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https://github.com/UpsilonNumworks/Upsilon.git
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99 lines
3.6 KiB
C++
99 lines
3.6 KiB
C++
#include "logistic_model.h"
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#include "../store.h"
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#include <math.h>
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#include <assert.h>
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#include <poincare/code_point_layout.h>
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#include <poincare/fraction_layout.h>
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#include <poincare/horizontal_layout.h>
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#include <poincare/vertical_offset_layout.h>
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using namespace Poincare;
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namespace Regression {
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Layout LogisticModel::layout() {
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if (m_layout.isUninitialized()) {
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m_layout = FractionLayout::Builder(
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CodePointLayout::Builder('c', k_layoutFont),
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HorizontalLayout::Builder({
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CodePointLayout::Builder('1', k_layoutFont),
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CodePointLayout::Builder('+', k_layoutFont),
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CodePointLayout::Builder('a', k_layoutFont),
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CodePointLayout::Builder(UCodePointMiddleDot, k_layoutFont),
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CodePointLayout::Builder('e', k_layoutFont),
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VerticalOffsetLayout::Builder(
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HorizontalLayout::Builder({
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CodePointLayout::Builder('-', k_layoutFont),
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CodePointLayout::Builder('b', k_layoutFont),
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CodePointLayout::Builder(UCodePointMiddleDot, k_layoutFont),
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CodePointLayout::Builder('X', k_layoutFont)
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}),
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VerticalOffsetLayoutNode::Position::Superscript
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)
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})
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);
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}
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return m_layout;
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}
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double LogisticModel::evaluate(double * modelCoefficients, double x) const {
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double a = modelCoefficients[0];
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double b = modelCoefficients[1];
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double c = modelCoefficients[2];
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return c/(1.0+a*exp(-b*x));
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}
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double LogisticModel::levelSet(double * modelCoefficients, double xMin, double step, double xMax, double y, Poincare::Context * context) {
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double a = modelCoefficients[0];
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double b = modelCoefficients[1];
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double c = modelCoefficients[2];
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if (a == 0 || b == 0 || c == 0 || y == 0) {
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return NAN;
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}
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double lnArgument = (c/y - 1)/a;
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if (lnArgument <= 0) {
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return NAN;
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}
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return -log(lnArgument)/b;
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}
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double LogisticModel::partialDerivate(double * modelCoefficients, int derivateCoefficientIndex, double x) const {
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double a = modelCoefficients[0];
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double b = modelCoefficients[1];
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double c = modelCoefficients[2];
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double denominator = 1.0 + a * exp(-b * x);
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if (derivateCoefficientIndex == 0) {
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// Derivate with respect to a: exp(-b*x)*(-1 * c/(1.0+a*exp(-b*x))^2)
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return -exp(-b * x) * c / (denominator * denominator);
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}
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if (derivateCoefficientIndex == 1) {
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// Derivate with respect to b: (-x)*a*exp(-b*x)*(-1/(1.0+a*exp(-b*x))^2)
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return x * a * exp(-b * x) * c / (denominator * denominator);
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}
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assert(derivateCoefficientIndex == 2);
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// Derivate with respect to c: (-x)*a*exp(-b*x)*(-1/(1.0+a*exp(-b*x))^2)
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return 1.0 / denominator;
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}
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void LogisticModel::specializedInitCoefficientsForFit(double * modelCoefficients, double defaultValue, Store * store, int series) const {
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assert(store != nullptr && series >= 0 && series < Store::k_numberOfSeries && !store->seriesIsEmpty(series));
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modelCoefficients[0] = defaultValue;
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modelCoefficients[1] = defaultValue;
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/* If the data is a standard logistic function, the ordinates are between 0
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* and c. Twice the standard vertical deviation is a rough estimate of c
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* that is "close enough" to c to seed the coefficient, without being too
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* dependent on outliers.*/
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modelCoefficients[2] = 2.0 * store->standardDeviationOfColumn(series, 1);
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/* TODO : Try two different sets of seeds to find a better fit for both
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* x = {0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0}
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* y = {5.0, 9.0, 40.0, 64.0, 144.0, 200.0, 269.0, 278.0, 290.0, 295.0}
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* (Coefficients should be {64.9, 1.0, 297.4})
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* And
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* x = {4, 3, 21, 1, 6}
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* y = {0, 4, 5, 4, 58}
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* (Coefficients should be {370162529, 4.266, 31.445} with R2=0.4 at least) */
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}
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}
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