public void ExpandRegionTest()
{
var points = new List <IPointIdFloat>()
{
new PointIdFloat(new List <float>()
{
1.5f, 1.5f, 1.5f
}),
new PointIdFloat(new List <float>()
{
1, 1, 1
}),
new PointIdFloat(new List <float>()
{
2, 1, 1
}),
new PointIdFloat(new List <float>()
{
1, 2, 1
}),
new PointIdFloat(new List <float>()
{
1, 1, 2
}),
new PointIdFloat(new List <float>()
{
3, 3, 3
}),
new PointIdFloat(new List <float>()
{
2, 2, 2
})
};
PointIdFloat.SetIds(points);
var clusters = new Dictionary <long, int>();
var visited = new HashSet <long>();
var p = points[0];
float epsilon = 1f;
var neighbours = DBScan.RegionQuery(points, p, epsilon);
Assert.IsTrue(neighbours.Contains(p));
DBScan.ExpandCluster(clusters, visited, points, p, neighbours.ToList(), 0, epsilon, 3);
Assert.IsTrue(clusters.ContainsKey(p.id));
foreach (var q in neighbours)
{
Assert.IsTrue(clusters.ContainsKey(q.id));
Assert.IsTrue(clusters[q.id] == clusters[p.id]);
}
Assert.IsFalse(clusters.ContainsKey(points.Count() + 1));
}
public void StartTest()
{
var points = new List <IPointIdFloat>()
{
new PointIdFloat(new List <float>()
{
1, 1, 1
}),
new PointIdFloat(new List <float>()
{
2, 1, 1
}),
new PointIdFloat(new List <float>()
{
1, 2, 1
}),
new PointIdFloat(new List <float>()
{
1, 1, 2
}),
new PointIdFloat(new List <float>()
{
3, 3, 3
}),
new PointIdFloat(new List <float>()
{
2, 2, 2
})
};
PointIdFloat.SetIds(points);
var dbscan = new DBScan(points);
var result = dbscan.Cluster(0.5f, 2);
Assert.IsTrue(result.Count == points.Count);
Assert.IsTrue(result.Values.Max() <= points.Count);
}
static void Main(string[] args)
{
var points = new List<List<double>>
{
new List<double> { 0.5, 0.5 },
new List<double> { 0.4, 0.4 },
new List<double> { 0.6, 0.6 },
new List<double> { 1.4, 0.5 },
new List<double> { 1.4, 0.4 },
new List<double> { 1.4, 0.6 },
new List<double> { 1.5, 2.5 },
new List<double> { 1.5, 2.4 },
new List<double> { 1.5, 2.6 },
new List<double> { 1.6, 2.5 },
new List<double> { 1.6, 2.4 },
new List<double> { 1.6, 2.6 },
new List<double> { 8, 7 },
};
var dbscan = new DBScan();
dbscan.DistanceFunction = new DistanceEuclidian();
dbscan.MinPts = 2;
dbscan.Epsilon = 0.29;
dbscan.Run(points);
for (var i = 0; i < dbscan.Clusters.Count(); i++)
{
Console.WriteLine("Cluster #" + i);
foreach (var vector in dbscan.Clusters[i].Vectors)
{
Console.WriteLine(string.Join(" ", vector));
}
}
}
public void RegionQueryPointsListTest()
{
var points = new List <IPointIdFloat>()
{
new PointIdFloat(new List <float>()
{
1, 1, 1
}),
new PointIdFloat(new List <float>()
{
2, 1, 1
}),
new PointIdFloat(new List <float>()
{
1, 2, 1
}),
new PointIdFloat(new List <float>()
{
1, 1, 2
}),
new PointIdFloat(new List <float>()
{
3, 3, 3
}),
new PointIdFloat(new List <float>()
{
2, 2, 2
})
};
PointIdFloat.SetIds(points);
var p = new PointIdFloat(new List <float>()
{
1.5f, 1.5f, 1.5f
});
Func <IPointIdFloat, Tuple <float, float, float> > keySelector = t => Tuple.Create(
KdTree.KdTreeNode.KeyByDepth(t, 0), 0.0f, 0.0f);
var result = DBScan.RegionQuery(points, p, 0.16f);
Assert.IsTrue(result.Count() == 0);
result = DBScan.RegionQuery(points, p, 1);
var expectedResult = new List <IPointIdFloat>()
{
new PointIdFloat(new List <float>()
{
1, 1, 1
}),
new PointIdFloat(new List <float>()
{
2, 1, 1
}),
new PointIdFloat(new List <float>()
{
1, 2, 1
}),
new PointIdFloat(new List <float>()
{
1, 1, 2
}),
new PointIdFloat(new List <float>()
{
2, 2, 2
})
};
PointIdFloat.SetIds(expectedResult);
Assert.IsTrue(SequenceEquivalent(result.ToList(), expectedResult, PointIdFloat.PointsComparison));
p = new PointIdFloat(new List <float>()
{
0.75f, 0.75f, 0.75f
});
result = DBScan.RegionQuery(points, p, (float)Math.Sqrt(2f / 3f));
expectedResult = new List <IPointIdFloat>()
{
new PointIdFloat(new List <float>()
{
1, 1, 1
}),
new PointIdFloat(new List <float>()
{
2, 1, 1
}),
new PointIdFloat(new List <float>()
{
1, 2, 1
}),
new PointIdFloat(new List <float>()
{
1, 1, 2
}),
};
PointIdFloat.SetIds(expectedResult);
Assert.IsTrue(SequenceEquivalent(result.ToList(), expectedResult, PointIdFloat.PointsComparison));
}
public void RegionQueryKdTreeTest()
{
var points = new List <IPointIdFloat>()
{
new PointIdFloat(new List <float>()
{
1, 1, 1
}),
new PointIdFloat(new List <float>()
{
2, 1, 1
}),
new PointIdFloat(new List <float>()
{
1, 2, 1
}),
new PointIdFloat(new List <float>()
{
1, 1, 2
}),
new PointIdFloat(new List <float>()
{
3, 3, 3
}),
new PointIdFloat(new List <float>()
{
2, 2, 2
})
};
PointIdFloat.SetIds(points);
var p = new PointIdFloat(new List <float>()
{
1.5f, 1.5f, 1.5f
});
KdTree kdt = new KdTree(points);
var result = kdt.PointsWithinDistance(p, 0.5f);
Assert.IsTrue(result.Count == 0);
result = DBScan.RegionQuery(kdt, p, 1);
var expectedResult = new List <IPointIdFloat>()
{
new PointIdFloat(new List <float>()
{
1, 1, 1
}),
new PointIdFloat(new List <float>()
{
2, 1, 1
}),
new PointIdFloat(new List <float>()
{
1, 2, 1
}),
new PointIdFloat(new List <float>()
{
1, 1, 2
}),
new PointIdFloat(new List <float>()
{
2, 2, 2
})
};
PointIdFloat.SetIds(expectedResult);
Assert.IsTrue(SequenceEquivalent(result.ToList(), expectedResult, PointIdFloat.PointsComparison));
p = new PointIdFloat(new List <float>()
{
0.75f, 0.75f, 0.75f
});
result = kdt.PointsWithinDistance(p, (float)Math.Sqrt(2));
expectedResult = new List <IPointIdFloat>()
{
new PointIdFloat(new List <float>()
{
1, 1, 1
}),
new PointIdFloat(new List <float>()
{
2, 1, 1
}),
new PointIdFloat(new List <float>()
{
1, 2, 1
}),
new PointIdFloat(new List <float>()
{
1, 1, 2
}),
};
PointIdFloat.SetIds(expectedResult);
Assert.IsTrue(SequenceEquivalent(result.ToList(), expectedResult, PointIdFloat.PointsComparison));
}
/// <summary>
/// Estimates traffic delay from the collection of travel times
/// </summary>
/// <param name="travelTimes"></param>
/// <param name="model"></param>
void EstimateTrafficDelay(IEnumerable<TravelTime> travelTimes, Model model)
{
List<TravelTimeDelay> delays = new List<TravelTimeDelay>();
foreach (var traveltime in travelTimes) {
double delay = 0;
if (model.TrafficSignalsDelay.Probability > 0 && traveltime.Stops.Count > 0)
delay = traveltime.TotalTravelTime.TotalSeconds - model.FreeFlowTravelTime - traveltime.Stops.Last().Length.TotalSeconds;
else
delay = traveltime.TotalTravelTime.TotalSeconds - model.FreeFlowTravelTime;
delay = Math.Max(0, delay);
delays.Add(new TravelTimeDelay() { TravelTime = traveltime, Delay = delay });
}
// avg delay as median of delays
delays.Sort(new Comparison<TravelTimeDelay>((TravelTimeDelay td1, TravelTimeDelay td2) => td1.Delay.CompareTo(td2.Delay)));
model.AvgDelay = delays[delays.Count / 2].Delay;
TrafficDelayMap delaysMap = new TrafficDelayMap(Properties.Settings.Default.ModelResolution, model.FreeFlowTravelTime);
List<List<TravelTimeDelay>> travelTimeClusters = null;
DBScan<TravelTimeDelay> clusterAnalyzer = new DBScan<TravelTimeDelay>(new DBScan<TravelTimeDelay>.FindNeighbours(FindNeighbours));
for (int i = _parameters.Length -1; i >= 0; i--) {
_parametersIndex = i;
int desiredClusterSize = (int)Math.Max(Properties.Settings.Default.MinimalClusterSize, travelTimes.Count() * Properties.Settings.Default.ClusterSizePercentage / 100.0);
int clusterSize = Math.Min(travelTimes.Count(), desiredClusterSize);
travelTimeClusters = clusterAnalyzer.ClusterAnalysis(delays, clusterSize);
foreach (var cluster in travelTimeClusters) {
TrafficDelayInfo delayInfo = new TrafficDelayInfo();
if (_parameters[_parametersIndex].Dates == DatesHandling.Any)
delayInfo.AppliesTo = DayOfWeek.Any;
else if (_parameters[_parametersIndex].Dates == DatesHandling.WeekendWorkdays)
delayInfo.AppliesTo = (DayOfWeek.Workday & DayOfWeekHelper.FromDate(cluster[0].TravelTime.TimeStart)) > 0 ? DayOfWeek.Workday : DayOfWeek.Weekend;
else
delayInfo.AppliesTo = DayOfWeekHelper.FromDate(cluster[0].TravelTime.TimeStart);
cluster.Sort(new Comparison<TravelTimeDelay>((TravelTimeDelay td1, TravelTimeDelay td2) => td1.Delay.CompareTo(td2.Delay)));
delayInfo.Delay = cluster.Sum(tt => tt.Delay) / cluster.Count;
delayInfo.From = cluster.Min(tt => tt.TravelTime.TimeStart.TimeOfDay).Subtract(new TimeSpan(0, _parameters[_parametersIndex].MembersTimeDifference / 2, 0));
if (delayInfo.From < new TimeSpan(0))
delayInfo.From = new TimeSpan(0);
delayInfo.To = cluster.Max(tt => tt.TravelTime.TimeEnd.TimeOfDay).Add(new TimeSpan(0, _parameters[_parametersIndex].MembersTimeDifference / 2, 0));
if (delayInfo.To > new TimeSpan(23, 59, 59)) {
delayInfo.To = new TimeSpan(23, 59, 59);
}
delaysMap.AddDelay(delayInfo.From, delayInfo.To, delayInfo.AppliesTo, delayInfo.Delay);
}
if (travelTimeClusters.Sum(cluster => cluster.Count) > Properties.Settings.Default.ClusterAnalysisStopPercentage * delays.Count / 100)
break;
}
model.TrafficDelay.AddRange(delaysMap.GetDelays());
}