public CortexLayer <TIN, TOUT> AddModule(string moduleName, IHtmModule module)
{
this.HtmModules.Add(moduleName, module);
// connect
return(this);
}
private float Train(int inpBits, IHtmModule <object, object> network, HtmClassifier <double, ComputeCycle> cls, bool isNewBornMode = true)
{
float accurracy;
string outFolder = nameof(RunPowerPredictionExperiment);
Directory.CreateDirectory(outFolder);
CortexNetworkContext ctx = new CortexNetworkContext();
Dictionary <string, object> scalarEncoderSettings = getScalarEncoderDefaultSettings(inpBits);
//var dateTimeEncoderSettings = getFullDateTimeEncoderSettings();
ScalarEncoder scalarEncoder = new ScalarEncoder(scalarEncoderSettings);
//DateTimeEncoder dtEncoder = new DateTimeEncoder(dateTimeEncoderSettings, DateTimeEncoder.Precision.Hours);
string fileName = "TestFiles\\rec-center-hourly-short.csv";
using (StreamReader sr = new StreamReader(fileName))
{
string line;
int cnt = 0;
int matches = 0;
using (StreamWriter sw = new StreamWriter("out.csv"))
{
while ((line = sr.ReadLine()) != null)
{
cnt++;
//if (isNewBornMode && cnt > 100) break;
bool x = false;
if (x)
{
break;
}
List <int> output = new List <int>();
string[] tokens = line.Split(",");
// Encode scalar value
var result = scalarEncoder.Encode(tokens[1]);
output.AddRange(result);
// This part adds datetime components to the input vector.
//output.AddRange(new int[scalarEncoder.Offset]);
//DateTime dt = DateTime.Parse(tokens[0], CultureInfo.InvariantCulture);
// Encode date/time/hour.
//result = dtEncoder.Encode(new DateTimeOffset(dt, TimeSpan.FromMilliseconds(0)));
//output.AddRange(result);
// This performs a padding to the inputBits = 10404 = 102*102.
output.AddRange(new int[inpBits - output.Count]);
var outArr = output.ToArray();
Debug.WriteLine($"-------------- {tokens[1]} --------------");
if (isNewBornMode)
{
for (int j = 0; j < 10; j++)
{
// Output here are active cells.
var res = network.Compute(output.ToArray(), true);
Debug.WriteLine(Helpers.StringifyVector(((int[])res)));
}
}
else
{
var lyrOut = network.Compute(output.ToArray(), true) as ComputeCycle;;
double input = Convert.ToDouble(tokens[1], CultureInfo.InvariantCulture);
if (input == lastPredictedValue)
{
matches++;
Debug.WriteLine($"Match {input}");
}
else
{
Debug.WriteLine($"Missmatch Actual value: {input} - Predicted value: {lastPredictedValue}");
}
cls.Learn(input, lyrOut.ActiveCells.ToArray());
lastPredictedValue = cls.GetPredictedInputValue(lyrOut.PredictiveCells.ToArray());
sw.WriteLine($"{tokens[0]};{input.ToString(CultureInfo.InvariantCulture)};{lastPredictedValue.ToString(CultureInfo.InvariantCulture)}");
Debug.WriteLine($"W: {Helpers.StringifyVector(lyrOut.WinnerCells.Select(c => c.Index).ToArray())}");
Debug.WriteLine($"P: {Helpers.StringifyVector(lyrOut.PredictiveCells.Select(c => c.Index).ToArray())}");
Debug.WriteLine($"Current input: {input} Predicted Input: {lastPredictedValue}");
int[,] twoDimenArray = ArrayUtils.Make2DArray <int>(outArr, (int)Math.Sqrt(outArr.Length), (int)Math.Sqrt(output.Count));
var twoDimArray = ArrayUtils.Transpose(twoDimenArray);
NeoCortexUtils.DrawBitmap(twoDimArray, 1024, 1024, $"{outFolder}\\{tokens[0].Replace("/", "-").Replace(":", "-")}.png", Color.Yellow, Color.Black, text: input.ToString());
}
Debug.WriteLine($"NewBorn stage: {isNewBornMode} - record: {cnt}");
}
}
accurracy = (float)matches / (float)cnt * (float)100.0;
}
return(accurracy);
}
