6663b6c9
 adorian
projet complet av...
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#include "binomial_law.h"
#include <assert.h>
#include <cmath>
namespace Probability {
BinomialLaw::BinomialLaw() :
TwoParameterLaw(20.0, 0.5)
{
}
I18n::Message BinomialLaw::title() {
return I18n::Message::BinomialLaw;
}
Law::Type BinomialLaw::type() const {
return Type::Binomial;
}
bool BinomialLaw::isContinuous() const {
return false;
}
I18n::Message BinomialLaw::parameterNameAtIndex(int index) {
assert(index >= 0 && index < 2);
if (index == 0) {
return I18n::Message::N;
} else {
return I18n::Message::P;
}
}
I18n::Message BinomialLaw::parameterDefinitionAtIndex(int index) {
assert(index >= 0 && index < 2);
if (index == 0) {
return I18n::Message::RepetitionNumber;
} else {
return I18n::Message::SuccessProbability;
}
}
float BinomialLaw::xMin() {
float min = 0.0f;
float max = m_parameter1 > 0.0f ? m_parameter1 : 1.0f;
return min - k_displayLeftMarginRatio * (max - min);
}
float BinomialLaw::xMax() {
float min = 0.0f;
float max = m_parameter1;
if (max <= min) {
max = min + 1.0f;
}
return max + k_displayRightMarginRatio*(max - min);
}
float BinomialLaw::yMin() {
return -k_displayBottomMarginRatio*yMax();
}
float BinomialLaw::yMax() {
int maxAbscissa = m_parameter2 < 1.0f ? (m_parameter1+1)*m_parameter2 : m_parameter1;
float result = evaluateAtAbscissa(maxAbscissa);
if (result <= 0.0f || std::isnan(result)) {
result = 1.0f;
}
return result*(1.0f+ k_displayTopMarginRatio);
}
bool BinomialLaw::authorizedValueAtIndex(float x, int index) const {
if (index == 0) {
/* As the cumulative probability are computed by looping over all discrete
* abscissa within the interesting range, the complexity of the cumulative
* probability is linear with the size of the range. Here we cap the maximal
* size of the range to 10000. If one day we want to increase or get rid of
* this cap, we should implement the explicite formula of the cumulative
* probability (which depends on an incomplete beta function) to make the
* comlexity O(1). */
if (x != (int)x || x < 0.0f || x > 99999.0f) {
return false;
}
return true;
}
if (x < 0.0f || x > 1.0f) {
return false;
}
return true;
}
double BinomialLaw::cumulativeDistributiveInverseForProbability(double * probability) {
if (m_parameter1 == 0.0 && (m_parameter2 == 0.0 || m_parameter2 == 1.0)) {
return NAN;
}
if (*probability >= 1.0) {
return m_parameter1;
}
return Law::cumulativeDistributiveInverseForProbability(probability);
}
double BinomialLaw::rightIntegralInverseForProbability(double * probability) {
if (m_parameter1 == 0.0 && (m_parameter2 == 0.0 || m_parameter2 == 1.0)) {
return NAN;
}
if (*probability <= 0.0) {
return m_parameter1;
}
return Law::rightIntegralInverseForProbability(probability);
}
template<typename T>
T BinomialLaw::templatedApproximateAtAbscissa(T x) const {
if (m_parameter1 == 0) {
if (m_parameter2 == 0 || m_parameter2 == 1) {
return NAN;
}
if (floor(x) == 0) {
return 1;
}
return 0;
}
if (m_parameter2 == 1) {
if (floor(x) == m_parameter1) {
return 1;
}
return 0;
}
if (m_parameter2 == 0) {
if (floor(x) == 0) {
return 1;
}
return 0;
}
if (x > m_parameter1) {
return 0;
}
T lResult = std::lgamma((T)(m_parameter1+1.0)) - std::lgamma(std::floor(x)+(T)1.0) - std::lgamma((T)m_parameter1 - std::floor(x)+(T)1.0)+
std::floor(x)*std::log((T)m_parameter2) + ((T)m_parameter1-std::floor(x))*std::log((T)(1.0-m_parameter2));
return std::exp(lResult);
}
}
template float Probability::BinomialLaw::templatedApproximateAtAbscissa(float x) const;
template double Probability::BinomialLaw::templatedApproximateAtAbscissa(double x) const;
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