[0, 10000, 1910041713]],
'random32_bsd' => [[0, 10000, 1663114331]],
'random64_bsd' => [[0, 10000, 864469165]],
'random128_bsd' => [[0, 10000, 1457025928]],
'random256_bsd' => [[0, 10000, 1216357476]],
'random_libc5' => [[0, 10000, 428084942]],
'random8_libc5' => [[0, 10000, 1910041713]],
'random32_libc5' => [[0, 10000, 1967452027]],
'random64_libc5' => [[0, 10000, 2106639801]],
'random128_libc5' => [[0, 10000, 428084942]],
'random256_libc5' => [[0, 10000, 116367984]],
'ranf' => [[0, 10000, 2152890433],
[2, 10000, 339327233]]
}.each { |type, args|
args.each_with_index { |(seed, n, result), i|
define_method("test_#{type}_#{i}") {
r, k = GSL::Rng.alloc(type), nil
r.set(seed) if seed != 0
n.times { k = r.get }
assert k == result, "#{r.name}, #{n} steps (#{k} observed vs #{result} expected)"
}
}
}
GSL::Rng.types.each { |type|
define_method("test_float_#{type}") { _rng_float_test(type) }
define_method("test_state_#{type}") { _rng_state_test(type) }
define_method("test_parallel_state_#{type}") { _rng_parallel_state_test(type) }
define_method("test_read_write_#{type}") { _rng_read_write_test(type) }
define_method("test_generic_#{type}") { _generic_rng_test(type) }
}
def _rng_float_test(type)
ri = GSL::Rng.alloc(type)
rf = GSL::Rng.alloc(type)
status = k = 0
begin
k = ri.get
u = rf.get
end while k == 0
c = k / u
N2.times {
k = ri.get
u = rf.get
if c * k != u
status = 1
break
end
}
assert status.zero?, "#{ri.name}, ratio of int to double (#{c} observed vs #{k/u} expected)"
end
def _rng_state_test(type)
r = GSL::Rng.alloc(type)
r_save = GSL::Rng.alloc(type)
N.times { r.get }
GSL::Rng.memcpy(r_save, r)
test_a = GSL::Vector.alloc(N)
test_b = GSL::Vector.alloc(N)
N.times { |i| test_a[i] = r.get }
GSL::Rng.memcpy(r, r_save)
N.times { |i| test_b[i] = r.get }
assert((0...N).all? { |i| test_b[i] == test_a[i] }, "#{r.name}, random number state consistency")
end
def _rng_parallel_state_test(type)
r1 = GSL::Rng.alloc(type)
r2 = GSL::Rng.alloc(type)
test_a = GSL::Vector.alloc(N)
test_b = GSL::Vector.alloc(N)
test_c = GSL::Vector.alloc(N)
test_d = GSL::Vector.alloc(N)
test_e = GSL::Vector.alloc(N)
test_f = GSL::Vector.alloc(N)
N.times { r1.get }
GSL::Rng.memcpy(r2, r1)
N.times { |i|
test_a[i] = r1.get
test_b[i] = r2.get
test_c[i] = r1.uniform_int(1234)
test_d[i] = r2.uniform_int(1234)
test_e[i] = r1.uniform
test_f[i] = r2.uniform
}
assert((0...N).all? { |i| test_a[i] == test_b[i] }, "#{r1.name}, parallel random number state consistency (a/b)")
assert((0...N).all? { |i| test_c[i] == test_d[i] }, "#{r1.name}, parallel random number state consistency (c/d)")
assert((0...N).all? { |i| test_e[i] == test_f[i] }, "#{r1.name}, parallel random number state consistency (e/f)")
end
def _rng_read_write_test(type)
r = GSL::Rng.alloc(type)
test_a = GSL::Vector.alloc(N)
test_b = GSL::Vector.alloc(N)
N.times { r.get }
r.fwrite('test.dat')
N.times { |i| test_a[i] = r.get }
r.fread('test.dat')
N.times { |i| test_b[i] = r.get }
assert((0...N).all? { |i| test_b[i] == test_a[i] }, "#{r.name}, random number generator read and write")
ensure
File.delete('test.dat') if FileTest.exist?('test.dat')
end
def _generic_rng_test(type)
r = GSL::Rng.alloc(type)
name, ran_max, ran_min = r.name, r.max, r.min
kmax, kmin, sigma = 0, 1000, 0.0
kmax, status = _rng_max_test(r, ran_max)
assert status.zero?, "#{name}, observed vs theoretical maximum (#{kmax} vs #{ran_max})"
kmin, status = _rng_min_test(r, ran_min, ran_max)
assert status.zero?, "#{name}, observed vs theoretical minimum (#{kmin} vs #{ran_min})"
sigma, status = _rng_sum_test(r)
assert status.zero?, "#{r.name}, sum test within acceptable sigma (observed #{sigma} sigma)"
sigma, status = _rng_bin_test(r)
assert status.zero?, "#{r.name}, bin test within acceptable chisq (observed #{sigma} sigma)"
r.set(1)
kmax, status = _rng_max_test(r, ran_max)
r.set(1)
kmin, s = _rng_min_test(r, ran_min, ran_max)
status |= s
r.set(1)
sigma, s = _rng_sum_test(r)
status |= s
r.set(12345)
kmax, s = _rng_max_test(r, ran_max)
status |= s
r.set(12345)
kmin, s = _rng_min_test(r, ran_min, ran_max)
status |= s
r.set(12345)
sigma, s = _rng_sum_test(r)
status |= s
assert status.zero?, "#{r.name}, maximum and sum tests for non-default seeds"
end
def _rng_max_test(r, ran_max)
max = 0
N2.times {
k = r.get
max = k if k > max
}
actual_uncovered = ran_max - max
expect_uncovered = ran_max.to_f / N2.to_f
[max, max > ran_max || actual_uncovered > 7 * expect_uncovered ? 1 : 0]
end
def _rng_min_test(r, ran_min, ran_max)
min = 1000000000
N2.times {
k = r.get
min = k if k < min
}
actual_uncovered = min - ran_min
expect_uncovered = ran_max.to_f / N2.to_f
[min, min < ran_min || actual_uncovered > 7 * expect_uncovered ? 1 : 0]
end
def _rng_sum_test(r)
sum = 0.0
N2.times {
x = r.uniform - 0.5
sum += x
}
sum /= N2.to_f
sigma = sum * Math.sqrt(12.0 * N2)
[sigma, sigma.abs > 3 || sigma.abs < 0.003 ? 1 : 0]
end
def _rng_bin_test(r)
count = GSL::Vector.calloc(BINS + EXTRA)
chisq = 0.0
N2.times { count[r.uniform_int(BINS)] += 1 }
BINS.times { |i|
x = N2.to_f / BINS
d = count[i] - x
chisq += d * d / x
}
BINS.upto(EXTRA - 1) { |i|
assert count[i].zero?, "#{r.name}, wrote outside range in bin test (#{i} observed vs #{BINS - 1} expected)"
}
[sigma = Math.sqrt(chisq / BINS), sigma.abs > 3 || sigma.abs < 0.003 ? 1 : 0]
end
end
|
