//
// GET: /Markers/
//public IEnumerable<Marker> GetAllUrines()
//{
// var rnd = new Random();
// var randomIndexs = Enumerable.Range(0, Common.Constants.NumberOfPoints).Select(r => rnd.Next(6000)).ToList();
// var result = new DataProvider()
// .GetAntiSocialDataV1()
// .Where(a => a.Urine)
// .Select((a, i) => new
// {
// index = i,
// m = new Marker
// {
// lng = a.Location.Longitude,
// lat = a.Location.Latitude,
// r = new Random().Next(100)
// }
// })
// .Where(item => randomIndexs.Contains(item.index))
// .Select(item => item.m)
// .ToList();
// return result;
//}
public IEnumerable<Marker> GetAllUrines()
{
var result = new DataProvider()
.GetAntiSocialDataV2()
.Where(a => a.Category == "urine")
.Select((a, i) => new Marker
{
lng = a.Location.Longitude,
lat = a.Location.Latitude,
r = a.Radius * 100
})
.ToList();
return result;
}
//
// GET: /Markers/
public IEnumerable<Marker> GetAllMarkers()
{
var rnd = new Random();
var randomIndexs = Enumerable.Range(0, 100).Select(r => rnd.Next(6000)).ToList();
var result = new DataProvider()
.GetAntiSocialDataV1()
.Where(a => a.Urine)
.Select((a, i) => new
{
index = i,
m = new Marker
{
lng = a.Location.Longitude,
lat = a.Location.Latitude,
r = new Random().Next(100)
}
})
.Where(item => randomIndexs.Contains(item.index))
.Select(item => item.m)
.ToList();
return result;
}
public IEnumerable<Marker> GetAllIBloods()
{
var rnd = new Random(100);
var randomIndexs = Enumerable.Range(0, Common.Constants.NumberOfPoints).Select(r => rnd.Next(6000)).ToList();
var result = new DataProvider()
.GetAntiSocialDataV1()
.Where(a => a.Blood)
.Select((a, i) => new
{
index = i,
m = new Marker
{
lng = a.Location.Longitude,
lat = a.Location.Latitude,
r = 50
}
})
//.Where(item => randomIndexs.Contains(item.index))
.Select(item => item.m)
.ToList();
return result;
}
public IEnumerable<Marker> GetAllClusteredBloods()
{
var result = new DataProvider()
.GetAntiSocialDataV1()
.Select(a =>
new Marker
{
lng = a.Location.Longitude,
lat = a.Location.Latitude,
r = 1 * 100
})
.ToList();
var rawData = result.Select(r => new double[] { r.lng, r.lat }).ToArray();
var clusters = Clustring.KMean(rawData, 5, 30);
var clustersGrouped = clusters.GroupBy(a => a);
var itemsInClusters = new List<Marker>();
var index = 0;
foreach (var item in clusters)
{
var newResult = Clustring.DataPointsPerCluster(rawData, index, clusters, 50).ToList();
foreach (var r in newResult)
{
itemsInClusters.Add(new Marker { lat = r.Item2, lng = r.Item1, r = 100 });
}
index++;
}
return itemsInClusters;
}
public static IEnumerable<Marker> GetClusterMakers(int clusterNumber)
{
var result = new DataProvider()
.GetAntiSocialDataV1()
.Select(a =>
new Marker
{
lng = a.Location.Longitude,
lat = a.Location.Latitude,
r = 1 * 100
})
.ToList();
var rawData = result.Select(r => new double[] { r.lng, r.lat }).ToArray();
var clusters = Clustring.KMean(rawData, 5, 1000);
var clustersGrouped = clusters.GroupBy(a => a);
var itemsInClusters = new List<Marker>();
return Clustring.DataPointsPerCluster(rawData, clusterNumber, clusters, 1000)
.Select(r => new Marker { lat = r.Item2, lng = r.Item1, r = 100 }).ToList();
}
//static void Main(string[] args)
//{
// try
// {
// Console.WriteLine("\nBegin outlier data detection using k-means clustering demo\n");
// Console.WriteLine("Loading all (height-weight) data into memory");
// string[] attributes = new string[] { "Height", "Weight" };
// double[][] rawData = new double[20][]; // in most cases data will be in a text file or SQl table
// rawData[0] = new double[] { 65.0, 220.0 }; // if data won't fit into memory, stream through external storage
// rawData[1] = new double[] { 73.0, 160.0 };
// rawData[2] = new double[] { 59.0, 110.0 };
// rawData[3] = new double[] { 61.0, 120.0 };
// rawData[4] = new double[] { 75.0, 150.0 };
// rawData[5] = new double[] { 67.0, 240.0 };
// rawData[6] = new double[] { 68.0, 230.0 };
// rawData[7] = new double[] { 70.0, 220.0 };
// rawData[8] = new double[] { 62.0, 130.0 };
// rawData[9] = new double[] { 66.0, 210.0 };
// rawData[10] = new double[] { 77.0, 190.0 };
// rawData[11] = new double[] { 75.0, 180.0 };
// rawData[12] = new double[] { 74.0, 170.0 };
// rawData[13] = new double[] { 70.0, 210.0 };
// rawData[14] = new double[] { 61.0, 110.0 };
// rawData[15] = new double[] { 58.0, 100.0 };
// rawData[16] = new double[] { 66.0, 230.0 };
// rawData[17] = new double[] { 59.0, 120.0 };
// rawData[18] = new double[] { 68.0, 210.0 };
// rawData[19] = new double[] { 61.0, 130.0 };
// Console.WriteLine("\nRaw data:\n");
// ShowMatrix(rawData, rawData.Length, true);
// int numAttributes = attributes.Length; // 2 in this demo (height,weight)
// int numClusters = 5; // vary this to experiment (must be between 2 and number data tuples)
// int maxCount = 30; // trial and error
// Console.WriteLine("\nBegin clustering data with k = " + numClusters + " and maxCount = " + maxCount);
// int[] clustering = Cluster(rawData, numClusters, numAttributes, maxCount);
// Console.WriteLine("\nClustering complete");
// Console.WriteLine("\nClustering in internal format: \n");
// ShowVector(clustering, true); // true -> newline after display
// Console.WriteLine("\nClustered data:");
// ShowClustering(rawData, numClusters, clustering, true);
// double[] outlier = Outlier(rawData, clustering, numClusters, 0);
// Console.WriteLine("Outlier for cluster 0 is:");
// ShowVector(outlier, true);
// Console.WriteLine("\nEnd demo\n");
// Console.ReadLine();
// }
// catch (Exception ex)
// {
// Console.WriteLine(ex.Message);
// Console.ReadLine();
// }
//} // Main
static void Main(string[] args)
{
try
{
Console.WriteLine("\nBegin outlier data detection using k-means clustering demo\n");
Console.WriteLine("Loading all (height-weight) data into memory");
string[] attributes = new string[] { "Height", "Weight" };
double[][] rawData = new DataProvider().GetAntiSocialDataV1()
.Where(a => a.Vomit)
.Select(r => new double[] { r.Location.Longitude, r.Location.Latitude }).ToArray();
Console.WriteLine("\nRaw data:\n");
//ShowMatrix(rawData, rawData.Length, true);
int numAttributes = attributes.Length; // 2 in this demo (height,weight)
int numClusters = 7; // vary this to experiment (must be between 2 and number data tuples)
int maxCount = 30; // trial and error
Console.WriteLine("\nBegin clustering data with k = " + numClusters + " and maxCount = " + maxCount);
int[] clustering = Cluster(rawData, numClusters, numAttributes, maxCount);
Console.WriteLine("\nClustering complete");
var datapointsInEachCluster = clustering.GroupBy(a => a);
Console.WriteLine("\nClustering in internal format: \n");
foreach (var item in datapointsInEachCluster.OrderBy(a => a.Key))
{
Console.WriteLine(string.Format("{0}, {1}", item.Key, item.Count()));
}
Console.WriteLine(string.Format("number of datapoints {0}, number of items {1}", rawData.Length, clustering.Length));
Console.WriteLine("\nClustered data:");
ShowClustering(rawData, numClusters, clustering, true);
double[] outlier = Outlier(rawData, clustering, numClusters, 0);
Console.WriteLine("Outlier for cluster 0 is:");
//ShowVector(outlier, true);
var outliersWithinCluster = outlier.GroupBy(a => a);
Console.WriteLine("\nOutliers with each cluster: \n");
foreach (var item in outliersWithinCluster.OrderBy(a => a.Key))
{
Console.WriteLine(string.Format("{0}, {1}", item.Key, item.Count()));
}
Console.WriteLine("\nEnd demo\n");
Console.ReadLine();
}
catch (Exception ex)
{
Console.WriteLine(ex.Message);
Console.ReadLine();
}
}
