static void Main(string[] args)
{
Console.WriteLine("Similarity & Distance Examples");
Console.WriteLine("------------------------------");
Console.WriteLine(Environment.NewLine);
InsightVector u = new InsightVector(new double[] { 1, 2, 3, 4, 5 });
Console.WriteLine("Vector u:");
Console.WriteLine(u.ToString());
Console.WriteLine(Environment.NewLine);
InsightVector v = new InsightVector(new double[] { 5, 4, 3, 2, 1 });
Console.WriteLine("Vector v:");
Console.WriteLine(v.ToString());
Console.WriteLine(Environment.NewLine);
double distance = u.DistanceFrom(v);
Console.WriteLine("Euclidean distance (u, v) = {0}", distance.ToString("F4"));
Console.WriteLine(Environment.NewLine);
distance = u.DistanceFrom(v, DistanceMethod.HammingDistance);
Console.WriteLine("Hamming distance (u, v) = {0}", distance.ToString("F4"));
Console.WriteLine(Environment.NewLine);
distance = u.DistanceFrom(v, DistanceMethod.ManhattanDistance);
Console.WriteLine("Manhattan distance (u, v) = {0}", distance.ToString("F4"));
Console.WriteLine(Environment.NewLine);
double similarity = u.SimilarityTo(v);
Console.WriteLine("Cosine similarity (u, v) = {0}", similarity.ToString("F4"));
Console.WriteLine(Environment.NewLine);
similarity = u.SimilarityTo(v, SimilarityMethod.JaccardCoefficient);
Console.WriteLine("Jaccard coefficient (u, v) = {0}", similarity.ToString("F4"));
Console.WriteLine(Environment.NewLine);
similarity = u.SimilarityTo(v, SimilarityMethod.PearsonCorrelation);
Console.WriteLine("Pearson correlation (u, v) = {0}", similarity.ToString("F4"));
Console.WriteLine(Environment.NewLine);
Console.ReadKey();
Console.WriteLine(Environment.NewLine);
Console.WriteLine("Covariance & Correlation Examples");
Console.WriteLine("------------------------------");
Console.WriteLine(Environment.NewLine);
InsightMatrix matrix = new InsightMatrix(new double[,] {
{ 2.1, 8 }, { 2.5, 12 }, { 4.0, 14 }, { 3.6, 10 }
});
Console.WriteLine("Example matrix:");
Console.WriteLine(matrix.ToString());
Console.WriteLine(Environment.NewLine);
var cov = matrix.CovarianceMatrix();
Console.WriteLine("Covariance matrix:");
Console.WriteLine(cov.ToString());
Console.WriteLine(Environment.NewLine);
var cor = matrix.CorrelationMatrix();
Console.WriteLine("Correlation matrix:");
Console.WriteLine(cor.ToString());
Console.WriteLine(Environment.NewLine);
Console.ReadKey();
Console.WriteLine(Environment.NewLine);
Console.WriteLine("Feature Extraction Examples");
Console.WriteLine("------------------------------");
Console.WriteLine(Environment.NewLine);
InsightMatrix matrix2 = new InsightMatrix(new double[,] {
{ 2.5, 2.4 }, { 0.5, 0.7 }, { 2.2, 2.9 }, { 1.9, 2.2 }, { 3.1, 3.0 }, { 2.3, 2.7 },
{ 2.0, 1.6 }, { 1.0, 1.1 }, { 1.5, 1.6 }, { 1.1, 0.9 }
});
Console.WriteLine("First test matrix:");
Console.WriteLine(matrix2.ToString());
Console.WriteLine(Environment.NewLine);
var pca = matrix2.ExtractFeatures(ExtractionMethod.PrincipalComponentAnalysis, 1);
Console.WriteLine("Result of principal components analysis:");
Console.WriteLine(pca.ToString());
Console.WriteLine(Environment.NewLine);
var svd = matrix2.ExtractFeatures(ExtractionMethod.SingularValueDecomposition, 1);
Console.WriteLine("Result of singular value decomposition:");
Console.WriteLine(svd.ToString());
Console.WriteLine(Environment.NewLine);
InsightMatrix matrix3 = new InsightMatrix(new double[,] {
{ 4, 2, 1 }, { 2, 4, 1 }, { 2, 3, 1 }, { 3, 6, 1 }, { 4, 4, 1 },
{ 9, 10, 2 }, { 6, 8, 2 }, { 9, 5, 2 }, { 8, 7, 2 }, { 10, 8, 2 }
});
Console.WriteLine("Second test matrix:");
Console.WriteLine(matrix3.ToString());
Console.WriteLine(Environment.NewLine);
var lda = matrix3.ExtractFeatures(ExtractionMethod.LinearDiscriminantAnalysis);
Console.WriteLine("Result of linear discriminant analysis:");
Console.WriteLine(lda.ToString());
Console.WriteLine(Environment.NewLine);
Console.ReadKey();
Console.WriteLine(Environment.NewLine);
Console.WriteLine("Optimization Examples");
Console.WriteLine("------------------------------");
Console.WriteLine(Environment.NewLine);
var simpleHillClimber = new SimpleHillClimbing<double>();
var transforms = new List<Func<double, double>>();
transforms.Add(x => x + 1);
transforms.Add(x => x - 1);
transforms.Add(x => x + 0.3);
transforms.Add(x => x - 0.3);
transforms.Add(x => x + 0.1);
transforms.Add(x => x - 0.1);
var solution = simpleHillClimber.FindMaxima(
0,
transforms,
x => Math.Abs(x) - (x * x));
Console.WriteLine("Simple hill climber solution = {0}", solution.Solution);
Console.WriteLine("Simple hill climber score = {0}", solution.Score);
Console.WriteLine(Environment.NewLine);
var steepestAscentHillClimber = new SteepestAscentHillClimbing<double>();
var solution2 = steepestAscentHillClimber.FindMaxima(
0,
transforms,
x => Math.Abs(x) - (x * x));
Console.WriteLine("Steepest ascent hill climber solution = {0}", solution2.Solution);
Console.WriteLine("Steepest ascent hill climber score = {0}", solution2.Score);
Console.WriteLine(Environment.NewLine);
var stocasticHillClimber = new StochasticHillClimbing<double>();
var solution3 = stocasticHillClimber.FindMaxima(
0,
transforms,
x => Math.Abs(x) - (x * x));
Console.WriteLine("Stocastic hill climber solution = {0}", solution3.Solution);
Console.WriteLine("Stocastic hill climber score = {0}", solution3.Score);
Console.WriteLine(Environment.NewLine);
Console.ReadKey();
Console.WriteLine(Environment.NewLine);
Console.WriteLine("Data Loading Examples");
Console.WriteLine("------------------------------");
Console.WriteLine(Environment.NewLine);
InsightMatrix iris = DataLoader.ImportFromCSV("../../../data/iris.data", ',', false, true);
Console.WriteLine("Iris data set:");
Console.WriteLine(iris.ToString());
Console.ReadKey();
Console.WriteLine(Environment.NewLine);
Console.WriteLine("Clustering Examples");
Console.WriteLine("------------------------------");
Console.WriteLine(Environment.NewLine);
var clusterResults = iris.Cluster(ClusteringMethod.KMeans);
Console.WriteLine("K-Means");
Console.WriteLine("Distortion = {0}", clusterResults.Distortion);
Console.WriteLine("Centroids:");
Console.WriteLine(clusterResults.Centroids.ToString());
Console.ReadKey();
var clusterResults2 = iris.Cluster(ClusteringMethod.KMeans, DistanceMethod.EuclideanDistance, 3, 10);
Console.WriteLine("K-Means (best of 10)");
Console.WriteLine("Distortion = {0}", clusterResults.Distortion);
Console.WriteLine("Centroids:");
Console.WriteLine(clusterResults.Centroids.ToString());
Console.ReadKey();
Console.WriteLine(Environment.NewLine);
Console.WriteLine("Linear Regression");
Console.WriteLine("------------------------------");
Console.WriteLine(Environment.NewLine);
Console.WriteLine("Loading regression sample data...");
InsightMatrix data1 = DataLoader.ImportFromCSV("../../../data/ex1data1.txt", ',', false, false);
Console.WriteLine("Training model...");
var model1 = new LinearRegression();
model1.Train(data1);
Console.WriteLine("Model training complete. Parameters:");
Console.WriteLine(model1.Theta.ToString());
Console.WriteLine(model1.Error.ToString());
Console.WriteLine("Predicting output for first data point...");
data1 = data1.RemoveColumn(data1.ColumnCount - 1);
var prediction = model1.Predict(data1.Row(0));
Console.WriteLine("Prediction = {0}", prediction);
Console.ReadKey();
Console.WriteLine(Environment.NewLine);
Console.WriteLine("Logistic Regression");
Console.WriteLine("------------------------------");
Console.WriteLine(Environment.NewLine);
Console.WriteLine("Loading classification sample data...");
InsightMatrix data2 = DataLoader.ImportFromCSV("../../../data/ex2data1.txt", ',', false, false);
Console.WriteLine("Training model...");
var model2 = new LogisticRegression();
model2.Train(data2);
Console.WriteLine("Model training complete. Parameters:");
Console.WriteLine(model2.Theta.ToString());
Console.WriteLine(model2.Error.ToString());
Console.WriteLine("Predicting output for first data point...");
data2 = data2.RemoveColumn(data2.ColumnCount - 1);
var classification = model2.Classify(data2.Row(0));
Console.WriteLine("Classification = {0}", classification);
Console.ReadKey();
}
