public static double CalculateError(IMLRegression method, IMLDataSet data)
{
ErrorCalculation calculation = new ErrorCalculation();
while (method is IMLContext)
{
((IMLContext) method).ClearContext();
break;
}
foreach (IMLDataPair pair in data)
{
IMLData data2 = method.Compute(pair.Input);
calculation.UpdateError(data2.Data, pair.Ideal.Data, pair.Significance);
}
return calculation.Calculate();
}
/// <summary>
/// Calculate an error for a method that uses regression.
/// </summary>
/// <param name="method">The method to evaluate.</param>
/// <param name="data">The training data to evaluate with.</param>
/// <returns>The error.</returns>
public static double CalculateError(IMLRegression method,
IMLDataSet data)
{
var errorCalculation = new ErrorCalculation();
// clear context
if (method is IMLContext)
{
((IMLContext) method).ClearContext();
}
// calculate error
foreach (IMLDataPair pair in data)
{
IMLData actual = method.Compute(pair.Input);
errorCalculation.UpdateError(actual, pair.Ideal, pair.Significance);
}
return errorCalculation.Calculate();
}
/// <inheritdoc/>
public override sealed void Iteration()
{
var errorCalculation = new ErrorCalculation();
foreach (IMLDataPair pair in _training)
{
// calculate the error
IMLData output = _network.Compute(pair.Input);
for (int currentAdaline = 0; currentAdaline < output.Count; currentAdaline++)
{
double diff = pair.Ideal[currentAdaline]
- output[currentAdaline];
// weights
for (int i = 0; i <= _network.InputCount; i++)
{
double input;
if (i == _network.InputCount)
{
input = 1.0d;
}
else
{
input = pair.Input[i];
}
_network.AddWeight(0, i, currentAdaline,
_learningRate*diff*input);
}
}
errorCalculation.UpdateError(output, pair.Ideal, pair.Significance);
}
// set the global error
Error = errorCalculation.Calculate();
}
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;
}
/// <inheritdoc />
public override sealed void Iteration()
{
if (_mustInit)
{
InitWeight();
}
var error = new ErrorCalculation();
foreach (IMLDataPair pair in _training)
{
IMLData xout = _network.ComputeInstar(pair.Input);
int j = EngineArray.IndexOfLargest(xout.Data);
for (int i = 0; i < _network.OutstarCount; i++)
{
double delta = _learningRate
*(pair.Ideal[i] - _network.WeightsInstarToOutstar[j, i]);
_network.WeightsInstarToOutstar.Add(j, i, delta);
}
IMLData out2 = _network.ComputeOutstar(xout);
error.UpdateError(out2.Data, pair.Ideal.Data, pair.Significance);
}
Error = error.Calculate();
}
public override sealed void Iteration()
{
if (this._x268cb8b20222b0dc)
{
this.xabfa4e7d76a2422c();
}
ErrorCalculation calculation = new ErrorCalculation();
using (IEnumerator<IMLDataPair> enumerator = this._x823a2b9c8bf459c5.GetEnumerator())
{
IMLDataPair pair;
IMLData data;
int num;
int num2;
double num3;
IMLData data2;
goto Label_005E;
Label_0023:
if (num2 < this._x87a7fc6a72741c2e.OutstarCount)
{
goto Label_0091;
}
Label_0032:
data2 = this._x87a7fc6a72741c2e.ComputeOutstar(data);
calculation.UpdateError(data2.Data, pair.Ideal.Data, pair.Significance);
Label_005E:
if (!enumerator.MoveNext() && ((((uint) num2) - ((uint) num3)) <= uint.MaxValue))
{
goto Label_014D;
}
goto Label_00D2;
Label_0084:
num2++;
goto Label_0124;
Label_0091:
num3 = this._x9b481c22b6706459 * (pair.Ideal[num2] - this._x87a7fc6a72741c2e.WeightsInstarToOutstar[num, num2]);
this._x87a7fc6a72741c2e.WeightsInstarToOutstar.Add(num, num2, num3);
goto Label_0084;
Label_00D2:
pair = enumerator.Current;
data = this._x87a7fc6a72741c2e.ComputeInstar(pair.Input);
num = EngineArray.IndexOfLargest(data.Data);
num2 = 0;
if ((((uint) num2) <= uint.MaxValue) && (-2 == 0))
{
goto Label_0032;
}
goto Label_0023;
Label_0124:
if ((((uint) num) - ((uint) num)) <= uint.MaxValue)
{
goto Label_0023;
}
}
Label_014D:
this.Error = calculation.Calculate();
}
/// <summary>
/// Evaluate the error for the specified model.
/// </summary>
///
/// <param name="param">The params for the SVN.</param>
/// <param name="prob">The problem to evaluate.</param>
/// <param name="target">The output values from the SVN.</param>
/// <returns>The calculated error.</returns>
private static double Evaluate(svm_parameter param, svm_problem prob,
double[] target)
{
int totalCorrect = 0;
var error = new ErrorCalculation();
if ((param.svm_type == svm_parameter.EPSILON_SVR)
|| (param.svm_type == svm_parameter.NU_SVR))
{
for (int i = 0; i < prob.l; i++)
{
double ideal = prob.y[i];
double actual = target[i];
error.UpdateError(actual, ideal);
}
return error.Calculate();
}
for (int i = 0; i < prob.l; i++)
{
if (target[i] == prob.y[i])
{
++totalCorrect;
}
}
return Format.HundredPercent*totalCorrect/prob.l;
}
/// <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];
for (int i = 0; i < data.Count; i++)
{
IMLDataPair pair = data[i];
Compute(pair.Input, actual);
errorCalculation.UpdateError(actual, pair.Ideal, pair.Significance);
}
return errorCalculation.Calculate();
}
/// <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();
}
/// <summary>
/// Process training for pure batch mode (one single batch).
/// </summary>
protected void ProcessPureBatch()
{
var errorCalc = new ErrorCalculation();
_visited.Clear();
foreach (IMLDataPair pair in _training) {
var input = pair.Input;
var ideal = pair.Ideal;
var actual = _network.Compute(input);
var sig = pair.Significance;
errorCalc.UpdateError(actual, ideal, sig);
for (int i = 0; i < _network.OutputCount; i++) {
var diff = (ideal[i] - actual[i])
* sig;
IFreeformNeuron neuron = _network.OutputLayer.Neurons[i];
CalculateOutputDelta(neuron, diff);
CalculateNeuronGradient(neuron);
}
}
// Set the overall error.
Error = errorCalc.Calculate();
// Learn for all data.
Learn();
}
/// <summary>
/// Process training batches.
/// </summary>
protected void ProcessBatches()
{
int lastLearn = 0;
var errorCalc = new ErrorCalculation();
_visited.Clear();
foreach (IMLDataPair pair in _training) {
var input = pair.Input;
var ideal = pair.Ideal;
var actual = _network.Compute(input);
var sig = pair.Significance;
errorCalc.UpdateError(actual, ideal, sig);
for (int i = 0; i < _network.OutputCount; i++) {
double diff = (ideal[i] - actual[i])
* sig;
IFreeformNeuron neuron = _network.OutputLayer.Neurons[i];
CalculateOutputDelta(neuron, diff);
CalculateNeuronGradient(neuron);
}
// Are we at the end of a batch.
lastLearn++;
if( lastLearn>=BatchSize ) {
lastLearn = 0;
Learn();
}
}
// Handle any remaining data.
if( lastLearn>0 ) {
Learn();
}
// Set the overall error.
Error = errorCalc.Calculate();
}
public override sealed void Iteration()
{
ErrorCalculation calculation = new ErrorCalculation();
using (IEnumerator<IMLDataPair> enumerator = this._x823a2b9c8bf459c5.GetEnumerator())
{
IMLDataPair pair;
IMLData data;
int num;
double num2;
int num3;
double num4;
goto Label_0032;
Label_0015:
calculation.UpdateError(data.Data, pair.Ideal.Data, pair.Significance);
Label_0032:
if (enumerator.MoveNext())
{
goto Label_010B;
}
if ((((uint) num2) - ((uint) num2)) > uint.MaxValue)
{
goto Label_0071;
}
goto Label_0151;
Label_0058:
num3++;
Label_005E:
if (num3 <= this._x87a7fc6a72741c2e.InputCount)
{
goto Label_00AE;
}
num++;
Label_0071:
if (num < data.Count)
{
goto Label_00ED;
}
goto Label_0015;
Label_0084:
this._x87a7fc6a72741c2e.AddWeight(0, num3, num, (this._x9b481c22b6706459 * num2) * num4);
goto Label_0058;
Label_00A1:
num4 = 1.0;
goto Label_0084;
Label_00AE:
if (num3 == this._x87a7fc6a72741c2e.InputCount)
{
goto Label_00A1;
}
num4 = pair.Input[num3];
if ((((uint) num) + ((uint) num)) <= uint.MaxValue)
{
goto Label_0084;
}
goto Label_0058;
Label_00E6:
num = 0;
goto Label_0071;
Label_00ED:
num2 = pair.Ideal[num] - data[num];
num3 = 0;
goto Label_005E;
Label_010B:
pair = enumerator.Current;
if ((((uint) num4) & 0) != 0)
{
goto Label_00AE;
}
data = this._x87a7fc6a72741c2e.Compute(pair.Input);
goto Label_00E6;
}
Label_0151:
this.Error = calculation.Calculate();
}
public override sealed void Iteration()
{
if (this._mustInit)
this.InitWeight();
ErrorCalculation errorCalculation = new ErrorCalculation();
foreach (IMLDataPair mlDataPair in this._training)
{
IMLData instar = this._network.ComputeInstar(mlDataPair.Input);
int row = EngineArray.IndexOfLargest(instar);
for (int col = 0; col < this._network.OutstarCount; ++col)
{
double value_ren = this._learningRate * (mlDataPair.Ideal[col] - this._network.WeightsInstarToOutstar[row, col]);
this._network.WeightsInstarToOutstar.Add(row, col, value_ren);
}
IMLData outstar = this._network.ComputeOutstar(instar);
errorCalculation.UpdateError(outstar, mlDataPair.Ideal, mlDataPair.Significance);
}
this.Error = errorCalculation.Calculate();
}