public static void PrepareData(ref NetworkContainer container, ref CountNetworkTrainSettings settings)
{
if (settings.trainSettings.dataset != null)
{
throw new IncorrectActionOrderException("This action has already been completed.");
}
TestForErrors(ref settings);
settings.trainSettings = new EncogTrainSettings
{
maxTrainingError = 0.01
};
int correctFileCount = BaseTrainHelper.GetFileCount(settings.correctInputDirectory);
int incorrectFileCount = BaseTrainHelper.GetFileCount(settings.incorrectInputDirectory);
if (correctFileCount <= 0)
{
throw new FileNotFoundException("correctFiles not found.");
}
if (incorrectFileCount <= 0)
{
throw new FileNotFoundException("incorretFiles not found.");
}
settings.trainSettings.dataset = new double[correctFileCount + incorrectFileCount][];
settings.trainSettings.answers = new double[correctFileCount + incorrectFileCount][];
// Compute correct training data.
ComputeData(
settings.sampleCount,
settings.correctInputDirectory,
ref settings.trainSettings.dataset,
ref settings.trainSettings.answers,
1.0,
0);
// Compute incorrect training data.
ComputeData(
settings.sampleCount,
settings.incorrectInputDirectory,
ref settings.trainSettings.dataset,
ref settings.trainSettings.answers,
0.0,
correctFileCount);
}
// 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++;
}
}
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++;
}
}