public void TestBufferData()
{
File.Delete(Filename);
var set = new BufferedMLDataSet(Filename);
set.BeginLoad(2, 1);
for (int i = 0; i < XOR.XORInput.Length; i++)
{
var input = new BasicMLData(XOR.XORInput[i]);
var ideal = new BasicMLData(XOR.XORIdeal[i]);
set.Add(input, ideal);
}
set.EndLoad();
XOR.TestXORDataSet(set);
}
public void TestBufferData()
{
File.Delete(Filename);
var set = new BufferedMLDataSet(Filename);
set.BeginLoad(2, 1);
for (int i = 0; i < XOR.XORInput.Length; i++)
{
var input = new BasicMLData(XOR.XORInput[i]);
var ideal = new BasicMLData(XOR.XORIdeal[i]);
set.Add(input, ideal);
}
set.EndLoad();
XOR.TestXORDataSet(set);
}
/// <summary>
/// Convert a CSV file to a binary training file.
/// </summary>
/// <param name="csvFile">The CSV file.</param>
/// <param name="format">The format.</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>
/// <param name="expectSignificance">Should a significance column be expected.</param>
public static void ConvertCSV2Binary(String csvFile, CSVFormat format,
String binFile, int inputCount, int outputCount,
bool headers, bool expectSignificance)
{
new FileInfo(binFile).Delete();
var csv = new CSVMLDataSet(csvFile,
inputCount, outputCount, false, format, expectSignificance);
var buffer = new BufferedMLDataSet(binFile);
buffer.BeginLoad(inputCount, outputCount);
foreach (IMLDataPair pair in csv)
{
buffer.Add(pair);
}
buffer.EndLoad();
}
/// <summary>
/// Convert a CSV file to binary.
/// </summary>
/// <param name="csvFile">The CSV file to convert.</param>
/// <param name="format">The format.</param>
/// <param name="binFile">The binary file.</param>
/// <param name="input">The input.</param>
/// <param name="ideal">The ideal.</param>
/// <param name="headers">True, if headers are present.</param>
public static void ConvertCSV2Binary(FileInfo csvFile, CSVFormat format,
FileInfo binFile, int[] input, int[] ideal, bool headers)
{
binFile.Delete();
var csv = new ReadCSV(csvFile.ToString(), headers, format);
var buffer = new BufferedMLDataSet(binFile.ToString());
buffer.BeginLoad(input.Length, ideal.Length);
while (csv.Next())
{
var inputData = new BasicMLData(input.Length);
var idealData = new BasicMLData(ideal.Length);
// handle input data
for (int i = 0; i < input.Length; i++)
{
inputData[i] = csv.GetDouble(input[i]);
}
// handle input data
for (int i = 0; i < ideal.Length; i++)
{
idealData[i] = csv.GetDouble(ideal[i]);
}
// add to dataset
buffer.Add(inputData, idealData);
}
buffer.EndLoad();
}
/// <summary>
/// Called to generate the training file.
/// </summary>
public void Generate()
{
string[] list = Directory.GetFiles(_path);
_trainingFile.Delete();
var output = new BufferedMLDataSet(_trainingFile.ToString());
output.BeginLoad(Config.InputWindow, 1);
foreach (string file in list)
{
var fn = new FileInfo(file);
if (fn.Name.StartsWith("collected") && fn.Name.EndsWith(".csv"))
{
ProcessFile(file, output);
}
}
output.EndLoad();
output.Close();
}
public static void ConvertCSV2Binary(string csvFile, CSVFormat format, string binFile, int inputCount, int outputCount, bool headers, bool expectSignificance)
{
new FileInfo(binFile).Delete();
CSVMLDataSet set = new CSVMLDataSet(csvFile, inputCount, outputCount, false, format, expectSignificance);
BufferedMLDataSet set2 = new BufferedMLDataSet(binFile);
set2.BeginLoad(inputCount, outputCount);
if ((((uint) inputCount) & 0) == 0)
{
foreach (IMLDataPair pair in set)
{
set2.Add(pair);
}
set2.EndLoad();
}
}
