Example #1
0
        public LWDecisionModel(McoModel mcoModel, bool _excludeNullValues, string _classifierName)
        {
            classifierName = _classifierName;
            classifiers    = new HashMap <string, LWClassifier>();
            ISet <string> mcoEntryObjectKeys = mcoModel.McoEntryObjectKeys;

            foreach (string key in mcoEntryObjectKeys)
            {
                if (key.EndsWith(".moo", StringComparison.Ordinal))
                {
                    string prefixFileName = key.Substring(0, key.Length - 4);
                    classifiers[prefixFileName] = new LWClassifier(mcoModel, prefixFileName, _excludeNullValues);
                }
            }
        }
Example #2
0
//JAVA TO C# CONVERTER WARNING: Method 'throws' clauses are not available in .NET:
//ORIGINAL LINE: public boolean predict(org.maltparser.core.feature.FeatureModel featureModel, org.maltparser.parser.history.action.ComplexDecisionAction decision, boolean one_prediction) throws org.maltparser.core.exception.MaltChainedException
        public bool predict(FeatureModel featureModel, ComplexDecisionAction decision, bool one_prediction)
        {
            if (decision.numberOfDecisions() > 2)
            {
                throw new MaltChainedException("Number of decisions is greater than two,  which is unsupported in the light-weight parser (lw.parser)");
            }
            featureModel.update();
            bool success = true;

            for (int i = 0; i < decision.numberOfDecisions(); i++)
            {
                LWClassifier classifier = null;
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final org.maltparser.parser.history.action.SingleDecision singleDecision = decision.getSingleDecision(i);
                SingleDecision singleDecision = decision.getSingleDecision(i);
//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final StringBuilder classifierString = new StringBuilder();
                StringBuilder classifierString = new StringBuilder();

//JAVA TO C# CONVERTER WARNING: The original Java variable was marked 'final':
//ORIGINAL LINE: final StringBuilder decisionModelString = new StringBuilder();
                StringBuilder decisionModelString = new StringBuilder();
                if (singleDecision.RelationToNextDecision == RelationToNextDecision.BRANCHED)
                {
                    decisionModelString.Append("bdm");
                }
                else if (singleDecision.RelationToNextDecision == RelationToNextDecision.SEQUANTIAL)
                {
                    decisionModelString.Append("sdm");
                }
                else
                {
                    decisionModelString.Append("odm");
                }
                decisionModelString.Append(i);
                string decisionSymbol = "";
                if (i == 1 && singleDecision.RelationToNextDecision == RelationToNextDecision.BRANCHED)
                {
                    decisionSymbol = singleDecision.DecisionSymbol;
                    decisionModelString.Append(decisionSymbol);
                }
                decisionModelString.Append('.');
                FeatureVector featureVector = featureModel.getFeatureVector(decisionSymbol, singleDecision.TableContainer.TableContainerName);

                if (featureModel.hasDivideFeatureFunction())
                {
                    SingleFeatureValue featureValue = (SingleFeatureValue)featureModel.DivideFeatureFunction.FeatureValue;
                    classifierString.Append(decisionModelString);
                    classifierString.Append(string.Format("{0:D3}", featureValue.IndexCode));
                    classifierString.Append('.');
                    classifierString.Append(classifierName);
                    classifier = classifiers[classifierString.ToString()];
                    if (classifier != null)
                    {
                        FeatureVector dividefeatureVector = featureModel.getFeatureVector("/" + featureVector.SpecSubModel.SubModelName);
                        success = classifier.predict(dividefeatureVector, singleDecision, one_prediction) && success;
                        continue;
                    }
                    classifierString.Length = 0;
                }

                classifierString.Append(decisionModelString);
                classifierString.Append(classifierName);
                classifier = classifiers[classifierString.ToString()];
                if (classifier != null)
                {
                    success = classifier.predict(featureVector, singleDecision, one_prediction) && success;
                }
                else
                {
                    singleDecision.addDecision(1);
                }
                if (!singleDecision.continueWithNextDecision())
                {
                    break;
                }
            }
            return(success);
        }