public void CorrelateSimulationCube(MathNet.Numerics.LinearAlgebra.Matrix <double> Rho)
{
Matrix <double> cholesky = Rho.Cholesky().Factor;
Action <UInt64, UInt64> pfunc = (i, k) =>
{
double[] s = new double[number_of_dims];
for (UInt64 j = 0; j < number_of_dims; ++j)
{
s[j] = this[i, j, k];
}
s = (cholesky * Vector <double> .Build.Dense(s)).ToArray();
for (UInt64 j = 0; j < number_of_dims; ++j)
{
this[i, j, k] = s[j];
}
};
using (WorkConsumer consumer = new utils.WorkConsumer(System.Environment.ProcessorCount - 1))//
{
for (UInt64 i = 0; i < number_of_sims; ++i)
{
for (UInt64 k = 0; k < number_of_steps; ++k)
{
UInt64 locali = i;
UInt64 localk = k;
consumer.Enqueue(() => pfunc(locali, localk));
}
}
consumer.waitforEmptyQueue();
}
}
static double Train(Network network, MathNet.Numerics.LinearAlgebra.Matrix <double> inputActivationValues, int expectedResult, Func <Matrix <double>, int, double> errorFunction)
{
network.Layers.First.Value.ActivationMatrix = inputActivationValues;
network.FeedForward();
return(errorFunction(network.Layers.Last.Value.ActivationMatrix, expectedResult));
}
/// <summary>
/// ranks a given document using semantic algorithem and BM25 algorithem
/// </summary>
/// <param name="R"></param>
/// <param name="N"></param>
/// <param name="k1"></param>
/// <param name="k2"></param>
/// <param name="b"></param>
/// <param name="queryWordsData"></param>
/// <param name="dl"></param>
/// <param name="avdl"></param>
/// <param name="docId"></param>
/// <param name="q"></param>
/// <param name="Dk_T"></param>
/// <param name="docsMap"></param>
/// <returns></returns>
public static double rankDoc(int R, int N, double k1, double k2, double b, Dictionary <string /*word in query*/, queryWordData /*struct of data*/> queryWordsData, double dl, double avdl, string docId, MathNet.Numerics.LinearAlgebra.Vector <double> q, MathNet.Numerics.LinearAlgebra.Matrix <double> Dk_T, Dictionary <string, int> docsMap)
{
double rank;
rank = BM25(R, N, k1, k2, b, queryWordsData, dl, avdl, docId);/*LSI(docId,q, Dk_T, docsMap);*/
return(rank);
}
