public async Task <IActionResult> GetForDriver(int id)
{
if (!User.Identity.IsAuthenticated)
{
return(Challenge());
}
Driver driver = await _driverRepository.GetAsync(id);
AuthorizationResult authResult = await _authorizationService.AuthorizeAsync(User, driver, "DriverInfoPolicy");
if (!authResult.Succeeded)
{
return(Forbid());
}
IEnumerable <Leg> legs = await _legRepository.ListForDriverAsync(id);
foreach (Leg leg in legs)
{
leg.Driver = null; // to prevent self-referencing loops during serialization
}
return(Ok(legs));
}
public async Task ComputeDriverStatistics(int id)
{
if (driverStats == null)
{
driverStats = new Dictionary <int, DriverStatisticResults>();
}
Driver driver = await _driverRepository.GetAsync(id);
if (driver == null)
{
return;
}
IEnumerable <Leg> legs = await _legRepository.ListForDriverAsync(id);
DriverStatisticResults results = new DriverStatisticResults();
results.DriverID = id;
results.Pickups = legs.Select(leg => leg.NumOfPassengersPickedUp).Sum();
results.MilesDriven = legs.Select(leg => leg.Distance).Sum();
if (await _legRepository.CountDriverLegsAsync(id) > 0)
{
results.AveragePickupDelay = legs.Select(leg =>
leg.StartTime.Subtract(leg.PickupRequestTime.GetValueOrDefault(leg.StartTime)).TotalMinutes).Average();
}
results.TotalFares = legs.Select(leg => leg.Fare * leg.NumOfPassengersAboard).Sum();
results.TotalCosts = legs.Select(leg => leg.GetTotalFuelCost()).Sum();
driverStats[id] = results;
}
// GET: Drivers/Details/5
public async Task <IActionResult> Details(int?id)
{
var driver = await _driverRepository.GetAsync(id.Value);
if (driver == null)
{
return(NotFound());
}
if (!User.Identity.IsAuthenticated)
{
return(Challenge());
}
var authResult = await _authorizationService.AuthorizeAsync(User, driver, "DriverInfoPolicy");
if (!authResult.Succeeded)
{
return(Forbid());
}
await _driverStatisticsService.ComputeDriverStatistics(id.Value);
// total pickups
int pickups = _driverStatisticsService.GetPickupsBy(id.Value);
ViewData["Pickups"] = pickups + " passenger pickup" + (pickups == 1 ? "" : "s");
// total miles driven
decimal milesDriven = _driverStatisticsService.GetMilesDrivenBy(id.Value);
ViewData["MilesDriven"] = milesDriven + " mile" + ((milesDriven > 0 && milesDriven < 1) ? "" : "s") + " driven";
// average pickup delay in minutes
if (_driverStatisticsService.GetAveragePickupDelayBy(id.Value).HasValue)
{
double avgPickupDelay = _driverStatisticsService.GetAveragePickupDelayBy(id.Value).Value;
ViewData["AveragePickupDelay"] = avgPickupDelay + " minute" + ((avgPickupDelay > 0 && avgPickupDelay < 1) ? "" : "s");
}
decimal totalFares = _driverStatisticsService.GetTotalFaresBy(id.Value);
ViewData["TotalFares"] = "$" + totalFares;
decimal totalCosts = _driverStatisticsService.GetTotalCostsBy(id.Value);
ViewData["TotalCosts"] = "$" + totalCosts;
if (id == null)
{
return(NotFound());
}
await _legRepository.ListForDriverAsync(id.Value);
return(View(driver));
}
/* Learn from legs with specified request times in a given date range */
public async Task LearnFromDates(DateTime from, DateTime to)
{
IEnumerable <Leg> legs = await _legRepository.ListForDriverAsync(_DriverID, leg =>
leg.StartTime.CompareTo(from) >= 0 &&
leg.StartTime.CompareTo(to) < 0 &&
leg.PickupRequestTime.HasValue);
double[][] trainingInputs = legs.Select(leg =>
{
return(new double[]
{
leg.StartTime.Subtract(leg.PickupRequestTime.Value).TotalMinutes,
leg.ArrivalTime.Subtract(leg.StartTime).TotalMinutes,
decimal.ToDouble(leg.Fare)
});
}).ToArray();
_logisticRegressions.Clear();
_logisticRegressions.AddRange(_logisticRegressionAnalyses.Select((lra, i) => {
double[] trainingOutputs =
legs.Select(leg => leg.NumOfPassengersPickedUp > i + 1 ? 1.0 : 0.0).ToArray();
return(lra.Learn(trainingInputs, trainingOutputs));
}));
}
/// <summary>
/// Train on number of clusters using gap statistic
/// </summary>
private async Task ComputeK(int maxK = 100, int B = 10, int driverID = 0, DateTime?startDate = null, DateTime?endDate = null)
{
double[] Wk = new double[maxK];
double[][] Wref_kb = new double[maxK][];
double[] Gap = new double[maxK];
double[] sd = new double[maxK];
KMeansClusterCollection[] clusterCollections = new KMeansClusterCollection[maxK];
// obtain dataset
IEnumerable <Leg> legs = driverID == 0 ? await _legRepository.ListAsync()
: await _legRepository.ListForDriverAsync(driverID);
if (startDate == null)
{
startDate = DateTime.MinValue;
}
if (endDate == null)
{
endDate = DateTime.MaxValue;
}
legs = legs.Where(leg => leg.StartTime.CompareTo(startDate) >= 0 && leg.StartTime.CompareTo(endDate) < 0);
double[][] dataset = GetDataset(legs);
// first cluster the dataset varying K
for (int k = 1; k <= maxK; k++)
{
KMeans kMeans = new KMeans(k)
{
// distance function for geographic coordinates
Distance = new GeographicDistance()
};
clusterCollections[k - 1] = kMeans.Learn(dataset);
double[][][] clusterData = ClusterPoints(dataset, k, clusterCollections[k - 1]);
// sum of pairwise distances
Wk[k - 1] = ComputeWk(clusterData, clusterCollections[k - 1]);
}
// then generate the reference data sets
double[] lowerBounds = new double[4];
double[] boxDimensions = new double[4];
for (int i = 0; i < 4; i++)
{
lowerBounds[i] = dataset.Select(l => l[i]).Min();
boxDimensions[i] = dataset.Select(l => l[i]).Max() - lowerBounds[i];
}
CorrectLongitudeBounds(lowerBounds, boxDimensions, 1);
CorrectLongitudeBounds(lowerBounds, boxDimensions, 3);
Random random = new Random();
for (int k = 1; k <= maxK; k++)
{
Wref_kb[k - 1] = new double[B];
for (int c = 0; c < B; c++)
{
double[][] refDataset = new double[dataset.Length][];
for (int i = 0; i < refDataset.Length; i++)
{
double[] dataPoint = new double[4];
for (int j = 0; j < 4; j++)
{
dataPoint[j] = random.NextDouble() * boxDimensions[j] + lowerBounds[j];
if ((j == 1 || j == 3) && dataPoint[j] > 180)
{
dataPoint[j] -= 360;
}
}
refDataset[i] = dataPoint;
}
// cluster reference dataset
KMeans refKmeans = new KMeans(k);
refKmeans.Distance = new GeographicDistance();
KMeansClusterCollection refClusters = refKmeans.Learn(refDataset);
// points in each cluster
double[][][] refClusterData = ClusterPoints(refDataset, k, refClusters);
// compute pairwise distance sum for refDataset
Wref_kb[k - 1][c] = ComputeWk(refClusterData, refClusters);
}
// compute gap statistic
double l_avg = Wref_kb[k - 1].Select(x => Log(x)).Average();
Gap[k - 1] = l_avg - Log(Wk[k - 1]);
sd[k - 1] = Sqrt(Wref_kb[k - 1].Select(x => (Log(x) - l_avg) * (Log(x) - l_avg)).Average());
// decide optimal k
if (k > 1 && Gap[k - 2] >= Gap[k - 1] - sd[k - 1])
{
ClusterCollection = clusterCollections[k - 2];
NumberOfClustersLastChanged = DateTime.Now;
return;
}
}
}
