static void Main(string[] args) { Person[] people = new Person[10]; people[0] = new Adult("Davis", "Kim"); people[1] = new Child("Tom", "Kim"); foreach (var item in people.Where(x => x != null)) { Console.WriteLine($"{item.FirstName} {item.LastName}"); } }
static void Main(string[] args) { using (var ctx = new DeliveryContext()) { var adult = new Adult() { Id = "123456" }; ctx.Adults.Add(adult); ctx.SaveChanges(); } // Accord.Math.Random.Generator.Seed = 0; // // A common desire when doing clustering is to attempt to find how to // // weight the different components / columns of a dataset, giving them // // different importances depending on the end goal of the clustering task. // // Declare some observations // double[][] observations = // { // new double[] { -5, -2, -1 }, // new double[] { -5, -5, -6 }, // new double[] { 2, 1, 1 }, // new double[] { 1, 1, 2 }, // new double[] { 1, 2, 2 }, // new double[] { 3, 1, 2 }, // new double[] { 11, 5, 4 }, // new double[] { 15, 5, 6 }, // new double[] { 10, 5, 6 }, //}; // // Create a new K-Means algorithm // KMeans kmeans = new KMeans(k: 3) // { // // For example, let's say we would like to consider the importance of // // the first column as 0.1, the second column as 0.7 and the third 0.9 // Distance = new WeightedSquareEuclidean(new double[] { 0.1, 0.7, 1.1 }) // }; // // Compute and retrieve the data centroids // var clusters = kmeans.Learn(observations); // // Use the centroids to parition all the data // int[] labels = clusters.Decide(observations); // // for(int i=0;i<labels.Length;i++) // foreach(var a in clusters) // { // // Console.WriteLine(a.); // } }