// 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.");
}
}
// Convert data from a CSV file to the actual input array for the network.
private static void ComputeData(
uint sampleCount,
string inputDataDirectory,
ref double[][] outputData,
ref double[][] outputAnswers,
double outputValue,
int index)
{
DirectoryInfo inputDirectory = new DirectoryInfo(inputDataDirectory);
foreach (var file in inputDirectory.GetFiles("*.csv"))
{
List <ICSVFilter> baseFilters = new List <ICSVFilter>(1);
ICSVFilter quaternions = new CSVEvenColumnFilter();
baseFilters.Add(quaternions);
// Setup loader.
CSVLoaderSettings settings = new CSVLoaderSettings
{
filePath = file.FullName,
trimUp = 1,
trimDown = 0,
filters = baseFilters
};
var data = CSVLoader <Vector3> .LoadData(ref settings);
// Initialize image Transformer.
ImageTransformerSettings imageSettings = new ImageTransformerSettings
{
focusJoints = (LeapMotionJoint[])Enum.GetValues(typeof(LeapMotionJoint)),
samples = data,
size = sampleCount
};
ImageTransformer imageTransformer = new ImageTransformer();
BaseTrainHelper.Project1DInto2D(
imageTransformer.GetNeuralInput(imageSettings),
ref outputData,
index);
// Set answer to given value.
outputAnswers[index] = new double[] { outputValue };
index++;
}
}
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.");
}
}
private static void ComputeData(
uint networkInputSize,
string inputDataDirectory,
ref double[][] outputData,
ref double[][] outputAnswers,
double outputValue,
int index)
{
DirectoryInfo inputDirectory = new DirectoryInfo(inputDataDirectory);
foreach (var file in inputDirectory.GetFiles("*.csv"))
{
List <ICSVFilter> baseFilters = new List <ICSVFilter>(1);
ICSVFilter quaternions = new CSVEvenColumnFilter();
baseFilters.Add(quaternions);
// Setup loader.
CSVLoaderSettings settings = new CSVLoaderSettings
{
filePath = file.FullName,
trimUp = 1,
trimDown = 0,
filters = baseFilters
};
var data = CSVLoader <Vector3> .LoadData(ref settings);
// Initialize CountBased Transformer settings.
IntervalBasedTransformerSettings countSettings = new IntervalBasedTransformerSettings
{
sampleList = data,
count = networkInputSize
};
CountBasedTransformer countTransformer = new CountBasedTransformer();
BaseTrainHelper.Project1DInto2D(
countTransformer.GetNeuralInput(countSettings),
ref outputData,
index);
// Set answer to given value.
outputAnswers[index] = new double[] { outputValue };
index++;
}
}
