public code v1

2026-05-23 00:32:57 +02:00
commit 759a8968a2
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+package flintstones.method.todim.phase.gathering;
+
+import java.util.Arrays;
+import java.util.HashMap;
+import java.util.Map.Entry;
+
+import javax.inject.Inject;
+
+import org.eclipse.e4.core.contexts.IEclipseContext;
+
+import flintstones.entity.method.phase.PhaseMethod;
+import flintstones.entity.problemelement.ProblemElementKey;
+import flintstones.entity.problemelement.entities.Alternative;
+import flintstones.entity.problemelement.entities.Criterion;
+import flintstones.entity.problemelement.entities.Expert;
+import flintstones.entity.problemelement.entities.ProblemElement;
+import flintstones.entity.problemelement.entities.ProblemElementHelper;
+import flintstones.entity.valuation.Valuation;
+import flintstones.helper.data.HashMatrix;
+import flintstones.model.domain.service.IDomainService;
+import flintstones.model.problemelement.service.IProblemElementService;
+import flintstones.model.valuation.service.IValuationService;
+import flintstones.valuation.numeric.NumericValuation;
+
+public class phaseTodim extends PhaseMethod {
+
+	@Inject
+	IEclipseContext context;
+
+	@Inject
+	IValuationService valuationService;
+
+	@Inject
+	IDomainService domainService;
+
+	@Inject
+	IProblemElementService problemService;
+
+	// Atributos
+	HashMap<ProblemElementKey, Valuation> mapVAluations;
+	HashMatrix<Expert, Criterion, Double> hmWeights;
+	Alternative[] allAlternatives;
+	Criterion[] allCriterion;
+	Expert[] allexperts;
+
+	double[][] initialMatrix;
+	double[][] normalizeMatrix;
+	double[] weights;
+	double[] relativeWeights;
+	double[] scaleNormalization;
+
+	double maxWeight;
+	double totalRelativeWeight;
+	
+	public phaseTodim() {
+	}
+
+	@Override
+	public String getName() {
+		return "TODIM";
+	}
+
+	@SuppressWarnings("unchecked")
+	public void initialize() {
+		ProblemElement[] pes = problemService.getAll(Alternative.Type);
+		allAlternatives = ProblemElementHelper.asAlternatives(pes);
+
+		pes = problemService.getAll(Criterion.Type);
+		allCriterion = ProblemElementHelper.asCriterions(pes);
+
+		pes = problemService.getAll(Expert.Type);
+		allexperts = ProblemElementHelper.asExperts(pes);
+
+		hmWeights = (HashMatrix<Expert, Criterion, Double>) this.importData("criterionWeights");
+		
+		this.weights = getWeights();
+	}
+
+	public Criterion[] getCriteria() {
+		return this.allCriterion;
+	}
+
+	public Alternative[] getAlternatives() {
+		return this.allAlternatives;
+	}
+
+	public String[] getAlternativesNames() {
+		String[] str = new String[allAlternatives.length];
+
+		for (int i = 0; i < allAlternatives.length; i++)
+			str[i] = allAlternatives[i].getCanonicalName();
+
+		return str;
+	}
+
+	public String[] getCriterionNames() {
+		String[] str = new String[allCriterion.length];
+
+		for (int i = 0; i < allCriterion.length; i++)
+			str[i] = allCriterion[i].getCanonicalName();
+
+		return str;
+	}
+
+	public void executeMethod(Expert e, double[] scaleNormalization) {
+		this.scaleNormalization = scaleNormalization;
+		
+		this.initialMatrix = this.getValuations(e);
+
+		normalize();
+
+		getRelativeWeight();
+
+		// exportamos los datos a la ventana de resultados.
+		this.exportData("normalizeMatrix", normalizeMatrix);
+		this.exportData("relativeWeights", relativeWeights);
+		this.exportData("maxWeight", maxWeight);
+		this.exportData("totalRelativeWeight", totalRelativeWeight);
+
+	}
+
+	public HashMap<ProblemElementKey, Valuation> getMapValuations(Expert e) {
+		ProblemElement pE = problemService.getByCanonicalName(Expert.Type, e.getCanonicalName());
+		mapVAluations = valuationService.getAllValuationsKVWith(pE);
+		return mapVAluations;
+	}
+
+	public double[][] getValuations(Expert e) {
+
+		double[][] valuation = new double[allAlternatives.length][allCriterion.length];
+
+		ProblemElement pE = problemService.getByCanonicalName(Expert.Type, e.getCanonicalName());
+		mapVAluations = valuationService.getAllValuationsKVWith(pE);
+
+		int a = 0, b = 0;
+		for (Entry<ProblemElementKey, Valuation> entry : mapVAluations.entrySet()) {
+			NumericValuation nV = (NumericValuation) entry.getValue();
+
+			for (int i = 0; i < allAlternatives.length; i++)
+				if (entry.getKey().getAlternative().getId().equals(allAlternatives[i].getId())) {
+					a = i;
+					break;
+				}
+
+			for (int i = 0; i < allCriterion.length; i++)
+				if (entry.getKey().getCriterion().getId().equals(allCriterion[i].getId())) {
+					b = i;
+					break;
+				}
+
+			valuation[a][b] = Double.valueOf(nV.getValue());
+			a = 0;
+			b = 0;
+		}
+
+		return valuation;
+	}
+
+	private double[] getWeights() {
+		double[] ws = new double[allCriterion.length];
+		double w;
+
+		for (int i = 0; i < allCriterion.length; i++) {
+			w = hmWeights.get(allexperts[0], allCriterion[i]);
+			ws[i] = w;
+		}
+
+		return ws;
+	}
+
+	private void normalize() {
+		normalizeMatrix = new double[allAlternatives.length][allCriterion.length];
+		for (int i = 0; i < initialMatrix.length; i++)
+			for (int j = 0; j < initialMatrix[i].length; j++)
+				normalizeMatrix[i][j] = initialMatrix[i][j] / scaleNormalization[j];
+	}
+
+	private void getRelativeWeight() {
+
+		maxWeight = Arrays.stream(weights).max().getAsDouble();
+
+		relativeWeights = new double[allCriterion.length];
+		for (int i = 0; i < relativeWeights.length; i++)
+			relativeWeights[i] = weights[i] / maxWeight;
+		
+		
+		totalRelativeWeight = 0.0f;
+		for (int i = 0; i < relativeWeights.length; i++)
+			totalRelativeWeight += relativeWeights[i];
+	}
+}