6663b6c9
 adorian
projet complet av...
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#include "exponential_law.h"
#include <assert.h>
#include <cmath>
#include <float.h>
#include <ion.h>
namespace Probability {
ExponentialLaw::ExponentialLaw() :
OneParameterLaw(1.0f)
{
}
I18n::Message ExponentialLaw::title() {
return I18n::Message::ExponentialLaw;
}
Law::Type ExponentialLaw::type() const {
return Type::Exponential;
}
bool ExponentialLaw::isContinuous() const {
return true;
}
I18n::Message ExponentialLaw::parameterNameAtIndex(int index) {
assert(index == 0);
return I18n::Message::Lambda;
}
I18n::Message ExponentialLaw::parameterDefinitionAtIndex(int index) {
assert(index == 0);
return I18n::Message::LambdaExponentialDefinition;
}
float ExponentialLaw::xMin() {
float max = xMax();
return - k_displayLeftMarginRatio * max;
}
float ExponentialLaw::xMax() {
assert(m_parameter1 != 0.0f);
float result = 5.0f/m_parameter1;
if (result <= 0.0f) {
result = 1.0f;
}
return result*(1.0f+ k_displayRightMarginRatio);
}
float ExponentialLaw::yMin() {
return -k_displayBottomMarginRatio*yMax();
}
float ExponentialLaw::yMax() {
float result = m_parameter1;
if (result <= 0.0f || std::isnan(result)) {
result = 1.0f;
}
if (result <= 0.0f) {
result = 1.0f;
}
return result*(1.0f+ k_displayTopMarginRatio);
}
float ExponentialLaw::evaluateAtAbscissa(float x) const {
if (x < 0.0f) {
return NAN;
}
return m_parameter1*std::exp(-m_parameter1*x);
}
bool ExponentialLaw::authorizedValueAtIndex(float x, int index) const {
if (x <= 0.0f || x > 7500.0f) {
return false;
}
return true;
}
double ExponentialLaw::cumulativeDistributiveFunctionAtAbscissa(double x) const {
return 1.0 - std::exp((double)(-m_parameter1*x));
}
double ExponentialLaw::cumulativeDistributiveInverseForProbability(double * probability) {
if (*probability >= 1.0) {
return INFINITY;
}
if (*probability <= 0.0) {
return 0.0;
}
return -std::log(1.0 - *probability)/(double)m_parameter1;
}
}
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