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103 lines
3.8 KiB
C++
103 lines
3.8 KiB
C++
#ifndef REGRESSION_STORE_H
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#define REGRESSION_STORE_H
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#include "model/model.h"
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#include "model/cubic_model.h"
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#include "model/exponential_model.h"
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#include "model/linear_model.h"
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#include "model/logarithmic_model.h"
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#include "model/logistic_model.h"
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#include "model/power_model.h"
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#include "model/proportional_model.h"
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#include "model/quadratic_model.h"
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#include "model/quartic_model.h"
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#include "model/trigonometric_model.h"
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#include "../shared/interactive_curve_view_range.h"
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#include "../shared/double_pair_store.h"
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#include <escher/responder.h>
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#include <float.h>
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namespace Regression {
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class Store : public Shared::InteractiveCurveViewRange, public Shared::DoublePairStore {
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public:
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Store();
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void reset();
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// Clean pool
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void tidy();
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// Regression
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void setSeriesRegressionType(int series, Model::Type type);
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Model::Type seriesRegressionType(int series) {
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return m_regressionTypes[series];
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}
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Model * modelForSeries(int series) {
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assert(series >= 0 && series < k_numberOfSeries);
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assert((int)m_regressionTypes[series] >= 0 && (int)m_regressionTypes[series] < Model::k_numberOfModels);
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return regressionModel((int)m_regressionTypes[series]);
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}
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uint32_t * seriesChecksum() { return m_seriesChecksum; }
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// Dots
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/* Return the closest dot to abscissa x above the regression curve if
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* direction > 0, below otherwise */
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int closestVerticalDot(int direction, double x, double y, int currentSeries, int currentDot, int * nextSeries, Poincare::Context * globalContext);
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/* Return the closest dot to given dot, on the right if direction > 0,
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* on the left otherwise */
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int nextDot(int series, int direction, int dot);
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Model * regressionModel(Model::Type type) {
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return regressionModel((int) type);
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}
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// Window
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void setDefault() override;
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// Series
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bool seriesIsEmpty(int series) const override;
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// Calculation
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double * coefficientsForSeries(int series, Poincare::Context * globalContext);
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double doubleCastedNumberOfPairsOfSeries(int series) const;
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double squaredValueSumOfColumn(int series, int i, bool lnOfSeries = false) const;
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double columnProductSum(int series, bool lnOfSeries = false) const;
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double meanOfColumn(int series, int i, bool lnOfSeries = false) const;
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double varianceOfColumn(int series, int i, bool lnOfSeries = false) const;
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double standardDeviationOfColumn(int series, int i, bool lnOfSeries = false) const;
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double covariance(int series, bool lnOfSeries = false) const;
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double slope(int series, bool lnOfSeries = false) const;
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double yIntercept(int series, bool lnOfSeries = false) const;
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double yValueForXValue(int series, double x, Poincare::Context * globalContext);
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double xValueForYValue(int series, double y, Poincare::Context * globalContext);
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double correlationCoefficient(int series) const;
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double squaredCorrelationCoefficient(int series) const;
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private:
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constexpr static float k_displayHorizontalMarginRatio = 0.05f;
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void resetMemoization();
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float maxValueOfColumn(int series, int i) const; //TODO LEA why float ?
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float minValueOfColumn(int series, int i) const; //TODO LEA why float ?
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Model * regressionModel(int index);
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uint32_t m_seriesChecksum[k_numberOfSeries];
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Model::Type m_regressionTypes[k_numberOfSeries];
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LinearModel m_linearModel;
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ProportionalModel m_proportionalModel;
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QuadraticModel m_quadraticModel;
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CubicModel m_cubicModel;
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QuarticModel m_quarticModel;
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LogarithmicModel m_logarithmicModel;
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ExponentialModel m_exponentialModel;
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PowerModel m_powerModel;
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TrigonometricModel m_trigonometricModel;
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LogisticModel m_logisticModel;
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double m_regressionCoefficients[k_numberOfSeries][Model::k_maxNumberOfCoefficients];
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bool m_regressionChanged[k_numberOfSeries];
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Poincare::Preferences::AngleUnit m_angleUnit;
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};
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typedef double (Store::*ArgCalculPointer)(int, int, bool) const;
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typedef void (Store::*RangeMethodPointer)();
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}
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#endif
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