private void init(string pid1, string pid2) { SetDistance(); PointFact pf1 = _Facts[pid1] as PointFact; PointFact pf2 = _Facts[pid2] as PointFact; string p1URI = pf1.Id; string p2URI = pf2.Id; float v = (pf1.Point - pf2.Point).magnitude; MMTTerm lhs = new OMA( new OMS(MMTURIs.Metric), new List <MMTTerm> { new OMS(p1URI), new OMS(p2URI) } ); MMTTerm valueTp = new OMS(MMTURIs.RealLit); MMTTerm value = new OMF(v); //see point label MMTValueDeclaration mmtDecl = new MMTValueDeclaration(this.Label, lhs, valueTp, value); AddFactResponse.sendAdd(mmtDecl, out this._URI); }
public string RunNMFbasedOMF(int maxEpoch, double learnRate, double regularization, int factorCount, List <double> quantizer, int topN = 0) { if (!ReadyForNumerical) { GetReadyForNumerical(); } StringBuilder log = new StringBuilder(); log.AppendLine(Utils.PrintHeading("NMF based OMF")); // NMF Prediction // Get ratings from scorer, for both train and test // R_all contains indexes of all ratings both train and test DataMatrix R_all = new DataMatrix(R_unknown.UserCount, R_unknown.ItemCount); R_all.MergeNonOverlap(R_unknown); R_all.MergeNonOverlap(R_train.IndexesOfNonZeroElements()); Utils.StartTimer(); DataMatrix R_predictedByNMF = NMF.PredictRatings(R_train, R_all, maxEpoch, learnRate, regularization, factorCount); log.AppendLine(Utils.StopTimer()); // OMF Prediction log.AppendLine(Utils.PrintHeading("Ordinal Matrix Factorization with NMF as scorer")); Utils.StartTimer(); Dictionary <Tuple <int, int>, List <double> > OMFDistributionByUserItem; DataMatrix R_predicted; log.AppendLine(OMF.PredictRatings(R_train.Matrix, R_unknown.Matrix, R_predictedByNMF.Matrix, quantizer, out R_predicted, out OMFDistributionByUserItem)); log.AppendLine(Utils.StopTimer()); // Numerical Evaluation log.AppendLine(Utils.PrintValue("RMSE", RMSE.Evaluate(R_test, R_predicted).ToString("0.0000"))); log.AppendLine(Utils.PrintValue("MAE", MAE.Evaluate(R_test, R_predicted).ToString("0.0000"))); // TopN Evaluation if (topN != 0) { var topNItemsByUser = ItemRecommendationCore.GetTopNItemsByUser(R_predicted, topN); for (int n = 1; n <= topN; n++) { log.AppendLine(Utils.PrintValue("NCDG@" + n, NCDG.Evaluate(RelevantItemsByUser, topNItemsByUser, n).ToString("0.0000"))); } for (int n = 1; n <= topN; n++) { log.AppendLine(Utils.PrintValue("MAP@" + n, MAP.Evaluate(RelevantItemsByUser, topNItemsByUser, n).ToString("0.0000"))); } } // Save OMFDistribution to file if (!File.Exists(GetDataFileName("RatingOMF_"))) { Utils.IO <Dictionary <Tuple <int, int>, List <double> > > .SaveObject(OMFDistributionByUserItem, GetDataFileName("RatingOMF_")); } return(log.ToString()); }
public unsafe void AddSection(ushort iSection, OMF flags, int offset, int cb) { var inst = ISymNGenWriter2Inst; var func = (delegate * unmanaged <IntPtr, ushort, OMF, int, int, int>)(*(*(void ***)inst + 4)); int hr = func(inst, iSection, flags, offset, cb); if (hr != 0) { Marshal.ThrowExceptionForHR(hr); } }
public override bool Equals(object obj) { if (!(obj is OMF)) { return(false); } OMF omf = (OMF)obj; return(kind.Equals(omf.kind) && f.Equals(omf.f)); }
private MMTDeclaration generateNot90DegreeAngleDeclaration(float val, string p1URI, string p2URI, string p3URI) { MMTTerm lhs = new OMA( new OMS(MMTURIs.Angle), new List <MMTTerm> { new OMS(p1URI), new OMS(p2URI), new OMS(p3URI) } ); MMTTerm valueTp = new OMS(MMTURIs.RealLit); MMTTerm value = new OMF(val); return(new MMTValueDeclaration(this.Label, lhs, valueTp, value)); }
void ISymNGenWriter.AddSection(ushort iSection, OMF flags, int offset, int cb) => AddSection(iSection, flags, offset, cb);
public string RunPrefNMFbasedOMF(int maxEpoch, double learnRate, double regularizationOfUser, double regularizationOfItem, int factorCount, List <double> quantizer, int topN) { if (!ReadyForOrdinal) { GetReadyForOrdinal(); } StringBuilder log = new StringBuilder(); log.AppendLine(Utils.PrintHeading("PrefNMF based OMF")); // =============PrefNMF prediction on Train+Unknown============ // Get ratings from scorer, for both train and test // R_all contains indexes of all ratings both train and test // DataMatrix R_all = new DataMatrix(R_unknown.UserCount, R_unknown.ItemCount); // R_all.MergeNonOverlap(R_unknown); //R_all.MergeNonOverlap(R_train.IndexesOfNonZeroElements()); //PrefRelations PR_unknown = PrefRelations.CreateDiscrete(R_all); // R_all is far too slow, change the data structure //Dictionary<int, List<Tuple<int, int>>> PR_unknown = new Dictionary<int, List<Tuple<int, int>>>(); //Dictionary<int, List<int>> PR_unknown_cache = new Dictionary<int, List<int>>(); Dictionary <int, List <int> > ItemsByUser_train = R_train.GetItemsByUser(); Dictionary <int, List <int> > ItemsByUser_unknown = R_unknown.GetItemsByUser(); Dictionary <int, List <int> > PR_unknown = new Dictionary <int, List <int> >(ItemsByUser_train); List <int> keys = new List <int>(ItemsByUser_train.Keys); foreach (var key in keys) { PR_unknown[key].AddRange(ItemsByUser_unknown[key]); } /* * foreach (var row in R_unknown.Matrix.EnumerateRowsIndexed()) * { * int indexOfUser = row.Item1; * PR_unknown_cache[indexOfUser] = new List<int>(); * Vector<double> itemsOfUser = row.Item2; * foreach (var item in itemsOfUser.EnumerateIndexed(Zeros.AllowSkip)) * { * PR_unknown_cache[indexOfUser].Add(item.Item1); * } * } * foreach (var row in R_train.Matrix.EnumerateRowsIndexed()) * { * int indexOfUser = row.Item1; * Vector<double> itemsOfUser = row.Item2; * foreach (var item in itemsOfUser.EnumerateIndexed(Zeros.AllowSkip)) * { * PR_unknown_cache[indexOfUser].Add(item.Item1); * } * } */ Utils.StartTimer(); SparseMatrix PR_predicted = PrefNMF.PredictPrefRelations(PR_train, PR_unknown, maxEpoch, learnRate, regularizationOfUser, regularizationOfItem, factorCount, quantizer); // Both predicted and train need to be quantized // otherwise OMF won't accept //PR_predicted.quantization(0, 1.0, // new List<double> { Config.Preferences.LessPreferred, // Config.Preferences.EquallyPreferred, Config.Preferences.Preferred }); DataMatrix R_predictedByPrefNMF = new DataMatrix(PR_predicted);// new DataMatrix(PR_predicted.GetPositionMatrix()); // PR_train itself is already in quantized form! //PR_train.quantization(0, 1.0, new List<double> { Config.Preferences.LessPreferred, Config.Preferences.EquallyPreferred, Config.Preferences.Preferred }); DataMatrix R_train_positions = new DataMatrix(PR_train.GetPositionMatrix()); R_train_positions.Quantization(quantizer[0], quantizer[quantizer.Count - 1] - quantizer[0], quantizer); log.AppendLine(Utils.StopTimer()); // =============OMF prediction on Train+Unknown============ log.AppendLine(Utils.PrintHeading("Ordinal Matrix Factorization with PrefNMF as scorer")); Utils.StartTimer(); Dictionary <Tuple <int, int>, List <double> > OMFDistributionByUserItem; DataMatrix R_predicted; log.AppendLine(OMF.PredictRatings(R_train_positions.Matrix, R_unknown.Matrix, R_predictedByPrefNMF.Matrix, quantizer, out R_predicted, out OMFDistributionByUserItem)); log.AppendLine(Utils.StopTimer()); // TopN Evaluation var topNItemsByUser = ItemRecommendationCore.GetTopNItemsByUser(R_predicted, topN); for (int n = 1; n <= topN; n++) { log.AppendLine(Utils.PrintValue("NCDG@" + n, NCDG.Evaluate(RelevantItemsByUser, topNItemsByUser, n).ToString("0.0000"))); } for (int n = 1; n <= topN; n++) { log.AppendLine(Utils.PrintValue("MAP@" + n, MAP.Evaluate(RelevantItemsByUser, topNItemsByUser, n).ToString("0.0000"))); } // Save OMFDistribution to file if (!File.Exists(GetDataFileName("PrefOMF_"))) { Utils.IO <Dictionary <Tuple <int, int>, List <double> > > .SaveObject(OMFDistributionByUserItem, GetDataFileName("PrefOMF_")); } return(log.ToString()); }