static void trainNormal(TimeCanvas canvas, List <Sequence> trainSet, int iterations, bool bidirectional)
{
//canvas = new TimeCanvas(25, 7);
//canvas.c2 = c2;
for (int i = 0; i < iterations; i++)
{
foreach (Sequence s in trainSet)
{
canvas.Reset();
foreach (double[] item in s)
{
canvas.Train(item);
}
if (bidirectional)
{
Sequence rS = ObjectCopier.Clone(s);
rS.Reverse();
canvas.Reset();
foreach (double[] item in rS)
{
canvas.Train(item);
}
}
}
}
canvas.Save("debugNormal.csv");
}
static void Main(string[] args)
{
//----------------------------------------------------------------------------------------------
//Create the network
int timelineSize = 50;
TimeCells.TimeCanvas canvas = new TimeCanvas(2, timelineSize, null);
//TimeCells.TimeCanvas canvas = new TimeCanvas(2, 50, HarvesineDistance);
canvas.parameterLineCreationTreshold = 0.005;
canvas.parameterFFLearningRate = 0;
bool doTraining = true;
double avgTrajectoryLength = timelineSize;
//----------------------------------------------------------------------------------------------
//Load the training set
if (doTraining)
{
double[] minBound = new double[2], maxBound = new double[2];
//List<TaxiCourse> trainSet = LoadDataSet("C:\\Users\\Stephane\\Documents\\GitHub\\Myline\\Datasets\\Kaggle Taxis\\train.csv", ref minBound, ref maxBound, ref avgTrajectoryLength, -1);
List <TaxiCourse> trainSet = LoadDataSet("D:\\robotology\\src\\Myline\\CDZ.NET\\Datasets\\Kaggle Taxis\\train.csv", canvas.parameterLineCreationTreshold, ref minBound, ref maxBound, ref avgTrajectoryLength, -1);
Console.WriteLine("Range is: Lattitude=" + (maxBound[0] - minBound[0]) + "\t Longitude=" + (maxBound[1] - minBound[1]));
Console.WriteLine("MinMax are: Lattitude=" + maxBound[0] + "/" + minBound[0] + "\t Longitude=" + maxBound[1] + "/" + minBound[1]);
//List<TaxiCourse> testSet = LoadDataSet("C:\\Users\\Stephane\\Documents\\GitHub\\Myline\\Datasets\\Kaggle Taxis\\test.csv", ref minBoundTest, ref maxBoundTest, ref avgTrajectoryLengthTest, -1);
StreamWriter stats = new StreamWriter("stats.txt");
stats.WriteLine("Min " + minBound[0] + " " + minBound[1]);
stats.WriteLine("Max " + maxBound[0] + " " + maxBound[1]);
stats.WriteLine("Lines " + trainSet.Count);
stats.Close();
//----------------------------------------------------------------------------------------------
//Prepare the data
//ScaleTrajectoryData(trainSet, minBound, maxBound);
//ScaleTrajectoryData(testSet, minBound, maxBound);
AddTerminalSymbol(trainSet, new double[] { 999, 999 });
//Try to imprint (fast training)
int imprintIteration = 0;
Stopwatch watch2 = new Stopwatch();
watch2.Start();
foreach (TaxiCourse course in trainSet)
{
canvas.Imprint(course.polyline);
imprintIteration++;
if (imprintIteration % 1000 == 0)
{
Console.WriteLine("Epoch done : " + Math.Floor(100 * imprintIteration / (double)trainSet.Count) + "%");
}
}
watch2.Stop();
Console.WriteLine("Training set (" + trainSet.Count + " elements) processed in " + watch2.Elapsed + ".");
canvas.Save("SavedCanvas.csv");
}
else //if (false)
{
Console.WriteLine("Loading previously learnt canvas.");
canvas.Load("SavedCanvas.csv");
Console.WriteLine("Loading complete.");
}
//----------------------------------------------------------------------------------------------
//Load the test set
double[] minBoundTest = null, maxBoundTest = null;
double avgTrajectoryLengthTest = 0.0;
List <TaxiCourse> testSet = LoadDataSet("D:\\robotology\\src\\Myline\\CDZ.NET\\Datasets\\Kaggle Taxis\\test.csv", canvas.parameterLineCreationTreshold, ref minBoundTest, ref maxBoundTest, ref avgTrajectoryLengthTest, -1);
//----------------------------------------------------------------------------------------------
//Generate the test results
StreamWriter results = new StreamWriter("Results.csv");
results.WriteLine("\"TRIP_ID\",\"LATITUDE\",\"LONGITUDE\"");
foreach (TaxiCourse course in testSet)
{
canvas.Reset();
//Load the begining of the sequence
foreach (double[] point in course.polyline)
{
canvas.PresentInput(point, false, false);
canvas.PropagateActivity();
}
double[] prediction = new double[] { 0, 0 };
double[] lastPrediction = new double[] { 0, 0 };
Console.WriteLine("Predicting");
int iterationCount = 0;
while (prediction[0] != 999 && prediction[1] != 999 && iterationCount < 2 * avgTrajectoryLength)
{
prediction.CopyTo(lastPrediction, 0);
canvas.PresentInput(prediction, false, false);
canvas.PropagateActivity();
//We take the best prediction after the actual spot
List <KeyValuePair <double[], double> > sortedPredictions = canvas.PredictAll();
foreach (KeyValuePair <double[], double> pair in sortedPredictions)
{
if (pair.Key[0] != prediction[0] || pair.Key[1] != prediction[1])
{
pair.Key.CopyTo(prediction, 0);
break;
}
}
Console.WriteLine(prediction[0] + "\t\t" + prediction[1]);
iterationCount++;
}
results.WriteLine("\"" + course.tripID + "\"," + lastPrediction[1] + "," + lastPrediction[0]);
}
results.Close();
}