Changeset 13721 for trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/StudentTProcessModel.cs

Timestamp:

03/23/16 16:19:04 (9 years ago)

Author:

mkommend

Message:

#2591: Changed all GP covariance and mean functions to use int[] for column indices instead of IEnumerable<int>. Changed GP utils, GPModel and StudentTProcessModell as well to use fewer iterators and adapted unit tests to new interface.

File:

• trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/StudentTProcessModel.cs (modified) (5 diffs)

trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/StudentTProcessModel.cs

@@ -171 +171 @@
       try {
         CalculateModel(ds, rows, scaleInputs);
-      } catch (alglib.alglibexception ae) {
+      }
+      catch (alglib.alglibexception ae) {
         // wrap exception so that calling code doesn't have to know about alglib implementation
         throw new ArgumentException("There was a problem in the calculation of the Gaussian process model", ae);
@@ -188 +189 @@
 
       int n = x.GetLength(0);
+      var columns = Enumerable.Range(0, x.GetLength(1)).ToArray();
 
       // calculate cholesky decomposed (lower triangular) covariance matrix
-      var cov = covarianceFunction.GetParameterizedCovarianceFunction(covarianceParameter, Enumerable.Range(0, x.GetLength(1)));
+      var cov = covarianceFunction.GetParameterizedCovarianceFunction(covarianceParameter, columns);
       this.l = CalculateL(x, cov);
 
       // calculate mean
-      var mean = meanFunction.GetParameterizedMeanFunction(meanParameter, Enumerable.Range(0, x.GetLength(1)));
+      var mean = meanFunction.GetParameterizedMeanFunction(meanParameter, columns);
       double[] m = Enumerable.Range(0, x.GetLength(0))
         .Select(r => mean.Mean(x, r))
@@ -240 +242 @@
       double[] meanGradients = new double[meanFunction.GetNumberOfParameters(nAllowedVariables)];
       for (int k = 0; k < meanGradients.Length; k++) {
-        var meanGrad = Enumerable.Range(0, alpha.Length)
-        .Select(r => mean.Gradient(x, r, k));
-        meanGradients[k] = -Util.ScalarProd(meanGrad, alpha); //TODO not working yet, try to fix with gradient check
+        var meanGrad = new double[alpha.Length];
+        for (int g = 0; g < meanGrad.Length; g++)
+          meanGrad[g] = mean.Gradient(x, g, k);
+        meanGradients[k] = -Util.ScalarProd(meanGrad, alpha);//TODO not working yet, try to fix with gradient check
       }
 
@@ -336 +339 @@
         double[,] newX = GetData(dataset, allowedInputVariables, rows, inputScaling);
         int newN = newX.GetLength(0);
-
-        var Ks = new double[newN, n];
-        var mean = meanFunction.GetParameterizedMeanFunction(meanParameter, Enumerable.Range(0, newX.GetLength(1)));
+        var columns = Enumerable.Range(0, newX.GetLength(1)).ToArray();
+
+        var Ks = new double[newN][];
+        var mean = meanFunction.GetParameterizedMeanFunction(meanParameter, columns);
         var ms = Enumerable.Range(0, newX.GetLength(0))
         .Select(r => mean.Mean(newX, r))
         .ToArray();
-        var cov = covarianceFunction.GetParameterizedCovarianceFunction(covarianceParameter, Enumerable.Range(0, newX.GetLength(1)));
+        var cov = covarianceFunction.GetParameterizedCovarianceFunction(covarianceParameter, columns);
         for (int i = 0; i < newN; i++) {
+          Ks[i] = new double[n];
           for (int j = 0; j < n; j++) {
-            Ks[i, j] = cov.CrossCovariance(x, newX, j, i);
+            Ks[i][j] = cov.CrossCovariance(x, newX, j, i);
           }
         }
 
         return Enumerable.Range(0, newN)
-          .Select(i => ms[i] + Util.ScalarProd(Util.GetRow(Ks, i), alpha));
-      } catch (alglib.alglibexception ae) {
+          .Select(i => ms[i] + Util.ScalarProd(Ks[i], alpha));
+      }
+      catch (alglib.alglibexception ae) {
         // wrap exception so that calling code doesn't have to know about alglib implementation
         throw new ArgumentException("There was a problem in the calculation of the Gaussian process model", ae);
@@ -369 +375 @@
         var kss = new double[newN];
         double[,] sWKs = new double[n, newN];
-        var cov = covarianceFunction.GetParameterizedCovarianceFunction(covarianceParameter, Enumerable.Range(0, x.GetLength(1)));
-        
+        var cov = covarianceFunction.GetParameterizedCovarianceFunction(covarianceParameter, Enumerable.Range(0, x.GetLength(1)).ToArray());
+
         if (l == null) {
           l = CalculateL(x, cov);
         }
-        
+
         // for stddev 
         for (int i = 0; i < newN; i++)
           kss[i] = cov.Covariance(newX, i, i);
-        
+
         for (int i = 0; i < newN; i++) {
           for (int j = 0; j < n; j++) {
-            sWKs[j, i] = cov.CrossCovariance(x, newX, j, i) ;
+            sWKs[j, i] = cov.CrossCovariance(x, newX, j, i);
           }
         }
-        
+
         // for stddev 
         alglib.ablas.rmatrixlefttrsm(n, newN, l, 0, 0, false, false, 0, ref sWKs, 0, 0);
-        
+
         for (int i = 0; i < newN; i++) {
-          var sumV = Util.ScalarProd(Util.GetCol(sWKs, i), Util.GetCol(sWKs, i));
+          var col = Util.GetCol(sWKs, i).ToArray();
+          var sumV = Util.ScalarProd(col, col);
           kss[i] -= sumV;
-          kss[i] *= (nu + beta -2) / (nu + n - 2);
+          kss[i] *= (nu + beta - 2) / (nu + n - 2);
           if (kss[i] < 0) kss[i] = 0;
         }
         return kss;
-      } catch (alglib.alglibexception ae) {
+      }
+      catch (alglib.alglibexception ae) {
         // wrap exception so that calling code doesn't have to know about alglib implementation
         throw new ArgumentException("There was a problem in the calculation of the Gaussian process model", ae);