// Predict the output of a dataset using an existing network.
public static bool Predict(ref NetworkContainer container, ref CountNetworkPredictSettings settings)
{
if (settings.predictSettings.data == null)
{
throw new IncorrectActionOrderException("Prepare predictor before predicting.");
}
return(EncogWrapper.Predict(ref container, ref settings.predictSettings));
}
// Prepare input data for prediction.
public static void PreparePredictor(ref NetworkContainer container, ref ImageNetworkPredictSettings settings)
{
TestForErrors(ref settings);
EncogWrapper.LoadNetworkFromFS(ref container, settings.trainedNetwork);
List <ICSVFilter> baseFilters = new List <ICSVFilter>(1);
ICSVFilter quaternions = new CSVEvenColumnFilter();
baseFilters.Add(quaternions);
// Setup loader.
CSVLoaderSettings CSVSettings = new CSVLoaderSettings
{
filePath = settings.predictData,
trimUp = 1,
trimDown = 0,
filters = baseFilters
};
var data = CSVLoader <Vector3> .LoadData(ref CSVSettings);
// Initialize image Transformer.
ImageTransformerSettings imageSettings = new ImageTransformerSettings
{
focusJoints = (LeapMotionJoint[])Enum.GetValues(typeof(LeapMotionJoint)),
samples = data,
size = settings.imgSize
};
ImageTransformer imageTransformer = new ImageTransformer();
if (settings.predictSettings.threshold.Equals(null))
{
settings.predictSettings = new EncogPredictSettings
{
threshold = 0.9
};
}
settings.predictSettings.data = imageTransformer.GetNeuralInput(imageSettings);
if (settings.predictSettings.data.Length != container.network.InputCount)
{
throw new NoNetworkMatchException("Sample count doesn't match network input count.");
}
}
public static void PreparePredictor(ref NetworkContainer container, ref CountNetworkPredictSettings settings)
{
TestForErrors(ref settings);
EncogWrapper.LoadNetworkFromFS(ref container, settings.trainedNetwork);
List <ICSVFilter> baseFilters = new List <ICSVFilter>(1);
ICSVFilter quaternions = new CSVEvenColumnFilter();
baseFilters.Add(quaternions);
// Setup loader.
CSVLoaderSettings CSVSettings = new CSVLoaderSettings
{
filePath = settings.predictData,
trimUp = 1,
trimDown = 0,
filters = baseFilters
};
var data = CSVLoader <Vector3> .LoadData(ref CSVSettings);
// Initialize CountBased Transformer settings.
IntervalBasedTransformerSettings countSettings = new IntervalBasedTransformerSettings
{
sampleList = data,
count = settings.sampleCount
};
CountBasedTransformer countTransformer = new CountBasedTransformer();
if (settings.predictSettings.threshold.Equals(null))
{
settings.predictSettings = new EncogPredictSettings
{
threshold = 0.9
};
}
settings.predictSettings.data = countTransformer.GetNeuralInput(countSettings);
if (settings.predictSettings.data.Length != container.network.InputCount)
{
throw new NoNetworkMatchException("Sample count doesn't match network input count.");
}
}
public static void Train(ref NetworkContainer container, ref CountNetworkTrainSettings settings)
{
if (settings.trainSettings.dataset == null)
{
throw new IncorrectActionOrderException("Prepare data before training network.");
}
if (container == null)
{
throw new IncorrectActionOrderException("Prepare network before training network.");
}
EncogWrapper.Train(ref container, ref settings.trainSettings);
if (settings.outputDirectory[settings.outputDirectory.Length - 1].Equals("/"))
{
EncogWrapper.SaveNetworkToFS(ref container, settings.outputDirectory + settings.outputName + ".eg");
return;
}
EncogWrapper.SaveNetworkToFS(ref container, settings.outputDirectory + "/" + settings.outputName + ".eg");
}
// Prepare network for training, this is mainly setting up the layers and activation functions.
public static void PrepareNetwork(ref NetworkContainer container, ref ImageNetworkTrainSettings settings)
{
if (settings.trainSettings.dataset == null)
{
throw new IncorrectActionOrderException("Prepare data before preparing network.");
}
EncogWrapper.Instantiate(ref container);
EncogLayerSettings inputLayerSettings = new EncogLayerSettings
{
activationFunction = null,
hasBias = true,
neuronCount = settings.trainSettings.dataset[0].Length
};
EncogWrapper.AddLayer(ref container, ref inputLayerSettings);
EncogLayerSettings hiddenLayerOneSettings = new EncogLayerSettings
{
activationFunction = new ActivationElliott(),
hasBias = true,
neuronCount = 100
};
EncogWrapper.AddLayer(ref container, ref hiddenLayerOneSettings);
EncogLayerSettings outputLayerSettings = new EncogLayerSettings
{
activationFunction = new ActivationElliott(),
hasBias = false,
neuronCount = 1
};
EncogWrapper.AddLayer(ref container, ref outputLayerSettings);
EncogWrapper.FinalizeNetwork(ref container);
}
