本文整理汇总了C#中IMLDataSet.GetRecord方法的典型用法代码示例。如果您正苦于以下问题：C# IMLDataSet.GetRecord方法的具体用法？C# IMLDataSet.GetRecord怎么用？C# IMLDataSet.GetRecord使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类IMLDataSet的用法示例。
在下文中一共展示了IMLDataSet.GetRecord方法的3个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的C#代码示例。
示例1: CalculateError
/// <summary>
/// Calculate the error for this neural network. The error is calculated
/// using root-mean-square(RMS).
/// </summary>
///
/// <param name="data">The training set.</param>
/// <returns>The error percentage.</returns>
public double CalculateError(IMLDataSet data)
{
var errorCalculation = new ErrorCalculation();
var actual = new double[_outputCount];
IMLDataPair pair = BasicMLDataPair.CreatePair(data.InputSize,
data.IdealSize);
for (int i = 0; i < data.Count; i++)
{
data.GetRecord(i, pair);
Compute(pair.InputArray, actual);
errorCalculation.UpdateError(actual, pair.IdealArray,pair.Significance);
}
return errorCalculation.Calculate();
}示例2: CalculateError
public double CalculateError(IMLDataSet data)
{
double[] numArray;
IMLDataPair pair;
int num;
ErrorCalculation calculation = new ErrorCalculation();
goto Label_0057;
Label_0031:
num++;
Label_0035:
if (num < data.Count)
{
data.GetRecord((long) num, pair);
this.Compute(pair.InputArray, numArray);
calculation.UpdateError(numArray, pair.IdealArray, pair.Significance);
goto Label_0031;
}
if ((((uint) num) | 8) != 0)
{
return calculation.Calculate();
}
Label_0057:
numArray = new double[this._outputCount];
if (0 != 0)
{
goto Label_0031;
}
pair = BasicMLDataPair.CreatePair(data.InputSize, data.IdealSize);
num = 0;
goto Label_0035;
}示例3: CalculateError
/// <summary>
/// Calculate the error for the entire training set.
/// </summary>
///
/// <param name="training">Training set to use.</param>
/// <param name="deriv">Should we find the derivative.</param>
/// <returns>The error.</returns>
public double CalculateError(IMLDataSet training,
bool deriv)
{
double totErr;
double diff;
totErr = 0.0d;
if (deriv)
{
int num = (_network.SeparateClass)
? _network.InputCount*_network.OutputCount
: _network.InputCount;
for (int i = 0; i < num; i++)
{
_network.Deriv[i] = 0.0d;
_network.Deriv2[i] = 0.0d;
}
}
_network.Exclude = (int) training.Count;
IMLDataPair pair = BasicMLDataPair.CreatePair(
training.InputSize, training.IdealSize);
var xout = new double[_network.OutputCount];
for (int r = 0; r < training.Count; r++)
{
training.GetRecord(r, pair);
_network.Exclude = _network.Exclude - 1;
double err = 0.0d;
IMLData input = pair.Input;
IMLData target = pair.Ideal;
if (_network.OutputMode == PNNOutputMode.Unsupervised)
{
if (deriv)
{
IMLData output = ComputeDeriv(input, target);
for (int z = 0; z < _network.OutputCount; z++)
{
xout[z] = output[z];
}
}
else
{
IMLData output = _network.Compute(input);
for (int z = 0; z < _network.OutputCount; z++)
{
xout[z] = output[z];
}
}
for (int i = 0; i < _network.OutputCount; i++)
{
diff = input[i] - xout[i];
err += diff*diff;
}
}
else if (_network.OutputMode == PNNOutputMode.Classification)
{
var tclass = (int) target[0];
IMLData output;
if (deriv)
{
output = ComputeDeriv(input, pair.Ideal);
//output_4.GetData(0); //**FIX**?
}
else
{
output = _network.Compute(input);
//output_4.GetData(0); **FIX**?
}
xout[0] = output[0];
for (int i = 0; i < xout.Length; i++)
{
if (i == tclass)
{
diff = 1.0d - xout[i];
err += diff*diff;
}
else
{
err += xout[i]*xout[i];
}
}
}
else if (_network.OutputMode == PNNOutputMode.Regression)
//.........这里部分代码省略.........