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119 lines
4.3 KiB
C++
119 lines
4.3 KiB
C++
#include <quiz.h>
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#include <string.h>
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#include <assert.h>
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#include <apps/shared/global_context.h>
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#include "../model/model.h"
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#include "../regression_context.h"
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#include "../store.h"
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using namespace Poincare;
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using namespace Regression;
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/* The data was generated by choosing X1 and the coefficients of the regression,
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* then filling Y1 with the regression formula + random()/10. */
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void assert_regression_is(double * xi, double * yi, int numberOfPoints, Model::Type modelType, double * trueCoefficients) {
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double precision = 0.1;
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int series = 0;
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Regression::Store store;
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// Set the points and the regression type
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for (int i = 0; i < numberOfPoints; i++) {
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store.set(xi[i], series, 0, i);
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store.set(yi[i], series, 1, i);
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}
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Shared::GlobalContext globalContext;
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RegressionContext context(&store, &globalContext);
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store.setSeriesRegressionType(series, modelType);
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// Compute the coefficients
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double * coefficients = store.coefficientsForSeries(series, &context);
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// Compare the coefficients
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int numberOfCoefs = store.modelForSeries(series)->numberOfCoefficients();
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for (int i = 0; i < numberOfCoefs; i++) {
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quiz_assert(std::fabs(coefficients[i] - trueCoefficients[i]) < precision);
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}
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}
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QUIZ_CASE(linear_regression) {
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double x[] = {1.0, 8.0, 14.0, 79.0};
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double y[] = {-3.581, 20.296, 40.676, 261.623};
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double coefficients[] = {3.4, -7.0};
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assert_regression_is(x, y, 4, Model::Type::Linear, coefficients);
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}
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QUIZ_CASE(quadratic_regression) {
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double x[] = {-34.0, -12.0, 5.0, 86.0, -2.0};
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double y[] = {-8241.389, -1194.734, -59.163, - 46245.39, -71.774};
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double coefficients[] = {-6.5, 21.3, -3.2};
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assert_regression_is(x, y, 5, Model::Type::Quadratic, coefficients);
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}
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QUIZ_CASE(cubic_regression) {
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double x[] = {-3.0, -2.8, -1.0, 0.0, 12.0};
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double y[] = {691.261, 566.498, 20.203, -12.865, -34293.21};
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double coefficients[] = {-21.2, 16.0, 4.1, -12.9};
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assert_regression_is(x, y, 5, Model::Type::Cubic, coefficients);
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}
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QUIZ_CASE(quartic_regression) {
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double x[] = {1.6, 3.5, 3.5, -2.8, 6.4, 5.3, 2.9, -4.8, -5.7, 3.1};
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double y[] = {-112.667, -1479.824, -1479.805, 1140.276, -9365.505, -5308.355, -816.925, 5554.007, 9277.107, -1009.874};
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double coefficients[] = {0.6, -43, 21.5, 3.1, -0.5};
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assert_regression_is(x, y, 10, Model::Type::Quartic, coefficients);
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}
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QUIZ_CASE(logarithmic_regression) {
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double x[] = {0.2, 0.5, 5, 7};
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double y[] = {-11.952, -9.035, -1.695, -0.584};
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double coefficients[] = {3.2, -6.9};
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assert_regression_is(x, y, 4, Model::Type::Logarithmic, coefficients);
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}
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QUIZ_CASE(exponential_regression) {
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double x[] = {5.5, 5.6, 5.7, 5.8, 5.9, 6.0};
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double y[] = {-276.842, -299.956, -324.933, -352.0299, -381.314, -413.0775};
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double coefficients[] = {-3.4, 0.8};
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assert_regression_is(x, y, 6, Model::Type::Exponential, coefficients);
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}
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QUIZ_CASE(exponential_regression2) {
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double x[] = {0, 1, 2, 3};
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double y[] = {3000, 3315.513, 3664.208, 4049.576};
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double coefficients[] = {3000, .1};
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assert_regression_is(x, y, 4, Model::Type::Exponential, coefficients);
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}
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QUIZ_CASE(exponential_regression3) {
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double x[] = {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
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double y[] = {-1, -.3678794, -.1353353, -.04978707, -.01831564, -.006737947, -.002478752, -.000911882, -.0003354626, -.0001234098, -.00004539993};
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double coefficients[] = {-1, -1};
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assert_regression_is(x, y, 11, Model::Type::Exponential, coefficients);
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}
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QUIZ_CASE(power_regression) {
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double x[] = {1.0, 50.0, 34.0, 67.0, 20.0};
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double y[] = {71.860, 2775514, 979755.1, 6116830, 233832.9};
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double coefficients[] = {71.8, 2.7};
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assert_regression_is(x, y, 5, Model::Type::Power, coefficients);
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}
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// No case for trigonometric regression, because it has no unique solution
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QUIZ_CASE(logistic_regression) {
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/* This data was generated without the random error, otherwise it did not pass
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* the test. */
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double x1[] = {2.3, 5.6, 1.1, 4.3};
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double y1[] = {3.948, 4.694, 2.184, 4.656};
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double coefficients1[] = {6, 1.5, 4.7};
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assert_regression_is(x1, y1, 4, Model::Type::Logistic, coefficients1);
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// This data produced a wrong fit before
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double x2[] = {0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0};
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double y2[] = {5.0, 9.0, 40.0, 64.0, 144.0, 200.0, 269.0, 278.0, 290.0, 295.0};
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double coefficients2[] = {64.9, 1.0, 297.4};
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assert_regression_is(x2, y2, 10, Model::Type::Logistic, coefficients2);
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}
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