public void run(bool full)
{
this.actual = new WeatherActual(INPUT_SIZE, OUTPUT_SIZE);
//loads data from database
this.actual.load();
// Console.WriteLine("Samples read: " + this.actual.size());
if (full)
{
createNetwork();
generateTrainingSets();
trainNetworkBackprop();
saveNeuralNetwork("Kathmandu");
}
else
{
loadNeuralNetwork("Kathmandu");
}
//call the process here to trigger data to visualiztion
//display();
}
public void trainStation(string station)
{
this.actual = new WeatherActual(INPUT_SIZE, OUTPUT_SIZE);
this.actual.load(station);
createNetwork();
generateTrainingSets();
trainNetworkBackprop();
saveNeuralNetwork(station);
}
public List<WeatherSamples> prediction(DateTime date,string station, double airPollutionLevel)
{
this.actual = new WeatherActual(INPUT_SIZE, OUTPUT_SIZE);
loadNeuralNetwork(station);
List<WeatherSamples> list = new List<WeatherSamples>();
List<double[]> predictHistory = new List<double[]>();
double[] present = new double[INPUT_SIZE * 4];
double[] predict = new double[OUTPUT_SIZE * 3];
this.actual.getInputDataToPredict(date, present);
var tempDate = date;
for (int i = 0; i < 30; i++)
{
predict = this.network.ComputeOutputs(present);
predictHistory.Add(predict);
this.actual.getInputDataToPredictSeries(date, present,predictHistory,airPollutionLevel);
list.Add(new WeatherSamples {date=tempDate,minTemp=predict[0],maxTemp=predict[1],rainfall=predict[2],airPollutionLevel=airPollutionLevel,station=station });
tempDate=tempDate.AddDays(+1);
}
return list;
}
