private SparseArray <float> GetWarmBalancingFactors(SparseArray <float> attraction, out string balanceFileName)
{
SparseArray <float> balanceFactors = null;
if (BalanceFactors.ContainsFileName())
{
balanceFileName = BalanceFactors.GetFileName() + CurrentMultiSetIndex + ".bin";
if (File.Exists(balanceFileName))
{
balanceFactors = LoadBalanceFactors(balanceFileName);
}
else
{
balanceFactors = attraction.CreateSimilarArray <float>();
var flatFactors = balanceFactors.GetFlatData();
// initialize the factors to 1
for (int i = 0; i < flatFactors.Length; i++)
{
flatFactors[i] = 1.0f;
}
}
}
else
{
balanceFileName = null;
}
return(balanceFactors);
}
public SparseTwinIndex <float> ProcessFlow(SparseArray <float> O, SparseArray <float> D, int[] validIndexes, SparseArray <float> attractionStar = null)
{
int length = validIndexes.Length;
Productions = O;
Attractions = D;
if (attractionStar == null)
{
AttractionsStar = D.CreateSimilarArray <float>();
}
else
{
AttractionsStar = attractionStar;
}
FlowMatrix = Productions.CreateSquareTwinArray <float>();
if (Friction == null)
{
InitializeFriction(length);
}
var flatAttractionStar = AttractionsStar.GetFlatData();
float[] oldTotal = new float[flatAttractionStar.Length];
var flatAttractions = Attractions.GetFlatData();
for (int i = 0; i < length; i++)
{
flatAttractionStar[i] = 1f;
oldTotal[i] = flatAttractions[i];
}
int iteration = 0;
float[] columnTotals = new float[length];
var balanced = false;
do
{
if (ProgressCallback != null)
{
// this doesn't go to 100%, but that is alright since when we end, the progress
// of the calling model should assume we hit 100%
ProgressCallback(iteration / (float)MaxIterations);
}
Array.Clear(columnTotals, 0, columnTotals.Length);
if (Vector.IsHardwareAccelerated)
{
VectorProcessFlow(columnTotals, FlowMatrix.GetFlatData());
}
else
{
ProcessFlow(columnTotals);
}
balanced = Balance(columnTotals, oldTotal);
} while((++iteration) < MaxIterations && !balanced);
if (ProgressCallback != null)
{
ProgressCallback(1f);
}
return(FlowMatrix);
}
private void AverageAttractionAndProduction(ref SparseArray <float> production, ref SparseArray <float> attraction)
{
var newAttraction = attraction.CreateSimilarArray <float>();
var newProduction = production.CreateSimilarArray <float>();
var flatAttraction = attraction.GetFlatData();
var flatProduction = production.GetFlatData();
var newFlatProduction = newProduction.GetFlatData();
var newFlatAttraction = newAttraction.GetFlatData();
var productionTotal = VectorHelper.Sum(flatProduction, 0, flatProduction.Length);
var attractionTotal = VectorHelper.Sum(flatAttraction, 0, flatAttraction.Length);
var totalAverage = productionTotal + attractionTotal / 2.0f;
var ratio = totalAverage / attractionTotal;
if (float.IsNaN(ratio) || float.IsInfinity(ratio))
{
ratio = 0.0f;
}
VectorHelper.Multiply(newFlatAttraction, flatAttraction, ratio);
ratio = totalAverage / productionTotal;
if (float.IsNaN(ratio) || float.IsInfinity(ratio))
{
ratio = 0.0f;
}
VectorHelper.Multiply(newFlatProduction, flatProduction, 1.0f / ratio);
attraction = newAttraction;
production = newProduction;
}
private void WriteParallel(int[] validZones, SparseArray <IPerson[]> population, StreamWriter writer, int length)
{
var output = population.CreateSimilarArray <StringBuilder>();
var flatOutput = output.GetFlatData();
var flatPop = population.GetFlatData();
int current = 0;
Parallel.For(0, length,
delegate(int i)
{
var popInZone = flatPop[i];
if (popInZone == null)
{
return;
}
StringBuilder builder = new StringBuilder();
foreach (var person in popInZone)
{
builder.Append(validZones[i]);
if (person == null)
{
builder.AppendLine(", PERSON MISSING!");
continue;
}
builder.Append(',');
builder.Append(person.Age);
builder.Append(',');
builder.Append(person.Household != null ? person.Household.Cars : 0);
builder.Append(',');
builder.Append(person.SchoolZone != null ? person.SchoolZone.ZoneNumber : -1);
builder.Append(',');
builder.Append(person.WorkZone != null ? person.WorkZone.ZoneNumber : -1);
builder.Append(',');
builder.Append(person.EmploymentStatus);
builder.Append(',');
builder.Append(person.StudentStatus);
builder.Append(',');
builder.Append(person.Occupation);
builder.Append(',');
builder.Append(person.DriversLicense ? 1 : 0);
builder.Append(',');
builder.Append(person.ExpansionFactor);
builder.AppendLine();
}
flatOutput[i] = builder;
Interlocked.Increment(ref current);
Progress = (float)current / length;
});
for (int i = 0; i < length; i++)
{
var sbuilder = flatOutput[i];
if (sbuilder != null)
{
writer.Write(sbuilder.ToString());
}
}
Progress = 1;
}
public SparseTwinIndex<float> ProcessFlow(SparseArray<float> O, SparseArray<float> D, int[] validIndexes, SparseArray<float> attractionStar = null)
{
int length = validIndexes.Length;
Productions = O;
Attractions = D;
if(attractionStar == null)
{
AttractionsStar = D.CreateSimilarArray<float>();
}
else
{
AttractionsStar = attractionStar;
}
FlowMatrix = Productions.CreateSquareTwinArray<float>();
if(Friction == null)
{
InitializeFriction(length);
}
var flatAttractionStar = AttractionsStar.GetFlatData();
float[] oldTotal = new float[flatAttractionStar.Length];
var flatAttractions = Attractions.GetFlatData();
for(int i = 0; i < length; i++)
{
flatAttractionStar[i] = 1f;
oldTotal[i] = flatAttractions[i];
}
int iteration = 0;
float[] columnTotals = new float[length];
var balanced = false;
do
{
if(ProgressCallback != null)
{
// this doesn't go to 100%, but that is alright since when we end, the progress
// of the calling model should assume we hit 100%
ProgressCallback(iteration / (float)MaxIterations);
}
Array.Clear(columnTotals, 0, columnTotals.Length);
if(Vector.IsHardwareAccelerated)
{
VectorProcessFlow(columnTotals, FlowMatrix.GetFlatData());
}
else
{
ProcessFlow(columnTotals);
}
balanced = Balance(columnTotals, oldTotal);
} while((++iteration) < MaxIterations && !balanced);
if(ProgressCallback != null)
{
ProgressCallback(1f);
}
return FlowMatrix;
}
private SparseArray <int> SplitAndClear(int pop, SparseArray <float> splitPercentages, Random rand)
{
var flatSplitPercentages = splitPercentages.GetFlatData();
var length = flatSplitPercentages.Length;
var ret = splitPercentages.CreateSimilarArray <int>();
var flatRet = ret.GetFlatData();
var flatRemainder = new float[length];
float remainderTotal = 0;
int total = 0;
for (int i = 0; i < length; i++)
{
float element = (flatSplitPercentages[i] * pop);
total += (flatRet[i] = (int)Math.Floor(element));
flatRemainder[i] = element - flatRet[i];
}
int notAssigned = pop - total;
// Make sure that we do not over assign
remainderTotal = notAssigned;
for (int i = 0; i < notAssigned; i++)
{
var randPop = rand.NextDouble() * remainderTotal;
float ammountToReduce = 0;
int j = 0;
for ( ; j < length; j++)
{
randPop -= (ammountToReduce = flatRemainder[j]);
if (randPop <= 0)
{
remainderTotal -= ammountToReduce;
flatRemainder[j] = 0;
flatRet[j] += 1;
break;
}
}
if (j == length)
{
for (j = 0; j < length; j++)
{
if (flatRemainder[j] >= 0)
{
remainderTotal -= flatRemainder[j];
flatRemainder[j] = 0;
flatRet[j] += 1;
break;
}
}
}
}
return(ret);
}
private void MatchAttractionToProduction(ref SparseArray <float> production, ref SparseArray <float> attraction)
{
var newAttraction = attraction.CreateSimilarArray <float>();
var flatAttraction = attraction.GetFlatData();
var flatProduction = production.GetFlatData();
var newFlatAttraction = newAttraction.GetFlatData();
var ratio = VectorHelper.Sum(flatProduction, 0, flatProduction.Length) / VectorHelper.Sum(flatAttraction, 0, flatAttraction.Length);
if (float.IsNaN(ratio) || float.IsInfinity(ratio))
{
ratio = 0.0f;
}
VectorHelper.Multiply(newFlatAttraction, flatAttraction, ratio);
attraction = newAttraction;
}
private void InitializeFlows()
{
Progress = 0;
// we are going to need to split based on this information
ZoneArray = Root.ZoneSystem.ZoneArray;
var validZones = ZoneArray.ValidIndexies().ToArray();
//Generate the place of work place of residence OD's
SparseArray <float> o = ZoneArray.CreateSimilarArray <float>();
SparseArray <float> d = ZoneArray.CreateSimilarArray <float>();
var numCat = Categories.Count;
// Start burning that CPU
Thread.CurrentThread.Priority = ThreadPriority.Highest;
SparseTwinIndex <float> workplaceDistribution = null;
SparseTwinIndex <float> prevWorkplaceDistribution = null;
float[] friction;
float[] nextFriction = null;
for (int i = 0; i < numCat; i++)
{
CurrentOccupationIndex = i;
Task assignToPopulation = null;
if (i > 0)
{
assignToPopulation = new Task(delegate
{
if (prevWorkplaceDistribution != null)
{
// We actually are assigning to the previous category with this data so we need i - 1
AssignToWorkers(prevWorkplaceDistribution, Categories[i - 1]);
prevWorkplaceDistribution = null;
}
});
assignToPopulation.Start();
}
Task computeNextFriction = null;
if (i + 1 < numCat)
{
computeNextFriction = new Task(delegate
{
nextFriction = ComputeFriction(ZoneArray.GetFlatData(), Categories[i + 1], nextFriction);
});
computeNextFriction.Start();
}
Categories[i].Generate(o, d);
GravityModel gravityModel = new GravityModel(ImpedenceFunction, (progress => Progress = (progress / numCat) + ((float)i / numCat)), Epsilon, MaxIterations);
workplaceDistribution = gravityModel.ProcessFlow(o, d, validZones);
Progress = ((float)(i + 1) / numCat);
if (assignToPopulation != null)
{
assignToPopulation.Wait();
assignToPopulation.Dispose();
}
if (computeNextFriction != null)
{
computeNextFriction.Wait();
computeNextFriction.Dispose();
}
prevWorkplaceDistribution = workplaceDistribution;
friction = nextFriction;
nextFriction = friction;
}
nextFriction = null;
prevWorkplaceDistribution = null;
AssignToWorkers(workplaceDistribution, Categories[numCat - 1]);
// ok now we can relax
Thread.CurrentThread.Priority = ThreadPriority.Normal;
GC.Collect();
}
private void Pass1(SparseArray <IZone> zoneArray, IZone[] zones, int[] basePopulation, int[] zonalDifferences, List <KeyValuePair <int, int> >[] results,
List <ITashaHousehold>[] remainingHouseholds, List <int>[] lookupsForRegion)
{
SparseArray <List <int> > householdsByZoneIndexToRegion = zoneArray.CreateSimilarArray <List <int> >();
SetupSpatialListByElement(householdsByZoneIndexToRegion.GetFlatData());
Console.WriteLine("Preparing household Index");
Parallel.For(0, HouseholdsByZone.Count, (int i) =>
{
var list = householdsByZoneIndexToRegion.GetFlatData()[i];
var total = HouseholdsByZone.GetFlatData()[i].Count;
for (int j = 0; j < total; j++)
{
list.Add(-1);
}
});
Console.WriteLine("Building household index");
Parallel.For(0, HouseholdsByRegion.Count, (int flatRegionIndex) =>
{
if (flatRegionIndex == 0)
{
return;
}
var region = HouseholdsByRegion.GetFlatData()[flatRegionIndex];
var householdIndexArray = householdsByZoneIndexToRegion.GetFlatData();
var householdArray = HouseholdsByZone.GetFlatData();
for (int i = 0; i < region.Count; i++)
{
var currentHousehold = region[i];
var flatZone = zoneArray.GetFlatIndex(currentHousehold.HomeZone.ZoneNumber);
var household = householdArray[flatZone];
var householdIndex = householdIndexArray[flatZone];
int index = household.IndexOf(currentHousehold);
householdIndex[index] = i;
}
});
Console.WriteLine("Starting Pass 1");
Parallel.For(0, HouseholdsByRegion.Count, (int flatRegionIndex) =>
{
if (flatRegionIndex == 0)
{
return;
}
var regionNumber = HouseholdsByRegion.GetSparseIndex(flatRegionIndex);
var zonesToProcess = zones.Where(z => z.RegionNumber == regionNumber).ToArray();
var regionLookup = HouseholdsByRegion.GetFlatData()[flatRegionIndex];
var remaining = remainingHouseholds[flatRegionIndex];
var resultsForRegion = results[flatRegionIndex];
for (int z = 0; z < zonesToProcess.Length; z++)
{
int attempts = 0;
int flatZone = zoneArray.GetFlatIndex(zonesToProcess[z].ZoneNumber);
var householdIndex = householdsByZoneIndexToRegion.GetFlatData()[flatZone];
// the difference is negative so we need to add not subtract
int populationToAdd = zonalDifferences[flatZone] >= 0 ? basePopulation[flatZone] : basePopulation[flatZone] + zonalDifferences[flatZone];
var zonalHouseholds = HouseholdsByZone.GetFlatData()[flatZone];
int i = 0;
List <int> tossedHouseholds = new List <int>();
for (int pop = 0; pop < populationToAdd;)
{
if (attempts > 2)
{
throw new XTMFRuntimeException("In '" + Name + "' we were unable to assign a base population for zone '" + zonesToProcess[z].ZoneNumber + "'!");
}
for (i = 0; i < zonalHouseholds.Count; i++)
{
var hhld = zonalHouseholds[i];
var newPop = pop + hhld.Persons.Length;
if (newPop <= populationToAdd)
{
pop = newPop;
resultsForRegion.Add(new KeyValuePair <int, int>(flatZone, householdIndex[i]));
if (pop >= populationToAdd)
{
break;
}
}
else
{
tossedHouseholds.Add(i);
}
}
attempts++;
}
// if we didn't use everything add the rest of the households to the remaining list
var lookupIndex = lookupsForRegion[flatRegionIndex];
// first add all the households we excluded to finish the zone
foreach (var tossed in tossedHouseholds)
{
remaining.Add(zonalHouseholds[tossed]);
lookupIndex.Add(householdIndex[tossed]);
}
// then add the rest of the households we haven't looked at
if (attempts <= 1)
{
for (; i < zonalHouseholds.Count; i++)
{
remaining.Add(zonalHouseholds[i]);
lookupIndex.Add(householdIndex[i]);
}
}
}
});
}
private SparseArray<float> GetWarmBalancingFactors(SparseArray<float> attraction, out string balanceFileName)
{
SparseArray<float> balanceFactors = null;
if ( this.BalanceFactors.ContainsFileName() )
{
balanceFileName = this.BalanceFactors.GetFileName() + this.CurrentMultiSetIndex + ".bin";
if ( File.Exists( balanceFileName ) )
{
balanceFactors = LoadBalanceFactors( balanceFileName );
}
else
{
balanceFactors = attraction.CreateSimilarArray<float>();
var flatFactors = balanceFactors.GetFlatData();
// initialize the factors to 1
for ( int i = 0; i < flatFactors.Length; i++ )
{
flatFactors[i] = 1.0f;
}
}
}
else
{
balanceFileName = null;
}
return balanceFactors;
}
private void WriteParallel(int[] validZones, SparseArray<IPerson[]> population, StreamWriter writer, int length)
{
var output = population.CreateSimilarArray<StringBuilder>();
var flatOutput = output.GetFlatData();
var flatPop = population.GetFlatData();
int current = 0;
Parallel.For( 0, length,
delegate(int i)
{
var popInZone = flatPop[i];
if ( popInZone == null ) return;
StringBuilder builder = new StringBuilder();
foreach ( var person in popInZone )
{
builder.Append( validZones[i] );
if ( person == null )
{
builder.AppendLine( ", PERSON MISSING!" );
continue;
}
builder.Append( ',' );
builder.Append( person.Age );
builder.Append( ',' );
builder.Append( person.Household != null ? person.Household.Cars : 0 );
builder.Append( ',' );
builder.Append( person.SchoolZone != null ? person.SchoolZone.ZoneNumber : -1 );
builder.Append( ',' );
builder.Append( person.WorkZone != null ? person.WorkZone.ZoneNumber : -1 );
builder.Append( ',' );
builder.Append( (int)person.EmploymentStatus );
builder.Append( ',' );
builder.Append( (int)person.StudentStatus );
builder.Append( ',' );
builder.Append( (int)person.Occupation );
builder.Append( ',' );
builder.Append( person.DriversLicense ? 1 : 0 );
builder.Append( ',' );
builder.Append( person.ExpansionFactor );
builder.AppendLine();
}
flatOutput[i] = builder;
System.Threading.Interlocked.Increment( ref current );
this.Progress = (float)current / length;
} );
for ( int i = 0; i < length; i++ )
{
var sbuilder = flatOutput[i];
if ( sbuilder != null )
{
writer.Write( sbuilder.ToString() );
}
}
this.Progress = 1;
}
private SparseArray<int> SplitAndClear(int pop, SparseArray<float> splitPercentages, Random rand)
{
var flatSplitPercentages = splitPercentages.GetFlatData();
var length = flatSplitPercentages.Length;
var ret = splitPercentages.CreateSimilarArray<int>();
var flatRet = ret.GetFlatData();
var flatRemainder = new float[length];
float remainderTotal = 0;
int total = 0;
for ( int i = 0; i < length; i++ )
{
float element = ( flatSplitPercentages[i] * pop );
total += ( flatRet[i] = (int)Math.Floor( element ) );
flatRemainder[i] = element - flatRet[i];
}
int notAssigned = pop - total;
// Make sure that we do not over assign
remainderTotal = notAssigned;
for ( int i = 0; i < notAssigned; i++ )
{
var randPop = rand.NextDouble() * remainderTotal;
float ammountToReduce = 0;
int j = 0;
for ( ; j < length; j++ )
{
randPop -= ( ammountToReduce = flatRemainder[j] );
if ( randPop <= 0 )
{
remainderTotal -= ammountToReduce;
flatRemainder[j] = 0;
flatRet[j] += 1;
break;
}
}
if ( j == length )
{
for ( j = 0; j < length; j++ )
{
if ( flatRemainder[j] >= 0 )
{
remainderTotal -= flatRemainder[j];
flatRemainder[j] = 0;
flatRet[j] += 1;
break;
}
}
}
}
return ret;
}