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[apps/regression/model/exponential_model] Explicit fit

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This commit is contained in:
Ruben Dashyan
2019-05-28 12:17:47 +02:00
committed by Léa Saviot
parent 9f41a46ce7
commit 13c63f495c
2 changed files with 40 additions and 0 deletions
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39
apps/regression/model/exponential_model.cpp
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@@ -1,4 +1,5 @@
#include "exponential_model.h"
#include "../store.h"
#include <math.h>
#include <assert.h>
#include <poincare/code_point_layout.h>
@@ -46,6 +47,44 @@ double ExponentialModel::levelSet(double * modelCoefficients, double xMin, doubl
return log(y/a)/b;
}
void ExponentialModel::fit(Store * store, int series, double * modelCoefficients, Poincare::Context * context) {
/* By the change of variable z=ln(y), the equation y=a*exp(b*x) becomes
* z=c*x+d with c=b and d=ln(a). Although that change of variable does not
* preserve the regression error function, it turns an exponential regression
* problem into a linear one and we consider that the solution of the latter
* is good enough for our purpose.
* That being said, one should check that the y values are all positive. (If
* the y values are all negative, one may replace each of them by its
* opposite. In the case where y values happen to be zero or of opposite
* sign, we call the base class method as a fallback. */
double sumOfX = 0;
double sumOfY = 0;
double sumOfXX = 0;
double sumOfXY = 0;
const int numberOfPoints = store->numberOfPairsOfSeries(series);
const int sign = store->get(series, 1, 0) > 0 ? 1 : -1;
for (int p = 0; p < numberOfPoints; p++) {
const double x = store->get(series, 0, p);
const double z = store->get(series, 1, p) * sign;
if (z <= 0) {
return Model::fit(store, series, modelCoefficients, context);
}
const double y = log(z);
sumOfX += x;
sumOfY += y;
sumOfXX += x*x;
sumOfXY += x*y;
}
const double meanOfX = sumOfX / numberOfPoints;
const double meanOfY = sumOfY / numberOfPoints;
const double meanOfXX = sumOfXX / numberOfPoints;
const double meanOfXY = sumOfXY / numberOfPoints;
const double variance = meanOfXX - meanOfX * meanOfX;
const double covariance = meanOfXY - meanOfX * meanOfY;
modelCoefficients[1] = covariance / variance;
modelCoefficients[0] = sign * exp(meanOfY - modelCoefficients[1] * meanOfX);
}
double ExponentialModel::partialDerivate(double * modelCoefficients, int derivateCoefficientIndex, double x) const {
double a = modelCoefficients[0];
double b = modelCoefficients[1];

1
apps/regression/model/exponential_model.h
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@@ -12,6 +12,7 @@ public:
I18n::Message formulaMessage() const override { return I18n::Message::ExponentialRegressionFormula; }
double evaluate(double * modelCoefficients, double x) const override;
double levelSet(double * modelCoefficients, double xMin, double step, double xMax, double y, Poincare::Context * context) override;
void fit(Store * store, int series, double * modelCoefficients, Poincare::Context * context) override;
double partialDerivate(double * modelCoefficients, int derivateCoefficientIndex, double x) const override;
int numberOfCoefficients() const override { return 2; }
int bannerLinesCount() const override { return 2; }