mirror of
https://github.com/UpsilonNumworks/Upsilon.git
synced 2026-01-19 00:37:25 +01:00
106 lines
3.7 KiB
C++
106 lines
3.7 KiB
C++
#include "exponential_model.h"
|
|
#include "../store.h"
|
|
#include "../linear_model_helper.h"
|
|
#include <math.h>
|
|
#include <assert.h>
|
|
#include <poincare/code_point_layout.h>
|
|
#include <poincare/horizontal_layout.h>
|
|
#include <poincare/vertical_offset_layout.h>
|
|
|
|
using namespace Poincare;
|
|
|
|
namespace Regression {
|
|
|
|
Layout ExponentialModel::layout() {
|
|
if (m_layout.isUninitialized()) {
|
|
constexpr int size = 4;
|
|
Layout layoutChildren[size] = {
|
|
CodePointLayout::Builder('a', k_layoutFont),
|
|
CodePointLayout::Builder(UCodePointMiddleDot, k_layoutFont),
|
|
CodePointLayout::Builder('e', k_layoutFont),
|
|
VerticalOffsetLayout::Builder(
|
|
HorizontalLayout::Builder(
|
|
CodePointLayout::Builder('b', k_layoutFont),
|
|
CodePointLayout::Builder(UCodePointMiddleDot, k_layoutFont),
|
|
CodePointLayout::Builder('X', k_layoutFont)
|
|
),
|
|
VerticalOffsetLayoutNode::Position::Superscript
|
|
)
|
|
};
|
|
m_layout = HorizontalLayout::Builder(layoutChildren, size);
|
|
}
|
|
return m_layout;
|
|
}
|
|
|
|
double ExponentialModel::evaluate(double * modelCoefficients, double x) const {
|
|
double a = modelCoefficients[0];
|
|
double b = modelCoefficients[1];
|
|
// a*e^(bx)
|
|
return a*exp(b*x);
|
|
}
|
|
|
|
double ExponentialModel::levelSet(double * modelCoefficients, double xMin, double step, double xMax, double y, Poincare::Context * context) {
|
|
double a = modelCoefficients[0];
|
|
double b = modelCoefficients[1];
|
|
if (a == 0 || b == 0 || y/a <= 0) {
|
|
return NAN;
|
|
}
|
|
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] = LinearModelHelper::Slope(covariance, variance);
|
|
modelCoefficients[0] =
|
|
sign * exp(LinearModelHelper::YIntercept(meanOfY, meanOfX, modelCoefficients[1]));
|
|
}
|
|
|
|
double ExponentialModel::partialDerivate(double * modelCoefficients, int derivateCoefficientIndex, double x) const {
|
|
double a = modelCoefficients[0];
|
|
double b = modelCoefficients[1];
|
|
if (derivateCoefficientIndex == 0) {
|
|
// Derivate: exp(b*x)
|
|
return exp(b*x);
|
|
}
|
|
if (derivateCoefficientIndex == 1) {
|
|
// Derivate: a*x*exp(b*x)
|
|
return a*x*exp(b*x);
|
|
}
|
|
assert(false);
|
|
return 0.0;
|
|
}
|
|
|
|
}
|