/// <summary>
/// Estimates the time it takes to travel from one waypoint to another.
/// </summary>
/// <param name="from">The start node.</param>
/// <param name="to">The destination node.</param>
/// <returns>The estimated time it takes to travel between the nodes.</returns>
private double EstimateTravelTime(TimeWaypoint from, Waypoint to)
{
// Init, if not done yet
if (_timeGraph == null)
{
_timeGraph = new TimeGraph(_instance);
}
// --> Calculate time needed for driving the given distance
double travelTime = Distances.CalculateEuclid(from.OriginalWaypoint, to, _instance.WrongTierPenaltyDistance) / _timeGraph.Speed;
// Check whether we reached the goal already, thus, eliminating the need for turning
if (from.OriginalWaypoint == to)
{
// No turning necessary - only consider time for driving
return(travelTime);
}
else
{
// In addition to the drive time also consider the effort for turning
return
// --> Calculate time needed for turning towards new orientation
(Math.Abs(Circle.GetOrientationDifference(from.Orientation,
Circle.GetOrientation(from.OriginalWaypoint.X, from.OriginalWaypoint.Y, to.X, to.Y))) / TimeGraph.PI2 * _timeGraph.TurnSpeed +
// --> Add time for driving
travelTime);
}
}
private static IEnumerable<TextLine> GetLines(List<Word> words, double maxDist, AngleBounds withinLine, int maxDegreeOfParallelism)
{
TextDirection textDirection = words[0].TextDirection;
var groupedIndexes = ClusteringAlgorithms.ClusterNearestNeighbours(words, 2, Distances.Euclidean,
(pivot, candidate) => maxDist,
pivot => pivot.BoundingBox.BottomRight, candidate => candidate.BoundingBox.BottomLeft,
pivot => true,
(pivot, candidate) => withinLine.Contains(Distances.Angle(pivot.BoundingBox.BottomRight, candidate.BoundingBox.BottomLeft)),
maxDegreeOfParallelism).ToList();
Func<IEnumerable<Word>, IReadOnlyList<Word>> orderFunc = l => l.OrderBy(x => x.BoundingBox.Left).ToList();
if (textDirection == TextDirection.Rotate180)
{
orderFunc = l => l.OrderByDescending(x => x.BoundingBox.Right).ToList();
}
else if (textDirection == TextDirection.Rotate90)
{
orderFunc = l => l.OrderByDescending(x => x.BoundingBox.Top).ToList();
}
else if (textDirection == TextDirection.Rotate270)
{
orderFunc = l => l.OrderBy(x => x.BoundingBox.Bottom).ToList();
}
for (var a = 0; a < groupedIndexes.Count; a++)
{
yield return new TextLine(orderFunc(groupedIndexes[a].Select(i => words[i])));
}
}
public static WarehouseResult Optimize(WarehouseParameters warehouseParameters, CancellationToken ct, Random random)
{
WarehouseManager warehouseManager = new WarehouseManager();
double[][] distancesMatrix = warehouseManager.CreateWarehouseDistancesMatrix(warehouseParameters.WarehousePath);
Distances.Create(distancesMatrix);
Orders orders = new Orders(warehouseParameters.OrdersPath, warehouseManager.WarehouseSize);
GeneticWarehouse geneticWarehouse = new GeneticWarehouse(warehouseParameters.WarehouseGeneticAlgorithmParameters,
warehouseManager.WarehouseSize,
(population) =>
{
double[] fitness = new double[population.Length];
warehouseParameters.FitnessGeneticAlgorithmParameters.WriteCsv = false;
Parallel.For(0, population.Length, i =>
{
var results = Fitness.CalculateAllOrdersFitness(orders, population[i],
warehouseParameters.FitnessGeneticAlgorithmParameters, random);
fitness[i] = results.Sum(x => x.Fitness);
});
return(fitness);
}, ct, random);
var z = geneticWarehouse.Run();
var result = Fitness.CalculateAllOrdersFitness(orders, z.BestChromosome, warehouseParameters.FitnessGeneticAlgorithmParameters, random);
z.FinalFitness = result.Sum(x => x.Fitness);
z.FinalOrderPaths = result.Select(x => x.Path).ToArray();
return(z);
}
private static IEnumerable<TextBlock> GetLinesGroups(TextLine[] lines, double maxDist, int maxDegreeOfParallelism)
{
/**************************************************************************************************
* We want to measure the distance between two lines using the following method:
* We check if two lines are overlapping horizontally.
* If they are overlapping, we compute the middle point (new X coordinate) of the overlapping area.
* We finally compute the Euclidean distance between these two middle points.
* If the two lines are not overlapping, the distance is set to the max distance.
**************************************************************************************************/
double euclidianOverlappingMiddleDistance(PdfLine l1, PdfLine l2)
{
var left = Math.Max(l1.Point1.X, l2.Point1.X);
var d = (Math.Min(l1.Point2.X, l2.Point2.X) - left);
if (d < 0) return double.MaxValue; // not overlapping -> max distance
return Distances.Euclidean(
new PdfPoint(left + d / 2, l1.Point1.Y),
new PdfPoint(left + d / 2, l2.Point1.Y));
}
var groupedIndexes = ClusteringAlgorithms.ClusterNearestNeighbours(lines,
euclidianOverlappingMiddleDistance,
(pivot, candidate) => maxDist,
pivot => new PdfLine(pivot.BoundingBox.BottomLeft, pivot.BoundingBox.BottomRight),
candidate => new PdfLine(candidate.BoundingBox.TopLeft, candidate.BoundingBox.TopRight),
pivot => true, (pivot, candidate) => true,
maxDegreeOfParallelism).ToList();
for (int a = 0; a < groupedIndexes.Count; a++)
{
yield return new TextBlock(groupedIndexes[a].Select(i => lines[i]).ToList());
}
}
public Distances GetClosestOffice(double longitude, double latitude)
{
List <Distances> distances = new List <Distances> ();
List <Office> offices = new List <Office> ();
bool checky = _cxt.Offices.Any();
DataSeed seed = new DataSeed(_cxt);
if (!checky)
{
if (seed.CreateOffices())
{
offices = _cxt.Offices.ToList();
}
}
else
{
offices = _cxt.Offices.ToList();
}
foreach (Office office in offices)
{
double dist = DistanceBetweenPlaces(longitude, latitude, office.longitude, office.latitude);
Distances distance = new Distances {
OfficeId = office.OfficeId,
distance = dist,
description = office.Description
};
distances.Add(distance);
}
List <Distances> distos = distances.OrderBy(disto => disto.distance).ToList();
return(distos.First());
}
public RegressionModel Train(BaseVector[] x, float[] y, Parameters param, int ntheads)
{
int k = param.GetParam <int>("Number of neighbours").Value;
IDistance distance = Distances.GetDistanceFunction(param);
return(new KnnRegressionModel(x, y, k, distance));
}
/// <summary>
/// Returns true if MatrixResponse instances are equal
/// </summary>
/// <param name="input">Instance of MatrixResponse to be compared</param>
/// <returns>Boolean</returns>
public bool Equals(MatrixResponse input)
{
if (input == null)
{
return(false);
}
return
((
Distances == input.Distances ||
Distances != null &&
Distances.SequenceEqual(input.Distances)
) &&
(
Times == input.Times ||
Times != null &&
Times.SequenceEqual(input.Times)
) &&
(
Weights == input.Weights ||
Weights != null &&
Weights.SequenceEqual(input.Weights)
) &&
(
Info == input.Info ||
(Info != null &&
Info.Equals(input.Info))
));
}
/// <summary>
/// Find the cities that are closest to this one.
/// </summary>
/// <param name="numberOfCloseCities">When creating the initial population of tours, this is a greater chance
/// that a nearby city will be chosen for a link. This is the number of nearby cities that will be considered close.</param>
public void FindClosestCities(int numberOfCloseCities)
{
double shortestDistance;
int shortestCity = 0;
double[] dist = new double[Distances.Count];
Distances.CopyTo(dist);
if (numberOfCloseCities > Distances.Count - 1)
{
numberOfCloseCities = Distances.Count - 1;
}
closeCities.Clear();
for (int i = 0; i < numberOfCloseCities; i++)
{
shortestDistance = Double.MaxValue;
for (int cityNum = 0; cityNum < Distances.Count; cityNum++)
{
if (dist[cityNum] < shortestDistance)
{
shortestDistance = dist[cityNum];
shortestCity = cityNum;
}
}
closeCities.Add(shortestCity);
dist[shortestCity] = Double.MaxValue;
}
}
public void EuclideanDistanceTest()
{
var pR = new Bounds[, ]
{
{ B(0.6f, 0.8f), B(0.6f, 0.6f), B(0.2f, 0.2f), B(0.6f, 0.8f), B(0.1f, 0.2f) },
{ B(0.0f, 0.3f), B(0.3f, 0.4f), B(0.6f, 0.6f), B(0.0f, 0.3f), B(0.1f, 0.2f) },
{ B(0.2f, 0.8f), B(0.4f, 0.8f), B(0.0f, 0.2f), B(0.2f, 0.8f), B(0.0f, 0.2f) },
{ B(0.6f, 0.7f), B(0.5f, 0.5f), B(0.3f, 0.3f), B(0.6f, 0.7f), B(0.3f, 0.3f) }
};
var cQ = new Bounds[, ]
{
{ B(0.4f, 0.4f), B(0.3f, 0.4f), B(0.1f, 0.2f), B(0.4f, 0.4f), B(0.1f, 0.2f) },
{ B(0.7f, 0.7f), B(0.1f, 0.4f), B(0.0f, 0.2f), B(0.7f, 0.7f), B(0.1f, 0.2f) },
{ B(0.3f, 0.3f), B(0.6f, 0.6f), B(0.2f, 0.2f), B(0.2f, 0.3f), B(0.1f, 0.2f) },
{ B(0.1f, 0.3f), B(0.2f, 0.4f), B(0.8f, 0.8f), B(0.2f, 0.3f), B(0.2f, 0.2f) },
{ B(0.1f, 0.3f), B(0.0f, 0.4f), B(0.2f, 0.2f), B(0.2f, 0.3f), B(0.8f, 0.8f) },
};
var exp = new float[, ]
{
{ 0.23f, 0.19f, 0.27f, 0.43f, 0.46f },
{ 0.27f, 0.43f, 0.24f, 0.12f, 0.36f },
{ 0.24f, 0.28f, 0.25f, 0.42f, 0.43f },
{ 0.20f, 0.18f, 0.26f, 0.37f, 0.39f }
};
var result = Distances.EuclideanSetDistance(pR, cQ);
AssertSetsEqual(exp, result);
}
/// <summary>
/// Lida com a movimentação do baiacu
/// </summary>
void Update()
{
// Se a parede não for nula
if (wall != null)
{
distances = wall.GetDistances(player.transform);
// Verifica se ele já está longe o suficiente dos corais para fazer os cálculos para os próximos corais
if (distances.horizontalDistance > maxDist)
{
GetNextWall();
}
// Verifica se é necessário inflar o baiacu para ele não cair nos corais
if (distances.lowerWallDistance < minDist)
{
player.Inflate();
}
}
// Caso a parede seja nula, pega referência da próxima parede
else
{
GetNextWall();
}
}
//Dijkstra algorithm - go backwards - from goal cell to root cell
public Distances DijkstraShortestPathTo(MazeCell goal)
{
MazeCell currentCell = goal;
// навигационная цепочка
Distances breadcrumbTrail = new Distances(this.startingCell);
breadcrumbTrail[currentCell] = this[currentCell];
// starightforward algo - For each cell along the path, the neighboring cell with the lowest distance will be the next step of the solution
while (currentCell != startingCell)
{
foreach (MazeCell neighbourCell in currentCell.Links()) //get all linked cells of current cell
{
if (this[neighbourCell] < this[currentCell]) //find linked cell with min distance (closest to the startingCell)
{
if (!breadcrumbTrail.ContainsKey(neighbourCell)) // add to dictionary if not exists in dictionary
{
breadcrumbTrail.Add(neighbourCell, this[neighbourCell]);
}
else
{
breadcrumbTrail[neighbourCell] = this[neighbourCell]; // replace distance if less distance value found
}
currentCell = neighbourCell; // switch current cell to the neighbour
break;
}
}
}
return(breadcrumbTrail);
}
public void HammingDistanceTest()
{
var pR = new Bounds[, ]
{
{ B(0.6f, 0.8f), B(0.6f, 0.6f), B(0.2f, 0.2f), B(0.6f, 0.8f), B(0.1f, 0.2f) },
{ B(0.0f, 0.3f), B(0.3f, 0.4f), B(0.6f, 0.6f), B(0.0f, 0.3f), B(0.1f, 0.2f) },
{ B(0.2f, 0.8f), B(0.4f, 0.8f), B(0.0f, 0.2f), B(0.2f, 0.8f), B(0.0f, 0.2f) },
{ B(0.6f, 0.7f), B(0.5f, 0.5f), B(0.3f, 0.3f), B(0.6f, 0.7f), B(0.3f, 0.3f) }
};
var cQ = new Bounds[, ]
{
{ B(0.4f, 0.4f), B(0.3f, 0.4f), B(0.1f, 0.2f), B(0.4f, 0.4f), B(0.1f, 0.2f) },
{ B(0.7f, 0.7f), B(0.1f, 0.4f), B(0.0f, 0.2f), B(0.7f, 0.7f), B(0.1f, 0.2f) },
{ B(0.3f, 0.3f), B(0.6f, 0.6f), B(0.2f, 0.2f), B(0.2f, 0.3f), B(0.1f, 0.2f) },
{ B(0.1f, 0.3f), B(0.2f, 0.4f), B(0.8f, 0.8f), B(0.2f, 0.3f), B(0.2f, 0.2f) },
{ B(0.1f, 0.3f), B(0.0f, 0.4f), B(0.2f, 0.2f), B(0.2f, 0.3f), B(0.8f, 0.8f) },
};
var exp = new float[, ]
{
{ 0.18f, 0.13f, 0.17f, 0.38f, 0.40f },
{ 0.19f, 0.34f, 0.18f, 0.09f, 0.27f },
{ 0.19f, 0.20f, 0.18f, 0.33f, 0.35f },
{ 0.19f, 0.14f, 0.22f, 0.33f, 0.35f }
};
var result = Distances.HammingSetDistance(pR, cQ);
AssertSetsEqual(exp, result);
}
//Метод для нахождения ближайшего города
public void NearestTownDistance(int nearTownCount)
{
double closestDistance;
int nearestTown = 0;
double[] way = new double[Distances.Count];
Distances.CopyTo(way);
if (nearTownCount > Distances.Count - 1)
{
nearTownCount = Distances.Count - 1;
}
nearTown.Clear();
for (int i = 0; i < nearTownCount; i++)
{
closestDistance = Double.MaxValue;
for (int townNum = 0; townNum < Distances.Count; townNum++)
{
if (way[townNum] < nearestTown)
{
closestDistance = way[townNum];
nearestTown = townNum;
}
nearTown.Add(nearestTown);
way[nearestTown] = Double.MaxValue;
}
}
}
public void Disconnect(int cell_id)
{
foreach (var pair in Distances.Where(pair => (pair.Key.Id1 == cell_id || pair.Key.Id2 == cell_id)).ToList())
{
Distances.Remove(pair.Key);
}
}
public float GetDistance(int cell_id_1, int cell_id_2)
{
float dist = -1;
Distances.TryGetValue(new CellIdPair(cell_id_1, cell_id_2), out dist);
return(dist);
}
private static void Normalize()
{
var prevCity = City.SaoPaulo;
double totalDistance = 0;
foreach (var d in WorldCupChronogram.Days)
{
var currentDistance = double.MaxValue;
var localCity = City.SaoPaulo;
foreach (var m in d.Matches)
{
var localDistance = Distances.GetDistance(prevCity, m.City);
if (localDistance < currentDistance)
{
currentDistance = localDistance;
localCity = m.City;
}
}
prevCity = localCity;
totalDistance += currentDistance;
Console.WriteLine(Convert.ToString(localCity) + " - " + totalDistance);
}
Console.ReadLine();
}
/// <summary>
/// Sets the footer line count.
/// </summary>
/// <param name="value">The value.</param>
protected override void SetFooterLineCount(int value)
{
if (value == 0)
{
footerExtent = 0;
}
else
{
if (Distances.Count <= value)
{
footerExtent = 0;
return;
}
int n = Distances.Count - value;
// The Total distance must be reduced by the padding size of the Distance total size. Then it should be calculated. This issue is occured in Nested Grid. SD 9312.
// this will give the exact size of the footer Extent when padding distance is reduced from total distance.
if (!(Distances is DistanceCounterSubset)) // Nested Grid cells in GridControl is not DistanceRangeCounterCollection type.
{
footerExtent = Distances.TotalDistance - ((DistanceRangeCounterCollection)(Distances)).paddingDistance - Distances.GetCumulatedDistanceAt(n);
}
else
{
footerExtent = Distances.TotalDistance - Distances.GetCumulatedDistanceAt(n);
}
//footerExtent = Distances.GetCumulatedDistanceAt(Math.Min(value, Distances.Count));
}
}
// Wave algo - fill all nearby cells with distance from first cell to the last cell on grid
public virtual Distances FindDistanceForAllReachableLinkedCells()
{
Distances distances = new Distances(this);
List <MazeCell> frontier = new List <MazeCell>()
{
this
};
// Will expands until last connected cell found
while (frontier.Count > 0)
{
var newFrontier = new List <MazeCell>();
foreach (var frontierCell in frontier)
{
foreach (var linkedCell in frontierCell.Links())
{
if (distances.ContainsKey(linkedCell)) // if cell is visited then skip
{
continue;
}
distances.Add(linkedCell, distances[frontierCell] + 1);
newFrontier.Add(linkedCell);
}
}
frontier = newFrontier;
}
return(distances);
}
/// <summary>
/// 这个方法有很多用途
/// 【1】寻找路径
/// 【2】寻找最优出入口
/// 【3】给迷宫上色（依据距离某一个点的距离）
/// </summary>
/// <param name="start"></param>
/// <returns></returns>
Distances FloodMaze(IDijkstraNode start)
{
Distances diss = new Distances(start);
List <IDijkstraNode> frontiers = new List <IDijkstraNode>();
frontiers.Add(start);
//Algorithm from the book Mazes for programmers.
while (frontiers.Count > 0)
{
List <IDijkstraNode> new_frontiers = new List <IDijkstraNode>();
foreach (var front in frontiers)
{
foreach (var conn in front.connections)
{
if (diss.Visited(conn) == false)
{
diss.SetDistanceReferenceTo(conn, front);
new_frontiers.Add(conn);
}
}
}
frontiers = new_frontiers;
}
return(diss);
}
/// <summary>
/// This is called to decide about potentially pending orders.
/// This method is being timed for statistical purposes and is also ONLY called when <code>SituationInvestigated</code> is <code>false</code>.
/// Hence, set the field accordingly to react on events not tracked by this outer skeleton.
/// </summary>
protected override void DecideAboutPendingOrders()
{
foreach (var order in _pendingOrders.Where(o => o.Positions.All(p => Instance.StockInfo.GetActualStock(p.Key) >= p.Value)).ToArray())
{
// Check all pods for the maximum number of picks that can be done with them
List <Pod> bestPods = new List <Pod>();
int bestPodPicks = -1;
foreach (var pod in Instance.Pods.Where(p => !p.InUse))
{
// Calculate picks that can potentially be done with the pod
int picks = order.Positions.Sum(pos => Math.Min(pod.CountAvailable(pos.Key), pos.Value));
// Check whether we found even more possible picks with this pod
if (bestPodPicks < picks)
{
bestPods.Clear();
bestPods.Add(pod);
bestPodPicks = picks;
}
else
{
// Check whether the current pod belongs into the winner group
if (bestPodPicks == picks)
{
bestPods.Add(pod);
}
}
}
// Choose station nearest to one of the best pods
OutputStation chosenStation = null;
double shortestDistance = double.PositiveInfinity;
foreach (var station in Instance.OutputStations.Where(s => s.Active && s.FitsForReservation(order)))
{
foreach (var pod in bestPods)
{
double distance;
switch (_config.DistanceRule)
{
case NearBestPodOrderBatchingDistanceRule.Euclid: distance = Distances.CalculateEuclid(pod, station, Instance.WrongTierPenaltyDistance); break;
case NearBestPodOrderBatchingDistanceRule.Manhattan: distance = Distances.CalculateManhattan(pod, station, Instance.WrongTierPenaltyDistance); break;
case NearBestPodOrderBatchingDistanceRule.ShortestPath: distance = Distances.CalculateShortestPathPodSafe(pod.Waypoint, station.Waypoint, Instance); break;
default: throw new ArgumentException("Unknown distance rule: " + _config.DistanceRule);
}
if (distance < shortestDistance)
{
shortestDistance = distance;
chosenStation = station;
}
}
}
// If we found a station, assign the bundle to it
if (chosenStation != null)
{
AllocateOrder(order, chosenStation);
}
}
}
/// <summary>
/// Дополняет текущий отель по данным из отеля извне
/// </summary>
/// <returns>Успешность объединения</returns>
public bool TryMerge(Hotel other)
{
if (other == null || other.Id != Id || other.Supplier != Supplier)
{
return(false);
}
if (!other.Name.IsNullOrEmpty())
{
Name ??= new Dictionary <Language, string>();
Name.AddRangeWithNonrecurringKey(other.Name);
}
if (!other.Info.IsNullOrEmpty())
{
Info ??= new Dictionary <Language, string>();
Info.AddRangeWithNonrecurringKey(other.Info);
}
if (!other.Address.IsNullOrEmpty())
{
Address ??= new Dictionary <Language, List <string> >();
Address.AddRangeWithNonrecurringKey(other.Address);
}
if (!other.Features.IsNullOrEmpty())
{
Features ??= new Dictionary <Language, List <Feature> >();
Features.AddRangeWithNonrecurringKey(other.Features);
}
if (!other.Description.IsNullOrEmpty())
{
Description ??= new Dictionary <Language, string>();
Description.AddRangeWithNonrecurringKey(other.Description);
}
if (!other.Distances.IsNullOrEmpty())
{
Distances ??= new Dictionary <Language, List <Distance> >();
Distances.AddRangeWithNonrecurringKey(other.Distances);
}
CheckInTime = string.IsNullOrEmpty(CheckInTime) ? other.CheckInTime : CheckInTime;
CheckOutTime = string.IsNullOrEmpty(CheckOutTime) ? other.CheckOutTime : CheckOutTime;
CityId = string.IsNullOrEmpty(CityId) ? other.CityId : CityId;
ResortId = string.IsNullOrEmpty(ResortId) ? other.ResortId : ResortId;
HotelChainName = string.IsNullOrEmpty(HotelChainName) ? other.HotelChainName : HotelChainName;
Photos = Photos.IsNullOrEmpty() ? other.Photos : Photos;
Phones = Phones.IsNullOrEmpty() ? other.Phones : Phones;
StarRating ??= other.StarRating;
PosLatitude ??= other.PosLatitude;
PosLongitude ??= other.PosLongitude;
CustomerRating ??= other.CustomerRating;
return(true);
}
/// <summary>
/// Calculates the time to move from one time-waypoint to another.
/// </summary>
/// <param name="from">The start node.</param>
/// <param name="to">The destination node.</param>
/// <returns>The time for traveling from one node to the other.</returns>
public double GetTimeNeededToTravel(TimeWaypoint from, TimeWaypoint to)
{
return
// --> Calculate time needed for turning towards new orientation
(Math.Abs(Circle.GetOrientationDifference(from.Orientation, to.Orientation)) / PI2 * TurnSpeed +
// --> Calculate time needed for driving the given distance
Distances.CalculateEuclid(from.OriginalWaypoint, to.OriginalWaypoint, Instance.WrongTierPenaltyDistance) / Speed);
}
private void DrawDistances(Distances d, double hCellSize, double vCellSize) =>
d.Cells.ForEach(cellDistance => {
TextBlock tb = new TextBlock {
Text = cellDistance.Distance.ToString(),
Margin = new Thickness(cellDistance.Cell.Col * hCellSize + 10, cellDistance.Cell.Row * vCellSize + 10, 0, 0)
};
MazeCanvas.Children.Add(tb);
});
public override ClassificationModel Train(BaseVector[] x, int[][] y, int ngroups, Parameters param, int nthreads,
Action <double> reportProgress)
{
int k = param.GetParam <int>("Number of neighbours").Value;
IDistance distance = Distances.GetDistanceFunction(param);
return(new KnnClassificationModel(x, y, ngroups, k, distance));
}
public float GetDistance(int cell_id_1, int cell_id_2)
{
if (Distances.TryGetValue(cell_id_1, out var distance_table) && distance_table.TryGetValue(cell_id_2, out var dist))
{
return(dist);
}
return(0);
}
public void find_dist_to_all_rovers(List <Rover> fidos)
{
Distances.Clear();
for (int i = 0; i < num_rovers; i++)
{
Distances.Add(find_dist_to_rover(i, fidos));
}
}
public override RegressionModel Train(BaseVector[] x, int[] nominal, double[] y, Parameters param, int ntheads, Action <double> reportProgress)
{
x = ClassificationMethod.ToOneHotEncoding(x, nominal);
int k = param.GetParam <int>("Number of neighbours").Value;
IDistance distance = Distances.GetDistanceFunction(param);
return(new KnnRegressionModel(x, y, k, distance));
}
public WarehousePairwiseProductFrequencyResolver(Random random, double probability, int participantsCount) : base(random, probability)
{
_participantsCount = participantsCount;
_distancesMatrix = Distances.GetInstance().DistancesMatrix;
_warehousePointsByLocation = Enumerable.Range(0, _distancesMatrix.Length)
.OrderBy(x => _distancesMatrix[0][x]).ToArray();
}
public Ship(int id, List <Link> links)
{
Id = id;
foreach (var link in links.Where(l => l.HasFactory(Id)))
{
Distances.Add(link.GetOtherId(Id), link.Distance);
}
}
public IActionResult locate()
{
OrderServices orderservice = new OrderServices(_cxt);
// 9.081999<br>Longitude: 8.675277
Distances dist = orderservice.GetClosestOffice(8.675277, 9.081999);
return(Json(dist));
}
public Distances CellDistances()
{
var distances = new Distances(this);
var frontier = new List<Cell>() { this };
while (frontier.Count > 0)
{
var newFrontier = new List<Cell>();
foreach (var cell in frontier)
{
foreach (var linked in cell.Links)
{
if (!distances.ContainsKey(linked))
{
distances.Add(linked, distances[cell] + 1);
newFrontier.Add(linked);
}
}
}
frontier = newFrontier;
}
return distances;
}
public int compareTo(Distances o){
if ((this.distanceValue - o.distanceValue)>0)
return 1;
else if ((this.distanceValue - o.distanceValue)==0)
return 0;
else return -1;
}
