public Task <byte[]> Execute(byte[] data)
{
// prep the data
Int32 questionId = BitConverter.ToInt32(data, 0);
Console.WriteLine("Node task created with parameter: " + questionId);
return(Task.Factory.StartNew(() =>
{
// To prevent any x-threaded issues
Int32 q = questionId;
Random rnd = new Random(q);
// prep the regres algorithm
LinearRegression regres = new LinearRegression();
double[] y;
double[,] x;
double[] w; // weighting
using (SqlConnection conn = new SqlConnection(@"Data Source=kivu;Initial Catalog=StackoverflowAug2012;uid=sblackler;pwd=password"))
{
conn.Open();
var results = conn.Query <PostResult>(@"SELECT TOP 8000 id, PostScore, ViewCount FROM Posts WHERE AcceptedAnswerId IS NULL AND AnswerCount < 10 AND PostScore > (SELECT TOP 1 PostScore FROM Posts WHERE ID = @Q) ORDER BY PostScore DESC", new { Q = rnd.Next(0, 11750761) }).ToList();
y = results.Select(p => p.ID).ToArray();
Int32 i = 0;
x = new double[results.Count, results.Count];
foreach (var el in results)
{
for (Int32 j = 1; j < results.Count; j++)
{
x[i, j - 1] = (el.PostScore < 1 ? 1 : el.PostScore / el.ViewCount < 1 ? 1 : el.ViewCount) * j / 100;
}
}
}
w = new double[Math.Max(y.Length, x.Length)];
// Blank the array with the value 1
Extensions.MemSet(w, 1);
try
{
if (regres.Regress(y, x, w))
{
return BitConverter.GetBytes((int)regres.SEC[0]);
}
}
catch { return BitConverter.GetBytes(-1); }
// In case the regression fails.
return BitConverter.GetBytes(rnd.Next(0, 11750761));
}));
}
public List <double> Remove(List <double> Data, ref Stack <ITransformation> Transforms)
{
if (Transforms == null)
{
Transforms = new Stack <ITransformation>();
}
double[] y = new double[Data.Count];
double[,] x = new double[2, Data.Count];
double[] w = new double[Data.Count];
for (int j = 0; j < Data.Count; j++)
{
y[j] = GetY(Data[j]);
x[0, j] = 1; // constant term
x[1, j] = GetX(j + 1);
w[j] = 1.0;
}
double a = Double.MinValue;
double b = Double.MinValue;
LinearRegression lr = new LinearRegression();
if (lr.Regress(y, x, w))
{
a = lr.Coefficients[1];
b = lr.Coefficients[0];
}
List <double> detrendedData = (List <double>)Utilities.DeepClone(Data);
double lastX = 0.0;
for (int i = 0; i < Data.Count; i++)
{
double regression = GetRegression(a, b, (i + 1));
detrendedData[i] -= regression;
lastX = (i + 1);
}
Transforms.Push(GetTransform(a, b, lastX));
return(detrendedData);
}