public NetworkHelper(IList<Minutia> features, SOMParams somParams)
{
var trainingFeaturesCount = features.Count;
var inputs = new double[trainingFeaturesCount][];
var featureCharacteristicsCount = somParams.CharacteristicsCount;
for (var i = 0; i < trainingFeaturesCount; i++)
{
var feature = features[i];
inputs[i] = new double[featureCharacteristicsCount];
inputs[i][0] = feature.Angle * somParams.AngleMultiplier;
inputs[i][1] = (double)feature.MinutiaType * somParams.TypeMultiplier;
inputs[i][2] = feature.X * somParams.CoordsMultiplier;
inputs[i][3] = feature.Y * somParams.CoordsMultiplier;
inputs[i][4] = feature.GetHashCode()*somParams.HashCodeMultiplier;
}
_networkSize = CreateRectangleFromFeatures(trainingFeaturesCount);
_som = new DistanceNetwork(featureCharacteristicsCount, trainingFeaturesCount);
_teacher = new SOMLearning(_som, _networkSize[0], _networkSize[1]);
_inputs = inputs;
_learningRadius = somParams.LearningRadius;
_fixedLearningRate = somParams.LearningRate / 10;
_driftingLearningRate = _fixedLearningRate * 9;
}
public NetworkHelper(double[][] features, SOMParams somParams)
{
var trainingFeaturesCount = features.GetLength(0);
var featureCharacteristicsCount = somParams.CharacteristicsCount;
_networkSize = CreateRectangleFromFeatures(trainingFeaturesCount);
_som = new DistanceNetwork(featureCharacteristicsCount, trainingFeaturesCount);
_teacher = new SOMLearning(_som, _networkSize[0], _networkSize[1]);
_inputs = features;
_learningRadius = somParams.LearningRadius;
_fixedLearningRate = somParams.LearningRate / 10;
_driftingLearningRate = _fixedLearningRate * 9;
}
public static double[][] GetQueryFeaturesSet(Bitmap image, SOMParams somParams)
{
var featExtractor = new Ratha1995MinutiaeExtractor();
var features = featExtractor.ExtractFeatures(image);
var queryFeaturesSet = new double[features.Count][];
for (var i = 0; i < features.Count; i++)
{
// create new sample
var feature = features[i];
queryFeaturesSet[i] = new double[somParams.CharacteristicsCount];
queryFeaturesSet[i][0] = feature.Angle * somParams.AngleMultiplier;
queryFeaturesSet[i][1] = (double)feature.MinutiaType * somParams.TypeMultiplier;
queryFeaturesSet[i][2] = feature.X * somParams.CoordsMultiplier;
queryFeaturesSet[i][3] = feature.Y * somParams.CoordsMultiplier;
queryFeaturesSet[i][4] = feature.GetHashCode() * somParams.HashCodeMultiplier;
}
return queryFeaturesSet;
}
// On "Start" button click
private void startButton_Click(object sender, EventArgs e)
{
// get iterations count
try
{
iterations = Math.Max(5, Math.Min(1000000, int.Parse( iterationsBox.Text )));
}
catch
{
iterations = 500;
}
// get learning rate
try
{
learningRate = Math.Max(0.00001, Math.Min(1.0, double.Parse( rateBox.Text )));
}
catch
{
learningRate = 0.3;
}
// get radius
try
{
learningRadius = Math.Max(1, Math.Min(30, int.Parse( radiusBox.Text )));
}
catch
{
learningRadius = 3;
}
try
{
angleMultiplier = Math.Max(0.00001, Math.Min(100, double.Parse(textBoxAngleMultiplier.Text)));
}
catch
{
angleMultiplier = 1;
}
try
{
typeMultiplier = Math.Max(0.00001, Math.Min(100, double.Parse(textBoxTypeMultiplier.Text)));
}
catch
{
typeMultiplier = 1;
}
try
{
coordsMultiplier = Math.Max(0.00001, Math.Min(100, double.Parse(textBoxCoordsMultiplier.Text)));
}
catch
{
coordsMultiplier = 1;
}
try
{
hashCodeMultiplier = Math.Max(0, Math.Min(100, double.Parse(textBoxHashCodeMultiplier.Text)));
}
catch
{
hashCodeMultiplier = 1;
}
// update settings controls
UpdateSettings( );
_SOMParams = new SOMParams(learningRadius, learningRate, angleMultiplier, typeMultiplier, coordsMultiplier, hashCodeMultiplier, iterations, 5);
// generate training set
queryFeaturesSet = NetworkHelper.GetQueryFeaturesSet(inputImage, _SOMParams);
// disable all settings controls except "Stop" button
EnableControls( false );
// run worker thread
needToStop = false;
workerThread = new Thread( SearchSolution );
workerThread.Start( );
}
public double[][] LoadTaughtPeople(SOMParams somParams)
{
double[][] features;
var fileName = GetTaughtFeaturesFilenameByParams(somParams);
People = new List<Person>();
using (var sr = new StreamReader(fileName))
{
var lineCount = File.ReadAllLines(fileName).Count();
features = new double[lineCount - 1][];
var count = 0;
var line = sr.ReadLine();
var splitStringArray = new string[1] {";"};
line = sr.ReadLine();
var members = line.Split(splitStringArray, StringSplitOptions.None);
var person = new Person(members[0]);
var finger = new Fingerprint(int.Parse(members[1]));
var scan = new Scan(int.Parse(members[2]));
var feature = new Minutia((short)double.Parse(members[3]), (short)double.Parse(members[4]), double.Parse(members[5]))
{
MinutiaType = (MinutiaType)double.Parse(members[6])
};
scan.Features.Add(feature);
features[count] = new double[5];
features[count][2] = double.Parse(members[3]);
features[count][3] = double.Parse(members[4]);
features[count][0] = double.Parse(members[5]);
features[count][1] = double.Parse(members[6]);
features[count][4] = double.Parse(members[7]);
count++;
// Read lines from the file until the end of the file is reached.
while ((line = sr.ReadLine()) != null)
{
members = line.Split(splitStringArray, StringSplitOptions.None);
features[count] = new double[5];
features[count][2] = double.Parse(members[3]);
features[count][3] = double.Parse(members[4]);
features[count][0] = double.Parse(members[5]);
features[count][1] = double.Parse(members[6]);
features[count][4] = double.Parse(members[7]);
count++;
feature = new Minutia((short)double.Parse(members[3]), (short)double.Parse(members[4]), double.Parse(members[5]))
{
MinutiaType = (MinutiaType)double.Parse(members[6])
};
scan.Features.Add(feature);
var scanId = int.Parse(members[2]);
if (scan.Id != scanId)
{
finger.Scans.Add(scan);
scan = new Scan(scanId);
}
var fingerId = int.Parse(members[1]);
if (finger.Id != fingerId)
{
person.Fingerprints.Add(finger);
finger = new Fingerprint(fingerId);
}
if (person.Id != members[0])
{
People.Add(person);
person = new Person(members[0]);
}
}
finger.Scans.Add(scan);
person.Fingerprints.Add(finger);
People.Add(person);
sr.Close();
}
return features;
}
public void SaveTaughtPeople(double[][] features, SOMParams somParams)
{
var fileName = GetTaughtFeaturesFilenameByParams(somParams);
var fs = new StreamWriter(fileName, false);
fs.WriteLine("Person;Finger;Scan;FeatureX;FeatureY;FeatureAngle;FeatureType");
var count = 0;
foreach (var person in People)
{
foreach (var fingerprint in person.Fingerprints)
{
foreach (var scan in fingerprint.Scans)
{
var scanFeaturesCount = scan.Features.Count;
var scanFeaturesIter = 0;
while (scanFeaturesCount > scanFeaturesIter)
{
fs.WriteLine("{0};{1};{2};{3:f4};{4:f4};{5:f4};{6:f4};{7:f4}", person.Id, fingerprint.Id,
scan.Id, features[count][2], features[count][3], features[count][0], features[count][1],
features[count][4]);
count++;
scanFeaturesIter++;
}
}
}
}
fs.Close();
}
public static bool IsAlreadyTaughtAndSaved(SOMParams somParams)
{
return File.Exists(GetTaughtFeaturesFilenameByParams(somParams));
}
private static string GetTaughtFeaturesFilenameByParams(SOMParams somParams)
{
return string.Format(@"{0}{1}_{2}_{3}_{4}_{5}_{6}_{7}_{8}{9}", Folder, TaughtFeaturesDatabasePrefix,
somParams.LearningRadius, somParams.LearningRate * 100000, somParams.Iterations, somParams.AngleMultiplier * 100000,
somParams.TypeMultiplier * 100000, somParams.CoordsMultiplier * 100000, somParams.HashCodeMultiplier * 100000,
TaughtFeaturesDatabaseSuffix);
}
private void InitializeParams()
{
_somParamses = new List<SOMParams>();
foreach (var iteration in _iterations)
{
foreach (var learningRate in _learningRates)
{
foreach (var learningRadius in _learningRadiuses)
{
foreach (var angleMultiplier in _angleMultipliers)
{
foreach (var typeMultiplier in _typeMultipliers)
{
foreach (var coordsMultiplier in _coordsMultipliers)
{
foreach (var hashCodeMultiplier in _hashCodeMultipliers)
{
var somParams = new SOMParams(learningRadius, learningRate, angleMultiplier, typeMultiplier, coordsMultiplier, hashCodeMultiplier, iteration, 5);
_somParamses.Add(somParams);
}
}
}
}
}
}
}
}