Changeset 8933

Timestamp:

11/20/12 14:04:29 (12 years ago)

Author:

gkronber

Message:

#1902 corrected handling of length-parameter arrays in ARD functions and prevented stacking of mask covariance functions to make sure that the length-parameter and the enumerable of selected column indexes are equally long.

Location:

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

Files:

• CovarianceFunctions/CovarianceLinearArd.cs (modified) (1 diff)
• CovarianceFunctions/CovarianceMask.cs (modified) (2 diffs)
• CovarianceFunctions/CovarianceRationalQuadraticArd.cs (modified) (1 diff)
• CovarianceFunctions/CovarianceSquaredExponentialArd.cs (modified) (1 diff)
• Util.cs (modified) (4 diffs)

trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/CovarianceFunctions/CovarianceLinearArd.cs

@@ -99 +99 @@
       if (columnIndices == null) columnIndices = Enumerable.Range(0, x.GetLength(1));
 
+      int k = 0;
       foreach (int columnIndex in columnIndices) {
-        yield return -2.0 * x[i, columnIndex] * x[j, columnIndex] * inverseLength[columnIndex] * inverseLength[columnIndex];
+        yield return -2.0 * x[i, columnIndex] * x[j, columnIndex] * inverseLength[k] * inverseLength[k];
+        k++;
       }
     }

trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/CovarianceFunctions/CovarianceMask.cs

@@ -20 +20 @@
 #endregion
 
+using System;
 using System.Collections.Generic;
 using System.Linq;
@@ -111 +112 @@
 
     public double GetCovariance(double[,] x, int i, int j, IEnumerable<int> columnIndices) {
+      // cov mask overwrites the previously selected columnIndices 
+      // -> stacking of CovarianceMask is not supported
+      if (columnIndices != null && columnIndices.Count() != x.GetLength(1))
+        throw new InvalidOperationException("Stacking of masking covariance functions is not supported.");
+
       return cov.GetCovariance(x, i, j, selectedDimensions);
     }
 
     public IEnumerable<double> GetGradient(double[,] x, int i, int j, IEnumerable<int> columnIndices) {
+      if (columnIndices != null && columnIndices.Count() != x.GetLength(1))
+        throw new InvalidOperationException("Stacking of masking covariance functions is not supported.");
+
       return cov.GetGradient(x, i, j, selectedDimensions);
     }
 
     public double GetCrossCovariance(double[,] x, double[,] xt, int i, int j, IEnumerable<int> columnIndices) {
+      if (columnIndices != null && columnIndices.Count() != x.GetLength(1))
+        throw new InvalidOperationException("Stacking of masking covariance functions is not supported.");
+
       return cov.GetCrossCovariance(x, xt, i, j, selectedDimensions);
     }

trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/CovarianceFunctions/CovarianceRationalQuadraticArd.cs

@@ -148 +148 @@
                    : Util.SqrDist(x, i, j, inverseLength, columnIndices);
       double b = 1 + 0.5 * d / shape;
+      int k = 0;
       foreach (var columnIndex in columnIndices) {
-        yield return sf2 * Math.Pow(b, -shape - 1) * Util.SqrDist(x[i, columnIndex] * inverseLength[columnIndex], x[j, columnIndex] * inverseLength[columnIndex]);
+        yield return sf2 * Math.Pow(b, -shape - 1) * Util.SqrDist(x[i, columnIndex] * inverseLength[k], x[j, columnIndex] * inverseLength[k]);
+        k++;
       }
       yield return 2 * sf2 * Math.Pow(b, -shape);

trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/CovarianceFunctions/CovarianceSquaredExponentialArd.cs

@@ -122 +122 @@
                    ? 0.0
                    : Util.SqrDist(x, i, j, inverseLength, columnIndices);
-
+      int k = 0;
       foreach (var columnIndex in columnIndices) {
-        double sqrDist = Util.SqrDist(x[i, columnIndex] * inverseLength[columnIndex], x[j, columnIndex] * inverseLength[columnIndex]);
+        double sqrDist = Util.SqrDist(x[i, columnIndex] * inverseLength[k], x[j, columnIndex] * inverseLength[k]);
         yield return sf2 * Math.Exp(-d / 2.0) * sqrDist;
+        k++;
       }
 

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

@@ -48 +48 @@
       double ss = 0.0;
       if (columnIndices == null) columnIndices = Enumerable.Range(0, x.GetLength(1));
-      foreach (int k in columnIndices) {
-        double d = x[i, k] - xt[j, k];
+      foreach (int columnIndex in columnIndices) {
+        double d = x[i, columnIndex] - xt[j, columnIndex];
         ss += d * d;
       }
@@ -63 +63 @@
       if (columnIndices == null) columnIndices = Enumerable.Range(0, x.GetLength(1));
       int scaleIndex = 0;
-      foreach (int k in columnIndices) {
-        double d = x[i, k] - xt[j, k];
+      foreach (int columnIndex in columnIndices) {
+        double d = x[i, columnIndex] - xt[j, columnIndex];
         ss += d * d * scale[scaleIndex] * scale[scaleIndex];
         scaleIndex++;
       }
+      // must be at the end of scale after iterating over columnIndices
+      if (scaleIndex != scale.Length)
+        throw new ArgumentException("Lengths of scales and covariance functions does not match.");
       return ss;
     }
@@ -77 +80 @@
       double sum = 0.0;
       if (columnIndices == null) columnIndices = Enumerable.Range(0, x.GetLength(1));
-      foreach (int k in columnIndices) {
-        sum += x[i, k] * xt[j, k];
+      foreach (int columnIndex in columnIndices) {
+        sum += x[i, columnIndex] * xt[j, columnIndex];
       }
       return scale * scale * sum;
@@ -90 +93 @@
       if (columnIndices == null) columnIndices = Enumerable.Range(0, x.GetLength(1));
       int scaleIndex = 0;
-      foreach (int k in columnIndices) {
-        sum += x[i, k] * scale[scaleIndex] * xt[j, k] * scale[scaleIndex];
+      foreach (int columnIndex in columnIndices) {
+        sum += x[i, columnIndex] * scale[scaleIndex] * xt[j, columnIndex] * scale[scaleIndex];
         scaleIndex++;
       }
+      // must be at the end of scale after iterating over columnIndices
+      if (scaleIndex != scale.Length)
+        throw new ArgumentException("Lengths of scales and covariance functions does not match.");
+
       return sum;
     }