public void Do(MHSampler2Parameters parameters)
{
Log.Post("MHSampler - Start");
Build.Do();
GraphClones = new List<IGWGraph<SGLNodeData, SGLEdgeData, SGLGraphData>>();
//get parameter values
var numberChains = parameters.NumberChains;
var graph = parameters.Graph;
//create clones
GraphClones.Add(graph);
var catGraphCreator = new CategoryGraphCreator();
for (int i = 1; i < numberChains; i++)
{
var clone = graph.Clone(GraphDataConverter.SGLNodeDataConvert, GraphDataConverter.SGLEdgeDataConvert, GraphDataConverter.SGLGraphDataConvert);
//catGraphCreator.CreateCategoryGraph(clone);
GraphClones.Add(clone);
}
//init testsamples
GraphCloneSamples = new List<int[]>[GraphClones.Count];
for (int i = 0; i < GraphCloneSamples.Length; i++)
{
GraphCloneSamples[i] = new List<int[]>();
}
//start chains
for (int i = 0; i < GraphClones.Count; i++)
{
var chain = GraphClones[i];
//start the first chain with viterbi
switch (parameters.MHSampler2StartPoint)
{
case MHSampler2StartPoint.Random:
FindStartPointRandom(chain);
break;
case MHSampler2StartPoint.Viterbi:
FindStartPointViterbi(chain);
break;
default: break;
}
}
//iterate over prerun
for (int i = 0; i < parameters.PreRunLength; i++)
{
foreach (var chain in GraphClones)
{
ChangePosition(chain, 0);
}
}
//iterate over prerun
for (int i = 0; i < GraphClones.Count; i++)
{
var chain = GraphClones[i];
for (int k = 0; k < parameters.RunLength; k++)
{
ChangePosition(chain, 0);
var sample = new int[chain.Nodes.Count()];
foreach (var node in chain.Nodes)
{
sample[node.GraphId] = node.Data.TempAssign;
}
GraphCloneSamples[i].Add(sample);
}
}
// shuffle all samples into one sample.
FinalSample = GraphCloneSamples.SelectMany(s => s).RandomizeOrder().ToList();
}
public void Do(MHSamplerParameters parameters)
{
Log.Post("MHSampler - Start");
Build.Do();
Chains = new List <IGWGraph <SGLNodeData, SGLEdgeData, SGLGraphData> >();
//get parameter values
var numberChains = parameters.NumberChains;
var graph = parameters.Graph;
TestInterval = parameters.TestInterval;
ToleranceVariance = parameters.ToleranceVariance;
ToleranceMarginalDistribution = parameters.ToleranceMarginalDistribution;
ToleranceAutoCorrelation = parameters.ToleranceAutoCorrelation;
//don't allow less than 2 chains
if (numberChains < 2)
{
numberChains = 2;
}
//combinations for marginal distribution test
Combinations = new List <int[]>();
int[] arr = new int[numberChains];
for (int i = 0; i < numberChains; i++)
{
arr[i] = i;
}
CalcCombinations(arr, 2, 0, new int[2]);
//create clones
Chains.Add(graph);
var catGraphCreator = new CategoryGraphCreator();
for (int i = 1; i < numberChains; i++)
{
var clone = graph.Clone(GraphDataConverter.SGLNodeDataConvert, GraphDataConverter.SGLEdgeDataConvert, GraphDataConverter.SGLGraphDataConvert);
catGraphCreator.CreateCategoryGraph(clone);
Chains.Add(clone);
}
//init testsamples
TestSamples = new List <int[]> [Chains.Count];
for (int i = 0; i < TestSamples.Length; i++)
{
TestSamples[i] = new List <int[]>();
}
//start chains
for (int i = 0; i < Chains.Count; i++)
{
var chain = Chains[i];
//start the first chain with viterbi
if (i == 0)
{
FindStartPointViterbi(chain);
}
//rest with random for now
else
{
FindStartPointRandom(chain);
}
}
//iterate over prerun
for (int i = 0; i < parameters.PreRunLength; i++)
{
foreach (var chain in Chains)
{
ChangePosition(chain, 0);
}
}
//test if chains are mixed
bool chainsHaveMixed = false;
int time = 1;
int test = 1;
while (!chainsHaveMixed)
{
foreach (var chain in Chains)
{
ChangePosition(chain, time);
}
//make statistic tests every xth iteration
if (time % TestInterval == 0)
{
Console.WriteLine("test: " + test++);
chainsHaveMixed = StatisticTests(time);
//constant window
if (!chainsHaveMixed)
{
//chains aren't mixed -> set everything back to 0
time = 0;
foreach (var chain in Chains)
{
chain.Nodes.Each(n => { n.Data.TempCount = 0; });
chain.Nodes.Each(n => { n.Data.LastUpdate = 0; });
}
//clear testsamples
foreach (var samples in TestSamples)
{
samples.Clear();
}
}
}
time++;
}
// shuffle all samples into one sample.
FinalSample = TestSamples.SelectMany(s => s).RandomizeOrder().ToList();
Log.Post("Chains may have mixed. ");
Log.Post("Number of Chains: " + numberChains);
Log.Post("Total Transitions: " + (CountTransitionNodeProposal + CountTransitionNeighborhoodProposal + CountTransitionCategoryProposal));
Log.Post("Node Transistion Proposals: " + CountTransitionNodeProposal);
Log.Post("Node Transistions Accepted: " + CountTransitionNodeAccepted);
Log.Post("Neighborhood Transistion Proposals: " + CountTransitionNeighborhoodProposal);
Log.Post("Neighborhood Transistions Accepted: " + CountTransitionNeighborhoodAccepted);
Log.Post("Category Transition Proposals: " + CountTransitionCategoryProposal);
Log.Post("Category Transitions Accepted: " + CountTransitionCategoryAccepted);
Log.Post("Tests: " + (test - 1));
Log.Post("AutoCorrelation Test Failed: " + CountTestAutoCorrelationFailed);
Log.Post("Variance Test Failed: " + CountTestVarianceFailed);
Log.Post("Marginal Distribution Test Failed: " + CountTestMarginalDistributionFailed);
Log.Post(Environment.NewLine);
}
