#include "uniform_law.h" #include #include #include namespace Probability { UniformLaw::UniformLaw() : TwoParameterLaw(-1.0f, 1.0f) { } I18n::Message UniformLaw::title() { return I18n::Message::UniformLaw; } Law::Type UniformLaw::type() const { return Type::Uniform; } bool UniformLaw::isContinuous() const { return true; } I18n::Message UniformLaw::parameterNameAtIndex(int index) { assert(index >= 0 && index < 2); if (index == 0) { return I18n::Message::A; } else { return I18n::Message::B; } } I18n::Message UniformLaw::parameterDefinitionAtIndex(int index) { assert(index >= 0 && index < 2); if (index == 0) { return I18n::Message::IntervalDefinition; } else { return I18n::Message::Default; } } float UniformLaw::xMin() { assert(m_parameter2 >= m_parameter1); if (m_parameter2 - m_parameter1 < FLT_EPSILON) { return m_parameter1 - 1.0f; } return m_parameter1 - 0.6f*(m_parameter2 - m_parameter1); } float UniformLaw::xMax() { if (m_parameter2 - m_parameter1 < FLT_EPSILON) { return m_parameter1 + 1.0f; } return m_parameter2 + 0.6f*(m_parameter2 - m_parameter1); } float UniformLaw::yMin() { return -k_displayBottomMarginRatio*yMax(); } float UniformLaw::yMax() { float result = m_parameter2 - m_parameter1 < FLT_EPSILON ? k_diracMaximum : 1.0f/(m_parameter2-m_parameter1); if (result <= 0.0f || std::isnan(result) || std::isinf(result)) { result = 1.0f; } return result*(1.0f+ k_displayTopMarginRatio); } float UniformLaw::evaluateAtAbscissa(float t) const { if (m_parameter2 - m_parameter1 < FLT_EPSILON) { if (m_parameter1 - k_diracWidth<= t && t <= m_parameter2 + k_diracWidth) { return 2.0f*k_diracMaximum; } return 0.0f; } if (m_parameter1 <= t && t <= m_parameter2) { return (1.0f/(m_parameter2-m_parameter1)); } return 0.0f; } bool UniformLaw::authorizedValueAtIndex(float x, int index) const { if (index == 0) { return true; } if (m_parameter1 > x) { return false; } return true; } void UniformLaw::setParameterAtIndex(float f, int index) { TwoParameterLaw::setParameterAtIndex(f, index); if (index == 0 && m_parameter2 < m_parameter1) { m_parameter2 = m_parameter1+1.0f; } } double UniformLaw::cumulativeDistributiveFunctionAtAbscissa(double x) const { if (x <= m_parameter1) { return 0.0; } if (x < m_parameter2) { return (x-m_parameter1)/(m_parameter2-m_parameter1); } return 1.0; } double UniformLaw::cumulativeDistributiveInverseForProbability(double * probability) { if (*probability >= 1.0f) { return m_parameter2; } if (*probability <= 0.0f) { return m_parameter1; } return m_parameter1*(1-*probability)+*probability*m_parameter2; } }