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/*********************************************************************
* Software License Agreement (BSD License)
*
* Copyright (c) 2008, Willow Garage, Inc.
* All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions
* are met:
*
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above
* copyright notice, this list of conditions and the following
* disclaimer in the documentation and/or other materials provided
* with the distribution.
* * Neither the name of the Willow Garage nor the names of its
* contributors may be used to endorse or promote products derived
* from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
* "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
* LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
* FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
* COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
* INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
* BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
* CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
* LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
* ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
* POSSIBILITY OF SUCH DAMAGE.
*********************************************************************/
/* Author: Ioan Sucan */
#define BOOST_TEST_MODULE "Random"
#include <boost/test/unit_test.hpp>
#include "ompl/util/RandomNumbers.h"
#include "../../BoostTestTeamCityReporter.h"
#include <cmath>
#include <vector>
#include <cstdio>
using namespace ompl;
struct SetSeedTo1
{
SetSeedTo1(void)
{
ompl::RNG::setSeed(1);
}
};
// make sure the test is deterministic
static SetSeedTo1 proxy;
// define a convenience macro
#define BOOST_OMPL_EXPECT_NEAR(a, b, diff) BOOST_CHECK_SMALL((a) - (b), diff)
/* Just test we get some random values */
BOOST_AUTO_TEST_CASE(DifferentSeeds)
{
RNG r1, r2, r3, r4;
int same = 0;
int eq = 0;
const int N = 100;
for (int i = 0 ; i < N ; ++i)
{
int v1 = r1.uniformInt(0, 100);
int v2 = r2.uniformInt(0, 100);
int v3 = r3.uniformInt(0, 100);
int v4 = r4.uniformInt(0, 100);
printf("%d %d %d %d\n", v1, v2, v3, v4);
if (v1 == v2 && v2 == v3 && v3 == v4)
eq++;
if (v1 == r1.uniformInt(0, 100))
same++;
if (v2 == r2.uniformInt(0, 100))
same++;
if (v3 == r3.uniformInt(0, 100))
same++;
if (v4 == r4.uniformInt(0, 100))
same++;
}
BOOST_CHECK(!(eq > N / 2));
BOOST_CHECK(same < 2 * N);
}
BOOST_AUTO_TEST_CASE(ValidRangeInts)
{
RNG r;
const int N = 100;
const int V = 10000 * N;
std::vector<int> c(N + 1, 0);
for (int i = 0 ; i < V ; ++i)
{
int v = r.uniformInt(0, N);
BOOST_CHECK(v >= 0);
BOOST_CHECK(v <= N);
c[v]++;
}
for (unsigned int i = 0 ; i < c.size() ; ++i)
BOOST_CHECK(c[i] > V/N/3);
}
static const double NUM_INT_SAMPLES = 1000000;
static const double NUM_REAL_SAMPLES = 1000000;
/* The following widening factor is multiplied by the standard error of the mean
* in errUniformInt() and errUniformReal() to obtain a reasonable range to pass to BOOST_CHECK_CLOSE().
* 4 sigma events should only happen "twice a lifetime" on average, so this should be lienient enough. */
static const double STDERR_WIDENING_FACTOR = 4.0;
static double avgIntsN(int s, int l, const int N)
{
RNG r;
double sum = 0.0;
for (int i = 0 ; i < N ; ++i)
sum += r.uniformInt(s, l);
return sum / (double)N;
}
static double avgInts(int s, int l)
{
return avgIntsN(s, l, NUM_INT_SAMPLES);
}
static double errUniformInt(int s, int l)
{
const int length = l-s+1;
//standard error of mean for discrete uniform distribution over {s,s+1,...,l}
const double stdErr = sqrt((length*length-1)/(12.0*NUM_INT_SAMPLES));
return stdErr * STDERR_WIDENING_FACTOR + std::numeric_limits<double>::epsilon();
}
BOOST_AUTO_TEST_CASE(AvgInts)
{
BOOST_OMPL_EXPECT_NEAR(avgInts(0, 1), 0.5, errUniformInt(0,1));
BOOST_OMPL_EXPECT_NEAR(avgInts(0, 10), 5.0, errUniformInt(0,10));
BOOST_OMPL_EXPECT_NEAR(avgInts(-1, 1), 0.0, errUniformInt(-1,1));
BOOST_OMPL_EXPECT_NEAR(avgInts(-1, 0), -0.5, errUniformInt(-1,0));
BOOST_OMPL_EXPECT_NEAR(avgInts(-2, 4), 1.0, errUniformInt(-2,4));
BOOST_OMPL_EXPECT_NEAR(avgInts(2, 4), 3.0, errUniformInt(2,4));
BOOST_OMPL_EXPECT_NEAR(avgInts(-6, -2), -4.0, errUniformInt(-6,-2));
BOOST_OMPL_EXPECT_NEAR(avgIntsN(0, 0, 1000), 0.0, errUniformInt(0,0));
}
static double avgRealsN(double s, double l, const int N)
{
RNG r;
double sum = 0.0;
for (int i = 0 ; i < N ; ++i)
sum += r.uniformReal(s, l);
return sum / (double)N;
}
static double avgReals(double s, double l)
{
return avgRealsN(s, l, NUM_REAL_SAMPLES);
}
static double errUniformReal(double s, double l)
{
//standard error of mean for continuous uniform distribution over real interval [s,l].
const double stdErr = (l-s)*sqrt(1.0/(12.0*NUM_REAL_SAMPLES));
return stdErr * STDERR_WIDENING_FACTOR + std::numeric_limits<double>::epsilon();
}
BOOST_AUTO_TEST_CASE(AvgReals)
{
BOOST_OMPL_EXPECT_NEAR(avgReals(-0.1, 0.3), 0.1, errUniformReal(-0.1,0.3));
BOOST_OMPL_EXPECT_NEAR(avgReals(0, 1), 0.5, errUniformReal(0,1));
BOOST_OMPL_EXPECT_NEAR(avgReals(0, 10), 5.0, errUniformReal(0,10));
BOOST_OMPL_EXPECT_NEAR(avgReals(-1, 1), 0.0, errUniformReal(-1,1));
BOOST_OMPL_EXPECT_NEAR(avgReals(-1, 0), -0.5, errUniformReal(-1,0));
BOOST_OMPL_EXPECT_NEAR(avgReals(-2, 4), 1.0, errUniformReal(-2,4));
BOOST_OMPL_EXPECT_NEAR(avgReals(2, 4), 3.0, errUniformReal(2,4));
BOOST_OMPL_EXPECT_NEAR(avgReals(-6, -2), -4.0, errUniformReal(-6,-2));
BOOST_OMPL_EXPECT_NEAR(avgRealsN(0, 0, 1000), 0.0, errUniformReal(0,0));
}
static double avgNormalRealsN(double mean, double stddev, const int N)
{
RNG r;
double sum = 0.0;
for (int i = 0 ; i < N ; ++i)
sum += r.gaussian(mean, stddev);
return sum / (double)N;
}
static double avgNormalReals(double m, double s)
{
return avgNormalRealsN(m, s, NUM_REAL_SAMPLES);
}
static double errNormal(double stddev)
{
//standard error of mean for gaussian with given stddev
return STDERR_WIDENING_FACTOR * stddev / sqrt(NUM_REAL_SAMPLES);
}
BOOST_AUTO_TEST_CASE(NormalReals)
{
BOOST_OMPL_EXPECT_NEAR(avgNormalReals(10.0, 1.0), 10.0, errNormal(1.0));
}
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