void log(StreamWriter logFile, int recordingCount, bool learning, int world)
{
//Dictionary<Signal, double[,]> fullPrediction = CA3.Predict( CA3.modalities ); //Evaluate with all modalities
//c0sacc->c1
Dictionary <Signal, double[, ]> c1Pred = CA3.Predict(new List <CDZNET.Core.Signal>()
{
LEC_ColorT0, MEC
});
//c1sacc->c0
Dictionary <Signal, double[, ]> c0Pred = CA3.Predict(new List <CDZNET.Core.Signal>()
{
LEC_ColorT1, MEC
});
//c0c1->sacc
Dictionary <Signal, double[, ]> saccPred = CA3.Predict(new List <CDZNET.Core.Signal>()
{
LEC_ColorT0, LEC_ColorT1
});
pictureBoxEndPoint.Image = getBitmapFromColorCode(LEC_ColorT1.reality);
pictureBoxPredictedEndpoint.Image = getBitmapFromColorCode(c1Pred[LEC_ColorT1]);
pictureBoxEndPoint.Refresh();
pictureBoxPredictedEndpoint.Refresh();
Dictionary <string, double> directionalErrors = getDirectionalErrors(LEC_ColorT1.reality, c1Pred[LEC_ColorT1]);
if (logFile != null)
{
logFile.WriteLine(
recordingCount + "," +
learning + "," +
world + "," +
GetString(LEC_ColorT0.reality) + "," +
GetString(LEC_ColorT1.reality) + "," +
GetString(MEC.reality) + "," +
GetString(c1Pred[LEC_ColorT1]) + "," +
GetString(GetSquaredError(LEC_ColorT1.reality, c1Pred[LEC_ColorT1])) + "," +
directionalErrors["left"] + "," +
directionalErrors["top"] + "," +
directionalErrors["right"] + "," +
directionalErrors["bottom"]
//GetSumSquared(c1Pred[LEC_ColorT1], LEC_ColorT1.reality) + "," +
//GetString(c0Pred[LEC_ColorT0]) + "," +
//GetSumSquared(c0Pred[LEC_ColorT0], LEC_ColorT0.reality) + "," +
//GetString(saccPred[MEC]) + "," +
//GetSumSquared(saccPred[MEC], MEC.reality)
);
logFile.Flush();
}
}
void predictEgosphere()
{
CA3.learningLocked = true;
World predictedWorld = new World(world.Width, world.Height);
progressBarCurrentOp.Value = 0;
progressBarCurrentOp.Minimum = 0;
progressBarCurrentOp.Maximum = world.Width * world.Height;
progressBarCurrentOp.Step = 1;
//We test the predictions of MEC only
for (int x = 0; x < world.Width; x++)
{
for (int y = 0; y < world.Height; y++)
{
predictedWorld.cells[x, y] = new Cell();
//if (x >= retinaSize / 2 && x < world.Width - retinaSize / 2 && y >= retinaSize / 2 && y < world.Height - retinaSize / 2)
{
Array.Clear(MEC.reality, 0, MEC.reality.Length);
MEC.reality[x, 0] = 1.0;
MEC.reality[world.Width + y, 0] = 1.0;
Dictionary <Signal, double[, ]> prediction = CA3.Predict(new List <Signal> {
MEC
});
//We use only the center as the prediction
double[] orientationCode = new double[2]
{
prediction[LEC_Orientation][(retinaSize / 2) * 2, retinaSize / 2],
prediction[LEC_Orientation][(retinaSize / 2) * 2 + 1, retinaSize / 2]
};
predictedWorld.cells[x, y].orientation = Cell.getOrientationFromCode(orientationCode);
for (int compo = 0; compo < 4; compo++)
{
predictedWorld.cells[x, y].colorCode[compo] = prediction[LEC_Color][(retinaSize / 2) * 4 + compo, retinaSize / 2];
}
//predictedWorld.cells[x, y].frequency
progressBarCurrentOp.PerformStep();
}
}
}
Dictionary <string, Bitmap> egoSphereVisu = predictedWorld.toImages();
pictureBoxEgosphereColor.Image = egoSphereVisu["color"];
pictureBoxEgosphereColor.Refresh();
pictureBoxEgosphereOrientation.Image = egoSphereVisu["orientation"];
pictureBoxEgosphereOrientation.Refresh();
CA3.learningLocked = false;
}
