// Worker thread
private void SearchSolution( )
{
_resource = new MinutieResource();
NetworkHelper networkHelper;
if (MinutieResource.IsAlreadyTaughtAndSaved(_SOMParams))
{
networkHelper = new NetworkHelper(_resource.LoadTaughtPeople(_SOMParams), _SOMParams);
networkHelper.Teach(iterations, iterations-1);
SetText(currentIterationBox, _SOMParams.Iterations.ToString());
}
else
{
networkHelper = new NetworkHelper(_resource.GetAllFeatures(), _SOMParams);
var iter = 0;
while (!needToStop)
{
networkHelper.Teach(iterations, iter);
iter++;
SetText(currentIterationBox, iter.ToString());
if (iter >= iterations-1)
needToStop = true;
}
//_resource.SaveTaughtPeople(networkHelper._inputs, _SOMParams);
networkHelper.Teach(iterations, iter);
iter++;
SetText(currentIterationBox, iter.ToString());
}
// create map
map = new int[networkHelper._networkSize[0], networkHelper._networkSize[1], 3];
UpdateMap(networkHelper);
FindPerson();
// enable settings controls
EnableControls( true );
}
// Update map
private void UpdateMap(NetworkHelper network)
{
// get first layer
var layer = network._som.Layers[0];
// lock
Monitor.Enter( this );
// run through all neurons
for ( var i = 0; i < layer.Neurons.Length; i++ )
{
var neuron = layer.Neurons[i];
var x = i % network._networkSize[0];
var y = i / network._networkSize[0];
map[x, y, 0] = (int) neuron.Weights[2];
map[x, y, 1] = (int) neuron.Weights[3];
map[x, y, 2] = 0;
}
_winners = new List<int>();
// collect active neurons
foreach (var queryFeature in queryFeaturesSet)
{
network._som.Compute(queryFeature);
var w = network._som.GetWinner();
map[w % network._networkSize[0], w / network._networkSize[0], 2] = 1;
_winners.Add(w);
}
// unlock
Monitor.Exit( this );
//
mapPanel.Invalidate( );
}
public void StartTesting()
{
var inputImage = ImageLoader.LoadImage(AppDomain.CurrentDomain.BaseDirectory + _fileName);
Parallel.ForEach(_somParamses, new ParallelOptions { MaxDegreeOfParallelism = 4 }, (currentParams) =>
{
object clonedImage;
try
{
clonedImage = inputImage.Clone();
}
catch (InvalidOperationException ioe)
{
const string winnerException = "000";
var formattedWinnerWithParamsException = string.Format(@"{0};{1};{2};{3};{4};{5};{6};{7}", winnerException,
currentParams.LearningRadius, currentParams.LearningRate, currentParams.Iterations,
currentParams.AngleMultiplier,
currentParams.TypeMultiplier, currentParams.CoordsMultiplier,
currentParams.HashCodeMultiplier);
_winnersList.TryAdd(formattedWinnerWithParamsException);
return;
}
var queryFeaturesSet = NetworkHelper.GetQueryFeaturesSet((Bitmap)clonedImage, currentParams);
var resource = new MinutieResource();
NetworkHelper networkHelper;
if (MinutieResource.IsAlreadyTaughtAndSaved(currentParams))
{
networkHelper = new NetworkHelper(resource.LoadTaughtPeople(currentParams), currentParams);
networkHelper.Teach(currentParams.Iterations, currentParams.Iterations - 1);
}
else
{
networkHelper = new NetworkHelper(resource.GetAllFeatures(), currentParams);
var iter = 0;
while (!(iter >= currentParams.Iterations - 1))
{
networkHelper.Teach(currentParams.Iterations, iter);
iter++;
}
resource.SaveTaughtPeople(networkHelper._inputs, currentParams);
networkHelper.Teach(currentParams.Iterations, iter);
}
var winners = new List<int>();
foreach (var queryFeature in queryFeaturesSet)
{
networkHelper._som.Compute(queryFeature);
var w = networkHelper._som.GetWinner();
winners.Add(w);
}
var winner = resource.GetWinningPerson(winners);
var formattedWinnerWithParams = string.Format(@"{0};{1};{2};{3};{4};{5};{6};{7}", winner,
currentParams.LearningRadius, currentParams.LearningRate, currentParams.Iterations,
currentParams.AngleMultiplier,
currentParams.TypeMultiplier, currentParams.CoordsMultiplier,
currentParams.HashCodeMultiplier);
_winnersList.TryAdd(formattedWinnerWithParams);
});
SaveResults();
}