Merge branch 'release/v2.8.2'

# Conflicts:
#	pom.xml
#	src/main/java/es/ubu/lsi/ubumonitor/AppInfo.java

pom.xml

@@ -5,7 +5,8 @@
 
 	<groupId>es.ubu.lsi</groupId>
 	<artifactId>ubumonitor</artifactId>
-	<version>2.8.1</version>
+	<version>2.8.2</version>
+
 
 
 	<name>UBUMonitor</name>

src/main/java/es/ubu/lsi/ubumonitor/AppInfo.java

@@ -8,7 +8,8 @@
  */
 public class AppInfo {
 
-	public static final String VERSION = "2.8.1";
+
+	public static final String VERSION = "2.8.2";
 
 	public static final String APPLICATION_VERSION = "v" + VERSION;
 

src/main/java/es/ubu/lsi/ubumonitor/clustering/controller/AlgorithmExecuter.java

@@ -6,7 +6,6 @@
 import java.util.List;
 import java.util.Map;
 import java.util.stream.Collectors;
-import java.util.stream.Stream;
 
 import org.apache.commons.math3.exception.NumberIsTooSmallException;
 import org.apache.commons.math3.ml.clustering.Cluster;
@@ -28,10 +27,12 @@
  * Clase encargada de ejecutar un algoritmo de clustering.
  * 
  * @author Xing Long Ji
+ * @author Raúl Marticorena Sánchez
  *
  */
 public class AlgorithmExecuter {
 
+	private static final int MINUS_ONE = -1;
 	private Clusterer<UserData> clusterer;
 	private Distance distance;
 	private List<UserData> usersData;
@@ -184,40 +185,121 @@ public static List<Map<UserData, double[]>> clustersTo(int dim, List<ClusterWrap
 		if (clusters.isEmpty()) {
 			return Collections.emptyList();
 		}
-
-		List<double[]> centers = new ArrayList<>();
-		for (ClusterWrapper clusterWrapper : clusters) {
-			double[] center = clusterWrapper.getCenter();
-			if (center != null) {
-				centers.add(center);
-			}
-		}
-
-		double[][] matrix = Stream
-				.concat(clusters.stream().flatMap(ClusterWrapper::stream).map(UserData::getPoint), centers.stream())
+
+		// Only use the point data, not use centroids (corrected bug).
+		double[][] matrix = clusters.stream().flatMap(ClusterWrapper::stream).map(UserData::getPoint)
 				.toArray(double[][]::new);
-
-		PrincipalComponentAnalysis pca = new PrincipalComponentAnalysis();
-		List<Map<UserData, double[]>> points = new ArrayList<>();
-
+		
+		List<Map<UserData, double[]>> points = new ArrayList<>();			
+		// PCA with T-SNE 	
+		PrincipalComponentAnalysis pca = new PrincipalComponentAnalysis();		
 		if (matrix[0].length > dim) {
 			matrix = pca.pca(matrix, dim);
 		}
+
+		// Correct problem of twists with PCA projections
+		changeSignInMatrixProjection(matrix, pca, dim); // fix problem of symmetry wit PCA
+
+		// Add points
 		int i = 0;
 		for (List<UserData> list : clusters) {
 			Map<UserData, double[]> map = new LinkedHashMap<>();
 			for (UserData userData : list) {
 				map.put(userData, matrix[i++]);
 			}
 			points.add(map);
-		}
+		}		
 
-		// Add centroides
-		for (int j = 0; i < matrix.length; i++, j++) {
-			points.get(j).put(null, matrix[i]);
-		}
+		// We need to calculate the centroid projections in N dimensions...
+		double[][] centroids = calculateCentroids(clusters, matrix);
+		// Add centroids
+		for (int j = 0; j < centroids.length; j++) {
+			points.get(j).put(null, centroids[j]);
+		}		
 
 		return points;
 	}
-
+
+	/**
+	 * Calculate the projections in N dimensions for each centroid.
+	 * 
+	 * @param clusters clusters
+	 * @param matrix   data numerical points
+	 * @return current projections of centroids
+	 */
+	private static double[][] calculateCentroids(final List<ClusterWrapper> clusters, final double[][] matrix){
+		// number of clusters x number of dimensions after PCA
+		double[][] centroids = new double[clusters.size()][matrix[0].length];		
+
+		int position = 0;  // initial row position in data numerical matrix
+		int currentNumberOfCentroid = 0;
+		for (ClusterWrapper cluster : clusters) {
+			double[] centroid = obtainCentroid(matrix, position, position + cluster.size());
+			centroids[currentNumberOfCentroid++] = centroid; // add centroid 
+			position += cluster.size(); // move forward to the next cluster row
+		}		
+		return centroids;
+	}
+
+	/**
+	 * Obtain the projection in N dimensions for a cluster.
+	 * 
+	 * @param matrix data numerical points
+	 * @param begin initial row in the cluster
+	 * @param end	number of elements in the cluster
+	 * @return centroid coordinates
+	 */
+	private static double[] obtainCentroid(double[][] matrix, final int begin, final int end) {
+		int dimensions = matrix[0].length;
+		double[] centroid = new double[dimensions];
+		// add the x_i values in each dimension...
+		for (int i = begin; i < end; i++) {
+			for (int j = 0; j < dimensions; j++) {
+				centroid[j] += matrix[i][j];
+			}
+		}
+		// aritmetic mean for added values in each dimension
+		for (int j = 0; j < dimensions; j++) {
+			centroid[j] = centroid[j]/(end-begin);
+		}
+		return centroid;
+	}
+
+	/**
+	 * Corrects the sign problem with PCA.
+	 * 
+	 * The PCA algorithm can give different solutions with the only different of the
+	 * sign in the principal components. To correct the problem, we check the sign (of
+	 * the first value in the principal component) and in the case of being negative,
+	 * we change the corresponding sign in the projected column. In this way, it is 
+	 * guaranteed that the projections are always the same (without twists).
+	 * 
+	 * Thanks to César Ignacio García Osorio and Juan José Rodriguez Diez.
+	 * 
+	 * @param matrix matrix
+	 * @param pca pca
+	 * @param numberOfComponents number of components
+	 */
+	private static void changeSignInMatrixProjection(double[][] matrix, final PrincipalComponentAnalysis pca, final int numberOfComponents) {
+		for (int i = 0; i < numberOfComponents; i++) {
+			// first element sign in i component
+			boolean negativeComponent = pca.getBasisVector(i)[0] < 0 ? true : false; 
+			if (negativeComponent) {
+				changeSignInColumn(matrix, i); // Chaging sign in i-component (i-column)
+			}
+		}
+	}
+
+	/**
+	 * Change sign in all elements in column.
+	 * 
+	 * @param matrix matrix
+	 * @param column column to change sign
+	 */
+	private static void changeSignInColumn(double[][] matrix, final int column) {
+		for (int i = 0; i < matrix.length; i++) {
+			matrix[i][column] = matrix[i][column] * MINUS_ONE;
+		}
+	}
+
 }