// /param mustHalt has the SLIM-RNN to halt to be a valid solution?
public void search(uint maximalIteration, bool mustHalt, out bool wasSolved, out SlimRnn solutionRnn)
{
wasSolved = false;
solutionRnn = null;
slimRnn.learningAlgorithm = this;
for (uint levinSearchIteration = 1; levinSearchIteration <= maximalIteration; levinSearchIteration++)
{
iteration(levinSearchIteration, mustHalt, out wasSolved, out solutionRnn);
if (wasSolved)
{
goto reset;
}
}
reset:
slimRnn.learningAlgorithm = null;
}
public static void debugConnections(SlimRnn slimRnn)
{
Console.WriteLine("SlimRnn");
int neuronIndex = 0;
foreach (SlimRnnNeuron iNeuron in slimRnn.neurons)
{
Console.WriteLine(" neuron idx={0}", neuronIndex);
int connectionIndex = 0;
foreach (var iConnection in iNeuron.outNeuronsWithWeights)
{
Console.WriteLine(" connection idx={0} wasUsed={1} weight={2}", connectionIndex, iConnection.wasUsed, iConnection.weight);
connectionIndex++;
}
neuronIndex++;
}
}
// depth first search of the changes of the weights of the SLIM-RNN
//
// this version doesn't timeshare the computations (to save RAM)
// /param mustHalt has the SLIM-RNN to halt to be a valid solution?
void depthFirstSearch(uint levinSearchIteration, bool mustHalt, out bool wasSolved, out SlimRnn solutionRnn)
{
solutionRnn = null;
wasSolved = false;
WeightChangeTreeElement weightChangeTreeRoot = WeightChangeTreeElement.makeRoot();
currentWeightChangeTreeElement = null; // to ignore calls to ISlimRnnLearningAlgorithm.opportunityToAdjustWeight()
// scan all weights for eligable weights
List <SlimRnnNeuronWithWeight> eligibleWeights = new List <SlimRnnNeuronWithWeight>();
foreach (SlimRnnNeuron iNeuron in slimRnn.neurons)
{
eligibleWeights.AddRange(iNeuron.outNeuronsWithWeights.Where(v => v.isEligable));
}
// build tree of the possible weight changes
//
// by iterating over all elements in the trace and adding all possible weight values to the weightChangeTree
{
List <WeightChangeTreeElement>
weightChangeTreeLeafElements = new List <WeightChangeTreeElement> {
weightChangeTreeRoot
},
nextWeightChangeTreeLeafElements = new List <WeightChangeTreeElement>();
foreach (SlimRnnNeuronWithWeight iTrace in eligibleWeights)
{
foreach (WeightChangeTreeElement iWeightChangeTreeElement in weightChangeTreeLeafElements)
{
/*
* for (
* uint weightWithPropabilityTableIndex = 0;
* weightWithPropabilityTableIndex < weightWithPropabilityTable.Count;
* weightWithPropabilityTableIndex++
* ) {
*
* WeightChangeTreeElement createdWeightChangeTreeElement = WeightChangeTreeElement.make(iTrace, weightWithPropabilityTableIndex, *parent**iWeightChangeTreeElement);
* iWeightChangeTreeElement.children.Add(createdWeightChangeTreeElement);
* iWeightChangeTreeElement.childrenConnectionNeuronIndices.Add(new Tuple<uint, uint>(iTrace.source.neuronIndex, iTrace.target.neuronIndex));
*
* nextWeightChangeTreeLeafElements.Add(createdWeightChangeTreeElement); // keep track of new leaf elements of the weight change tree
* }*/
nextWeightChangeTreeLeafElements.AddRange(createWeightChangeTreeElementsForConnectionAndAddToParent(iTrace, iWeightChangeTreeElement));
}
weightChangeTreeLeafElements = nextWeightChangeTreeLeafElements;
nextWeightChangeTreeLeafElements = new List <WeightChangeTreeElement>();
}
}
// set all weights of the trace to zero
// we do this because the connections are this way inactive and this avoids any call to ISlimRnnLearningAlgorithm.opportunityToAdjustWeight() for
// connections which are already inside the weightChangeTree
foreach (var iNeuronWithWeight in eligibleWeights)
{
iNeuronWithWeight.weight = 0.0f;
}
// depth-first-search iterate and update the weightChange tree as necessary
List <DepthFirstSearchStackElement> stack = new List <DepthFirstSearchStackElement>();
stack.Clear();
stack.push(DepthFirstSearchStackElement.make(weightChangeTreeRoot));
while (!stack.isEmpty())
{
DepthFirstSearchStackElement topStackElement = stack.pop();
// calls to ISlimRnnLearningAlgorithm.opportunityToAdjustWeight() have to modify the tree
currentWeightChangeTreeElement = topStackElement.treeElement;
if (!currentWeightChangeTreeElement.isRoot)
{
// do modification of SLIM-RNN
// OPTIMIZATION TODO< check if we have to do this recursivly or if it leads to the right answer with the nonrecursive code, the recursive code is correct >
// nonrecursive code:
// currentWeightChangeTreeElement.neuronWithWeight.weight = weightWithPropabilityTable[(int)currentWeightChangeTreeElement.weightWithPropabilityTableIndex].weight;
// recursive code
WeightChangeTreeElement currentWeightUpdateElement = currentWeightChangeTreeElement;
for (;;)
{
if (/*unnecessary currentWeightUpdateElement == null ||*/ currentWeightUpdateElement.isRoot)
{
break;
}
currentWeightUpdateElement.neuronWithWeight.weight = weightWithPropabilityTable[(int)currentWeightUpdateElement.weightWithPropabilityTableIndex].weight;
currentWeightUpdateElement = currentWeightUpdateElement.parent;
}
// we need to label all connections which got already adapted
currentWeightUpdateElement = currentWeightChangeTreeElement;
for (;;)
{
if (/*unnecessary currentWeightUpdateElement == null ||*/ currentWeightUpdateElement.isRoot)
{
break;
}
var connectionTuple = new Tuple <uint, uint>(currentWeightUpdateElement.neuronWithWeight.source.neuronIndex, currentWeightUpdateElement.neuronWithWeight.target.neuronIndex);
Debug.Assert(!globalConnectionNeuronIndices.Contains(connectionTuple));
globalConnectionNeuronIndices.Add(connectionTuple);
currentWeightUpdateElement = currentWeightUpdateElement.parent;
}
// debug network
//SlimRnnDebug.debugConnections(slimRnn);
double tLim = calcTimebound(levinSearchIteration, topStackElement.treeElement);
bool slimRnnSolvedTask = tester.doesSlimRnnSolveTask(slimRnn, mustHalt, tLim);
if (slimRnnSolvedTask)
{
// the task has been solved with this network
wasSolved = true;
solutionRnn = slimRnn;
return;
}
// reset all touched connections to 0.0 to avoid any sideeffects
// OPTIMIZATION TODO< in the recursive version of depth-first-search we don't need to do this because we modify the network with each successive call,
// so in this version we don't have to reset the whole connections to null >
currentWeightUpdateElement = currentWeightChangeTreeElement;
for (;;)
{
if (/*unnecessary currentWeightUpdateElement == null ||*/ currentWeightUpdateElement.isRoot)
{
break;
}
currentWeightUpdateElement.neuronWithWeight.weight = 0.0f;
currentWeightUpdateElement = currentWeightUpdateElement.parent;
}
// we need to unlabel all connections which got already adapted
currentWeightUpdateElement = currentWeightChangeTreeElement;
for (;;)
{
if (/*unnecessary currentWeightUpdateElement == null ||*/ currentWeightUpdateElement.isRoot)
{
break;
}
var connectionTuple = new Tuple <uint, uint>(currentWeightUpdateElement.neuronWithWeight.source.neuronIndex, currentWeightUpdateElement.neuronWithWeight.target.neuronIndex);
Debug.Assert(globalConnectionNeuronIndices.Contains(connectionTuple));
globalConnectionNeuronIndices.Remove(connectionTuple);
currentWeightUpdateElement = currentWeightUpdateElement.parent;
}
}
// push all children for depth-first-search
foreach (var iTreeChildren in topStackElement.treeElement.children)
{
stack.push(DepthFirstSearchStackElement.make(iTreeChildren));
}
}
}
// /param mustHalt has the SLIM-RNN to halt to be a valid solution?
void iteration(uint levinSearchIteration, bool mustHalt, out bool wasSolved, out SlimRnn solutionRnn)
{
depthFirstSearch(levinSearchIteration, mustHalt, out wasSolved, out solutionRnn);
}
public UniversalSlimRnnSearch(SlimRnn slimRnn, ITaskSolvedAndVerifiedTester tester)
{
this.slimRnn = slimRnn;
this.tester = tester;
}
