DataForClustering.java

package kb.howtokb.clustering;

import java.util.List;

import kb.howtokb.clustering.sim.CategorySimilarity;
import kb.howtokb.clustering.sim.SimilarityComputation;
import kb.howtokb.taskframe.WikiHowTaskFrame;
import kb.howtokb.utils.AdjacencyBackedSparseMatrix;
import kb.howtokb.utils.SparseSimMatrix;


public class DataForClustering {
	
	/*Get similarity matrix of a cluster
	 * s(i,j) is similarity between i and j
	 * 0 at diagonal
	 */
	public static double[][] getSimilarityMatrix(List<WikiHowTaskFrame> list, CategorySimilarity cs) throws Exception{
		double [][] res = new double[list.size()][list.size()];
		for (int i=0; i<list.size()-1; i++){
			for (int j=i+1; j<list.size(); j++){
				double sim = SimilarityComputation.getSimilarity(cs, list.get(i), list.get(j));
				res[i][j] = sim;
				res[j][i] = sim;
				System.out.print(sim + " ");
			}
			System.out.println();
		}
		for (int i=0; i<list.size(); i++){
			res[i][i] = 0;
		}
		return res;
	}
	
	/*Get similarity matrix of a cluster
	 * s(i,j) is similarity between i and j
	 * 1 at diagonal
	 */
	public static double[][] getFullSimilarityMatrix(List<WikiHowTaskFrame> list, CategorySimilarity cs) throws Exception{
		double [][] res = new double[list.size()][list.size()];
		for (int i=0; i<list.size()-1; i++){
			for (int j=i+1; j<list.size(); j++){
				double sim = SimilarityComputation.getSimilarity(cs, list.get(i), list.get(j));
				res[i][j] = sim;
				res[j][i] = sim;
				System.out.print(sim + " ");
			}
			System.out.println();
		}
		for (int i=0; i<list.size(); i++){
			res[i][i] = 1;
		}
		return res;
	}
	
	/*Get sparse similarity matrix of a cluster
	 * s(i,j) is similarity between i and j
	 * 
	 */
	public static SparseSimMatrix getSparseSimilarityMatrix(List<WikiHowTaskFrame> list, CategorySimilarity cs, double threshold) throws Exception{
		SparseSimMatrix res = new SparseSimMatrix((float) threshold);
		for (int i=0; i<list.size()-1; i++){
			for (int j=i+1; j<list.size(); j++){
				double sim = SimilarityComputation.getSimilarity(cs, list.get(i), list.get(j));
				res.set(i, j, (float)sim);
				System.out.print(sim + " ");
			}
			System.out.println();
		}
		for (int i=0; i<list.size(); i++){
			res.set(i, i, (float) 1.0);
		}
		return res;
	}
	
	/*Get dynamic sparse similarity matrix of a cluster
	 * s(i,j) is similarity between i and j
	 * 
	 */
	public static AdjacencyBackedSparseMatrix getAdjacencyBackedSparseSimilarityMatrix(List<WikiHowTaskFrame> list, CategorySimilarity cs, double threshold) throws Exception{
		int n = list.size();
		AdjacencyBackedSparseMatrix res = new AdjacencyBackedSparseMatrix((float) threshold, n);
		for (int i=0; i<list.size()-1; i++){
			for (int j=i+1; j<list.size(); j++){
				double sim = SimilarityComputation.getSimilarity(cs, list.get(i), list.get(j));
				res.set(i, j, (float)sim);
				System.out.print(sim + " ");
			}
			System.out.println();
		}
		for (int i=0; i<list.size(); i++){
			res.set(i, i, (float) 1.0);
		}
		return res;
	}
	
//	/*
//	 * Get Unnormalized Laplacian Matrix
//	 */
//	public static double[][] getUnnormalizedLaplacianMatrix(double[][] simMatrix){
//		double[][] res = new double[simMatrix.length][simMatrix.length];
//		for (int i=0; i<simMatrix.length; i++){
//			double temp = 0;
//			for (int j=0; j<simMatrix[i].length; j++){
//				if (i != j){
//					res[i][j] = 0 - simMatrix[i][j];
//					temp += simMatrix[i][j];
//				}
//			}
//			res[i][i] = temp;
//		}
//		return res;
//	}
//	
//	/*
//	 * Get Normalized Laplacian Matrix
//	 */
//	public static double[][] getNormalizedLaplacianMatrix(double[][] simMatrix){
//		double[][] res = getUnnormalizedLaplacianMatrix(simMatrix);
//		for (int i=0; i<res.length - 1; i++){
//			for (int j=i+1; j < res.length; j++){
//				double tmp = res[i][j] / Math.sqrt(res[i][i] * res[j][j]);
//				res[i][j] = tmp;
//				res[j][i] = tmp;
//			}
//			res[i][i] = 1;
//		}
//		res[res.length - 1][res.length - 1] = 1;
//		return res;
//	}
//	
//	/*
//	 * EigenDecomposition
//	 * Return matrix to do k-means
//	 */
//	public static double[][] getMatrixForKMean(double[][] laplacian, int k){
//		double [][] res = new double[laplacian.length][k];
//		
//		//Doing eigen decomposition
//		EigenvalueDecomposition eigen = new EigenvalueDecomposition(
//				new DenseDoubleMatrix2D(laplacian));
//		double[][] U = eigen.getV().toArray();
//		System.out.println(eigen.getV().toString());
//		//Get only k eigenvectors corresponding to k smallest eigenvalues
//		// Y = (U_n U_n-1 ....U_n-k+1) where lamda_n <= lamda_n-1 <= ... <= lamda_n-k+1
//		for (int i=0; i < U.length; i++){
//			for (int j=U.length - 1; j >= (U.length - 1) - k + 1; j--){
//				res[i][U.length - 1 - j] = U[i][j];
//			}
//		}
//		
////		//Normalize rows of Y to use for k-means on row of Y
////		for (int i=0; i<res.length; i++){
////			double sum = Arrays.stream(res[i]).sum();
////			if (sum != 0){
////				for (int j=0; j<res[i].length; j++){
////					res[i][j] /= sum;
////					System.out.print(res[i][j] + " ");
////				}
////			}
////			System.out.println();
////		}
//		
//		//Norm 1
//		double[] tmp = new double[res.length];
//		for (int i=0; i<res.length; i++){
//			tmp[i] = 0;
//			for (int j=0; j<res[i].length; j++){
//				tmp[i] += Math.pow(res[i][j], 2);
//			}
//			tmp[i] = Math.sqrt(tmp[i]);
//		}
//		for (int i=0; i<res.length; i++){
//			if (tmp[i] != 0){
//				for (int j=0; j<res[i].length; j++){
//					res[i][j] /= tmp[i];
//					System.out.print(res[i][j] + " ");
//				}
//			}
//			System.out.println();
//		}
//		
//		return res;
//	}
//	
//	
//	/*
//	 * EigenDecomposition
//	 * Return matrix to do k-means
//	 */
//	public static double[][] getMatrixForKMean(CategorySimilarity cs, List<WikiHowTaskFrame> list, boolean unnormalized, int k) throws Exception{
//		//Get similarity matrix
//		double[][] simMatrix = getSimilarityMatrix(list, cs);
//		//Get laplacian matrix
//		double[][] laplacian;
//		if (unnormalized){
//			laplacian = getUnnormalizedLaplacianMatrix(simMatrix);
//		}else laplacian = getNormalizedLaplacianMatrix(simMatrix);
//		
//		return getMatrixForKMean(laplacian, k);
//	}
}