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// ProbabDistrib.cpp
// Implements the cProbabDistrib class representing a discrete probability distribution curve and random generator
#include "Globals.h"
#include "ProbabDistrib.h"
cProbabDistrib::cProbabDistrib(int a_MaxValue) :
m_MaxValue(a_MaxValue), m_Sum(-1)
{
}
void cProbabDistrib::SetPoints(const cProbabDistrib::cPoints & a_Points)
{
ASSERT(!a_Points.empty());
m_Sum = 0;
m_Cumulative.clear();
m_Cumulative.reserve(a_Points.size() + 1);
int ProbSum = 0;
int LastProb = 0;
int LastValue = -1;
if (a_Points[0].m_Value != 0)
{
m_Cumulative.emplace_back(0, 0); // Always push in the [0, 0] point for easier search algorithm bounds
LastValue = 0;
}
for (cPoints::const_iterator itr = a_Points.begin(), end = a_Points.end(); itr != end; ++itr)
{
if (itr->m_Value == LastValue)
{
continue;
}
// Add the current trapezoid to the sum:
ProbSum += (LastProb + itr->m_Probability) * (itr->m_Value - LastValue) / 2;
LastProb = itr->m_Probability;
LastValue = itr->m_Value;
m_Cumulative.emplace_back(itr->m_Value, ProbSum);
} // for itr - a_Points[]
if (LastValue != m_MaxValue)
{
m_Cumulative.emplace_back(m_MaxValue, 0); // Always push in the last point for easier search algorithm bounds
}
m_Sum = ProbSum;
}
bool cProbabDistrib::SetDefString(const AString & a_DefString)
{
AStringVector Points = StringSplitAndTrim(a_DefString, ";");
if (Points.empty())
{
return false;
}
cPoints Pts;
for (AStringVector::const_iterator itr = Points.begin(), end = Points.end(); itr != end; ++itr)
{
AStringVector Split = StringSplitAndTrim(*itr, ",");
if (Split.size() != 2)
{
// Bad format
return false;
}
int Value = atoi(Split[0].c_str());
int Prob = atoi(Split[1].c_str());
if (((Value == 0) && (Split[0] != "0")) || ((Prob == 0) && (Split[1] != "0")))
{
// Number parse error
return false;
}
Pts.emplace_back(Value, Prob);
} // for itr - Points[]
SetPoints(Pts);
return true;
}
int cProbabDistrib::Random(MTRand & a_Rand) const
{
return MapValue(a_Rand.RandInt(m_Sum));
}
int cProbabDistrib::MapValue(int a_OrigValue) const
{
ASSERT(a_OrigValue >= 0);
ASSERT(a_OrigValue < m_Sum);
// Binary search through m_Cumulative for placement:
size_t Lo = 0;
size_t Hi = m_Cumulative.size() - 1;
while (Hi - Lo > 1)
{
size_t Mid = (Lo + Hi) / 2;
int MidProbab = m_Cumulative[Mid].m_Probability;
if (MidProbab < a_OrigValue)
{
Lo = Mid;
}
else
{
Hi = Mid;
}
}
ASSERT(Hi - Lo == 1);
// Linearly interpolate between Lo and Hi:
int ProbDif = m_Cumulative[Hi].m_Probability - m_Cumulative[Lo].m_Probability;
ProbDif = (ProbDif != 0) ? ProbDif : 1;
int ValueDif = m_Cumulative[Hi].m_Value - m_Cumulative[Lo].m_Value;
return m_Cumulative[Lo].m_Value + (a_OrigValue - m_Cumulative[Lo].m_Probability) * ValueDif / ProbDif;
}
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