// used for interactivly checking if the learning algorithm works correctly
public static void interactiveCheckLearningAlgorithm()
{
SlimRnn rnn = new SlimRnn();
rnn.neurons.Add(SlimRnnNeuron.makeInputNeuron()); // [0] input
rnn.neurons.Add(SlimRnnNeuron.makeInputNeuron()); // [1] input
rnn.neurons.Add(SlimRnnNeuron.makeInputNeuron()); // [2] input
rnn.neurons.Add(new SlimRnnNeuron(SlimRnnNeuron.EnumType.ADDITIVE)); // [3] termination/output
rnn.neurons.Add(new SlimRnnNeuron(SlimRnnNeuron.EnumType.ADDITIVE)); // [4] output
rnn.outputNeuronsStartIndex = 3;
rnn.numberOfOutputNeurons = 2;
rnn.terminatingNeuronIndex = 3;
rnn.terminatingNeuronThreshold = 0.5f;
rnn.numberOfInputNeurons = 3;
rnn.t_lim = 5; // are actually less steps but fine for testing
rnn.world = new TestWorld();
// insert unused neurons as possible neurons for learning
rnn.neurons[0].outNeuronsWithWeights.Add(new SlimRnnNeuronWithWeight(rnn.neurons[0], rnn.neurons[3], 0.0f, 0, true));
rnn.neurons[0].outNeuronsWithWeights.Add(new SlimRnnNeuronWithWeight(rnn.neurons[0], rnn.neurons[4], 0.0f, 1, true));
rnn.neurons[1].outNeuronsWithWeights.Add(new SlimRnnNeuronWithWeight(rnn.neurons[1], rnn.neurons[3], 1.0f, 0));
rnn.neurons[1].outNeuronsWithWeights.Add(new SlimRnnNeuronWithWeight(rnn.neurons[1], rnn.neurons[4], 0.0f, 1, true));
rnn.neurons[2].outNeuronsWithWeights.Add(new SlimRnnNeuronWithWeight(rnn.neurons[2], rnn.neurons[3], 1.0f, 0));
rnn.initializeNeurons();
UniversalSlimRnnSearch learningAlgorithm = new UniversalSlimRnnSearch(rnn, new AlwaysSuccessfulTester());
learningAlgorithm.weightWithPropabilityTable = new List <UniversalSlimRnnSearch.WeightWithPropability> {
/*new UniversalSlimRnnSearch.WeightWithPropability(-50.0f, 0.01),
* new UniversalSlimRnnSearch.WeightWithPropability(-40.0f, 0.02),
* new UniversalSlimRnnSearch.WeightWithPropability(-30.0f, 0.02),
* new UniversalSlimRnnSearch.WeightWithPropability(-20.0f, 0.02),
* new UniversalSlimRnnSearch.WeightWithPropability(-10.0f, 0.02),
*
* new UniversalSlimRnnSearch.WeightWithPropability(-9.0f, 0.02),
* new UniversalSlimRnnSearch.WeightWithPropability(-8.0f, 0.02),
* new UniversalSlimRnnSearch.WeightWithPropability(-7.0f, 0.02),
* new UniversalSlimRnnSearch.WeightWithPropability(-6.0f, 0.02),
* new UniversalSlimRnnSearch.WeightWithPropability(-5.0f, 0.02),
* new UniversalSlimRnnSearch.WeightWithPropability(-4.0f, 0.02),
* new UniversalSlimRnnSearch.WeightWithPropability(-3.0f, 0.02),
* new UniversalSlimRnnSearch.WeightWithPropability(-2.0f, 0.02),
* new UniversalSlimRnnSearch.WeightWithPropability(-1.5f, 0.03),
* new UniversalSlimRnnSearch.WeightWithPropability(-1.0f, 0.03),
* new UniversalSlimRnnSearch.WeightWithPropability(-0.99f, 0.03),
* new UniversalSlimRnnSearch.WeightWithPropability(-0.98f, 0.03),
* new UniversalSlimRnnSearch.WeightWithPropability(-0.95f, 0.03),
* new UniversalSlimRnnSearch.WeightWithPropability(-0.92f, 0.03),
* new UniversalSlimRnnSearch.WeightWithPropability(-0.9f, 0.03),
* new UniversalSlimRnnSearch.WeightWithPropability(-0.85f, 0.03),
* new UniversalSlimRnnSearch.WeightWithPropability(-0.8f, 0.03),
* new UniversalSlimRnnSearch.WeightWithPropability(-0.75f, 0.03),
* new UniversalSlimRnnSearch.WeightWithPropability(-0.7f, 0.03),
* new UniversalSlimRnnSearch.WeightWithPropability(-0.65f, 0.03),
* new UniversalSlimRnnSearch.WeightWithPropability(-0.6f, 0.03),
* new UniversalSlimRnnSearch.WeightWithPropability(-0.55f, 0.03),
* new UniversalSlimRnnSearch.WeightWithPropability(-0.5f, 0.03),
* new UniversalSlimRnnSearch.WeightWithPropability(-0.45f, 0.03),
* new UniversalSlimRnnSearch.WeightWithPropability(-0.4f, 0.03),
* new UniversalSlimRnnSearch.WeightWithPropability(-0.35f, 0.03),
* new UniversalSlimRnnSearch.WeightWithPropability(-0.3f, 0.03),
* new UniversalSlimRnnSearch.WeightWithPropability(-0.25f, 0.03),
* new UniversalSlimRnnSearch.WeightWithPropability(-0.2f, 0.03),
* new UniversalSlimRnnSearch.WeightWithPropability(-0.15f, 0.03),
* new UniversalSlimRnnSearch.WeightWithPropability(-0.1f, 0.03),
* new UniversalSlimRnnSearch.WeightWithPropability(-0.05f, 0.01),
* new UniversalSlimRnnSearch.WeightWithPropability(-0.02f, 0.01),
* new UniversalSlimRnnSearch.WeightWithPropability(-0.01f, 0.01),
*
* // TODO< positive side >
*/
// just for testing
new UniversalSlimRnnSearch.WeightWithPropability(1.0f, 0.4),
new UniversalSlimRnnSearch.WeightWithPropability(-1.0f, 0.4),
new UniversalSlimRnnSearch.WeightWithPropability(0.5f, 0.1),
new UniversalSlimRnnSearch.WeightWithPropability(-0.5f, 0.1),
};
// invoke learning algorithm
bool wasSolved;
SlimRnn solutionRnn;
learningAlgorithm.search(1, true, out wasSolved, out solutionRnn);
}
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;
}
