private static GamePlayerBenchmark.SampleState SelectOrCreateChildAtRandom(Random r, GraphicalModel model, int[] variables, int[] variableSizes, IList <GamePlayerBenchmark.SampleState> children, ConcatVector[] humanFeatureVectors)
{
int i = r.NextInt(variables.Length);
int variable = variables[i];
int observation = r.NextInt(variableSizes[i]);
foreach (GamePlayerBenchmark.SampleState s in children)
{
if (s.variable == variable && s.observation == observation)
{
return(s);
}
}
int humanObservationVariable = 0;
foreach (GraphicalModel.Factor f in model.factors)
{
foreach (int j in f.neigborIndices)
{
if (j >= humanObservationVariable)
{
humanObservationVariable = j + 1;
}
}
}
GraphicalModel.Factor f_1 = model.AddFactor(new int[] { variable, humanObservationVariable }, new int[] { variableSizes[i], variableSizes[i] }, null);
model.factors.Remove(f_1);
GamePlayerBenchmark.SampleState newState = new GamePlayerBenchmark.SampleState(f_1, variable, observation);
children.Add(newState);
return(newState);
}
//////////////////////////////////////////////////////////////
// This is an implementation of something like MCTS, trying to take advantage of the general speed gains due to fast
// CliqueTree caching of dot products. It doesn't actually do any clever selection, preferring to select observations
// at random.
//////////////////////////////////////////////////////////////
private static void Gameplay(Random r, GraphicalModel model, ConcatVector weights, ConcatVector[] humanFeatureVectors)
{
IList <int> variablesList = new List <int>();
IList <int> variableSizesList = new List <int>();
foreach (GraphicalModel.Factor f in model.factors)
{
for (int i = 0; i < f.neigborIndices.Length; i++)
{
int j = f.neigborIndices[i];
if (!variablesList.Contains(j))
{
variablesList.Add(j);
variableSizesList.Add(f.featuresTable.GetDimensions()[i]);
}
}
}
int[] variables = variablesList.Stream().MapToInt(null).ToArray();
int[] variableSizes = variableSizesList.Stream().MapToInt(null).ToArray();
IList <GamePlayerBenchmark.SampleState> childrenOfRoot = new List <GamePlayerBenchmark.SampleState>();
CliqueTree tree = new CliqueTree(model, weights);
int initialFactors = model.factors.Count;
// Run some "samples"
long start = Runtime.CurrentTimeMillis();
long marginalsTime = 0;
for (int i_1 = 0; i_1 < 1000; i_1++)
{
log.Info("\tTaking sample " + i_1);
Stack <GamePlayerBenchmark.SampleState> stack = new Stack <GamePlayerBenchmark.SampleState>();
GamePlayerBenchmark.SampleState state = SelectOrCreateChildAtRandom(r, model, variables, variableSizes, childrenOfRoot, humanFeatureVectors);
long localMarginalsTime = 0;
// Each "sample" is 10 moves deep
for (int j = 0; j < 10; j++)
{
// log.info("\t\tFrame "+j);
state.Push(model);
System.Diagnostics.Debug.Assert((model.factors.Count == initialFactors + j + 1));
///////////////////////////////////////////////////////////
// This is the thing we're really benchmarking
///////////////////////////////////////////////////////////
if (state.cachedMarginal == null)
{
long s = Runtime.CurrentTimeMillis();
state.cachedMarginal = tree.CalculateMarginalsJustSingletons();
localMarginalsTime += Runtime.CurrentTimeMillis() - s;
}
stack.Push(state);
state = SelectOrCreateChildAtRandom(r, model, variables, variableSizes, state.children, humanFeatureVectors);
}
log.Info("\t\t" + localMarginalsTime + " ms");
marginalsTime += localMarginalsTime;
while (!stack.Empty())
{
stack.Pop().Pop(model);
}
System.Diagnostics.Debug.Assert((model.factors.Count == initialFactors));
}
log.Info("Marginals time: " + marginalsTime + " ms");
log.Info("Avg time per marginal: " + (marginalsTime / 200) + " ms");
log.Info("Total time: " + (Runtime.CurrentTimeMillis() - start));
}