mirror of
https://github.com/UpsilonNumworks/Upsilon.git
synced 2026-01-19 00:37:25 +01:00
2b59509fdd
Each country comes with an set of preferences, built at compile time by apps/i18n.py, used to define : - the exam mode - the method for computing quartiles - the unit system in which to output the results with units Functions to access those preferences are available in via sharedGlobalPreferences. Change-Id: I220ebaa9b9e8954dfe33cd51936f47505b98978d
370 lines
13 KiB
C++
370 lines
13 KiB
C++
#include <quiz.h>
|
|
#include <apps/i18n.h>
|
|
#include <apps/global_preferences.h>
|
|
#include <assert.h>
|
|
#include <math.h>
|
|
#include <cmath>
|
|
#include "../store.h"
|
|
#include <poincare/helpers.h>
|
|
#include <poincare/test/helper.h>
|
|
|
|
using namespace Poincare;
|
|
|
|
namespace Statistics {
|
|
|
|
void assert_value_approximately_equal_to(double d1, double d2, double precision, double reference) {
|
|
quiz_assert((std::isnan(d1) && std::isnan(d2))
|
|
|| (std::isinf(d1) && std::isinf(d2) && d1 * d2 > 0.0 /*same sign*/)
|
|
|| IsApproximatelyEqual(d1, d2, precision, reference));
|
|
}
|
|
|
|
/* SublistMethod is the method for computing quartiles used in most
|
|
* countries, which defines quartiles as the medians of the left and right
|
|
* subsets of data.
|
|
* FrequencyMethod is the method used in France and Italy, which defines the
|
|
* quartiles as the 25th and 75th percentile, in terms of cumulated
|
|
* frequencies. */
|
|
void assert_data_statictics_equal_to(
|
|
double v[],
|
|
double n[],
|
|
int numberOfData,
|
|
double trueSumOfOccurrences,
|
|
double trueMaxValue,
|
|
double trueMinValue,
|
|
double trueRange,
|
|
double trueMean,
|
|
double trueVariance,
|
|
double trueStandardDeviation,
|
|
double trueSampleStandardDeviation,
|
|
double trueFirstQuartileSublistMethod,
|
|
double trueThirdQuartileSublistMethod,
|
|
double trueQuartileRangeSublistMethod,
|
|
double trueFirstQuartileFrequencyMethod,
|
|
double trueThirdQuartileFrequencyMethod,
|
|
double trueQuartileRangeFrequencyMethod,
|
|
double trueMedian,
|
|
double trueSum,
|
|
double trueSquaredValueSum) {
|
|
Store store;
|
|
int seriesIndex = 0;
|
|
|
|
// Set the data in the store
|
|
for (int i = 0; i < numberOfData; i++) {
|
|
store.set(v[i], seriesIndex, 0, i);
|
|
store.set(n[i], seriesIndex, 1, i);
|
|
}
|
|
|
|
double precision = 1e-3;
|
|
|
|
double sumOfOccurrences = store.sumOfOccurrences(seriesIndex);
|
|
double maxValue = store.maxValue(seriesIndex);
|
|
double minValue = store.minValue(seriesIndex);
|
|
double range = store.range(seriesIndex);
|
|
double mean = store.mean(seriesIndex);
|
|
double variance = store.variance(seriesIndex);
|
|
double standardDeviation = store.standardDeviation(seriesIndex);
|
|
double sampleStandardDeviation = store.sampleStandardDeviation(seriesIndex);
|
|
double median = store.median(seriesIndex);
|
|
double sum = store.sum(seriesIndex);
|
|
double squaredValueSum = store.squaredValueSum(seriesIndex);
|
|
|
|
// Check the positive statistics
|
|
quiz_assert(range >= 0.0);
|
|
quiz_assert(variance >= 0.0);
|
|
quiz_assert(standardDeviation >= 0.0);
|
|
quiz_assert(sampleStandardDeviation >= 0.0);
|
|
quiz_assert(squaredValueSum >= 0.0);
|
|
|
|
// Compare the statistics
|
|
double reference = trueSquaredValueSum;
|
|
assert_value_approximately_equal_to(variance, trueVariance, precision, reference);
|
|
assert_value_approximately_equal_to(squaredValueSum, trueSquaredValueSum, precision, reference);
|
|
|
|
reference = std::sqrt(trueSquaredValueSum);
|
|
assert_value_approximately_equal_to(trueStandardDeviation * trueStandardDeviation, trueVariance, precision, reference);
|
|
assert_value_approximately_equal_to(sumOfOccurrences, trueSumOfOccurrences, precision, reference);
|
|
assert_value_approximately_equal_to(mean, trueMean, precision, reference);
|
|
assert_value_approximately_equal_to(standardDeviation, trueStandardDeviation, precision, reference);
|
|
assert_value_approximately_equal_to(sampleStandardDeviation, trueSampleStandardDeviation, precision, reference);
|
|
assert_value_approximately_equal_to(median, trueMedian, precision, reference);
|
|
assert_value_approximately_equal_to(sum, trueSum, precision, reference);
|
|
|
|
// Perfect match
|
|
assert_value_approximately_equal_to(maxValue, trueMaxValue, 0.0, 0.0);
|
|
assert_value_approximately_equal_to(minValue, trueMinValue, 0.0, 0.0);
|
|
assert_value_approximately_equal_to(range, trueRange, 0.0, 0.0);
|
|
|
|
// Compare the country specific statistics
|
|
I18n::Country country;
|
|
double quartileRange;
|
|
bool shouldUseFrequencyMethod;
|
|
for (int c = 0; c < I18n::NumberOfCountries; c++) {
|
|
country = static_cast<I18n::Country>(c);
|
|
GlobalPreferences::sharedGlobalPreferences()->setCountry(country);
|
|
quartileRange = store.quartileRange(seriesIndex);
|
|
quiz_assert(quartileRange >= 0.0);
|
|
shouldUseFrequencyMethod = GlobalPreferences::sharedGlobalPreferences()->methodForQuartiles() == I18n::MethodForQuartiles::CumulatedFrequency;
|
|
assert_value_approximately_equal_to(store.firstQuartile(seriesIndex), shouldUseFrequencyMethod ? trueFirstQuartileFrequencyMethod : trueFirstQuartileSublistMethod, precision, reference);
|
|
assert_value_approximately_equal_to(store.thirdQuartile(seriesIndex), shouldUseFrequencyMethod ? trueThirdQuartileFrequencyMethod : trueThirdQuartileSublistMethod, precision, reference);
|
|
assert_value_approximately_equal_to(quartileRange, shouldUseFrequencyMethod ? trueQuartileRangeFrequencyMethod : trueQuartileRangeSublistMethod, 0.0, 0.0);
|
|
}
|
|
}
|
|
|
|
QUIZ_CASE(data_statistics) {
|
|
|
|
/* 1 2 3 4
|
|
* 1 1 1 1 */
|
|
|
|
constexpr int listLength1 = 4;
|
|
double v1[listLength1] = {1.0, 2.0, 3.0, 4.0};
|
|
double n1[listLength1] = {1.0, 1.0, 1.0, 1.0};
|
|
assert_data_statictics_equal_to(
|
|
v1,
|
|
n1,
|
|
listLength1,
|
|
/* sumOfOccurrences */ 4.0,
|
|
/* maxValue */ 4.0,
|
|
/* minValue */ 1.0,
|
|
/* range */ 3.0,
|
|
/* mean */ 2.5,
|
|
/* variance */ 1.25,
|
|
/* standardDeviation */ 1.118,
|
|
/* sampleStandardDeviation */ 1.291,
|
|
/* firstQuartileSublistMethod */ 1.5,
|
|
/* thirdQuartileSublistMethod */ 3.5,
|
|
/* quartileRangeSublistMethod */ 2.0,
|
|
/* firstQuartileFrequencyMethod */ 1.0,
|
|
/* thirdQuartileFrequencyMethod */ 3.0,
|
|
/* quartileRangeFrequencyMethod */ 2.0,
|
|
/* median */ 2.5,
|
|
/* sum */ 10.0,
|
|
/* squaredValueSum */ 30.0);
|
|
|
|
|
|
/* 1 2 3 4 5 6 7 8 9 10 11
|
|
* 1 1 1 1 1 1 1 1 1 1 1 */
|
|
|
|
constexpr int listLength2 = 11;
|
|
double v2[listLength2] = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0};
|
|
double n2[listLength2] = {1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0};
|
|
assert_data_statictics_equal_to(
|
|
v2,
|
|
n2,
|
|
listLength2,
|
|
/* sumOfOccurrences */ 11.0,
|
|
/* maxValue */ 11.0,
|
|
/* minValue */ 1.0,
|
|
/* range */ 10.0,
|
|
/* mean */ 6.0,
|
|
/* variance */ 10.0,
|
|
/* standardDeviation */ 3.1623,
|
|
/* sampleStandardDeviation */ 3.3166,
|
|
/* firstQuartileSublistMethod */ 3.0,
|
|
/* thirdQuartileSublistMethod */ 9.0,
|
|
/* quartileRangeSublistMethod */ 6.0,
|
|
/* firstQuartileFrequencyMethod */ 3.0,
|
|
/* thirdQuartileFrequencyMethod */ 9.0,
|
|
/* quartileRangeFrequencyMethod */ 6.0,
|
|
/* median */ 6.0,
|
|
/* sum */ 66.0,
|
|
/* squaredValueSum */ 506.0);
|
|
|
|
/* 1 2 3 4 5 6 7 8 9 10 11 12
|
|
* 1 1 1 1 1 1 1 1 1 1 1 1 */
|
|
|
|
constexpr int listLength3 = 12;
|
|
double v3[listLength3] = {1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0};
|
|
double n3[listLength3] = {1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0};
|
|
assert_data_statictics_equal_to(
|
|
v3,
|
|
n3,
|
|
listLength3,
|
|
/* sumOfOccurrences */ 12.0,
|
|
/* maxValue */ 12.0,
|
|
/* minValue */ 1.0,
|
|
/* range */ 11.0,
|
|
/* mean */ 6.5,
|
|
/* variance */ 11.917,
|
|
/* standardDeviation */ 3.4521,
|
|
/* sampleStandardDeviation */ 3.6056,
|
|
/* firstQuartileSublistMethod */ 3.5,
|
|
/* thirdQuartileSublistMethod */ 9.5,
|
|
/* quartileRangeSublistMethod */ 6.0,
|
|
/* firstQuartileFrequencyMethod */ 3.0,
|
|
/* thirdQuartileFrequencyMethod */ 9.0,
|
|
/* quartileRangeFrequencyMethod */ 6.0,
|
|
/* median */ 6.5,
|
|
/* sum */ 78.0,
|
|
/* squaredValueSum */ 650.0);
|
|
|
|
/* 1 2 3 5 10
|
|
* 0.2 0.05 0.3 0.0001 0.4499 */
|
|
|
|
constexpr int listLength4 = 5;
|
|
double v4[listLength4] = {1.0, 2.0, 3.0, 5.0, 10.0};
|
|
double n4[listLength4] = {0.2, 0.05, 0.3, 0.0001, 0.4499};
|
|
assert_data_statictics_equal_to(
|
|
v4,
|
|
n4,
|
|
listLength4,
|
|
/* sumOfOccurrences */ 1.0,
|
|
/* maxValue */ 10.0,
|
|
/* minValue */ 1.0,
|
|
/* range */ 9.0,
|
|
/* mean */ 5.6995,
|
|
/* variance */ 15.6082,
|
|
/* standardDeviation */ 3.9507,
|
|
/* sampleStandardDeviation */ INFINITY,
|
|
/* firstQuartileSublistMethod */ 2.0,
|
|
/* thirdQuartileSublistMethod */ 10.0,
|
|
/* quartileRangeSublistMethod */ 8.0,
|
|
/* firstQuartileFrequencyMethod */ 2.0,
|
|
/* thirdQuartileFrequencyMethod */ 10.0,
|
|
/* quartileRangeFrequencyMethod */ 8.0,
|
|
/* median */ 3.0,
|
|
/* sum */ 5.6995,
|
|
/* squaredValueSum */ 48.0925);
|
|
|
|
/* 1 -2 3 5 10
|
|
* 0.4 0.00005 0.9 0.4 0.5 */
|
|
|
|
constexpr int listLength5 = 5;
|
|
double v5[listLength5] = {1.0, -2.0, 3.0, 5.0, 10.0};
|
|
double n5[listLength5] = {0.4, 0.00005, 0.9, 0.4, 0.5};
|
|
assert_data_statictics_equal_to(
|
|
v5,
|
|
n5,
|
|
listLength5,
|
|
/* sumOfOccurrences */ 2.2,
|
|
/* maxValue */ 10.0,
|
|
/* minValue */ -2.0,
|
|
/* range */ 12.0,
|
|
/* mean */ 4.5908,
|
|
/* variance */ 10.06,
|
|
/* standardDeviation */ 3.1719,
|
|
/* sampleStandardDeviation */ 4.2947,
|
|
/* firstQuartileSublistMethod */ 3.0,
|
|
/* thirdQuartileSublistMethod */ 5.0,
|
|
/* quartileRangeSublistMethod */ 2.0,
|
|
/* firstQuartileFrequencyMethod */ 3.0,
|
|
/* thirdQuartileFrequencyMethod */ 5.0,
|
|
/* quartileRangeFrequencyMethod */ 2.0,
|
|
/* median */ 3.0,
|
|
/* sum */ 10.1,
|
|
/* squaredValueSum */ 68.500);
|
|
|
|
/* -7 -10 12 5 -2
|
|
* 4 5 3 1 9 */
|
|
|
|
constexpr int listLength6 = 6;
|
|
double v6[listLength6] = {-7.0, -10.0, 1.0, 2.0, 5.0, -2.0};
|
|
double n6[listLength6] = {4.0, 5.0, 3.0, 0.5, 1.0, 9.0};
|
|
assert_data_statictics_equal_to(
|
|
v6,
|
|
n6,
|
|
listLength6,
|
|
/* sumOfOccurrences */ 22.5,
|
|
/* maxValue */ 5.0,
|
|
/* minValue */ -10.0,
|
|
/* range */ 15.0,
|
|
/* mean */ -3.8667,
|
|
/* variance */ 18.9155,
|
|
/* standardDeviation */ 4.3492,
|
|
/* sampleStandardDeviation */ 4.4492,
|
|
/* firstQuartileSublistMethod */ -7.0,
|
|
/* thirdQuartileSublistMethod */ -2.0,
|
|
/* quartileRangeSublistMethod */ 5.0,
|
|
/* firstQuartileFrequencyMethod */ -7.0,
|
|
/* thirdQuartileFrequencyMethod */ -2.0,
|
|
/* quartileRangeFrequencyMethod */ 5.0,
|
|
/* median */ -2.0,
|
|
/* sum */ -87.0,
|
|
/* squaredValueSum */ 762.0);
|
|
|
|
/* 1 1 1 10 3 -1 3
|
|
* 1 1 1 0 0 0 1 */
|
|
|
|
constexpr int listLength7 = 7;
|
|
double v7[listLength7] = {1.0, 1.0, 1.0, 10.0, 3.0, -1.0, 3.0};
|
|
double n7[listLength7] = {1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 1.0};
|
|
assert_data_statictics_equal_to(
|
|
v7,
|
|
n7,
|
|
listLength7,
|
|
/* sumOfOccurrences */ 4.0,
|
|
/* maxValue */ 3.0,
|
|
/* minValue */ 1.0,
|
|
/* range */ 2.0,
|
|
/* mean */ 1.5,
|
|
/* variance */ 0.75,
|
|
/* standardDeviation */ 0.866,
|
|
/* sampleStandardDeviation */ 1.0,
|
|
/* firstQuartileSublistMethod */ 1.0,
|
|
/* thirdQuartileSublistMethod */ 2.0,
|
|
/* quartileRangeSublistMethod */ 1.0,
|
|
/* firstQuartileFrequencyMethod */ 1.0,
|
|
/* thirdQuartileFrequencyMethod */ 1.0,
|
|
/* quartileRangeFrequencyMethod */ 0.0,
|
|
/* median */ 1.0,
|
|
/* sum */ 6.0,
|
|
/* squaredValueSum */ 12.0);
|
|
|
|
/* 1 2 3 4
|
|
* 0 1 0 1 */
|
|
|
|
constexpr int listLength8 = 4;
|
|
double v8[listLength8] = {1.0, 2.0, 3.0, 4.0};
|
|
double n8[listLength8] = {0.0, 1.0, 0.0, 1.0};
|
|
assert_data_statictics_equal_to(
|
|
v8,
|
|
n8,
|
|
listLength8,
|
|
/* sumOfOccurrences */ 2.0,
|
|
/* maxValue */ 4.0,
|
|
/* minValue */ 2.0,
|
|
/* range */ 2.0,
|
|
/* mean */ 3.0,
|
|
/* variance */ 1.0,
|
|
/* standardDeviation */ 1.0,
|
|
/* sampleStandardDeviation */ 1.414,
|
|
/* firstQuartileSublistMethod */ 2.0,
|
|
/* thirdQuartileSublistMethod */ 4.0,
|
|
/* quartileRangeSublistMethod */ 2.0,
|
|
/* firstQuartileFrequencyMethod */ 2.0,
|
|
/* thirdQuartileFrequencyMethod */ 4.0,
|
|
/* quartileRangeFrequencyMethod */ 2.0,
|
|
/* median */ 3.0,
|
|
/* sum */ 6.0,
|
|
/* squaredValueSum */ 20.0);
|
|
|
|
/* -996.85840734641
|
|
* 9 */
|
|
|
|
constexpr int listLength9 = 1;
|
|
double v9[listLength9] = {-996.85840734641};
|
|
double n9[listLength9] = {9};
|
|
assert_data_statictics_equal_to(
|
|
v9,
|
|
n9,
|
|
listLength9,
|
|
/* sumOfOccurrences */ 9.0,
|
|
/* maxValue */ -996.85840734641,
|
|
/* minValue */ -996.85840734641,
|
|
/* range */ 0.0,
|
|
/* mean */ -996.85840734641,
|
|
/* variance */ 0.0,
|
|
/* standardDeviation */ 0.0,
|
|
/* sampleStandardDeviation */ 0.0,
|
|
/* firstQuartileSublistMethod */ -996.85840734641,
|
|
/* thirdQuartileSublistMethod */ -996.85840734641,
|
|
/* quartileRangeSublistMethod */ 0.0,
|
|
/* firstQuartileFrequencyMethod */ -996.85840734641,
|
|
/* thirdQuartileFrequencyMethod */ -996.85840734641,
|
|
/* quartileRangeFrequencyMethod */ 0.0,
|
|
/* median */ -996.85840734641,
|
|
/* sum */ -8971.72566611769,
|
|
/* squaredValueSum */ 8943540.158675);
|
|
}
|
|
|
|
}
|