/// <summary>
/// Convert a CSV file to a binary training file.
/// </summary>
/// <param name="csvFile">The CSV file.</param>
/// <param name="binFile">The binary file.</param>
/// <param name="inputCount">The number of input values.</param>
/// <param name="outputCount">The number of output values.</param>
/// <param name="headers">True, if there are headers on the3 CSV.</param>
public static void ConvertCSV2Binary(String csvFile,
String binFile, int inputCount, int outputCount,
bool headers)
{
File.Delete(binFile);
CSVNeuralDataSet csv = new CSVNeuralDataSet(csvFile.ToString(),
inputCount, outputCount, false);
BufferedNeuralDataSet buffer = new BufferedNeuralDataSet(binFile);
buffer.BeginLoad(50, 6);
foreach (INeuralDataPair pair in csv)
{
buffer.Add(pair);
}
buffer.EndLoad();
}
/// <summary>
/// Evaluate disk.
/// </summary>
private void EvalBinary()
{
String file = "temp.egb";
BasicNeuralDataSet training = RandomTrainingFactory.Generate(
1000, 10000, 10, 10, -1, 1);
// create the binary file
File.Delete(file);
BufferedNeuralDataSet training2 = new BufferedNeuralDataSet(file);
training2.Load(training);
long stop = (10 * Evaluate.MILIS);
int record = 0;
INeuralDataPair pair = BasicNeuralDataPair.CreatePair(10, 10);
Stopwatch watch = new Stopwatch();
watch.Start();
int iterations = 0;
while (watch.ElapsedMilliseconds < stop)
{
iterations++;
training2.GetRecord(record++, pair);
if (record >= training2.Count)
{
record = 0;
}
}
training2.Close();
iterations /= 100000;
this.report.Report(EncogBenchmark.STEPS, EncogBenchmark.STEP4,
"Disk(binary) dataset, result: "
+ Format.FormatInteger(iterations));
File.Delete(file);
this.binaryScore = iterations;
}
