//  JKHeatMapEngine.swift
//  JKTinder
//  Created by Joseph Kalash on 2/10/18.
//  Copyright © 2018 Joseph Kalash. All rights reserved.

import Alamofire
import DeepDiff

// Note: This manager only deals with profile IDs and does not store any other info. Once user selectes a node in the
// cluster data, the array of profileIDs are passed to the next VC where the profile details are fetched and displayed to the user

struct DistanceEstimate : Hashable {
    var distance : JKTrilateration.Circle   //The central point estimate
    var weight : Double                     //Estimated error on the distance

    // JKTrilateration.Circle already conforms to Hashable
    var hashValue: Int {
        return distance.hashValue
    }

    // JKTrilateration.Circle already conforms to Hashable thus conforms to Equatable as well
    static func == (lhs: DistanceEstimate, rhs: DistanceEstimate) -> Bool {
        return lhs.distance == rhs.distance && lhs.weight == rhs.weight
    }
}

struct LocationEstimate : Hashable {
    var estimatedDistances : Set<DistanceEstimate>  //The set of estimated distances
    var estimatedLocation : Vector2?

    // JKTrilateration.Circle already conforms to Hashable
    var hashValue: Int {
        return estimatedDistances.hashValue
    }

    // JKTrilateration.Circle already conforms to Hashable thus conforms to Equatable as well
    static func == (lhs: LocationEstimate, rhs: LocationEstimate) -> Bool {
        return lhs.estimatedDistances == rhs.estimatedDistances && lhs.estimatedLocation == rhs.estimatedLocation
    }

    mutating func locate() {

        var knownPos : [[Double]] = []
        var dist : [Double] = []

        let distances : [JKTrilateration.Circle] = self.estimatedDistances.flatMap({ $0.distance })
        for d in distances {
            knownPos.append([d.center.x, d.center.y])
            dist.append(d.radius)
        }

        //Convert distance to km
        dist = dist.map({ $0 / 1000 })

        let weights = self.estimatedDistances.flatMap({ $0.weight })

        let optimizer = NonLinearLeastSquareOptimizer(knownPos: knownPos, dist: &dist, weights: weights)
        self.estimatedLocation = optimizer.getLocation()
    }
}

//Continuously run :
//1. Fetch random point in the boundary defined by circle at center of mapView / radius zoomlevel
//2. Request nearby profiles and store all distance estimates in userLocations data structure
//3. Run Levmarq least square optimization on all profiles that have at least 3 entry points
//4. Refresh locatedUsers with new/updated estimated locations
//5. Refresh MapView with the new points

class JKHeatMapEngine {


    // Stores all points found for given profile IDs. Maps profile IDs to a set of estimates
    // Each estimated point distance has a weight; the error on the computation (used to regularize in ML training)
    // Also holds that have been located, alongside their location. Maps a profile id to a location.
    internal var userLocations : [String : LocationEstimate] = [:]

    public var runner : DataRequest? {
        didSet {
            oldValue?.cancel()  //Cancel any existing sequest when being reassigned
        }
    }

    //Repeatedly call the runner until either method called again or runner canceled by the controller
    public func fetchUsers(mapCenter : Vector2, mapZoomRadius : Double, callback : @escaping (_ diff: Array<Change<[String : Vector2]>>) -> Void) {
        let randomPoint = Utils.randomPointInCircle(JKTrilateration.Circle(center: mapCenter, radius: mapZoomRadius))

        runner = APIManager.current.fetchNearbyAt(loc: randomPoint, callback: { (json, error) in

            guard let dict = json, let profiles = dict["profiles"] as? [NSDictionary] else {
                self.fetchUsers(mapCenter: mapCenter, mapZoomRadius: mapZoomRadius, callback: callback)
                return
            }

            //Store to compute DeepDiff
            var profileIdsToLocate : [String] = []

            //A user either has a distance shared --> Append a point
            for i in 0 ..< profiles.count {
                if let profileId = profiles[i]["profileId"] as? String {
                    var estimate : DistanceEstimate? = nil
                    if let distance = profiles[i]["distance"] as? Double {
                        estimate = DistanceEstimate(distance: JKTrilateration.Circle(center: randomPoint, radius: distance), weight: 5.0)
                    }
                    else {
                        //Find the approximate distance
                        if let (distance, w) = Utils.estimateDistance(of: i, profiles: profiles) {
                            estimate = DistanceEstimate(distance: JKTrilateration.Circle(center: randomPoint, radius: distance), weight: w)
                        }
                    }

                    //If we somehow found no estimate, skip
                    if estimate == nil { continue }

                    //Check if user location already a record
                    if self.userLocations[profileId] != nil {
                       self.userLocations[profileId]!.estimatedDistances.insert(estimate!) //Appent to existing set

                        //Locates profiles whose distance found is anywhere between 3rd and 6th
                        if self.userLocations[profileId]!.estimatedDistances.count == 3 {
                            profileIdsToLocate.append(profileId)
                        }
                    }
                    else {
                        //Create initial set
                       self.userLocations[profileId] = LocationEstimate(estimatedDistances: [estimate!], estimatedLocation: nil)
                    }
                }
            }

            //Compute distances
            for id in profileIdsToLocate {
                self.userLocations[id]!.locate()
            }

            //Repeat as long as we were able to locate users
            if profileIdsToLocate.count > 0 {
                self.fetchUsers(mapCenter: mapCenter, mapZoomRadius: mapZoomRadius, callback: callback)
            }
        }, applyFilterParams: false)
    }

}