generic-library/cxx
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

Beefed up the tests for all of the distributions to include checks against the expected skewness and kurtosis

Browse Source 
git-svn-id: https://llvm.org/svn/llvm-project/libcxx/trunk@103910 91177308-0d34-0410-b5e6-96231b3b80d8
This commit is contained in:
Howard Hinnant
2010-05-16 17:56:20 +00:00
parent 9cb70437e2
commit df40dc6c1a
22 changed files with 2182 additions and 297 deletions
Download Patch File Download Diff File Expand all files Collapse all files

76
test/numerics/rand/rand.dis/rand.dist.bern/rand.dist.bern.bin/eval_param.pass.cpp
View File

@@ -39,60 +39,120 @@ int main()
const int N = 100000;
std::vector<D::result_type> u;
for (int i = 0; i < N; ++i)
u.push_back(d(g, p));
{
D::result_type v = d(g, p);
assert(0 <= v && v <= p.t());
u.push_back(v);
}
double mean = std::accumulate(u.begin(), u.end(),
double(0)) / u.size();
double var = 0;
double skew = 0;
double kurtosis = 0;
for (int i = 0; i < u.size(); ++i)
var += sqr(u[i] - mean);
{
double d = (u[i] - mean);
double d2 = sqr(d);
var += d2;
skew += d * d2;
kurtosis += d2 * d2;
}
var /= u.size();
double dev = std::sqrt(var);
skew /= u.size() * dev * var;
kurtosis /= u.size() * var * var;
kurtosis -= 3;
double x_mean = p.t() * p.p();
double x_var = x_mean*(1-p.p());
double x_skew = (1-2*p.p()) / std::sqrt(x_var);
double x_kurtosis = (1-6*p.p()*(1-p.p())) / x_var;
assert(std::abs(mean - x_mean) / x_mean < 0.01);
assert(std::abs(var - x_var) / x_var < 0.01);
assert(std::abs(skew - x_skew) / x_skew < 0.01);
assert(std::abs(kurtosis - x_kurtosis) / x_kurtosis < 0.01);
}
{
typedef std::binomial_distribution<> D;
typedef D::param_type P;
typedef std::minstd_rand G;
typedef std::mt19937 G;
G g;
D d(16, .75);
P p(30, .03125);
const int N = 100000;
std::vector<D::result_type> u;
for (int i = 0; i < N; ++i)
u.push_back(d(g, p));
{
D::result_type v = d(g, p);
assert(0 <= v && v <= p.t());
u.push_back(v);
}
double mean = std::accumulate(u.begin(), u.end(),
double(0)) / u.size();
double var = 0;
double skew = 0;
double kurtosis = 0;
for (int i = 0; i < u.size(); ++i)
var += sqr(u[i] - mean);
{
double d = (u[i] - mean);
double d2 = sqr(d);
var += d2;
skew += d * d2;
kurtosis += d2 * d2;
}
var /= u.size();
double dev = std::sqrt(var);
skew /= u.size() * dev * var;
kurtosis /= u.size() * var * var;
kurtosis -= 3;
double x_mean = p.t() * p.p();
double x_var = x_mean*(1-p.p());
double x_skew = (1-2*p.p()) / std::sqrt(x_var);
double x_kurtosis = (1-6*p.p()*(1-p.p())) / x_var;
assert(std::abs(mean - x_mean) / x_mean < 0.01);
assert(std::abs(var - x_var) / x_var < 0.01);
assert(std::abs(skew - x_skew) / x_skew < 0.01);
assert(std::abs(kurtosis - x_kurtosis) / x_kurtosis < 0.01);
}
{
typedef std::binomial_distribution<> D;
typedef D::param_type P;
typedef std::minstd_rand G;
typedef std::mt19937 G;
G g;
D d(16, .75);
P p(40, .25);
const int N = 100000;
std::vector<D::result_type> u;
for (int i = 0; i < N; ++i)
u.push_back(d(g, p));
{
D::result_type v = d(g, p);
assert(0 <= v && v <= p.t());
u.push_back(v);
}
double mean = std::accumulate(u.begin(), u.end(),
double(0)) / u.size();
double var = 0;
double skew = 0;
double kurtosis = 0;
for (int i = 0; i < u.size(); ++i)
var += sqr(u[i] - mean);
{
double d = (u[i] - mean);
double d2 = sqr(d);
var += d2;
skew += d * d2;
kurtosis += d2 * d2;
}
var /= u.size();
double dev = std::sqrt(var);
skew /= u.size() * dev * var;
kurtosis /= u.size() * var * var;
kurtosis -= 3;
double x_mean = p.t() * p.p();
double x_var = x_mean*(1-p.p());
double x_skew = (1-2*p.p()) / std::sqrt(x_var);
double x_kurtosis = (1-6*p.p()*(1-p.p())) / x_var;
assert(std::abs(mean - x_mean) / x_mean < 0.01);
assert(std::abs(var - x_var) / x_var < 0.01);
assert(std::abs(skew - x_skew) / x_skew < 0.03);
assert(std::abs(kurtosis - x_kurtosis) / x_kurtosis < 0.01);
}
}