public void IGMNDataTest2()
{
IGMNData a = new IGMNData(igmn, new Vector(new double[] { 0, 0 }));
a.Gaussian.Covariance.Covariance.WriteToFile("orig_full_cov.txt");
a.Gaussian.Covariance.InverseCovariance.WriteToFile("orig_full_invcov.txt");
a.Gaussian.Mean.WriteToFile("orig_full_mean.txt");
Console.Out.WriteLine(a.Gaussian.Covariance.Determinant);
a.InputGaussian.Covariance.Covariance.WriteToFile("orig_inp_cov.txt");
a.InputGaussian.Covariance.InverseCovariance.WriteToFile("orig_inp_invcov.txt");
a.InputGaussian.Mean.WriteToFile("orig_inp_mean.txt");
Console.Out.WriteLine(a.InputGaussian.Covariance.Determinant);
a.RefineWithData(new Vector(new double[] { 1, 1 }), 0.5);
a.RefineWithData(new Vector(new double[] { 2, 2 }), 0.3333333333333333333);
a.RefineWithData(new Vector(new double[] { 3, 3 }), 0.25);
a.Gaussian.Covariance.Covariance.WriteToFile("mod_full_cov.txt");
a.Gaussian.Covariance.InverseCovariance.WriteToFile("mod_full_invcov.txt");
a.Gaussian.Mean.WriteToFile("mod_full_mean.txt");
Console.Out.WriteLine(a.Gaussian.Covariance.Determinant);
a.InputGaussian.Covariance.Covariance.WriteToFile("mod_inp_cov.txt");
a.InputGaussian.Covariance.InverseCovariance.WriteToFile("mod_inp_invcov.txt");
a.InputGaussian.Mean.WriteToFile("mod_inp_mean.txt");
Console.Out.WriteLine(a.InputGaussian.Covariance.Determinant);
}
public void Train(Vector x)
{
double sum = 0;
XTVector min = convertVector(x); min[min.Length - 1] = double.MinValue;
XTVector max = convertVector(x); max[max.Length - 1] = double.MaxValue;
List<IGMNData> possibleRelevant2 = xtree.rangeQuery(new MBR(min, max));
for (int i = 0; i < possibleRelevant2.Count; ++i)
{
sum += possibleRelevant2[i].Accumlator;
possibleRelevant2[i].Age++;
}
sum /= possibleRelevant2.Count;
List<IGMNData> possibleRelevant = xtree.pointQuery(convertVector(x));
List<double> posterior = new List<double>();
double sum2 = 0;
List<IGMNData> relevant = new List<IGMNData>();
for (int i = 0; i < possibleRelevant.Count; ++i)
{
double e;
double relev = possibleRelevant[i].Gaussian.Likelihood(x, out e) * (possibleRelevant[i].Accumlator / sum); // likelihood * prior
if (e > RelevanceLevel)
{
posterior.Add(relev);
sum2 += relev;
relevant.Add(possibleRelevant[i]);
}
}
if (relevant.Count == 0)
{
IGMNData newdata = new IGMNData(this, x);
cortical.Add(newdata);
xtree.Insert(newdata);
}
else
{
//relevance normalization
for (int i = 0; i < posterior.Count; ++i)
{
posterior[i] /= sum2;
}
//true relevant gaussians update
for (int i = 0; i < relevant.Count; ++i)
{
relevant[i].Accumlator += posterior[i];
double w = posterior[i] / relevant[i].Accumlator;
xtree.Delete(relevant[i]);
relevant[i].RefineWithData(x, w);//0.25|w
xtree.Insert(relevant[i]);
}
}
//delete unneeded gaussians
List<IGMNData> deleteList = new List<IGMNData>();
foreach (IGMNData g in possibleRelevant2)
{
if ((g.Age > AGE_MIN) && (g.Accumlator < ACCUMLATOR_MIN))
{
deleteList.Add(g);
}
}
foreach (IGMNData g in deleteList)
{
cortical.Remove(g);
xtree.Delete(g);
}
}
public void IGMNDataTest()
{
IGMNData a = new IGMNData(null, new Vector(new double[] { 1, 2, 3, 4, 5, 4, 3, 2, 1 }));
a.Gaussian.Covariance.Covariance.WriteToFile("orig_full_cov.txt");
a.Gaussian.Covariance.InverseCovariance.WriteToFile("orig_full_invcov.txt");
a.Gaussian.Mean.WriteToFile("orig_full_mean.txt");
Console.Out.WriteLine(a.Gaussian.Covariance.Determinant);
a.InputGaussian.Covariance.Covariance.WriteToFile("orig_inp_cov.txt");
a.InputGaussian.Covariance.InverseCovariance.WriteToFile("orig_inp_invcov.txt");
a.InputGaussian.Mean.WriteToFile("orig_inp_mean.txt");
Console.Out.WriteLine(a.InputGaussian.Covariance.Determinant);
a.RefineWithData(new Vector(new double[] { 4, 5, 1, 2, 8, 3, 3, 8, 1 }), 0.5);
a.RefineWithData(new Vector(new double[] { 8, 3, 3, 1, 2, 8, 1, 4, 5 }), 0.5);
a.Gaussian.Covariance.Covariance.WriteToFile("mod_full_cov.txt");
a.Gaussian.Covariance.InverseCovariance.WriteToFile("mod_full_invcov.txt");
a.Gaussian.Mean.WriteToFile("mod_full_mean.txt");
Console.Out.WriteLine(a.Gaussian.Covariance.Determinant);
a.InputGaussian.Covariance.Covariance.WriteToFile("mod_inp_cov.txt");
a.InputGaussian.Covariance.InverseCovariance.WriteToFile("mod_inp_invcov.txt");
a.InputGaussian.Mean.WriteToFile("mod_inp_mean.txt");
Console.Out.WriteLine(a.InputGaussian.Covariance.Determinant);
}
