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.);
// }
}