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[apps/regression] Improve initial coefficient value

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Change-Id: I2026b8de7e031f7e22921be2def800fa71d7a4e4
This commit is contained in:
Hugo Saint-Vignes
2020-11-25 12:41:05 +01:00
committed by EmilieNumworks
parent 1025fd9937
commit 06d7104776
2 changed files with 14 additions and 2 deletions
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12
apps/regression/model/trigonometric_model.cpp
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@@ -86,6 +86,18 @@ void TrigonometricModel::specializedInitCoefficientsForFit(double * modelCoeffic
modelCoefficients[0] = 3.0*store->standardDeviationOfColumn(series, 1);
// Init the "y delta" coefficient
modelCoefficients[k_numberOfCoefficients - 1] = store->meanOfColumn(series, 1);
// Init the b coefficient
double rangeX = store->maxValueOfColumn(series, 0) - store->minValueOfColumn(series, 0);
if (rangeX > 0) {
/* b/2π represents the frequency of the sine (in radians). Instead of
* initializing it to 0, we use the inverse of X series' range as an order
* of magnitude for it. It can help avoiding a regression that overfits the
* data with a very high frequency. This period also depends on the
* angleUnit. We take it into account so that it doesn't impact the result
* (although coefficients b and c depends on the angleUnit). */
double radian = toRadians(Poincare::Preferences::sharedPreferences()->angleUnit());
modelCoefficients[1] = (2.0 * M_PI / radian) / rangeX;
}
}
Expression TrigonometricModel::expression(double * modelCoefficients) {

4
apps/regression/store.h
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@@ -73,12 +73,12 @@ public:
double yValueForXValue(int series, double x, Poincare::Context * globalContext);
double xValueForYValue(int series, double y, Poincare::Context * globalContext);
double correlationCoefficient(int series) const; // R
float maxValueOfColumn(int series, int i) const; //TODO LEA why float ?
float minValueOfColumn(int series, int i) const; //TODO LEA why float ?
private:
double computeDeterminationCoefficient(int series, Poincare::Context * globalContext);
constexpr static float k_displayHorizontalMarginRatio = 0.05f;
void resetMemoization();
float maxValueOfColumn(int series, int i) const; //TODO LEA why float ?
float minValueOfColumn(int series, int i) const; //TODO LEA why float ?
Model * regressionModel(int index);
uint32_t m_seriesChecksum[k_numberOfSeries];
Model::Type m_regressionTypes[k_numberOfSeries];