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build1/epsilon-master/apps/probability/law/binomial_law.cpp 3.72 KB
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;