예제 #1
0
        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}");
            }
        }
예제 #2
0
        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.);
            //                }
        }