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| author | madmaxoft@gmail.com <madmaxoft@gmail.com@0a769ca7-a7f5-676a-18bf-c427514a06d6> | 2013-03-14 10:52:57 +0100 |
|---|---|---|
| committer | madmaxoft@gmail.com <madmaxoft@gmail.com@0a769ca7-a7f5-676a-18bf-c427514a06d6> | 2013-03-14 10:52:57 +0100 |
| commit | ff403fdbf0b4c372057fc8369797ddf652cfd727 (patch) | |
| tree | 0f9f22c6a8ea41e879cbd49011ccd2c9f25d2604 /source/ProbabDistrib.cpp | |
| parent | Changed DelayedFluidSimulatorData to be a vector rather than a list, performance doubled :) (diff) | |
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Diffstat (limited to 'source/ProbabDistrib.cpp')
| -rw-r--r-- | source/ProbabDistrib.cpp | 141 |
1 files changed, 141 insertions, 0 deletions
diff --git a/source/ProbabDistrib.cpp b/source/ProbabDistrib.cpp new file mode 100644 index 000000000..fbc9f9428 --- /dev/null +++ b/source/ProbabDistrib.cpp @@ -0,0 +1,141 @@ +
+// ProbabDistrib.cpp
+
+// Implements the cProbabDistrib class representing a discrete probability distribution curve and random generator
+
+#include "Globals.h"
+#include "ProbabDistrib.h"
+#include "MersenneTwister.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 = 0;
+ if (a_Points[0].m_Value != 0)
+ {
+ m_Cumulative.push_back(cPoint(0, 0)); // Always push in the [0, 0] point for easier search algorithm bounds
+ }
+ 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.push_back(cPoint(itr->m_Value, ProbSum));
+ } // for itr - a_Points[]
+ if (LastValue != m_MaxValue)
+ {
+ m_Cumulative.push_back(cPoint(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.push_back(cPoint(Value, Prob));
+ } // for itr - Points[]
+
+ SetPoints(Pts);
+ return true;
+}
+
+
+
+
+
+int cProbabDistrib::Random(MTRand & a_Rand) const
+{
+ int v = a_Rand.randInt(m_Sum);
+ return MapValue(v);
+}
+
+
+
+
+
+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)
+ {
+ int 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;
+ 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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