| public Solve ( System.Matrix B ) : System.Matrix | ||
| B | System.Matrix | A Matrix with as many rows as A and any number of columns. |
| return | System.Matrix | |
public override void Iteration()
{
LUDecomposition decomposition = null;
double num;
double num2;
double num3;
double num4;
int num5;
int num6;
IMLData data;
double num7;
double num8;
if ((((uint) num4) | 15) != 0)
{
goto Label_0399;
}
goto Label_01C7;
Label_0022:
base.PostIteration();
if ((((uint) num5) & 0) == 0)
{
return;
}
goto Label_01C7;
Label_0044:
this._xd7d571ecee49d1e4 = Math.Abs((double) ((this._x8557b7ee760663f3 - this._xc7c4e9c099884228) / (2.0 * num)));
Label_0073:
this.Error = num;
goto Label_0022;
Label_00F1:
if ((num4 >= num3) && (this._x3271cefb1a159639 < 1E+25))
{
this._x3271cefb1a159639 *= 10.0;
num5 = 0;
Label_01EF:
if (num5 < this._xe2982b936ae423cd)
{
this._xc410e3804222557a[num5][num5] = this._x3cb63876dda4b74a[num5] + (this._x3271cefb1a159639 + this._x6ad505c7ef981b0e);
num5++;
if ((((uint) num7) + ((uint) num3)) >= 0)
{
goto Label_01EF;
}
}
else
{
if ((((uint) num7) + ((uint) num8)) > uint.MaxValue)
{
return;
}
decomposition = new LUDecomposition(this._x05fb16197e552de6);
if (!decomposition.IsNonsingular)
{
goto Label_00F1;
}
this._xdadd8f92d75a3aba = decomposition.Solve(this._x878c4eb3cef19a5a);
}
if (((uint) num6) >= 0)
{
if (0 != 0)
{
goto Label_0340;
}
goto Label_01C7;
}
goto Label_029A;
}
this._x3271cefb1a159639 /= 10.0;
if (this._x6c57e14b737e51f6 && (decomposition != null))
{
num8 = Trace(decomposition.Inverse());
this._xc7c4e9c099884228 = this._xe2982b936ae423cd - (this._x6ad505c7ef981b0e * num8);
if (((uint) num7) < 0)
{
goto Label_0044;
}
this._x6ad505c7ef981b0e = ((double) this._xe2982b936ae423cd) / ((2.0 * num2) + num8);
if ((((uint) num4) | 0xfffffffe) != 0)
{
goto Label_0044;
}
goto Label_0022;
}
goto Label_0073;
Label_0137:
num7 = this._x61830ac74d65acc3.Ideal[0] - data[0];
num += num7 * num7;
num6++;
Label_0161:
if (num6 < this._x8557b7ee760663f3)
{
this._xb12276308f0fa6d9.GetRecord((long) num6, this._x61830ac74d65acc3);
data = this._x87a7fc6a72741c2e.Compute(this._x61830ac74d65acc3.Input);
goto Label_0137;
}
num /= 2.0;
num4 = (this._xd7d571ecee49d1e4 * num) + (this._x6ad505c7ef981b0e * num2);
goto Label_00F1;
Label_01C7:
num2 = this.UpdateWeights();
num = 0.0;
num6 = 0;
goto Label_0161;
Label_029A:
if ((((uint) num7) | 0xff) == 0)
{
goto Label_0137;
}
this._x3271cefb1a159639 /= 10.0;
goto Label_00F1;
Label_0340:
num4 = num3 + 1.0;
if ((((uint) num4) - ((uint) num6)) >= 0)
{
goto Label_029A;
}
Label_0399:
base.PreIteration();
this._x2f33d779e5a20b28 = NetworkCODEC.NetworkToArray(this._x87a7fc6a72741c2e);
if ((((uint) num8) | 4) == 0)
{
goto Label_0022;
}
IComputeJacobian jacobian = new JacobianChainRule(this._x87a7fc6a72741c2e, this._xb12276308f0fa6d9);
num = jacobian.Calculate(this._x2f33d779e5a20b28);
num2 = this.x01818299df58497e();
this.CalculateHessian(jacobian.Jacobian, jacobian.RowErrors);
num3 = (this._xd7d571ecee49d1e4 * num) + (this._x6ad505c7ef981b0e * num2);
goto Label_0340;
}
/// <inheritdoc />
public override void Iteration()
{
if (!_initComplete)
{
_hessian.Init(_network, Training);
_initComplete = true;
}
PreIteration();
_hessian.Clear();
_weights = NetworkCODEC.NetworkToArray(_network);
_hessian.Compute();
double currentError = _hessian.SSE;
SaveDiagonal();
double startingError = currentError;
bool done = false;
bool singular;
while (!done)
{
ApplyLambda();
var decomposition = new LUDecomposition(_hessian.HessianMatrix);
singular = decomposition.IsNonsingular;
if (singular)
{
_deltas = decomposition.Solve(_hessian.Gradients);
UpdateWeights();
currentError = CalculateError();
}
if (!singular || currentError >= startingError)
{
_lambda *= ScaleLambda;
if (_lambda > LambdaMax)
{
_lambda = LambdaMax;
done = true;
}
}
else
{
_lambda /= ScaleLambda;
done = true;
}
}
Error = currentError;
PostIteration();
}
/// <summary>
/// Perform one iteration.
/// </summary>
///
public override void Iteration()
{
LUDecomposition decomposition = null;
PreIteration();
_weights = NetworkCODEC.NetworkToArray(_network);
IComputeJacobian j = new JacobianChainRule(_network,
_indexableTraining);
double sumOfSquaredErrors = j.Calculate(_weights);
double sumOfSquaredWeights = CalculateSumOfSquaredWeights();
// this.setError(j.getError());
CalculateHessian(j.Jacobian, j.RowErrors);
// Define the objective function
// bayesian regularization objective function
double objective = _beta*sumOfSquaredErrors + _alpha
*sumOfSquaredWeights;
double current = objective + 1.0d;
// Start the main Levenberg-Macquardt method
_lambda /= ScaleLambda;
// We'll try to find a direction with less error
// (or where the objective function is smaller)
while ((current >= objective)
&& (_lambda < LambdaMax))
{
_lambda *= ScaleLambda;
// Update diagonal (Levenberg-Marquardt formula)
for (int i = 0; i < _parametersLength; i++)
{
_hessian[i][i] = _diagonal[i]
+ (_lambda + _alpha);
}
// Decompose to solve the linear system
decomposition = new LUDecomposition(_hessianMatrix);
// Check if the Jacobian has become non-invertible
if (!decomposition.IsNonsingular)
{
continue;
}
// Solve using LU (or SVD) decomposition
_deltas = decomposition.Solve(_gradient);
// Update weights using the calculated deltas
sumOfSquaredWeights = UpdateWeights();
// Calculate the new error
sumOfSquaredErrors = 0.0d;
for (int i = 0; i < _trainingLength; i++)
{
_indexableTraining.GetRecord(i, _pair);
IMLData actual = _network
.Compute(_pair.Input);
double e = _pair.Ideal[0]
- actual[0];
sumOfSquaredErrors += e*e;
}
sumOfSquaredErrors /= 2.0d;
// Update the objective function
current = _beta*sumOfSquaredErrors + _alpha
*sumOfSquaredWeights;
// If the object function is bigger than before, the method
// is tried again using a greater dumping factor.
}
// If this iteration caused a error drop, then next iteration
// will use a smaller damping factor.
_lambda /= ScaleLambda;
if (_useBayesianRegularization && (decomposition != null))
{
// Compute the trace for the inverse Hessian
double trace = Trace(decomposition.Inverse());
// Poland update's formula:
_gamma = _parametersLength - (_alpha*trace);
_alpha = _parametersLength
/(2.0d*sumOfSquaredWeights + trace);
_beta = Math.Abs((_trainingLength - _gamma)
/(2.0d*sumOfSquaredErrors));
}
Error = sumOfSquaredErrors;
PostIteration();
}
/// <summary>
/// Perform one iteration.
/// </summary>
public override void Iteration()
{
LUDecomposition decomposition;
PreIteration();
_hessian.Clear();
_weights = NetworkCODEC.NetworkToArray(_network);
_hessian.Compute();
double currentError = _hessian.SSE;
SaveDiagonal();
double startingError = currentError;
bool done = false;
while (!done)
{
ApplyLambda();
decomposition = new LUDecomposition(_hessian.HessianMatrix);
if (decomposition.IsNonsingular)
{
_deltas = decomposition.Solve(_hessian.Gradients);
UpdateWeights();
currentError = CalculateError();
if (currentError < startingError)
{
_lambda /= LevenbergMarquardtTraining.ScaleLambda;
done = true;
}
}
if (!done)
{
_lambda *= LevenbergMarquardtTraining.ScaleLambda;
if (_lambda > LevenbergMarquardtTraining.LambdaMax)
{
_lambda = LevenbergMarquardtTraining.LambdaMax;
done = true;
}
}
}
Error = currentError;
PostIteration();
}
