Changeset 12819
 Timestamp:
 07/30/15 18:09:15 (6 years ago)
 File:

 1 edited
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trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/GaussianProcessModel.cs
r12817 r12819 85 85 private double[] covarianceParameter; 86 86 87 [Storable] 88 private double[,] l; 89 90 [Storable] 91 private double[,] x; 87 private double[,] l; // used to be storable in previous versions (is calculated lazily now) 88 private double[,] x; // scaled training dataset, used to be storable in previous versions (is calculated lazily now) 89 90 // BackwardsCompatibility3.4 91 #region Backwards compatible code, remove with 3.5 92 [Storable(Name = "l")] // restore if available but don't store anymore 93 private double[,] l_storable { 94 set { this.l = value; } 95 get { 96 if (trainingDataset == null) return l; // this model has been created with an old version 97 else return null; // if the training dataset is available l should not be serialized 98 } 99 } 100 [Storable(Name = "x")] // restore if available but don't store anymore 101 private double[,] x_storable { 102 set { this.x = value; } 103 get { 104 if (trainingDataset == null) return x; // this model has been created with an old version 105 else return null; // if the training dataset is available x should not be serialized 106 } 107 } 108 #endregion 109 110 111 [Storable] 112 private IDataset trainingDataset; // it is better to store the original training dataset completely because this is more efficient in persistence 113 [Storable] 114 private int[] trainingRows; 115 92 116 [Storable] 93 117 private Scaling inputScaling; … … 101 125 this.covarianceFunction = cloner.Clone(original.covarianceFunction); 102 126 this.inputScaling = cloner.Clone(original.inputScaling); 127 this.trainingDataset = cloner.Clone(original.trainingDataset); 103 128 this.negativeLogLikelihood = original.negativeLogLikelihood; 104 129 this.targetVariable = original.targetVariable; … … 112 137 113 138 // shallow copies of arrays because they cannot be modified 139 this.trainingRows = original.trainingRows; 114 140 this.allowedInputVariables = original.allowedInputVariables; 115 141 this.alpha = original.alpha; … … 137 163 .ToArray(); 138 164 sqrSigmaNoise = Math.Exp(2.0 * hyp.Last()); 139 140 165 CalculateModel(ds, rows); 141 166 } 142 167 143 168 private void CalculateModel(IDataset ds, IEnumerable<int> rows) { 144 inputScaling = new Scaling(ds, allowedInputVariables, rows); 145 x = AlglibUtil.PrepareAndScaleInputMatrix(ds, allowedInputVariables, rows, inputScaling); 169 this.trainingDataset = (IDataset)ds.Clone(); 170 this.trainingRows = rows.ToArray(); 171 this.inputScaling = new Scaling(trainingDataset, allowedInputVariables, rows); 172 this.x = CalculateX(trainingDataset, allowedInputVariables, rows, inputScaling); 146 173 var y = ds.GetDoubleValues(targetVariable, rows); 147 174 148 175 int n = x.GetLength(0); 149 l = new double[n, n]; 150 151 // calculate means and covariances 176 177 // calculate cholesky decomposed (lower triangular) covariance matrix 178 var cov = covarianceFunction.GetParameterizedCovarianceFunction(covarianceParameter, Enumerable.Range(0, x.GetLength(1))); 179 this.l = CalculateL(x, cov, sqrSigmaNoise); 180 181 // calculate mean 152 182 var mean = meanFunction.GetParameterizedMeanFunction(meanParameter, Enumerable.Range(0, x.GetLength(1))); 153 183 double[] m = Enumerable.Range(0, x.GetLength(0)) … … 155 185 .ToArray(); 156 186 157 var cov = covarianceFunction.GetParameterizedCovarianceFunction(covarianceParameter, Enumerable.Range(0, x.GetLength(1))); 158 for (int i = 0; i < n; i++) { 159 for (int j = i; j < n; j++) { 160 l[j, i] = cov.Covariance(x, i, j) / sqrSigmaNoise; 161 if (j == i) l[j, i] += 1.0; 162 } 163 } 164 165 166 // cholesky decomposition 187 188 189 // calculate sum of diagonal elements for likelihood 190 double diagSum = Enumerable.Range(0, n).Select(i => Math.Log(l[i, i])).Sum(); 191 192 // solve for alpha 193 double[] ym = y.Zip(m, (a, b) => a  b).ToArray(); 194 167 195 int info; 168 196 alglib.densesolverreport denseSolveRep; 169 170 var res = alglib.trfac.spdmatrixcholesky(ref l, n, false);171 if (!res) throw new ArgumentException("Matrix is not positive semidefinite");172 173 // calculate sum of diagonal elements for likelihood174 double diagSum = Enumerable.Range(0, n).Select(i => Math.Log(l[i, i])).Sum();175 176 // solve for alpha177 double[] ym = y.Zip(m, (a, b) => a  b).ToArray();178 197 179 198 alglib.spdmatrixcholeskysolve(l, n, false, ym, out info, out denseSolveRep, out alpha); … … 230 249 } 231 250 251 private static double[,] CalculateX(IDataset ds, IEnumerable<string> allowedInputVariables, IEnumerable<int> rows, Scaling inputScaling) { 252 return AlglibUtil.PrepareAndScaleInputMatrix(ds, allowedInputVariables, rows, inputScaling); 253 } 254 255 private static double[,] CalculateL(double[,] x, ParameterizedCovarianceFunction cov, double sqrSigmaNoise) { 256 int n = x.GetLength(0); 257 var l = new double[n, n]; 258 259 // calculate covariances 260 for (int i = 0; i < n; i++) { 261 for (int j = i; j < n; j++) { 262 l[j, i] = cov.Covariance(x, i, j) / sqrSigmaNoise; 263 if (j == i) l[j, i] += 1.0; 264 } 265 } 266 267 // cholesky decomposition 268 var res = alglib.trfac.spdmatrixcholesky(ref l, n, false); 269 if (!res) throw new ArgumentException("Matrix is not positive semidefinite"); 270 return l; 271 } 272 232 273 233 274 public override IDeepCloneable Clone(Cloner cloner) { … … 258 299 259 300 private IEnumerable<double> GetEstimatedValuesHelper(IDataset dataset, IEnumerable<int> rows) { 301 if (x == null) { 302 this.x = CalculateX(trainingDataset, allowedInputVariables, trainingRows, inputScaling); 303 } 304 int n = x.GetLength(0); 305 260 306 var newX = AlglibUtil.PrepareAndScaleInputMatrix(dataset, allowedInputVariables, rows, inputScaling); 261 307 int newN = newX.GetLength(0); 262 int n = x.GetLength(0); 308 263 309 var Ks = new double[newN, n]; 264 310 var mean = meanFunction.GetParameterizedMeanFunction(meanParameter, Enumerable.Range(0, newX.GetLength(1))); … … 278 324 279 325 public IEnumerable<double> GetEstimatedVariance(IDataset dataset, IEnumerable<int> rows) { 326 if (x == null) { 327 this.x = CalculateX(trainingDataset, allowedInputVariables, trainingRows, inputScaling); 328 } 329 int n = x.GetLength(0); 330 280 331 var newX = AlglibUtil.PrepareAndScaleInputMatrix(dataset, allowedInputVariables, rows, inputScaling); 281 332 int newN = newX.GetLength(0); 282 int n = x.GetLength(0);283 333 284 334 var kss = new double[newN]; 285 335 double[,] sWKs = new double[n, newN]; 286 336 var cov = covarianceFunction.GetParameterizedCovarianceFunction(covarianceParameter, Enumerable.Range(0, x.GetLength(1))); 337 338 if (l == null) { 339 l = CalculateL(x, cov, sqrSigmaNoise); 340 } 287 341 288 342 // for stddev
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