| public static Generate ( long seed, int count, int inputCount, int idealCount, double min, double max ) : BasicMLDataSet | ||
| seed | long | The seed value to use, the same seed value will always produce /// the same results. |
| count | int | How many training items to generate. |
| inputCount | int | How many input numbers. |
| idealCount | int | How many ideal numbers. |
| min | double | The minimum random number. |
| max | double | The maximum random number. |
| Résultat | BasicMLDataSet | |
/// <summary>
/// Evaluate memory.
/// </summary>
private void EvalMemory()
{
BasicMLDataSet training = RandomTrainingFactory.Generate(
1000, 10000, 10, 10, -1, 1);
const long stop = (10 * Evaluate.Milis);
int record = 0;
IMLDataPair pair;
int iterations = 0;
var watch = new Stopwatch();
watch.Start();
while (true)
{
iterations++;
pair = training[record++];
if (record >= training.Count)
{
record = 0;
}
if ((iterations & 0xff) == 0 && watch.ElapsedMilliseconds >= stop)
{
break;
}
}
iterations /= 100000;
_report.Report(Steps, Step2,
"Memory dataset, result: " + Format.FormatInteger(iterations));
_memoryScore = iterations;
}
/// <summary>
/// Evaluate memory.
/// </summary>
private void EvalMemory()
{
BasicNeuralDataSet training = RandomTrainingFactory.Generate(
1000, 10000, 10, 10, -1, 1);
long stop = (10 * Evaluate.MILIS);
int record = 0;
INeuralDataPair pair = BasicNeuralDataPair.CreatePair(10, 10);
int iterations = 0;
Stopwatch watch = new Stopwatch();
watch.Start();
while (watch.ElapsedMilliseconds < stop)
{
iterations++;
training.GetRecord(record++, pair);
if (record >= training.Count)
{
record = 0;
}
}
iterations /= 100000;
this.report.Report(EncogBenchmark.STEPS, EncogBenchmark.STEP3,
"Memory dataset, result: " + Format.FormatInteger(iterations));
this.memoryScore = iterations;
}
/// <summary>
/// Evaluate disk.
/// </summary>
private void EvalBinary()
{
FileInfo file = FileUtil.CombinePath(new FileInfo(Path.GetTempPath()), "temp.egb");
BasicMLDataSet training = RandomTrainingFactory.Generate(
1000, 10000, 10, 10, -1, 1);
// create the binary file
if (file.Exists)
{
file.Delete();
}
var training2 = new BufferedMLDataSet(file.ToString());
training2.Load(training);
const long stop = (10 * Evaluate.Milis);
int record = 0;
IMLDataPair pair;
var watch = new Stopwatch();
watch.Start();
int iterations = 0;
while (true)
{
iterations++;
pair = training[record++];
if (record >= training.Count)
{
record = 0;
}
if ((iterations & 0xff) == 0 && watch.ElapsedMilliseconds >= stop)
{
break;
}
}
training2.Close();
iterations /= 100000;
_report.Report(Steps, Step3,
"Disk(binary) dataset, result: "
+ Format.FormatInteger(iterations));
if (file.Exists)
{
file.Delete();
}
_binaryScore = iterations;
}
/// <summary>
/// Evaluate training.
/// </summary>
/// <param name="input">Input neurons.</param>
/// <param name="hidden1">Hidden 1 neurons.</param>
/// <param name="hidden2">Hidden 2 neurons.</param>
/// <param name="output">Output neurons.</param>
/// <returns>The result of the evaluation.</returns>
public static int EvaluateTrain(int input, int hidden1, int hidden2,
int output)
{
BasicNetwork network = EncogUtility.SimpleFeedForward(input,
hidden1, hidden2, output, true);
IMLDataSet training = RandomTrainingFactory.Generate(1000,
10000, input, output, -1, 1);
return(EvaluateTrain(network, training));
}
/// <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;
}
/// <summary>
/// Evaluate training, use OpenCL.
/// </summary>
/// <param name="device">The OpenCL device, null for CPU.</param>
/// <param name="input">Input neurons.</param>
/// <param name="hidden1">Hidden 1 neurons.</param>
/// <param name="hidden2">Hidden 2 neurons.</param>
/// <param name="output">Output neurons.</param>
/// <returns>The result of the evaluation.</returns>
public static int EvaluateTrain(EncogCLDevice device, int input, int hidden1, int hidden2,
int output)
{
BasicNetwork network = EncogUtility.SimpleFeedForward(input,
hidden1, hidden2, output, true);
INeuralDataSet training = RandomTrainingFactory.Generate(1000,
10000, input, output, -1, 1);
OpenCLTrainingProfile profile = null;
#if !SILVERLIGHT
if (device != null)
{
profile = new OpenCLTrainingProfile(device);
}
#endif
return(EvaluateTrain(profile, network, training));
}
