package net.bmahe.genetics4j.samples.clustering;
   
   import java.util.HashMap;
   import java.util.HashSet;
   import java.util.Map;
   import java.util.Set;
   
   import org.apache.commons.lang3.Validate;
   
  import net.bmahe.genetics4j.core.Fitness;
  
  public class FitnessUtils {
  
  	// tag::a_i[]
  	private final static double a_i(final double[][] data, final double[][] distances,
  			final Map<Integer, Set<Integer>> clusterToMembers, final int clusterIndex, final int i) {
  		Validate.notNull(data);
  		Validate.notNull(distances);
  		Validate.notNull(clusterToMembers);
  
  		final var members = clusterToMembers.get(clusterIndex);
  
  		double sumDistances = 0.0;
  		for (final int memberIndex : members) {
  			if (memberIndex != i) {
  				sumDistances += distances[i][memberIndex];
  			}
  		}
  
  		return sumDistances / ((double) members.size() - 1.0d);
  	}
  	// end::a_i[]
  
  	// tag::b_i[]
  	private final static double b_i(final double[][] data, final double[][] distances,
  			final Map<Integer, Set<Integer>> clusterToMembers, final int numClusters, final int clusterIndex,
  			final int i) {
  		Validate.notNull(data);
  		Validate.notNull(distances);
  		Validate.notNull(clusterToMembers);
  		Validate.isTrue(numClusters > 0);
  		Validate.inclusiveBetween(0, numClusters - 1, clusterIndex);
  
  		double minMean = -1;
  		for (int otherClusterIndex = 0; otherClusterIndex < numClusters; otherClusterIndex++) {
  
  			if (otherClusterIndex != clusterIndex) {
  
  				final var members = clusterToMembers.get(otherClusterIndex);
  
  				if (members != null && members.size() > 0) {
  					double sumDistances = 0.0;
  					for (final int memberIndex : members) {
  						sumDistances += distances[i][memberIndex];
  					}
  
  					final double meanDistance = sumDistances / members.size();
  
  					if (minMean < 0 || meanDistance < minMean) {
  						minMean = meanDistance;
  					}
  				}
  			}
  		}
  
  		return minMean;
  	}
  	// end::b_i[]
  
  	public final static int[] assignDataToClusters(final double[][] data, double[][] distances,
  			final double[][] clusters) {
  
  		final double[] closestClusterDistance = new double[data.length];
  		final int[] closestClusterIndex = new int[data.length];
  
  		for (int i = 0; i < data.length; i++) {
  			closestClusterIndex[i] = -1;
  
  			final double dataX = data[i][0];
  			final double dataY = data[i][1];
  
  			for (int clusterIndex = 0; clusterIndex < clusters.length; clusterIndex++) {
  				final double clusterX = clusters[clusterIndex][0];
  				final double clusterY = clusters[clusterIndex][1];
  
  				final double distance = Math
  						.sqrt(((clusterX - dataX) * (clusterX - dataX)) + ((clusterY - dataY) * (clusterY - dataY)));
  
  				if (closestClusterIndex[i] == -1 || distance < closestClusterDistance[i]) {
  					closestClusterIndex[i] = clusterIndex;
  					closestClusterDistance[i] = distance;
  				}
  			}
  		}
  
  		return closestClusterIndex;
  	}
  
  	public final static double computeSilhouetteScore(final double[][] data, double[][] distances, final int numClusters,
 			final Map<Integer, Set<Integer>> clusterToMembers, final int[] closestClusterIndex, final int i) {
 
 		final int clusterI = closestClusterIndex[i];
 
 		double silhouetteScore = 0.0d;
 		if (clusterToMembers.getOrDefault(clusterI, Set.of())
 				.size() > 1) {
 			final double ai = a_i(data, distances, clusterToMembers, clusterI, i);
 			final double bi = b_i(data, distances, clusterToMembers, numClusters, clusterI, i);
 
 			silhouetteScore = (bi - ai) / Math.max(ai, bi);
 		}
 
 		return silhouetteScore;
 	}
 
 	public final static double computeSumSquaredErrors(final double[][] data, double[][] distances,
 			final double[][] clusters, final Map<Integer, Set<Integer>> clusterToMembers,
 			final int[] closestClusterIndex) {
 
 		double sumSquareErrors = 0.0d;
 		for (int i = 0; i < data.length; i++) {
 			final double[] cluster = clusters[closestClusterIndex[i]];
 
 			sumSquareErrors += (cluster[0] - data[i][0]) * (cluster[0] - data[i][0]);
 			sumSquareErrors += (cluster[1] - data[i][1]) * (cluster[1] - data[i][1]);
 		}
 
 		return sumSquareErrors;
 	}
 
 	// tag::fitness[]
 	public final static Fitness<Double> computeFitness(final int numDataPoints, final double[][] data,
 			double[][] distances, final int numClusters) {
 		Validate.notNull(data);
 		Validate.notNull(distances);
 		Validate.isTrue(numDataPoints > 0);
 		Validate.isTrue(numDataPoints == data.length);
 		Validate.isTrue(numDataPoints == distances.length);
 		Validate.isTrue(numClusters > 0);
 
 		return (genoType) -> {
 
 			final double[][] clusters = PhenotypeUtils.toPhenotype(genoType);
 
 			final int[] closestClusterIndex = assignDataToClusters(data, distances, clusters);
 
 			final Map<Integer, Set<Integer>> clusterToMembers = new HashMap<>();
 
 			for (int i = 0; i < numDataPoints; i++) {
 				final var members = clusterToMembers.computeIfAbsent(closestClusterIndex[i], k -> new HashSet<>());
 				members.add(i);
 			}
 
 			double sum_si = 0.0;
 			for (int i = 0; i < numDataPoints; i++) {
 				sum_si += computeSilhouetteScore(data, distances, numClusters, clusterToMembers, closestClusterIndex, i);
 			}
 
 			return sum_si;
 		};
 	}
 	// end::fitness[]
 
 	// Copy/pasted for the Clustering doc tag::fitness_with_sse[]
 	public final static Fitness<Double> computeFitnessWithSSE(final int numDataPoints, final double[][] data,
 			double[][] distances, final int numClusters) {
 		Validate.notNull(data);
 		Validate.notNull(distances);
 		Validate.isTrue(numDataPoints > 0);
 		Validate.isTrue(numDataPoints == data.length);
 		Validate.isTrue(numDataPoints == distances.length);
 		Validate.isTrue(numClusters > 0);
 
 		return (genoType) -> {
 
 			final double[][] clusters = PhenotypeUtils.toPhenotype(genoType);
 
 			final int[] closestClusterIndex = assignDataToClusters(data, distances, clusters);
 
 			final Map<Integer, Set<Integer>> clusterToMembers = new HashMap<>();
 
 			for (int i = 0; i < numDataPoints; i++) {
 				final var members = clusterToMembers.computeIfAbsent(closestClusterIndex[i], k -> new HashSet<>());
 				members.add(i);
 			}
 
 			double sum_si = 0.0;
 			for (int i = 0; i < numDataPoints; i++) {
 				sum_si += computeSilhouetteScore(data, distances, numClusters, clusterToMembers, closestClusterIndex, i);
 			}
 
 			final double sumSquaredError = computeSumSquaredErrors(data,
 					distances,
 					clusters,
 					clusterToMembers,
 					closestClusterIndex);
 
 			return sum_si + (1.0 / sumSquaredError);
 		};
 	}
 	// end::fitness_with_sse[]
 
 }