StatisticsTest.h 9.45 KB
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#ifndef STATISTICSTEST_H_
#define STATISTICSTEST_H_

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#include "MantidKernel/Statistics.h"
#include <boost/math/special_functions/fpclassify.hpp>
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#include <cxxtest/TestSuite.h>
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#include <algorithm>
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#include <cmath>
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#include <vector>
#include <string>

using namespace Mantid::Kernel;
using std::string;
using std::vector;

class StatisticsTest : public CxxTest::TestSuite
{
public:
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  void test_Doubles_And_Default_Flags_Calculates_All_Stats()
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  {
    vector<double> data;
    data.push_back(17.2);
    data.push_back(18.1);
    data.push_back(16.5);
    data.push_back(18.3);
    data.push_back(12.6);

    Statistics stats = getStatistics(data);

    TS_ASSERT_EQUALS(stats.mean, 16.54);
    TS_ASSERT_DELTA(stats.standard_deviation, 2.0732, 0.0001);
    TS_ASSERT_EQUALS(stats.minimum, 12.6);
    TS_ASSERT_EQUALS(stats.maximum, 18.3);
    TS_ASSERT_EQUALS(stats.median, 17.2);
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  }

  void test_Doubles_With_Sorted_Data()
  {
    vector<double> data;
    data.push_back(17.2);
    data.push_back(18.1);
    data.push_back(16.5);
    data.push_back(18.3);
    data.push_back(12.6);
    sort(data.begin(), data.end());

    Statistics stats = getStatistics(data, (StatOptions::Median|StatOptions::SortedData));

    TS_ASSERT(boost::math::isnan(stats.mean));
    TS_ASSERT(boost::math::isnan(stats.standard_deviation));
    TS_ASSERT(boost::math::isnan(stats.minimum));
    TS_ASSERT(boost::math::isnan(stats.maximum));
    TS_ASSERT_EQUALS(stats.median, 17.2);
  }

  void test_Unsorted_Data_With_Sorted_Flag_Gives_Expected_Incorrect_Result_For_Median()
  {
    vector<double> data;
    data.push_back(17.2);
    data.push_back(18.1);
    data.push_back(16.5);
    data.push_back(18.3);
    data.push_back(12.6);

    Statistics stats = getStatistics(data, (StatOptions::Median|StatOptions::SortedData));

    TS_ASSERT(boost::math::isnan(stats.mean));
    TS_ASSERT(boost::math::isnan(stats.standard_deviation));
    TS_ASSERT(boost::math::isnan(stats.minimum));
    TS_ASSERT(boost::math::isnan(stats.maximum));
    TS_ASSERT_EQUALS(stats.median, 16.5);
  }
  
  void test_Doubles_With_Corrected_StdDev_Calculates_Mean()
  {
    vector<double> data;
    data.push_back(17.2);
    data.push_back(18.1);
    data.push_back(16.5);
    data.push_back(18.3);
    data.push_back(12.6);
    sort(data.begin(), data.end());

    Statistics stats = getStatistics(data, StatOptions::CorrectedStdDev);

    TS_ASSERT_EQUALS(stats.mean, 16.54);
    TS_ASSERT_DELTA(stats.standard_deviation, 2.3179, 0.0001);
    TS_ASSERT_EQUALS(stats.minimum, 12.6);
    TS_ASSERT_EQUALS(stats.maximum, 18.3);
    TS_ASSERT(boost::math::isnan(stats.median));
  }

  void test_Types_Can_Be_Disabled_With_Flags() {
    vector<double> data;
    data.push_back(17.2);
    data.push_back(18.1);
    data.push_back(16.5);
    data.push_back(18.3);
    data.push_back(12.6);
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    Statistics justMean = getStatistics(data, StatOptions::Mean);
    TS_ASSERT_EQUALS(justMean.mean, 16.54);
    TS_ASSERT(boost::math::isnan(justMean.standard_deviation));
    TS_ASSERT(boost::math::isnan(justMean.minimum));
    TS_ASSERT(boost::math::isnan(justMean.maximum));
    TS_ASSERT(boost::math::isnan(justMean.median));
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  }
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  void testZscores()
  {
    vector<double> data;
    data.push_back(12);
    data.push_back(13);
    data.push_back(9);
    data.push_back(18);
    data.push_back(7);
    data.push_back(9);
    data.push_back(14);
    data.push_back(16);
    data.push_back(10);
    data.push_back(12);
    data.push_back(7);
    data.push_back(13);
    data.push_back(14);
    data.push_back(19);
    data.push_back(10);
    data.push_back(16);
    data.push_back(12);
    data.push_back(16);
    data.push_back(19);
    data.push_back(11);

    std::vector<double> Zscore = getZscore(data);
    TS_ASSERT_DELTA(Zscore[4], 1.6397, 0.0001);
    TS_ASSERT_DELTA(Zscore[6], 0.3223, 0.0001);
    std::vector<double> ZModscore = getModifiedZscore(data);
    TS_ASSERT_DELTA(ZModscore[4], 1.2365, 0.0001);
    TS_ASSERT_DELTA(ZModscore[6], 0.3372, 0.0001);
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  }

  void testDoubleSingle()
  {
    vector<double> data;
    data.push_back(42.);

    Statistics stats = getStatistics(data);

    TS_ASSERT_EQUALS(stats.mean, 42.);
    TS_ASSERT_EQUALS(stats.standard_deviation, 0.);
    TS_ASSERT_EQUALS(stats.minimum, 42.);
    TS_ASSERT_EQUALS(stats.maximum, 42.);
    TS_ASSERT_EQUALS(stats.median, 42.);
  }

  void testInt32Even()
  {
    vector<int32_t> data;
    data.push_back(1);
    data.push_back(2);
    data.push_back(3);
    data.push_back(4);
    data.push_back(5);
    data.push_back(6);

    Statistics stats = getStatistics(data);

    TS_ASSERT_EQUALS(stats.mean, 3.5);
    TS_ASSERT_DELTA(stats.standard_deviation, 1.7078, 0.0001);
    TS_ASSERT_EQUALS(stats.minimum, 1.);
    TS_ASSERT_EQUALS(stats.maximum, 6.);
    TS_ASSERT_EQUALS(stats.median, 3.5);
  }

  bool my_isnan(const double number)
  {
    return number != number;
  }

  void testString()
  {
    vector<string> data;
    data.push_back("hi there");

    Statistics stats = getStatistics(data);

    TS_ASSERT(my_isnan(stats.mean));
    TS_ASSERT(my_isnan(stats.standard_deviation));
    TS_ASSERT(my_isnan(stats.minimum));
    TS_ASSERT(my_isnan(stats.maximum));
    TS_ASSERT(my_isnan(stats.median));
  }
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  /** Test function to calculate Rwp
    */
  void testRwp()
  {
    vector<double> obsY(4);
    vector<double> calY(4);
    vector<double> obsE(4);

    obsY[0] = 1.0;
    calY[0] = 1.1;
    obsE[0] = 1.0;

    obsY[1] = 2.0;
    calY[1] = 2.1;
    obsE[1] = 1.2;

    obsY[2] = 3.0;
    calY[2] = 3.5;
    obsE[2] = 1.4;

    obsY[3] = 1.0;
    calY[3] = 1.3;
    obsE[3] = 1.0;

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    Rfactor rfactor = getRFactor(obsY, calY, obsE);
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    TS_ASSERT_DELTA(rfactor.Rwp, 0.1582, 0.0001);
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  }

  /** Test throw exception
    */
  void testRwpException1()
  {
    vector<double> obsY(4);
    vector<double> calY(4);
    vector<double> obsE(3);

    obsY[0] = 1.0;
    calY[0] = 1.1;
    obsE[0] = 1.0;

    obsY[1] = 2.0;
    calY[1] = 2.1;
    obsE[1] = 1.2;
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    obsY[2] = 3.0;
    calY[2] = 3.5;
    obsE[2] = 1.4;

    obsY[3] = 1.0;
    calY[3] = 1.3;

    TS_ASSERT_THROWS_ANYTHING(getRFactor(obsY, calY, obsE));
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  }
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  /** Test throw exception on empty array
    */
  void testRwpException2()
  {
    vector<double> obsY;
    vector<double> calY;
    vector<double> obsE;

    TS_ASSERT_THROWS_ANYTHING(getRFactor(obsY, calY, obsE));
  }

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  /// Test moment calculations about origin and mean
  void test_getMoments()
  {
    const double mean = 5.;
    const double sigma = 4.;
    const double deltaX = .2;
    const size_t numX = 200;
    // calculate to have same number of points left and right of function
    const double offsetX = mean - (.5 * deltaX * static_cast<double>(numX));
    // variance about origin
    double expVar = mean*mean+sigma*sigma;
    // skew about origin
    double expSkew = mean*mean*mean+3.*mean*sigma*sigma;

    // x-values to try out
    vector<double> x;
    for (size_t i = 0; i < numX; ++i)
      x.push_back(static_cast<double>(i) * deltaX + offsetX);

    // just declare so we can have test of exception handling
    vector<double> y;

    TS_ASSERT_THROWS(getMomentsAboutOrigin(x, y), std::out_of_range);

    // now calculate the y-values
    for (size_t i = 0; i < numX; ++i)
    {
      double temp = (x[i]-mean)/sigma;
      y.push_back(exp(-.5*temp*temp)/(sigma * sqrt(2.*M_PI)));
    }

    // Normal distribution values are taken from the wikipedia page
    {
      std::cout << "Normal distribution about origin" << std::endl;
      vector<double> aboutOrigin = getMomentsAboutOrigin(x, y);
      TS_ASSERT_EQUALS(aboutOrigin.size(), 4);
      TS_ASSERT_DELTA(aboutOrigin[0], 1., .0001);
      TS_ASSERT_DELTA(aboutOrigin[1], mean, .0001);
      TS_ASSERT_DELTA(aboutOrigin[2], expVar, .001*expVar);
      TS_ASSERT_DELTA(aboutOrigin[3], expSkew, .001*expSkew);

      std::cout << "Normal distribution about mean" << std::endl;
      vector<double> aboutMean = getMomentsAboutMean(x, y);
      TS_ASSERT_EQUALS(aboutMean.size(), 4);
      TS_ASSERT_DELTA(aboutMean[0], 1., .0001);
      TS_ASSERT_DELTA(aboutMean[1], 0., .0001);
      TS_ASSERT_DELTA(aboutMean[2], sigma*sigma, .001*expVar);
      TS_ASSERT_DELTA(aboutMean[3], 0., .0001*expSkew);
    }

    // Now a gaussian function as a histogram
    y.clear();
    for (size_t i = 0; i < numX-1; ++i) // one less y than x makes it a histogram
    {
      double templeft = (x[i]-mean)/sigma;
      templeft = exp(-.5*templeft*templeft)/(sigma * sqrt(2.*M_PI));
      double tempright = (x[i+1]-mean)/sigma;
      tempright = exp(-.5*tempright*tempright)/(sigma * sqrt(2.*M_PI));
      y.push_back(.5*deltaX*(templeft+tempright));
//      std::cout << i << ":\t" << x[i] << "\t" << y[i] << std::endl;
    }

    // Normal distribution values are taken from the wikipedia page
    {
      std::cout << "Normal distribution about origin" << std::endl;
      vector<double> aboutOrigin = getMomentsAboutOrigin(x, y);
      TS_ASSERT_EQUALS(aboutOrigin.size(), 4);
      TS_ASSERT_DELTA(aboutOrigin[0], 1., .0001);
      TS_ASSERT_DELTA(aboutOrigin[1], mean, .0001);
      TS_ASSERT_DELTA(aboutOrigin[2], expVar, .001*expVar);
      TS_ASSERT_DELTA(aboutOrigin[3], expSkew, .001*expSkew);

      std::cout << "Normal distribution about mean" << std::endl;
      vector<double> aboutMean = getMomentsAboutMean(x, y);
      TS_ASSERT_EQUALS(aboutMean.size(), 4);
      TS_ASSERT_DELTA(aboutMean[0], 1., .0001);
      TS_ASSERT_DELTA(aboutMean[1], 0., .0001);
      TS_ASSERT_DELTA(aboutMean[2], sigma*sigma, .001*expVar);
      TS_ASSERT_DELTA(aboutMean[3], 0., .0001*expSkew);
    }
  }

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};

#endif // STATISTICSTEST_H_