public static void Main()
{
modshogun.init_shogun_with_defaults();
double shift_coef = 1.0;
double[,] traindata_real = Load.load_numbers("../data/fm_train_real.dat");
double[,] testdata_real = Load.load_numbers("../data/fm_test_real.dat");
RealFeatures feats_train = new RealFeatures(traindata_real);
RealFeatures feats_test = new RealFeatures(testdata_real);
EuclideanDistance distance = new EuclideanDistance(feats_train, feats_train);
MultiquadricKernel kernel = new MultiquadricKernel(feats_train, feats_test, shift_coef, distance);
double[,] km_train = kernel.get_kernel_matrix();
kernel.init(feats_train, feats_test);
double[,] km_test = kernel.get_kernel_matrix();
foreach(double item in km_train) {
Console.Write(item);
}
foreach(double item in km_test) {
Console.Write(item);
}
modshogun.exit_shogun();
}
public void ReviewersThatHaveReviewedUniqueTitlesShouldNotBeSimilar()
{
var r1 = ReviewerBuilder.BuildReviewer1();
var r2 = ReviewerBuilder.BuildAReviewerThatReviewedSomethingUnique();
euclideanDistance = new EuclideanDistance(r1, r2);
Assert.AreEqual(0, euclideanDistance.Score());
}
public static void Main()
{
modshogun.init_shogun_with_defaults();
int k = 3;
double[,] traindata_real = Load.load_numbers("../data/fm_train_real.dat");
double[,] testdata_real = Load.load_numbers("../data/fm_test_real.dat");
double[] trainlab = Load.load_labels("../data/label_train_multiclass.dat");
RealFeatures feats_train = new RealFeatures(traindata_real);
RealFeatures feats_test = new RealFeatures(testdata_real);
EuclideanDistance distance = new EuclideanDistance(feats_train, feats_train);
MulticlassLabels labels = new MulticlassLabels(trainlab);
KNN knn = new KNN(k, distance, labels);
knn.train();
double[] out_labels = MulticlassLabels.obtain_from_generic(knn.apply(feats_test)).get_labels();
foreach(double item in out_labels) {
Console.Write(item);
}
modshogun.exit_shogun();
}
public static void Main() {
modshogun.init_shogun_with_defaults();
double[,] traindata_real = Load.load_numbers("../data/fm_train_real.dat");
double[,] testdata_real = Load.load_numbers("../data/fm_test_real.dat");
RealFeatures feats_train = new RealFeatures(traindata_real);
RealFeatures feats_test = new RealFeatures(testdata_real);
EuclideanDistance distance = new EuclideanDistance(feats_train, feats_train);
distance.set_disable_sqrt(true);
double[,] dm_train = distance.get_distance_matrix();
distance.init(feats_train, feats_test);
double[,] dm_test = distance.get_distance_matrix();
foreach(double item in dm_train) {
Console.Write(item);
}
foreach(double item in dm_test) {
Console.Write(item);
}
}
public static void Main()
{
modshogun.init_shogun_with_defaults();
double degree = 1.0;
double[,] traindata_real = Load.load_numbers("../data/fm_train_real.dat");
double[,] testdata_real = Load.load_numbers("../data/fm_test_real.dat");
RealFeatures feats_train = new RealFeatures(traindata_real);
RealFeatures feats_test = new RealFeatures(testdata_real);
EuclideanDistance distance = new EuclideanDistance(feats_train, feats_train);
PowerKernel kernel = new PowerKernel(feats_train, feats_test, degree, distance);
double[,] km_train = kernel.get_kernel_matrix();
kernel.init(feats_train, feats_test);
double[,] km_test = kernel.get_kernel_matrix();
foreach (double item in km_train)
Console.Write(item);
foreach (double item in km_test)
Console.Write(item);
}
public void PointsDistanceEuclidean()
{
Point p1 = new Point(10.0, 15.0);
Point p2 = new Point(7.0, 19.0);
IDistanceMetric metric = new EuclideanDistance();
PointsDistance distance = new PointsDistance(metric);
Assert.AreEqual(5.0, distance.CalculateDistance(p1, p2));
}
public Node(double[] weights, int x, int y)
{
Weights = weights;
Coordinate = new Coordinate(x, y);
Count = 0;
_distanceMeasure = new EuclideanDistance();
}
static void Main(string[] args)
{
IKMeans _kMeans = new KMeans();
IDistance _distance = new EuclideanDistance();
Random rnd = new Random(0);
int objCount = 10;
int intrstsCount = 2;
int clusterCount = 2;
List <ClusterEntityDTO> clusters = new List <ClusterEntityDTO>();
for (int i = 0; i < objCount; i++)
{
ClusterEntityDTO dTO = new ClusterEntityDTO
{
Name = string.Format("{0:yyyyMMddHHmmss}", DateTime.Now),
Id = i
};
List <Interest> interests = new List <Interest>();
Console.WriteLine("___" + dTO.Name + "___");
for (int j = 0; j < intrstsCount; j++)
{
double rand = Math.Round(rnd.NextDouble() * 200.0);
Interest intr = new Interest
{
Name = string.Format("intr{0:yyyyMMddHHmmss}", DateTime.Now),
Weight = rand,
Weight2 = rand
};
Console.WriteLine("--- " + intr.Weight + "---");
interests.Add(intr);
}
Console.WriteLine("-------------------------------");
dTO.Interests = interests;
clusters.Add(dTO);
}
Console.WriteLine("-----------------------------------");
Console.WriteLine("-----------------------------------");
Console.WriteLine("-----------------------------------");
var result = _kMeans.Clustering(clusters, _distance, clusterCount);
for (int i = 0; i < objCount; i++)
{
Console.WriteLine("___" + result[i].Name + "___");
for (int j = 0; j < intrstsCount; j++)
{
Console.WriteLine("---" + result[i].Interests[j].Weight2 + "---");
}
if (i < objCount - 1 && result[i].CentroidId != result[i + 1].CentroidId)
{
Console.WriteLine("%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%");
}
}
Console.ReadLine();
}
public void TestDistanceCalc()
{
ICalculateDistance calc = new EuclideanDistance();
double[] pos1 = { 0.5, 1.0, 2.5 };
double[] pos2 = { 0.1, 2.0, -2.5 };
Assert.AreEqual(5.1146, calc.Calculate(pos1, pos2), 0.001);
}
public void EuclideanCalculateDistance_for3and4_expected5()
{
double[] a = { 10.0, 34.0 };
double[] b = { 13.0, 38.0 };
EuclideanDistance metric = new EuclideanDistance();
double result = metric.CalculateDistance(a, b);
Assert.AreEqual(5.0, result);
}
public void TwoSimilarReviewersShouldHaveOneLikeReview()
{
var r1 = ReviewerBuilder.BuildOneReviewMax();
var r2 = ReviewerBuilder.BuildOneReviewMin();
euclideanDistance = new EuclideanDistance(r1, r2);
Assert.IsNotEmpty(euclideanDistance.FindSharedItems());
}
public TrainModel(FeatureExtractionType eType, int EuclideanCentroidPointIndex, ClassifierType cType)
{
this.cType = cType;
Trained = false;
if (eType == FeatureExtractionType.EuclideanDistance)
{
featureExtraction = new EuclideanDistance(EuclideanCentroidPointIndex);
}
}
public void DifferentDimensionException()
{
EuclideanDistance distance = new EuclideanDistance();
Vector v1 = new Vector(new double[] { 1, 2, 3 });
Vector v2 = new Vector(new double[] { 1, 2, 3, 4 });
Assert.Throws <ArgumentException>(() => distance.Measure(v1, v2));
Assert.Throws <ArgumentException>(() => distance.Measure(v2, v1));
}
public TrainModel(FeatureExtractionType eType, ClassifierType cType)
{
this.cType = cType;
Trained = false;
if (eType == FeatureExtractionType.EuclideanDistance)
{
featureExtraction = new EuclideanDistance(14);
}
}
public void Initialize()
{
// Composition root (for unit tests)
// Compose objects here using Dependency Injection
IDistanceMetric metric = new EuclideanDistance();
PointsDistance distance = new PointsDistance(metric);
this.cut = new ShootingRange(distance);
}
public void TestClustering()
{
var hclust = new HierarchicalClustering();
var vals = TestUtils.ReadMatrix("Examples/clustering_array_copy_error.txt.gz");
var data = new FloatMatrixIndexer(vals);
var distance = new EuclideanDistance();
hclust.TreeClusterKmeans(data, MatrixAccess.Columns, distance, HierarchicalClusterLinkage.Average, false,
false, 1, 300, 1, 10,
(i) => { });
}
public override float[] PredictStrength(BaseVector x)
{
double[] projectedTest = MatrixUtils.VectorTimesMatrix(x, projection);
float[] distances = new float[ngroups];
IDistance distance = new EuclideanDistance();
for (int j = 0; j < ngroups; j++)
{
distances[j] = -(float)distance.Get(projectedTest, projectedGroupMeans[j]);
}
return(distances);
}
public EuclideanDistanceTests()
{
Range range = new Range(lowerLimit, upperLimit);
this.dimensionList = new List <Dimension>(EuclideanDistanceTests.dimensionsCount);
for (int i = 0; i < EuclideanDistanceTests.dimensionsCount; i++)
{
((List <Dimension>) this.dimensionList).Add(new Dimension(range));
}
this.euclideanDistance1 = new EuclideanDistance(this.dimensionList);
}
public void Distance()
{
EuclideanDistance distance = new EuclideanDistance();
Vector v1 = new Vector(new double[] { 4, 3, 2 });
Vector v2 = new Vector(new double[] { 2, 3, 4 });
Assert.AreEqual(Math.Sqrt(8), distance.Measure(v1, v2));
Assert.AreEqual(distance.Measure(v2, v1), distance.Measure(v1, v2));
Assert.AreEqual(0, distance.Measure(v1, v1));
Assert.AreEqual(0, distance.Measure(v2, v2));
}
public static void Main()
{
modshogun.init_shogun_with_defaults();
double sigma = 1.0;
double[,] traindata_real = Load.load_numbers("../data/fm_train_real.dat");
double[,] testdata_real = Load.load_numbers("../data/fm_test_real.dat");
RealFeatures feats_train = new RealFeatures(traindata_real);
RealFeatures feats_test = new RealFeatures(testdata_real);
EuclideanDistance distance = new EuclideanDistance(feats_train, feats_train);
CauchyKernel kernel = new CauchyKernel(feats_train, feats_train, sigma, distance);
double[,] km_train = kernel.get_kernel_matrix();
kernel.init(feats_train, feats_test);
double[,] km_test = kernel.get_kernel_matrix();
// Parse and Display km_train
Console.Write("km_train:\n");
int numRows = km_train.GetLength(0);
int numCols = km_train.GetLength(1);
for (int i = 0; i < numRows; i++)
{
for (int j = 0; j < numCols; j++)
{
Console.Write(km_train[i, j] + " ");
}
Console.Write("\n");
}
// Parse and Display km_test
Console.Write("\nkm_test:\n");
numRows = km_test.GetLength(0);
numCols = km_test.GetLength(1);
for (int i = 0; i < numRows; i++)
{
for (int j = 0; j < numCols; j++)
{
Console.Write(km_test[i, j] + " ");
}
Console.Write("\n");
}
modshogun.exit_shogun();
}
public static void Main()
{
modshogun.init_shogun_with_defaults();
double width = 1.7;
double[,] traindata_real = Load.load_numbers("../data/fm_train_real.dat");
double[,] testdata_real = Load.load_numbers("../data/fm_test_real.dat");
RealFeatures feats_train = new RealFeatures(traindata_real);
RealFeatures feats_test = new RealFeatures(testdata_real);
EuclideanDistance distance = new EuclideanDistance();
DistanceKernel kernel = new DistanceKernel(feats_train, feats_test, width, distance);
double[,] dm_train = distance.get_distance_matrix();
distance.init(feats_train, feats_test);
double[,] dm_test = distance.get_distance_matrix();
// Parse and Display km_train
Console.Write("dm_train:\n");
int numRows = dm_train.GetLength(0);
int numCols = dm_train.GetLength(1);
for (int i = 0; i < numRows; i++)
{
for (int j = 0; j < numCols; j++)
{
Console.Write(dm_train[i, j] + " ");
}
Console.Write("\n");
}
// Parse and Display km_test
Console.Write("\ndm_test:\n");
numRows = dm_test.GetLength(0);
numCols = dm_test.GetLength(1);
for (int i = 0; i < numRows; i++)
{
for (int j = 0; j < numCols; j++)
{
Console.Write(dm_test[i, j] + " ");
}
Console.Write("\n");
}
modshogun.exit_shogun();
}
public void TestRun()
{
var anneal = new DiscreteAnnealSubclass(1000, 400, 1);
while (!anneal.Done)
{
anneal.Iteration();
}
ICalculateDistance dist = new EuclideanDistance();
Assert.AreEqual(1000, anneal.K);
Assert.AreEqual(0, dist.Calculate(anneal.Best, DiscreteAnnealSubclass.Ideal), AIFH.DefaultPrecision);
Assert.AreEqual(0, anneal.BestScore, AIFH.DefaultPrecision);
}
private void btnProcessData_Click(object sender, EventArgs e)
{
IDistance eucledeanDistance = new EuclideanDistance();
BackgroundWorker backgroundWorker = new BackgroundWorker();
backgroundWorker.WorkerReportsProgress = true;
backgroundWorker.DoWork += (o, eventArgs) =>
{
for (int k = (int)start.Value; k <= (int)end.Value; k++)
{
List <double[]> distanceResult = new List <double[]>();
_kMeans = new KMeans(k, eucledeanDistance);
Centroid[] centroids = _kMeans.Run(_trainingSet.ToArray());
double result = 0;
foreach (double[] point in _trainingSet)
{
double minValue = Double.MaxValue;
foreach (Centroid centroid in centroids)
{
double distance = eucledeanDistance.Run(point, centroid.Array);
if (distance < minValue)
{
minValue = distance;
}
}
result += minValue;
}
result /= _trainingSet.Count;
_kmeansPointList.Add(new DPoint(k, result));
double progressPercent = (100 * k) / (int)end.Value;
((BackgroundWorker)o).ReportProgress((int)progressPercent, new UserState {
Distortion = result, Index = k
});
}
};
backgroundWorker.ProgressChanged += (o, eventArgsProgressChanged) =>
{
chart1.DataBind();
progressBar1.Value = eventArgsProgressChanged.ProgressPercentage;
UserState temp = eventArgsProgressChanged.UserState as UserState;
listBox1.Items.Add(String.Format("K={0}, Dist: {1}", temp.Index, Math.Round(temp.Distortion, 2)));
};
backgroundWorker.RunWorkerAsync();
}
public void TestGeneralDistanceMatrix()
{
var distance = new EuclideanDistance();
var distanceMatrix = new GenericDistanceMatrix(new FloatMatrixIndexer(new float[, ] {
{ 0, 0 }, { 1, 0 }, { 3, 0 }
}), distance);
Assert.AreEqual(0, distanceMatrix[0, 0]);
Assert.AreEqual(0, distanceMatrix[2, 2]);
Assert.AreEqual(distanceMatrix[1, 2], distanceMatrix[2, 1]);
Assert.AreEqual(1, distanceMatrix[1, 0]);
Assert.AreEqual(2, distanceMatrix[1, 2]);
distanceMatrix[0, 1] = -1;
Assert.AreEqual(-1, distanceMatrix[0, 1]);
}
public static void Main() {
modshogun.init_shogun_with_defaults();
int merges = 3;
double[,] fm_train = Load.load_numbers("../data/fm_train_real.dat");
RealFeatures feats_train = new RealFeatures(fm_train);
EuclideanDistance distance = new EuclideanDistance(feats_train, feats_train);
Hierarchical hierarchical = new Hierarchical(merges, distance);
hierarchical.train();
double[] out_distance = hierarchical.get_merge_distances();
int[,] out_cluster = hierarchical.get_cluster_pairs();
}
private double[,] CreateDimenstionMatrix(List <Place> places)
{
int placesCount = places.Count;
double[,] dimenstionMatrix = new double[placesCount, placesCount];
for (int i = 0; i < placesCount; i++)
{
for (int j = 0; j < placesCount; j++)
{
dimenstionMatrix[i, j] = EuclideanDistance.FindEuclideanDistance(places.ElementAt(i), places.ElementAt(j));
}
}
return(dimenstionMatrix);
}
public static void Main()
{
modshogun.init_shogun_with_defaults();
int merges = 3;
double[,] fm_train = Load.load_numbers("../data/fm_train_real.dat");
RealFeatures feats_train = new RealFeatures(fm_train);
EuclideanDistance distance = new EuclideanDistance(feats_train, feats_train);
Hierarchical hierarchical = new Hierarchical(merges, distance);
hierarchical.train();
double[] out_distance = hierarchical.get_merge_distances();
int[,] out_cluster = hierarchical.get_cluster_pairs();
}
public static void Main()
{
modshogun.init_shogun_with_defaults();
double sigma = 1.0;
double[,] traindata_real = Load.load_numbers("../data/fm_train_real.dat");
double[,] testdata_real = Load.load_numbers("../data/fm_test_real.dat");
RealFeatures feats_train = new RealFeatures(traindata_real);
RealFeatures feats_test = new RealFeatures(testdata_real);
EuclideanDistance distance = new EuclideanDistance(feats_train, feats_train);
CircularKernel kernel = new CircularKernel(feats_train, feats_train, sigma, distance);
double[,] km_train = kernel.get_kernel_matrix();
kernel.init(feats_train, feats_test);
double[,] km_test=kernel.get_kernel_matrix();
// Parse and Display km_train
Console.Write("km_train:\n");
int numRows = km_train.GetLength(0);
int numCols = km_train.GetLength(1);
for(int i = 0; i < numRows; i++){
for(int j = 0; j < numCols; j++){
Console.Write(km_train[i,j] +" ");
}
Console.Write("\n");
}
// Parse and Display km_test
Console.Write("\nkm_test:\n");
numRows = km_test.GetLength(0);
numCols = km_test.GetLength(1);
for(int i = 0; i < numRows; i++){
for(int j = 0; j < numCols; j++){
Console.Write(km_test[i,j] +" ");
}
Console.Write("\n");
}
modshogun.exit_shogun();
}
public static void Main()
{
modshogun.init_shogun_with_defaults();
double width = 1.7;
double[,] traindata_real = Load.load_numbers("../data/fm_train_real.dat");
double[,] testdata_real = Load.load_numbers("../data/fm_test_real.dat");
RealFeatures feats_train = new RealFeatures(traindata_real);
RealFeatures feats_test = new RealFeatures(testdata_real);
EuclideanDistance distance = new EuclideanDistance();
DistanceKernel kernel = new DistanceKernel(feats_train, feats_test, width, distance);
double[,] dm_train = distance.get_distance_matrix();
distance.init(feats_train, feats_test);
double[,] dm_test = distance.get_distance_matrix();
// Parse and Display km_train
Console.Write("dm_train:\n");
int numRows = dm_train.GetLength(0);
int numCols = dm_train.GetLength(1);
for(int i = 0; i < numRows; i++){
for(int j = 0; j < numCols; j++){
Console.Write(dm_train[i,j] +" ");
}
Console.Write("\n");
}
// Parse and Display km_test
Console.Write("\ndm_test:\n");
numRows = dm_test.GetLength(0);
numCols = dm_test.GetLength(1);
for(int i = 0; i < numRows; i++){
for(int j = 0; j < numCols; j++){
Console.Write(dm_test[i,j] +" ");
}
Console.Write("\n");
}
modshogun.exit_shogun();
}
public static void Main() {
modshogun.init_shogun_with_defaults();
int k = 3;
// already tried init_random(17)
Math.init_random(17);
double[,] fm_train = Load.load_numbers("../data/fm_train_real.dat");
RealFeatures feats_train = new RealFeatures(fm_train);
EuclideanDistance distance = new EuclideanDistance(feats_train, feats_train);
KMeans kmeans = new KMeans(k, distance);
kmeans.train();
double[,] out_centers = kmeans.get_cluster_centers();
kmeans.get_radiuses();
}
public float GetPathLength()
{
if (Path.Length == 0)
{
return(0f);
}
var euclideanDistance = new EuclideanDistance();
var length = 0f;
var previousWaypoint = Path[0];
for (var i = 1; i < Path.Length; i++)
{
var waypoint = Path[i];
length += euclideanDistance.GetDistance(previousWaypoint, waypoint);
previousWaypoint = waypoint;
}
return(length);
}
static void Main()
{
Console.WriteLine("Biathlon");
Point target = ReadInitialPoint();
double radius = 3.0;
// Composition root (for normal program run)
// Compose objects here using Dependency Injection
IDistanceMetric metric = new EuclideanDistance();
PointsDistance distance = new PointsDistance(metric);
ShootingRange sr = new ShootingRange(distance);
Point found = sr.SearchFirstNearest(target, radius);
CheckHit(found);
Console.ReadLine();
}
public void TestClusterNodeFormat()
{
var hclust = new HierarchicalClustering();
var data = new FloatMatrixIndexer(new float[, ] {
{ 1, 2, 3 }, { 2, 3, 4 }
});
var distance = new EuclideanDistance();
var rowTree = hclust.TreeCluster(data, MatrixAccess.Rows, distance, HierarchicalClusterLinkage.Maximum, false,
false, 1, i => { });
var rowTreeR = HierarchicalClusterNode.FromRFormat(new[] { -1 }, new[] { -2 }, new[] { 1.732051 });
Assert.AreEqual(rowTreeR[0], rowTree[0]);
var colTree = hclust.TreeCluster(data, MatrixAccess.Columns, distance, HierarchicalClusterLinkage.Maximum, false,
false, 1, i => { });
var colTreeR = HierarchicalClusterNode.FromRFormat(new[] { -1, -3 }, new[] { -2, 1 }, new[] { 1.414214, 2.828428 });
CollectionAssert.AreEqual(colTree, colTreeR);
}
public static void Main()
{
modshogun.init_shogun_with_defaults();
int k = 3;
// already tried init_random(17)
Math.init_random(17);
double[,] fm_train = Load.load_numbers("../data/fm_train_real.dat");
RealFeatures feats_train = new RealFeatures(fm_train);
EuclideanDistance distance = new EuclideanDistance(feats_train, feats_train);
KMeans kmeans = new KMeans(k, distance);
kmeans.train();
double[,] out_centers = kmeans.get_cluster_centers();
kmeans.get_radiuses();
}
/// <summary>
///
/// </summary>
/// <returns></returns>
public override double EvaluatePartition()
{
UpdatesCentroids();
EuclideanDistance ed = new EuclideanDistance();
ed.AttributesToCalculateProximity = Set.Attributes.Values;
double result = 0;
foreach (var cluster in Structuring.Clusters.Values)
{
foreach (var item in cluster.Elements)
{
result += ed.CalculateProximity(item, cluster.Centroid);
}
}
return(Math.Sqrt(result / Set.ElementsCount));
}
static void Main(string[] args)
{
Console.WriteLine("Starting: {0}", DateTime.Now);
String filePath = args[0];
String outputFilePath = args[1];
int k = Int32.Parse(args[2]);
int distance = Int32.Parse(args[3]);
int numThreads = Int32.Parse(args[4]);
var samples = CSVIO.Load <float>(filePath);
Similarity sim = new EuclideanDistance();
if (distance == 2)
{
sim = new PearsonSimilarity();
}
if (distance == 3)
{
sim = new CosineSimilarity();
}
Console.WriteLine("Using distance measure: {0} on {1} samples of dimensionality: {2}",
sim, samples.Count, samples[0].Length);
Console.WriteLine("Beginning Clustering: {0}", DateTime.Now);
var clusters = Cluster(samples, sim, numThreads);
Cluster root = clusters[0];
Console.WriteLine("Finished Clustering: {0}", DateTime.Now);
var classifications = Classify(samples, root, k);
CSVIO.Save <int>(outputFilePath, classifications);
Console.WriteLine("Finished: {0}", DateTime.Now);
}
public void DataGettingTest()
{
DataGetter dg = new DataGetter();
var data = dg.GetData("approximation1.txt", ' ');
var one = dg.GetTrainingDataWithOneOutput("approximation1.txt", 1);
var test = dg.GetTrainingDataWithOneOutput("approximation_test.txt", 1);
var two = dg.GetTrainingDataWithChosenInputs("classification.txt", new bool[] { true, true, true, true });
{ Console.WriteLine("test"); }
var distCal = new EuclideanDistance();
RBFNetwork network = new RBFNetwork(distCal,
new GaussianRadialBasis(),
new KNNWidthCalculator(distCal, 2, 1),
new RandomNeuronPositioner(),
2, one[0].DesiredOutput.Count, one[0].Input.Count);
network.Train(new BackpropagationTrainingParameters(0.5, 20, 0, -1, 1, one), test);
var output = network.ProcessInput(test[0].Input);
}
public void SetUp() {
LoadData();
myEuclideanDistance = new EuclideanDistance();
}
public void SetUp()
{
euclideanDistance = new EuclideanDistance(ReviewerBuilder.BuildReviewer1(), ReviewerBuilder.BuildReviewer2());
}
/// <summary>
/// display a GUI and generate the WEKA based clusterer
/// </summary>
/// <param name="InstancesList">list of the weka instance</param>
/// <returns>weka clusterer</returns>
public Clusterer BuildClusterer(cParamAlgo ClusteringAlgo, cExtendedTable Input)
{
this.InputTable = Input;
foreach (var item in Input)
{
this.ListDescriptors.Add(item.Name);
}
cListValuesParam Parameters = ClusteringAlgo.GetListValuesParam();
Clusterer ClustererToReturn = null;
Instances ListInstancesWithoutClasses = CreateInstancesWithoutClass(Input);
#region EM
if (ClusteringAlgo.Name == "EM")
{
ClustererToReturn = new EM();
if (Parameters.ListCheckValues.Get("checkBoxAutomatedClassNum").Value)
((EM)ClustererToReturn).setNumClusters(-1);
else
((EM)ClustererToReturn).setNumClusters((int)Parameters.ListDoubleValues.Get("numericUpDownNumClasses").Value);
((EM)ClustererToReturn).setMaxIterations((int)Parameters.ListDoubleValues.Get("numericUpDownMaxIterations").Value);
((EM)ClustererToReturn).setMinStdDev((double)Parameters.ListDoubleValues.Get("numericUpDownMinStdev").Value);
((EM)ClustererToReturn).setSeed((int)Parameters.ListDoubleValues.Get("numericUpDownSeedNumber").Value);
ClustererToReturn.buildClusterer(ListInstancesWithoutClasses);
this.NumberOfClusters = ClustererToReturn.numberOfClusters();
}
#endregion
#region K Means
else if (ClusteringAlgo.Name == "K-Means")
{
ClustererToReturn = new SimpleKMeans();
((SimpleKMeans)ClustererToReturn).setNumClusters((int)Parameters.ListDoubleValues.Get("numericUpDownNumClasses").Value);
((SimpleKMeans)ClustererToReturn).setSeed((int)Parameters.ListDoubleValues.Get("numericUpDownSeedNumber").Value);
string DistanceType = (string)Parameters.ListTextValues.Get("comboBoxDistance").Value;
if (DistanceType == "Euclidean")
{
EuclideanDistance ED = new EuclideanDistance();
ED.setDontNormalize(!(bool)Parameters.ListCheckValues.Get("checkBoxNormalize").Value);
((SimpleKMeans)ClustererToReturn).setDistanceFunction(ED);
}
else if (DistanceType == "Manhattan")
{
ManhattanDistance MD = new ManhattanDistance();
MD.setDontNormalize(!(bool)Parameters.ListCheckValues.Get("checkBoxNormalize").Value);
((SimpleKMeans)ClustererToReturn).setDistanceFunction(MD);
}
else return null;
ClustererToReturn.buildClusterer(ListInstancesWithoutClasses);
this.NumberOfClusters = ClustererToReturn.numberOfClusters();
}
#endregion
//#region K Means++
//else if (ClusteringAlgo.Name == "K-Means++")
//{
// ClustererToReturn = new SimpleKMeans();
// ((SimpleKMeans)ClustererToReturn).setNumClusters((int)Parameters.ListDoubleValues.Get("numericUpDownNumClasses").Value);
// ((SimpleKMeans)ClustererToReturn).setSeed((int)Parameters.ListDoubleValues.Get("numericUpDownSeedNumber").Value);
// string DistanceType = (string)Parameters.ListTextValues.Get("comboBoxDistance").Value;
// if (DistanceType == "Euclidean")
// {
// EuclideanDistance ED = new EuclideanDistance();
// ED.setDontNormalize(!(bool)Parameters.ListCheckValues.Get("checkBoxNormalize").Value);
// ((SimpleKMeans)ClustererToReturn).setDistanceFunction(ED);
// }
// else if (DistanceType == "Manhattan")
// {
// ManhattanDistance MD = new ManhattanDistance();
// MD.setDontNormalize(!(bool)Parameters.ListCheckValues.Get("checkBoxNormalize").Value);
// ((SimpleKMeans)ClustererToReturn).setDistanceFunction(MD);
// }
// else return null;
// ClustererToReturn.buildClusterer(ListInstancesWithoutClasses);
// this.NumberOfClusters = ClustererToReturn.numberOfClusters();
//}
//#endregion
#region hierarchical
else if (ClusteringAlgo.Name == "Hierarchical")
{
ClustererToReturn = new weka.clusterers.HierarchicalClusterer();
string OptionDistance = " -N " + (int)Parameters.ListDoubleValues.Get("numericUpDownNumClasses").Value;
string DistanceType = (string)Parameters.ListTextValues.Get("comboBoxDistance").Value;
OptionDistance += " -A \"weka.core.";
switch (DistanceType)
{
case "Euclidean":
OptionDistance += "EuclideanDistance";
break;
case "Manhattan":
OptionDistance += "ManhattanDistance";
break;
case "Chebyshev":
OptionDistance += "ChebyshevDistance";
break;
default:
break;
}
if (!(bool)Parameters.ListCheckValues.Get("checkBoxNormalize").Value)
OptionDistance += " -D";
OptionDistance += " -R ";
OptionDistance += "first-last\"";
string WekaOption = "-L " + (string)Parameters.ListTextValues.Get("comboBoxLinkType").Value + OptionDistance;
((HierarchicalClusterer)ClustererToReturn).setOptions(weka.core.Utils.splitOptions(WekaOption));
ClustererToReturn.buildClusterer(ListInstancesWithoutClasses);
this.NumberOfClusters = ClustererToReturn.numberOfClusters();
}
#endregion
#region Farthest First
else if (ClusteringAlgo.Name == "FarthestFirst")
{
ClustererToReturn = new weka.clusterers.FarthestFirst();
((FarthestFirst)ClustererToReturn).setNumClusters((int)Parameters.ListDoubleValues.Get("numericUpDownNumClasses").Value);
((FarthestFirst)ClustererToReturn).setSeed((int)Parameters.ListDoubleValues.Get("numericUpDownSeedNumber").Value);
ClustererToReturn.buildClusterer(ListInstancesWithoutClasses);
this.NumberOfClusters = ClustererToReturn.numberOfClusters();
}
#endregion
#region CobWeb
else if (ClusteringAlgo.Name == "CobWeb")
{
ClustererToReturn = new weka.clusterers.Cobweb();
((Cobweb)ClustererToReturn).setSeed((int)Parameters.ListDoubleValues.Get("numericUpDownSeedNumber").Value);
((Cobweb)ClustererToReturn).setAcuity((double)Parameters.ListDoubleValues.Get("numericUpDownAcuity").Value);
((Cobweb)ClustererToReturn).setCutoff((double)Parameters.ListDoubleValues.Get("numericUpDownCutOff").Value);
ClustererToReturn.buildClusterer(ListInstancesWithoutClasses);
this.NumberOfClusters = ClustererToReturn.numberOfClusters();
}
#endregion
#region Manual
else if (ClusteringAlgo.Name == "Manual")
{
string DescriptorName = (string)Parameters.ListTextValues.Get("comboBoxForDescriptorManualClustering").Value;
// this.Classes = new double[ListInstancesWithoutClasses.numInstances()];
for (int IdxPt = 0; IdxPt < this.Classes.Count / 2; IdxPt++)
{
this.Classes[IdxPt] = 2;
}
this.NumberOfClusters = 2;
// break;
//int IdxDesc = -1;
//foreach (string item in this.ListDescriptors)
//{
// IdxDesc++;
// if (item == DescriptorName) break;
//}
//int Idx=0;
//foreach (Instance item in ListInstancesWithoutClasses)
//{
// this.Classes.Add(((int)item.value(IdxDesc)) % cGlobalInfo.ListCellularPhenotypes.Count);
//}
//// re - ordonner les valeurs du discripteur afin que les classes se suivent sans laisser de classe vide !!
//this.NumberOfClusters = cGlobalInfo.ListCellularPhenotypes.Count;
}
#endregion
else
{
System.Windows.Forms.MessageBox.Show("Clustering method not implemented !", "Error", MessageBoxButtons.OK, MessageBoxIcon.Error);
return null;
}
return ClustererToReturn;
}
public void ReviewersThatHaveTheSameTasteShouldHaveAPerfectScore()
{
var r1 = ReviewerBuilder.BuildReviewer1();
euclideanDistance = new EuclideanDistance(r1, r1);
Assert.AreEqual(1.0, euclideanDistance.Score());
}
public void ReviewersThatHaveTheSameTasteShouldHaveAllSimilarItems()
{
var r1 = ReviewerBuilder.BuildReviewer1();
euclideanDistance = new EuclideanDistance(r1, r1);
Assert.AreEqual(6, euclideanDistance.FindSharedItems().Count);
}
public double GetSimilarity(string str1, string str2, string type)
{
IStringMetric stringMetric;
switch (type)
{
case AlgorithmTypes.BlockDistance:
stringMetric = new BlockDistance();
break;
case AlgorithmTypes.ChapmanLengthDeviation:
stringMetric = new ChapmanLengthDeviation();
break;
case AlgorithmTypes.ChapmanMeanLength:
stringMetric = new ChapmanMeanLength();
break;
case AlgorithmTypes.CosineSimilarity:
stringMetric = new CosineSimilarity();
break;
case AlgorithmTypes.DiceSimilarity:
stringMetric = new DiceSimilarity();
break;
case AlgorithmTypes.EuclideanDistance:
stringMetric = new EuclideanDistance();
break;
case AlgorithmTypes.JaccardSimilarity:
stringMetric = new JaccardSimilarity();
break;
case AlgorithmTypes.Jaro:
stringMetric = new Jaro();
break;
case AlgorithmTypes.JaroWinkler:
stringMetric = new JaroWinkler();
break;
case AlgorithmTypes.Levenstein:
stringMetric = new Levenstein();
break;
case AlgorithmTypes.MatchingCoefficient:
stringMetric = new MatchingCoefficient();
break;
case AlgorithmTypes.MongeElkan:
stringMetric = new MongeElkan();
break;
case AlgorithmTypes.NeedlemanWunch:
stringMetric = new NeedlemanWunch();
break;
case AlgorithmTypes.OverlapCoefficient:
stringMetric = new OverlapCoefficient();
break;
case AlgorithmTypes.QGramsDistance:
stringMetric = new QGramsDistance();
break;
case AlgorithmTypes.SmithWaterman:
stringMetric = new SmithWaterman();
break;
case AlgorithmTypes.SmithWatermanGotoh:
stringMetric = new SmithWatermanGotoh();
break;
case AlgorithmTypes.SmithWatermanGotohWindowedAffine:
stringMetric = new SmithWatermanGotohWindowedAffine();
break;
default:
stringMetric = new SmithWatermanGotoh();
break;
}
var similarity = stringMetric.GetSimilarity(str1.Trim(), str2.Trim());
return(similarity);
}
//Constructor
/// <summary>
/// Creates initialized instance
/// </summary>
/// <param name="sourceNeuron">Source neuron</param>
/// <param name="targetNeuron">Target neuron</param>
/// <param name="weight">Synapse weight</param>
public BaseSynapse(INeuron sourceNeuron,
INeuron targetNeuron,
double weight
)
{
//Neurons to be connected
SourceNeuron = sourceNeuron;
TargetNeuron = targetNeuron;
//Euclidean distance
Distance = EuclideanDistance.Compute(SourceNeuron.Placement.ReservoirCoordinates, TargetNeuron.Placement.ReservoirCoordinates);
//Weight sign and signal range conversion rules
if (SourceNeuron.Role == CommonEnums.NeuronRole.Input)
{
//Source is input neuron
if (TargetNeuron.OutputType == CommonEnums.NeuronSignalType.Spike)
{
//Target is spiking
//Ensure positive weight
Weight = Math.Abs(weight);
//Convert signal to <0,1>
_add = -SourceNeuron.OutputRange.Min;
_div = SourceNeuron.OutputRange.Span;
}
else
{
//Target is also analog
//Weight is unchanged
Weight = weight;
//No signal conversion
_add = 0;
_div = 1;
}
}
else
{
//Source is reservoir neuron
if (SourceNeuron.OutputType == CommonEnums.NeuronSignalType.Spike)
{
//Source reservoir neuron is spiking
if (TargetNeuron.OutputType == CommonEnums.NeuronSignalType.Spike)
{
//Target is also spiking
//Weight dependent on source neuron role
Weight = Math.Abs(weight) * ((SourceNeuron.Role == CommonEnums.NeuronRole.Excitatory) ? 1 : -1);
//No signal conversion
_add = 0;
_div = 1;
}
else
{
//Target is analog
//Weight is unchanged
Weight = weight;
//Convert signal to target neuron range
_add = (TargetNeuron.OutputRange.Min - SourceNeuron.OutputRange.Min);
_div = (SourceNeuron.OutputRange.Span / TargetNeuron.OutputRange.Span);
}
}
else
{
//Source reservoir neuron is analog
if (TargetNeuron.OutputType == CommonEnums.NeuronSignalType.Spike)
{
//Target is spiking
//Weight dependent on source neuron role
Weight = Math.Abs(weight) * ((SourceNeuron.Role == CommonEnums.NeuronRole.Excitatory) ? 1 : -1);
//Convert signal to <0,1>
_add = -SourceNeuron.OutputRange.Min;
_div = SourceNeuron.OutputRange.Span;
}
else
{
//Target is also analog
//Weight is unchanged
Weight = weight;
//No signal conversion
_add = 0;
_div = 1;
}
}
}
//Set Delay to 0 as default. It can be changed later by SetDelay method.
Delay = 0;
//Efficacy statistics
EfficacyStat = new BasicStat(false);
return;
}
public static double ApproximatelyEquals(this string firstWord, string secondWord, SimMetricType simMetricType = SimMetricType.Levenstein)
{
switch (simMetricType)
{
case SimMetricType.BlockDistance:
var sim2 = new BlockDistance();
return(sim2.GetSimilarity(firstWord, secondWord));
case SimMetricType.ChapmanLengthDeviation:
var sim3 = new ChapmanLengthDeviation();
return(sim3.GetSimilarity(firstWord, secondWord));
case SimMetricType.CosineSimilarity:
var sim4 = new CosineSimilarity();
return(sim4.GetSimilarity(firstWord, secondWord));
case SimMetricType.DiceSimilarity:
var sim5 = new DiceSimilarity();
return(sim5.GetSimilarity(firstWord, secondWord));
case SimMetricType.EuclideanDistance:
var sim6 = new EuclideanDistance();
return(sim6.GetSimilarity(firstWord, secondWord));
case SimMetricType.JaccardSimilarity:
var sim7 = new JaccardSimilarity();
return(sim7.GetSimilarity(firstWord, secondWord));
case SimMetricType.Jaro:
var sim8 = new Jaro();
return(sim8.GetSimilarity(firstWord, secondWord));
case SimMetricType.JaroWinkler:
var sim9 = new JaroWinkler();
return(sim9.GetSimilarity(firstWord, secondWord));
case SimMetricType.MatchingCoefficient:
var sim10 = new MatchingCoefficient();
return(sim10.GetSimilarity(firstWord, secondWord));
case SimMetricType.MongeElkan:
var sim11 = new MongeElkan();
return(sim11.GetSimilarity(firstWord, secondWord));
case SimMetricType.NeedlemanWunch:
var sim12 = new NeedlemanWunch();
return(sim12.GetSimilarity(firstWord, secondWord));
case SimMetricType.OverlapCoefficient:
var sim13 = new OverlapCoefficient();
return(sim13.GetSimilarity(firstWord, secondWord));
case SimMetricType.QGramsDistance:
var sim14 = new QGramsDistance();
return(sim14.GetSimilarity(firstWord, secondWord));
case SimMetricType.SmithWaterman:
var sim15 = new SmithWaterman();
return(sim15.GetSimilarity(firstWord, secondWord));
case SimMetricType.SmithWatermanGotoh:
var sim16 = new SmithWatermanGotoh();
return(sim16.GetSimilarity(firstWord, secondWord));
case SimMetricType.SmithWatermanGotohWindowedAffine:
var sim17 = new SmithWatermanGotohWindowedAffine();
return(sim17.GetSimilarity(firstWord, secondWord));
case SimMetricType.ChapmanMeanLength:
var sim18 = new ChapmanMeanLength();
return(sim18.GetSimilarity(firstWord, secondWord));
default:
var sim1 = new Levenstein();
return(sim1.GetSimilarity(firstWord, secondWord));
}
}
