public ClassificationType[,] Classify(LasFile file)
{
var sw = Stopwatch.StartNew();
ClassificationType[,] output = new ClassificationType[_divisionCountX, _divisionCountY];
SubgroupOfPoints[,] values = Utills.GroupPoints(file, _divisionCountX, _divisionCountY);
Statistics stats = new Statistics();
stats.Count = _divisionCountX * _divisionCountY;
for (int i = 0; i < _divisionCountX; i++)
{
for (int j = 0; j < _divisionCountY; j++)
{
double avgHeight = values[i, j].avgHeight;
double avgIntensity = values[i, j].avgIntensity;
double avgDistance = values[i, j].avgDistance;
OpenTK.Vector3 slopeVector = values[i, j].slopeVector;
IMLData classed = Network.Compute(new BasicMLData(
new double[] { avgDistance, avgHeight, avgIntensity, slopeVector[0], slopeVector[1], slopeVector[2] }));
output[i, j] = Utills.QuickClassess[classed.IndexOfMax()];
ClassificationType ct;
if (!Utills.QuickClassess.TryGetValue(values[i, j].classIndex, out ct))
{
continue;
}
if (output[i, j] != ct)
{
stats.ClassErrors[(int)ct]++;
}
stats.PredictionMatrix[(int)output[i, j], (int)ct]++;
stats.PredictionMatrix[(int)ct, (int)output[i, j]]++;
stats.ClassCount[(int)output[i, j]]++;
stats.ClassRealCount[(int)ct]++;
}
Console.WriteLine(i);
}
Console.Write(stats.ToString());
sw.Stop();
Console.WriteLine("Czas trwania [" + sw.Elapsed.TotalSeconds.ToString() + "s]");
stats.SaveMatrixAsCSV();
return(output);
}
public ClassificationType[,] Classify(LasFile file, int divCountX, int divCountY)
{
Stopwatch swTotal = Stopwatch.StartNew();
Stopwatch sw = Stopwatch.StartNew();
Console.WriteLine("Preparing testing dataset...");
LasPointDataRecords points = file.LasPointDataRecords;
ClassificationType[,] output = new ClassificationType[divCountX, divCountY];
SubgroupOfPoints[,] values = Utills.GroupPoints(file, divCountX, divCountY);
Statistics stats = new Statistics();
stats.Count = divCountX * divCountY;
sw.Stop();
Console.WriteLine("Preparing testing dataset completed [" + sw.Elapsed.TotalSeconds.ToString() + "s]");
Stopwatch sw2 = Stopwatch.StartNew();
Console.WriteLine("Classification in progress...");
int noiseCount = 0;
for (int i = 0; i < divCountX; i++)
{
for (int j = 0; j < divCountY; j++)
{
if (values[i, j].classIndex == 7)
{
output[i, j] = ClassificationType.Noise;
noiseCount++;
}
else
{
double avgHeight = values[i, j].avgHeight;
double avgIntensity = values[i, j].avgIntensity;
double avgDistance = values[i, j].avgDistance;
OpenTK.Vector3 slopeVector = values[i, j].slopeVector;
output[i, j] = Utills.ClassificationClasses[knn.Compute(new double[] {
avgDistance, avgHeight, avgIntensity, slopeVector[0],
slopeVector[1], slopeVector[2]
})];
ClassificationType ct;
if (!Utills.QuickClassess.TryGetValue(values[i, j].classIndex, out ct))
{
continue;
}
if (output[i, j] != ct)
{
stats.ClassErrors[(int)ct]++;
}
stats.PredictionMatrix[(int)output[i, j], (int)ct]++;
stats.PredictionMatrix[(int)ct, (int)output[i, j]]++;
stats.ClassCount[(int)output[i, j]]++;
stats.ClassRealCount[(int)ct]++;
}
}
//Console.WriteLine(i);
}
Console.Write(stats.ToString());
sw2.Stop();
Console.WriteLine("Classification completed [" + sw2.Elapsed.TotalSeconds.ToString() + "s]");
swTotal.Stop();
Console.WriteLine("Total time: [" + swTotal.Elapsed.TotalSeconds.ToString() + "s]");
Console.WriteLine("Noise count: " + noiseCount.ToString());
stats.SaveMatrixAsCSV();
return(output);
}