/**
 * Copyright (C) 2013-2017 Vasilis Vryniotis 
 *
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 *     http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 */
package com.datumbox.framework.core.mathematics.distances;

import com.datumbox.framework.common.dataobjects.AssociativeArray;
import com.datumbox.framework.common.dataobjects.TypeInference;

import java.util.*;

/**
 * The Distance class provides methods to estimate various types of distances
 * between Associative Arrays.
 * 
 * @author Vasilis Vryniotis 
 */
public class Distance {
    
    /**
     * Estimates the euclidean distance of two Associative Arrays.
     * 
     * @param a1
     * @param a2
     * @return 
     */
    public static double euclidean(AssociativeArray a1, AssociativeArray a2) {
        Map columnDistances = columnDistances(a1, a2, null);
        
        double distance = 0.0;
        for(double columnDistance : columnDistances.values()) {
            distance+=(columnDistance*columnDistance);
        }
        
        return Math.sqrt(distance);
    }
    
    /**
     * Estimates the weighted euclidean distance of two Associative Arrays.
     * 
     * @param a1
     * @param a2
     * @param columnWeights
     * @return 
     */
    public static double euclideanWeighted(AssociativeArray a1, AssociativeArray a2, Map columnWeights) {
        Map columnDistances = columnDistances(a1, a2, columnWeights.keySet());
        
        double distance = 0.0;
        for(Map.Entry entry : columnDistances.entrySet()) {
            double columnDistance = entry.getValue();
            distance+=(columnDistance*columnDistance)*columnWeights.get(entry.getKey());
        }
        return Math.sqrt(distance);
    }
    
    /**
     * Estimates the manhattan distance of two Associative Arrays.
     * 
     * @param a1
     * @param a2
     * @return 
     */
    public static double manhattan(AssociativeArray a1, AssociativeArray a2) {
        Map columnDistances = columnDistances(a1, a2, null);
        
        double distance = 0.0;
        for(double columnDistance : columnDistances.values()) {
            distance+=Math.abs(columnDistance);
        }
        
        return distance;
    }
    
    /**
     * Estimates the weighted manhattan distance of two Associative Arrays.
     * 
     * @param a1
     * @param a2
     * @param columnWeights
     * @return 
     */
    public static double manhattanWeighted(AssociativeArray a1, AssociativeArray a2, Map columnWeights) {
        Map columnDistances = columnDistances(a1, a2, columnWeights.keySet());
        
        double distance = 0.0;
        for(Map.Entry entry : columnDistances.entrySet()) {
            distance+=Math.abs(entry.getValue())*columnWeights.get(entry.getKey());
        }
        
        return distance;
    }
    
    /**
     * Estimates the maximum distance of two Associative Arrays.
     * 
     * @param a1
     * @param a2
     * @return 
     */
    public static double maximum(AssociativeArray a1, AssociativeArray a2) {
        Map columnDistances = columnDistances(a1, a2, null);
        
        double distance=0.0;
        for(double columnDistance : columnDistances.values()) {
            columnDistance = Math.abs(columnDistance);
            if(distance columnDistances(AssociativeArray a1, AssociativeArray a2, Set