// /param correctnessDemonstrationSuccess is true if the task was correctly solved by the (returned) solver and all existing solvers can't solve the task
void solverModificationAndCorrectnessDemonstration(ITaskAndSolverModification p, ITask task, out Solver solver, out bool correctnessDemonstrationSuccess)
{
var allExistingTasks = solverTuples.Select(v => v.task);
context.solverModificationAndCorrectnessDemonstration(p, task, /* passing in this parameter is a hack */ previousS, allExistingTasks, out solver, out correctnessDemonstrationSuccess);
}
public void solverModificationAndCorrectnessDemonstration(ITaskAndSolverModification pParameter, ITask taskParameter, Solver previousS, IEnumerable <ITask> allExistingTasks, out Solver solver, out bool correctnessDemonstrationSuccess)
{
var p = (Environment2dTaskAndSolverModification)pParameter;
var task = (Environment2dTask)taskParameter;
correctnessDemonstrationSuccess = false;
solver = null;
UniversalSlimRnnSearch slimRnnSearch = new UniversalSlimRnnSearch(previousS.slimRnn, new Environment2dPowerplayNetworkTester(task, allExistingTasks));
slimRnnSearch.weightWithPropabilityTable = returnNormalizedPropabilitiesOfTable();
{ // solver modification
// hard coded modification
// make a connection between the 1.0 input neuron and the neuron which causes a move to the right
previousS.slimRnn.neurons[(int)neuronIndexConstantOne].outNeuronsWithWeights.Add(
new SlimRnnNeuronWithWeight(previousS.slimRnn.neurons[(int)neuronIndexConstantOne], previousS.slimRnn.neurons[(int)previousS.slimRnn.outputNeuronsStartIndex], 0.0f, 0, true));
// TODO< not hardcoded modification >
}
// let the SLIM-RNN search-algorithm search for a network which solves the task the fastest way
uint maximumIteration = 3;
bool mustHalt = true;
bool wasSolved;
SlimRnn solutionRnn;
// we have to set the world because UniversalSlimRnnSearch.search() uses the world
///slimRnnSearch.world = new TaskSlimRnnWorld(task);
// the search is doing "solver modification" _and_ "correctness demonstration"
slimRnnSearch.search(maximumIteration, mustHalt, out wasSolved, out solutionRnn);
// rollback changes
// if it was solved then keep used eligable weights
if (wasSolved)
{
int breakpointHere = 1;
// we have to rollback/remove eligable weights which didn't get used
foreach (SlimRnnNeuron iNeuron in previousS.slimRnn.neurons)
{
iNeuron.outNeuronsWithWeights = new List <SlimRnnNeuronWithWeight>(
iNeuron.outNeuronsWithWeights.Where(v => !v.isEligable || (v.isEligable && (v.weight != 0.0f)))
);
}
// set all used eligable connections to normal connections
foreach (SlimRnnNeuron iNeuron in previousS.slimRnn.neurons)
{
foreach (var iConnection in iNeuron.outNeuronsWithWeights)
{
Debug.Assert(!iConnection.isEligable || (iConnection.isEligable && iConnection.weight != 0.0f));
iConnection.isEligable = false;
}
}
}
else
{
// we have to rollback/remove all eligable weights
foreach (SlimRnnNeuron iNeuron in previousS.slimRnn.neurons)
{
iNeuron.outNeuronsWithWeights = new List <SlimRnnNeuronWithWeight>(iNeuron.outNeuronsWithWeights.Where(v => !v.isEligable));
}
}
// if we solved the task with the SLIM-RNN then we have to create a solver which represents our new problem solver
// Solver has of course the SLIM-RNN which solved the problem
if (wasSolved)
{
solver = new Solver();
solver.slimRnn = solutionRnn;
}
if (!wasSolved)
{
// if it was not solved we can't demonstrate the correctness
correctnessDemonstrationSuccess = false;
return;
}
correctnessDemonstrationSuccess = true;
}