Changeset 13160
 Timestamp:
 11/15/15 13:54:43 (5 years ago)
 Location:
 trunk/sources
 Files:

 6 edited
Legend:
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 Added
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trunk/sources/HeuristicLab.Algorithms.DataAnalysis.Views/3.4/HeuristicLab.Algorithms.DataAnalysis.Views3.4.csproj
r13100 r13160 167 167 <DependentUpon>NcaDimensionReductionView.cs</DependentUpon> 168 168 </Compile> 169 <Compile Include="OneRClassificationModelView.cs" /> 169 <Compile Include="OneRClassificationModelView.cs"> 170 <SubType>UserControl</SubType> 171 </Compile> 170 172 <Compile Include="OneRClassificationModelView.Designer.cs"> 171 173 <DependentUpon>OneRClassificationModelView.cs</DependentUpon> 
trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/GaussianProcessCovarianceOptimizationProblem.cs
r12954 r13160 26 26 using HeuristicLab.Data; 27 27 using HeuristicLab.Encodings.SymbolicExpressionTreeEncoding; 28 using HeuristicLab.Optimization; 28 29 using HeuristicLab.Parameters; 29 30 using HeuristicLab.Persistence.Default.CompositeSerializers.Storable; … … 233 234 } 234 235 235 public void ObjectiveFunction(double[] x, ref double func, double[] grad, object obj) { 236 public override void Analyze(ISymbolicExpressionTree[] trees, double[] qualities, ResultCollection results, IRandom random) { 237 if (!results.ContainsKey("Best Solution Quality")) { 238 results.Add(new Result("Best Solution Quality", typeof(DoubleValue))); 239 } 240 if (!results.ContainsKey("Best Tree")) { 241 results.Add(new Result("Best Tree", typeof(ISymbolicExpressionTree))); 242 } 243 if (!results.ContainsKey("Best Solution")) { 244 results.Add(new Result("Best Solution", typeof(GaussianProcessRegressionSolution))); 245 } 246 247 var bestQuality = qualities.Max(); 248 249 if (results["Best Solution Quality"].Value == null  bestQuality > ((DoubleValue)results["Best Solution Quality"].Value).Value) { 250 var bestIdx = Array.IndexOf(qualities, bestQuality); 251 var bestClone = (ISymbolicExpressionTree)trees[bestIdx].Clone(); 252 results["Best Tree"].Value = bestClone; 253 results["Best Solution Quality"].Value = new DoubleValue(bestQuality); 254 results["Best Solution"].Value = CreateSolution(bestClone, random); 255 } 256 } 257 258 private IItem CreateSolution(ISymbolicExpressionTree tree, IRandom random) { 259 // again tune the hyperparameters. 260 // this is suboptimal because 1) more effort and 2) we cannot be sure to find the same local optimum 261 var meanFunction = new MeanConst(); 262 var problemData = ProblemData; 263 var ds = problemData.Dataset; 264 var targetVariable = problemData.TargetVariable; 265 var allowedInputVariables = problemData.AllowedInputVariables.ToArray(); 266 var nVars = allowedInputVariables.Length; 267 var trainingRows = problemData.TrainingIndices.ToArray(); 268 var bestObjValue = new double[1] { double.MinValue }; 269 270 // use the same covariance function for each restart 271 var covarianceFunction = TreeToCovarianceFunction(tree); 272 // data that is necessary for the objective function 273 var data = Tuple.Create(ds, targetVariable, allowedInputVariables, trainingRows, (IMeanFunction)meanFunction, covarianceFunction, bestObjValue); 274 275 // allocate hyperparameters 276 var hyperParameters = new double[meanFunction.GetNumberOfParameters(nVars) + covarianceFunction.GetNumberOfParameters(nVars) + 1]; // mean + cov + noise 277 278 // initialize hyperparameters 279 hyperParameters[0] = ds.GetDoubleValues(targetVariable).Average(); // mean const 280 281 for (int i = 0; i < covarianceFunction.GetNumberOfParameters(nVars); i++) { 282 hyperParameters[1 + i] = random.NextDouble() * 2.0  1.0; 283 } 284 hyperParameters[hyperParameters.Length  1] = 1.0; // s² = exp(2), TODO: other inits better? 285 286 // use alglib.bfgs for hyperparameter optimization ... 287 double epsg = 0; 288 double epsf = 0.00001; 289 double epsx = 0; 290 double stpmax = 1; 291 int maxits = ConstantOptIterations; 292 alglib.mincgstate state; 293 alglib.mincgreport rep; 294 295 alglib.mincgcreate(hyperParameters, out state); 296 alglib.mincgsetcond(state, epsg, epsf, epsx, maxits); 297 alglib.mincgsetstpmax(state, stpmax); 298 alglib.mincgoptimize(state, ObjectiveFunction, null, data); 299 300 alglib.mincgresults(state, out hyperParameters, out rep); 301 302 if (rep.terminationtype >= 0) { 303 304 var model = new GaussianProcessModel(ds, targetVariable, allowedInputVariables, trainingRows, hyperParameters, meanFunction, covarianceFunction); 305 return model.CreateRegressionSolution(ProblemData); 306 } else return null; 307 } 308 309 private void ObjectiveFunction(double[] x, ref double func, double[] grad, object obj) { 236 310 // we want to optimize the model likelihood by changing the hyperparameters and also return the gradient for each hyperparameter 237 311 var data = (Tuple<IDataset, string, string[], int[], IMeanFunction, ICovarianceFunction, double[]>)obj; … … 252 326 var gradients = model.HyperparameterGradients; 253 327 Array.Copy(gradients, grad, gradients.Length); 254 } catch ( Exception) {328 } catch (ArgumentException) { 255 329 // building the GaussianProcessModel might fail, in this case we return the worst possible objective value 256 330 func = 1.0E+300; 
trunk/sources/HeuristicLab.Algorithms.DataAnalysis/3.4/GaussianProcess/GaussianProcessModel.cs
r13121 r13160 165 165 .ToArray(); 166 166 sqrSigmaNoise = Math.Exp(2.0 * hyp.Last()); 167 CalculateModel(ds, rows, scaleInputs); 167 try { 168 CalculateModel(ds, rows, scaleInputs); 169 } catch (alglib.alglibexception ae) { 170 // wrap exception so that calling code doesn't have to know about alglib implementation 171 throw new ArgumentException("There was a problem in the calculation of the Gaussian process model", ae); 172 } 168 173 } 169 174 … … 306 311 307 312 private IEnumerable<double> GetEstimatedValuesHelper(IDataset dataset, IEnumerable<int> rows) { 308 if (x == null) { 309 x = GetData(trainingDataset, allowedInputVariables, trainingRows, inputScaling); 310 } 311 int n = x.GetLength(0); 312 313 double[,] newX = GetData(dataset, allowedInputVariables, rows, inputScaling); 314 int newN = newX.GetLength(0); 315 316 var Ks = new double[newN, n]; 317 var mean = meanFunction.GetParameterizedMeanFunction(meanParameter, Enumerable.Range(0, newX.GetLength(1))); 318 var ms = Enumerable.Range(0, newX.GetLength(0)) 319 .Select(r => mean.Mean(newX, r)) 320 .ToArray(); 321 var cov = covarianceFunction.GetParameterizedCovarianceFunction(covarianceParameter, Enumerable.Range(0, newX.GetLength(1))); 322 for (int i = 0; i < newN; i++) { 323 for (int j = 0; j < n; j++) { 324 Ks[i, j] = cov.CrossCovariance(x, newX, j, i); 325 } 326 } 327 328 return Enumerable.Range(0, newN) 329 .Select(i => ms[i] + Util.ScalarProd(Util.GetRow(Ks, i), alpha)); 313 try { 314 if (x == null) { 315 x = GetData(trainingDataset, allowedInputVariables, trainingRows, inputScaling); 316 } 317 int n = x.GetLength(0); 318 319 double[,] newX = GetData(dataset, allowedInputVariables, rows, inputScaling); 320 int newN = newX.GetLength(0); 321 322 var Ks = new double[newN, n]; 323 var mean = meanFunction.GetParameterizedMeanFunction(meanParameter, Enumerable.Range(0, newX.GetLength(1))); 324 var ms = Enumerable.Range(0, newX.GetLength(0)) 325 .Select(r => mean.Mean(newX, r)) 326 .ToArray(); 327 var cov = covarianceFunction.GetParameterizedCovarianceFunction(covarianceParameter, Enumerable.Range(0, newX.GetLength(1))); 328 for (int i = 0; i < newN; i++) { 329 for (int j = 0; j < n; j++) { 330 Ks[i, j] = cov.CrossCovariance(x, newX, j, i); 331 } 332 } 333 334 return Enumerable.Range(0, newN) 335 .Select(i => ms[i] + Util.ScalarProd(Util.GetRow(Ks, i), alpha)); 336 } catch (alglib.alglibexception ae) { 337 // wrap exception so that calling code doesn't have to know about alglib implementation 338 throw new ArgumentException("There was a problem in the calculation of the Gaussian process model", ae); 339 } 330 340 } 331 341 332 342 public IEnumerable<double> GetEstimatedVariance(IDataset dataset, IEnumerable<int> rows) { 333 if (x == null) { 334 x = GetData(trainingDataset, allowedInputVariables, trainingRows, inputScaling); 335 } 336 int n = x.GetLength(0); 337 338 var newX = GetData(dataset, allowedInputVariables, rows, inputScaling); 339 int newN = newX.GetLength(0); 340 341 var kss = new double[newN]; 342 double[,] sWKs = new double[n, newN]; 343 var cov = covarianceFunction.GetParameterizedCovarianceFunction(covarianceParameter, Enumerable.Range(0, x.GetLength(1))); 344 345 if (l == null) { 346 l = CalculateL(x, cov, sqrSigmaNoise); 347 } 348 349 // for stddev 350 for (int i = 0; i < newN; i++) 351 kss[i] = cov.Covariance(newX, i, i); 352 353 for (int i = 0; i < newN; i++) { 354 for (int j = 0; j < n; j++) { 355 sWKs[j, i] = cov.CrossCovariance(x, newX, j, i) / Math.Sqrt(sqrSigmaNoise); 356 } 357 } 358 359 // for stddev 360 alglib.ablas.rmatrixlefttrsm(n, newN, l, 0, 0, false, false, 0, ref sWKs, 0, 0); 361 362 for (int i = 0; i < newN; i++) { 363 var sumV = Util.ScalarProd(Util.GetCol(sWKs, i), Util.GetCol(sWKs, i)); 364 kss[i] += sqrSigmaNoise; // kss is V(f), add noise variance of predictive distibution to get V(y) 365 kss[i] = sumV; 366 if (kss[i] < 0) kss[i] = 0; 367 } 368 return kss; 343 try { 344 if (x == null) { 345 x = GetData(trainingDataset, allowedInputVariables, trainingRows, inputScaling); 346 } 347 int n = x.GetLength(0); 348 349 var newX = GetData(dataset, allowedInputVariables, rows, inputScaling); 350 int newN = newX.GetLength(0); 351 352 var kss = new double[newN]; 353 double[,] sWKs = new double[n, newN]; 354 var cov = covarianceFunction.GetParameterizedCovarianceFunction(covarianceParameter, Enumerable.Range(0, x.GetLength(1))); 355 356 if (l == null) { 357 l = CalculateL(x, cov, sqrSigmaNoise); 358 } 359 360 // for stddev 361 for (int i = 0; i < newN; i++) 362 kss[i] = cov.Covariance(newX, i, i); 363 364 for (int i = 0; i < newN; i++) { 365 for (int j = 0; j < n; j++) { 366 sWKs[j, i] = cov.CrossCovariance(x, newX, j, i) / Math.Sqrt(sqrSigmaNoise); 367 } 368 } 369 370 // for stddev 371 alglib.ablas.rmatrixlefttrsm(n, newN, l, 0, 0, false, false, 0, ref sWKs, 0, 0); 372 373 for (int i = 0; i < newN; i++) { 374 var sumV = Util.ScalarProd(Util.GetCol(sWKs, i), Util.GetCol(sWKs, i)); 375 kss[i] += sqrSigmaNoise; // kss is V(f), add noise variance of predictive distibution to get V(y) 376 kss[i] = sumV; 377 if (kss[i] < 0) kss[i] = 0; 378 } 379 return kss; 380 } catch (alglib.alglibexception ae) { 381 // wrap exception so that calling code doesn't have to know about alglib implementation 382 throw new ArgumentException("There was a problem in the calculation of the Gaussian process model", ae); 383 } 369 384 } 370 385 } 
trunk/sources/HeuristicLab.ExtLibs/HeuristicLab.SharpDX/2.6.3/HeuristicLab.SharpDX2.6.3

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Plugin.cs

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trunk/sources/HeuristicLab.ExtLibs/HeuristicLab.SharpDX/2.6.3/HeuristicLab.SharpDX2.6.3/Properties

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trunk/sources/HeuristicLab.Tests/HeuristicLab3.3/Samples/GPMultiplexerSampleTest.cs
r12012 r13160 98 98 99 99 var osga = new OffspringSelectionGeneticAlgorithm(); 100 osga.Name = "Genetic Programming  Multiplexer 11 problem";100 osga.Name = "Genetic Programming  Multiplexer 11 Problem"; 101 101 osga.Description = "A genetic programming algorithm that solves the 11bit multiplexer problem."; 102 102 osga.Problem = problem;
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