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[apps] Multiple series in regression/store

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This commit is contained in:
Léa Saviot
2018-05-21 13:19:01 +02:00
parent 25713dc950
commit 7637183055
2 changed files with 95 additions and 95 deletions
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154
apps/regression/store.cpp
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@@ -16,7 +16,7 @@ Store::Store() :
/* Dots */
int Store::closestVerticalDot(int direction, float x) {
int Store::closestVerticalDot(int series, int direction, float x) {
float nextX = INFINITY;
float nextY = INFINITY;
int selectedDot = -1;
@@ -25,26 +25,26 @@ int Store::closestVerticalDot(int direction, float x) {
* - the next dot is the closest one in abscissa to x
* - the next dot is above the regression curve if direction == 1 and below
* otherwise */
for (int index = 0; index < m_numberOfPairs; index++) {
if ((m_xMin <= m_data[0][index] && m_data[0][index] <= m_xMax) &&
(std::fabs(m_data[0][index] - x) < std::fabs(nextX - x)) &&
((m_data[1][index] - yValueForXValue(m_data[0][index]) >= 0) == (direction > 0))) {
for (int index = 0; index < m_numberOfPairs[series]; index++) {
if ((m_xMin <= m_data[series][0][index] && m_data[series][0][index] <= m_xMax) &&
(std::fabs(m_data[series][0][index] - x) < std::fabs(nextX - x)) &&
((m_data[series][1][index] - yValueForXValue(m_data[series][0][index]) >= 0) == (direction > 0))) {
// Handle edge case: if 2 dots have the same abscissa but different ordinates
if (nextX != m_data[0][index] || ((nextY - m_data[1][index] >= 0) == (direction > 0))) {
nextX = m_data[0][index];
nextY = m_data[1][index];
if (nextX != m_data[series][0][index] || ((nextY - m_data[series][1][index] >= 0) == (direction > 0))) {
nextX = m_data[series][0][index];
nextY = m_data[series][1][index];
selectedDot = index;
}
}
}
// Compare with the mean dot
double meanX = meanOfColumn(0);
double meanY = meanOfColumn(1);
double meanX = meanOfColumn(series, 0);
double meanY = meanOfColumn(series, 1);
if (m_xMin <= meanX && meanX <= m_xMax &&
(std::fabs(meanX - x) < std::fabs(nextX - x)) &&
((meanY - yValueForXValue(meanX) >= 0) == (direction > 0))) {
if (nextX != meanX || ((nextY - meanY >= 0) == (direction > 0))) {
selectedDot = m_numberOfPairs;
selectedDot = m_numberOfPairs[series];
}
}
return selectedDot;
@@ -53,54 +53,54 @@ int Store::closestVerticalDot(int direction, float x) {
int Store::nextDot(int direction, int dot) {
float nextX = INFINITY;
int selectedDot = -1;
double meanX = meanOfColumn(0);
double meanX = meanOfColumn(series, 0);
float x = meanX;
if (dot >= 0 && dot < m_numberOfPairs) {
if (dot >= 0 && dot < m_numberOfPairs[series]) {
x = get(0, dot);
}
/* We have to scan the Store in opposite ways for the 2 directions to ensure to
* select all dots (even with equal abscissa) */
if (direction > 0) {
for (int index = 0; index < m_numberOfPairs; index++) {
for (int index = 0; index < m_numberOfPairs[series]; index++) {
/* The conditions to test are in this order:
* - the next dot is the closest one in abscissa to x
* - the next dot is not the same as the selected one
* - the next dot is at the right of the selected one */
if (std::fabs(m_data[0][index] - x) < std::fabs(nextX - x) &&
if (std::fabs(m_date[series][0][index] - x) < std::fabs(nextX - x) &&
(index != dot) &&
(m_data[0][index] >= x)) {
(m_date[series][0][index] >= x)) {
// Handle edge case: 2 dots have same abscissa
if (m_data[0][index] != x || (index > dot)) {
nextX = m_data[0][index];
if (m_date[series][0][index] != x || (index > dot)) {
nextX = m_date[series][0][index];
selectedDot = index;
}
}
}
// Compare with the mean dot
if (std::fabs(meanX - x) < std::fabs(nextX - x) &&
(m_numberOfPairs != dot) &&
(m_numberOfPairs[series] != dot) &&
(meanX >= x)) {
if (meanX != x || (x > dot)) {
selectedDot = m_numberOfPairs;
selectedDot = m_numberOfPairs[series];
}
}
} else {
// Compare with the mean dot
if (std::fabs(meanX - x) < std::fabs(nextX - x) &&
(m_numberOfPairs != dot) &&
(m_numberOfPairs[series] != dot) &&
(meanX <= x)) {
if (meanX != x || (m_numberOfPairs < dot)) {
if (meanX != x || (m_numberOfPairs[series] < dot)) {
nextX = meanX;
selectedDot = m_numberOfPairs;
selectedDot = m_numberOfPairs[series];
}
}
for (int index = m_numberOfPairs-1; index >= 0; index--) {
if (std::fabs(m_data[0][index] - x) < std::fabs(nextX - x) &&
for (int index = m_numberOfPairs[series]-1; index >= 0; index--) {
if (std::fabs(m_date[series][0][index] - x) < std::fabs(nextX - x) &&
(index != dot) &&
(m_data[0][index] <= x)) {
(m_date[series][0][index] <= x)) {
// Handle edge case: 2 dots have same abscissa
if (m_data[0][index] != x || (index < dot)) {
nextX = m_data[0][index];
if (m_date[series][0][index] != x || (index < dot)) {
nextX = m_date[series][0][index];
selectedDot = index;
}
}
@@ -111,9 +111,9 @@ int Store::nextDot(int direction, int dot) {
/* Window */
void Store::setDefault() {
float min = minValueOfColumn(0);
float max = maxValueOfColumn(0);
void Store::setDefault(int series) {
float min = minValueOfColumn(series, 0);
float max = maxValueOfColumn(series, 0);
float range = max - min;
setXMin(min - k_displayLeftMarginRatio*range);
setXMax(max + k_displayRightMarginRatio*range);
@@ -122,98 +122,98 @@ void Store::setDefault() {
/* Calculations */
float Store::maxValueOfColumn(int i) {
float Store::maxValueOfColumn(int series, int i) {
float max = -FLT_MAX;
for (int k = 0; k < m_numberOfPairs; k++) {
if (m_data[i][k] > max) {
max = m_data[i][k];
for (int k = 0; k < m_numberOfPairs[series]; k++) {
if (m_data[series][i][k] > max) {
max = m_data[series][i][k];
}
}
return max;
}
float Store::minValueOfColumn(int i) {
float Store::minValueOfColumn(int series, int i) {
float min = FLT_MAX;
for (int k = 0; k < m_numberOfPairs; k++) {
if (m_data[i][k] < min) {
min = m_data[i][k];
for (int k = 0; k < m_numberOfPairs[series]; k++) {
if (m_data[series][i][k] < min) {
min = m_data[series][i][k];
}
}
return min;
}
double Store::squaredValueSumOfColumn(int i) {
double Store::squaredValueSumOfColumn(int series, int i) {
double result = 0;
for (int k = 0; k < m_numberOfPairs; k++) {
result += m_data[i][k]*m_data[i][k];
for (int k = 0; k < m_numberOfPairs[series]; k++) {
result += m_data[series][i][k]*m_data[series][i][k];
}
return result;
}
double Store::columnProductSum() {
double Store::columnProductSum(int series) {
double result = 0;
for (int k = 0; k < m_numberOfPairs; k++) {
result += m_data[0][k]*m_data[1][k];
for (int k = 0; k < m_numberOfPairs[series]; k++) {
result += m_date[series][0][k]*m_date[series][1][k];
}
return result;
}
double Store::meanOfColumn(int i) {
return m_numberOfPairs == 0 ? 0 : sumOfColumn(i)/m_numberOfPairs;
double Store::meanOfColumn(int series, int i) {
return m_numberOfPairs[series] == 0 ? 0 : sumOfColumn(series, i)/m_numberOfPairs[series];
}
double Store::varianceOfColumn(int i) {
double mean = meanOfColumn(i);
return squaredValueSumOfColumn(i)/m_numberOfPairs - mean*mean;
double Store::varianceOfColumn(int series, int i) {
double mean = meanOfColumn(series, i);
return squaredValueSumOfColumn(series, i)/m_numberOfPairs[series] - mean*mean;
}
double Store::standardDeviationOfColumn(int i) {
return std::sqrt(varianceOfColumn(i));
double Store::standardDeviationOfColumn(int series, int i) {
return std::sqrt(varianceOfColumn(series, i));
}
double Store::covariance() {
return columnProductSum()/m_numberOfPairs - meanOfColumn(0)*meanOfColumn(1);
double Store::covariance(int series) {
return columnProductSum(series)/m_numberOfPairs[series] - meanOfColumn(series, 0)*meanOfColumn(series, 1);
}
double Store::slope() {
return covariance()/varianceOfColumn(0);
double Store::slope(int series) {
return covariance(series)/varianceOfColumn(series, 0);
}
double Store::yIntercept() {
return meanOfColumn(1) - slope()*meanOfColumn(0);
double Store::yIntercept(int series) {
return meanOfColumn(series, 1) - slope(series)*meanOfColumn(series, 0);
}
double Store::yValueForXValue(double x) {
return slope()*x+yIntercept();
double Store::yValueForXValue(int series, double x) {
return slope(series)*x+yIntercept(series);
}
double Store::xValueForYValue(double y) {
return std::fabs(slope()) < DBL_EPSILON ? NAN : (y - yIntercept())/slope();
double Store::xValueForYValue(int series, double y) {
return std::fabs(slope(series)) < DBL_EPSILON ? NAN : (y - yIntercept(series))/slope(series);
}
double Store::correlationCoefficient() {
double sd0 = standardDeviationOfColumn(0);
double sd1 = standardDeviationOfColumn(1);
return (sd0 == 0.0 || sd1 == 0.0) ? 1.0 : covariance()/(sd0*sd1);
double Store::correlationCoefficient(int series) {
double sd0 = standardDeviationOfColumn(series, 0);
double sd1 = standardDeviationOfColumn(series, 1);
return (sd0 == 0.0 || sd1 == 0.0) ? 1.0 : covariance(series)/(sd0*sd1);
}
double Store::squaredCorrelationCoefficient() {
double cov = covariance();
double v0 = varianceOfColumn(0);
double v1 = varianceOfColumn(1);
double Store::squaredCorrelationCoefficient(int series) {
double cov = covariance(series);
double v0 = varianceOfColumn(series, 0);
double v1 = varianceOfColumn(series, 1);
return (v0 == 0.0 || v1 == 0.0) ? 1.0 : cov*cov/(v0*v1);
}
InteractiveCurveViewRangeDelegate::Range Store::computeYRange(InteractiveCurveViewRange * interactiveCurveViewRange) {
InteractiveCurveViewRangeDelegate::Range Store::computeYRange(int series, InteractiveCurveViewRange * interactiveCurveViewRange) {
float min = FLT_MAX;
float max = -FLT_MAX;
for (int k = 0; k < m_numberOfPairs; k++) {
if (m_xMin <= m_data[0][k] && m_data[0][k] <= m_xMax) {
if (m_data[1][k] < min) {
min = m_data[1][k];
for (int k = 0; k < m_numberOfPairs[series]; k++) {
if (m_xMin <= m_date[series][0][k] && m_date[series][0][k] <= m_xMax) {
if (m_date[series][1][k] < min) {
min = m_date[series][1][k];
}
if (m_data[1][k] > max) {
max = m_data[1][k];
if (m_date[series][1][k] > max) {
max = m_date[series][1][k];
}
}
}

36
apps/regression/store.h
View File

@@ -12,37 +12,37 @@ public:
// Dots
/* Return the closest dot to x above the regression curve if direction > 0,
* below otherwise*/
int closestVerticalDot(int direction, float x);
int closestVerticalDot(int series, int direction, float x);
/* Return the closest dot to dot given on the right if direction > 0,
* on the left otherwise*/
int nextDot(int direction, int dot);
int nextDot(int series, int direction, int dot);
// Window
void setDefault() override;
// Calculation
double numberOfPairs() const { return m_numberOfPairs; }
double squaredValueSumOfColumn(int i);
double columnProductSum();
double meanOfColumn(int i);
double varianceOfColumn(int i);
double standardDeviationOfColumn(int i);
double covariance();
double slope();
double yIntercept();
double yValueForXValue(double x);
double xValueForYValue(double y);
double correlationCoefficient();
double squaredCorrelationCoefficient();
double numberOfPairs(int series) const { return m_numberOfPairs; }
double squaredValueSumOfColumn(int series, int i);
double columnProductSum(int series);
double meanOfColumn(int series, int i);
double varianceOfColumn(int series, int i);
double standardDeviationOfColumn(int series, int i);
double covariance(int series);
double slope(int series);
double yIntercept(int series);
double yValueForXValue(int series, double x);
double xValueForYValue(int series, double y);
double correlationCoefficient(int series);
double squaredCorrelationCoefficient(int series);
private:
constexpr static float k_displayTopMarginRatio = 0.12f;
constexpr static float k_displayRightMarginRatio = 0.05f;
constexpr static float k_displayBottomMarginRatio = 0.5f;
constexpr static float k_displayLeftMarginRatio = 0.05f;
InteractiveCurveViewRangeDelegate::Range computeYRange(InteractiveCurveViewRange * interactiveCurveViewRange) override;
InteractiveCurveViewRangeDelegate::Range computeYRange(int series, InteractiveCurveViewRange * interactiveCurveViewRange) override;
float addMargin(float x, float range, bool isMin) override;
float maxValueOfColumn(int i);
float minValueOfColumn(int i);
float maxValueOfColumn(int series, int i);
float minValueOfColumn(int series, int i);
};
typedef double (Store::*ArgCalculPointer)(int);