| public FindBestRange ( double low, double high, int numberOfPoints, bool useLog, double minError, ICalculationCriteria network ) : void | ||
| low | double | The low gamma to begin the search with. |
| high | double | The high gamma to end the search with. |
| numberOfPoints | int | If you do set this to negative, set x2 and y2 to the correct values. |
| useLog | bool | Should we progress "logarithmically" from low to high. |
| minError | double | We are done if the error is below this. |
| network | ICalculationCriteria | The network to evaluate. |
| Результат | void | |
/// <summary>
///
/// </summary>
///
public override sealed void Iteration()
{
if (!_samplesLoaded)
{
_network.Samples = new BasicMLDataSet(_training);
_samplesLoaded = true;
}
var globalMinimum = new GlobalMinimumSearch();
var dermin = new DeriveMinimum();
int k;
if (_network.OutputMode == PNNOutputMode.Classification)
{
k = _network.OutputCount;
}
else
{
k = _network.OutputCount + 1;
}
_dsqr = new double[_network.InputCount];
_v = new double[_network.InputCount*k];
_w = new double[_network.InputCount*k];
var x = new double[_network.InputCount];
var bs = new double[_network.InputCount];
var direc = new double[_network.InputCount];
var g = new double[_network.InputCount];
var h = new double[_network.InputCount];
var dwk2 = new double[_network.InputCount];
if (_network.Trained)
{
for (int i = 0; i < _network.InputCount; i++)
{
x[i] = _network.Sigma[i];
}
globalMinimum.Y2 = 1.0e30d;
}
else
{
globalMinimum.FindBestRange(_sigmaLow, _sigmaHigh,
_numSigmas, true, _maxError, this);
for (int i = 0; i < _network.InputCount; i++)
{
x[i] = globalMinimum.X2;
}
}
double d = dermin.Calculate(32767, _maxError, 1.0e-8d,
_minImprovement, this, _network.InputCount, x,
globalMinimum.Y2, bs, direc, g, h, dwk2);
globalMinimum.Y2 = d;
for (int i = 0; i < _network.InputCount; i++)
{
_network.Sigma[i] = x[i];
}
_network.Error = Math.Abs(globalMinimum.Y2);
_network.Trained = true; // Tell other routines net is trained
return;
}
/// <summary>
/// Derive the minimum, using a conjugate gradient method.
/// </summary>
///
/// <param name="maxIterations">The max iterations.</param>
/// <param name="maxError">Stop at this error rate.</param>
/// <param name="eps">The machine's precision.</param>
/// <param name="tol">The convergence tolerance.</param>
/// <param name="network">The network to get the error from.</param>
/// <param name="n">The number of variables.</param>
/// <param name="x">The independent variable.</param>
/// <param name="ystart">The start for y.</param>
/// <param name="bs">Work vector, must have n elements.</param>
/// <param name="direc">Work vector, must have n elements.</param>
/// <param name="g">Work vector, must have n elements.</param>
/// <param name="h">Work vector, must have n elements.</param>
/// <param name="deriv2">Work vector, must have n elements.</param>
/// <returns>The best error.</returns>
public double Calculate(int maxIterations, double maxError,
double eps, double tol,
ICalculationCriteria network, int n, double[] x,
double ystart, double[] bs, double[] direc,
double[] g, double[] h, double[] deriv2)
{
var globalMinimum = new GlobalMinimumSearch();
double fbest = network.CalcErrorWithMultipleSigma(x, direc, deriv2,
true);
double prevBest = 1.0e30d;
for (int i = 0; i < n; i++)
{
direc[i] = -direc[i];
}
EngineArray.ArrayCopy(direc, g);
EngineArray.ArrayCopy(direc, h);
int convergenceCounter = 0;
int poorCj = 0;
// Main loop
for (int iteration = 0; iteration < maxIterations; iteration++)
{
if (fbest < maxError)
{
break;
}
EncogLogging.Log(EncogLogging.LevelInfo,
"Beginning internal Iteration #" + iteration + ", currentError=" + fbest + ",target=" + maxError);
// Check for convergence
double toler;
if (prevBest <= 1.0d)
{
toler = tol;
}
else
{
toler = tol * prevBest;
}
// Stop if there is little improvement
if ((prevBest - fbest) <= toler)
{
if (++convergenceCounter >= 3)
{
break;
}
}
else
{
convergenceCounter = 0;
}
double dot2 = 0;
double dlen = 0;
double dot1 = dot2 = dlen = 0.0d;
double high = 1.0e-4d;
for (int i = 0; i < n; i++)
{
bs[i] = x[i];
if (deriv2[i] > high)
{
high = deriv2[i];
}
dot1 += direc[i] * g[i]; // Directional first derivative
dot2 += direc[i] * direc[i] * deriv2[i]; // and second
dlen += direc[i] * direc[i]; // Length of search vector
}
double scale;
if (Math.Abs(dot2) < EncogFramework.DefaultDoubleEqual)
{
scale = 0;
}
else
{
scale = dot1 / dot2;
}
high = 1.5d / high;
if (high < 1.0e-4d)
{
high = 1.0e-4d;
}
if (scale < 0.0d)
{
scale = high;
}
else if (scale < 0.1d * high)
{
scale = 0.1d * high;
}
else if (scale > 10.0d * high)
{
scale = 10.0d * high;
}
prevBest = fbest;
globalMinimum.Y2 = fbest;
globalMinimum.FindBestRange(0.0d, 2.0d * scale, -3, false, maxError,
network);
if (globalMinimum.Y2 < maxError)
{
if (globalMinimum.Y2 < fbest)
{
for (int i = 0; i < n; i++)
{
x[i] = bs[i] + globalMinimum.Y2 * direc[i];
if (x[i] < 1.0e-10d)
{
x[i] = 1.0e-10d;
}
}
fbest = globalMinimum.Y2;
}
else
{
for (int i = 0; i < n; i++)
{
x[i] = bs[i];
}
}
break;
}
if (convergenceCounter > 0)
{
fbest = globalMinimum.Brentmin(20, maxError, eps, 1.0e-7d,
network, globalMinimum.Y2);
}
else
{
fbest = globalMinimum.Brentmin(10, maxError, 1.0e-6d, 1.0e-5d,
network, globalMinimum.Y2);
}
for (int i = 0; i < n; i++)
{
x[i] = bs[i] + globalMinimum.X2 * direc[i];
if (x[i] < 1.0e-10d)
{
x[i] = 1.0e-10d;
}
}
double improvement = (prevBest - fbest) / prevBest;
if (fbest < maxError)
{
break;
}
for (int i = 0; i < n; i++)
{
direc[i] = -direc[i]; // negative gradient
}
double gam = Gamma(n, g, direc);
if (gam < 0.0d)
{
gam = 0.0d;
}
if (gam > 10.0d)
{
gam = 10.0d;
}
if (improvement < 0.001d)
{
++poorCj;
}
else
{
poorCj = 0;
}
if (poorCj >= 2)
{
if (gam > 1.0d)
{
gam = 1.0d;
}
}
if (poorCj >= 6)
{
poorCj = 0;
gam = 0.0d;
}
FindNewDir(n, gam, g, h, direc);
}
return(fbest);
}
/// <summary>
/// Derive the minimum, using a conjugate gradient method.
/// </summary>
///
/// <param name="maxIterations">The max iterations.</param>
/// <param name="maxError">Stop at this error rate.</param>
/// <param name="eps">The machine's precision.</param>
/// <param name="tol">The convergence tolerance.</param>
/// <param name="network">The network to get the error from.</param>
/// <param name="n">The number of variables.</param>
/// <param name="x">The independent variable.</param>
/// <param name="ystart">The start for y.</param>
/// <param name="bs">Work vector, must have n elements.</param>
/// <param name="direc">Work vector, must have n elements.</param>
/// <param name="g">Work vector, must have n elements.</param>
/// <param name="h">Work vector, must have n elements.</param>
/// <param name="deriv2">Work vector, must have n elements.</param>
/// <returns>The best error.</returns>
public double Calculate(int maxIterations, double maxError,
double eps, double tol,
ICalculationCriteria network, int n, double[] x,
double ystart, double[] bs, double[] direc,
double[] g, double[] h, double[] deriv2)
{
var globalMinimum = new GlobalMinimumSearch();
double fbest = network.CalcErrorWithMultipleSigma(x, direc, deriv2,
true);
double prevBest = 1.0e30d;
for (int i = 0; i < n; i++)
{
direc[i] = -direc[i];
}
EngineArray.ArrayCopy(direc, g);
EngineArray.ArrayCopy(direc, h);
int convergenceCounter = 0;
int poorCj = 0;
// Main loop
for (int iteration = 0; iteration < maxIterations; iteration++)
{
if (fbest < maxError)
{
break;
}
EncogLogging.Log(EncogLogging.LevelInfo,
"Beginning internal Iteration #" + iteration + ", currentError=" + fbest + ",target=" + maxError);
// Check for convergence
double toler;
if (prevBest <= 1.0d)
{
toler = tol;
}
else
{
toler = tol*prevBest;
}
// Stop if there is little improvement
if ((prevBest - fbest) <= toler)
{
if (++convergenceCounter >= 3)
{
break;
}
}
else
{
convergenceCounter = 0;
}
double dot2 = 0;
double dlen = 0;
double dot1 = dot2 = dlen = 0.0d;
double high = 1.0e-4d;
for (int i= 0; i < n; i++)
{
bs[i] = x[i];
if (deriv2[i] > high)
{
high = deriv2[i];
}
dot1 += direc[i]*g[i]; // Directional first derivative
dot2 += direc[i]*direc[i]*deriv2[i]; // and second
dlen += direc[i]*direc[i]; // Length of search vector
}
double scale;
if (Math.Abs(dot2) < EncogFramework.DefaultDoubleEqual)
{
scale = 0;
}
else
{
scale = dot1/dot2;
}
high = 1.5d/high;
if (high < 1.0e-4d)
{
high = 1.0e-4d;
}
if (scale < 0.0d)
{
scale = high;
}
else if (scale < 0.1d*high)
{
scale = 0.1d*high;
}
else if (scale > 10.0d*high)
{
scale = 10.0d*high;
}
prevBest = fbest;
globalMinimum.Y2 = fbest;
globalMinimum.FindBestRange(0.0d, 2.0d*scale, -3, false, maxError,
network);
if (globalMinimum.Y2 < maxError)
{
if (globalMinimum.Y2 < fbest)
{
for (int i = 0; i < n; i++)
{
x[i] = bs[i] + globalMinimum.Y2*direc[i];
if (x[i] < 1.0e-10d)
{
x[i] = 1.0e-10d;
}
}
fbest = globalMinimum.Y2;
}
else
{
for (int i = 0; i < n; i++)
{
x[i] = bs[i];
}
}
break;
}
if (convergenceCounter > 0)
{
fbest = globalMinimum.Brentmin(20, maxError, eps, 1.0e-7d,
network, globalMinimum.Y2);
}
else
{
fbest = globalMinimum.Brentmin(10, maxError, 1.0e-6d, 1.0e-5d,
network, globalMinimum.Y2);
}
for (int i = 0; i < n; i++)
{
x[i] = bs[i] + globalMinimum.X2*direc[i];
if (x[i] < 1.0e-10d)
{
x[i] = 1.0e-10d;
}
}
double improvement = (prevBest - fbest)/prevBest;
if (fbest < maxError)
{
break;
}
for (int i = 0; i < n; i++)
{
direc[i] = -direc[i]; // negative gradient
}
double gam = Gamma(n, g, direc);
if (gam < 0.0d)
{
gam = 0.0d;
}
if (gam > 10.0d)
{
gam = 10.0d;
}
if (improvement < 0.001d)
{
++poorCj;
}
else
{
poorCj = 0;
}
if (poorCj >= 2)
{
if (gam > 1.0d)
{
gam = 1.0d;
}
}
if (poorCj >= 6)
{
poorCj = 0;
gam = 0.0d;
}
FindNewDir(n, gam, g, h, direc);
}
return fbest;
}
/// <summary>
///
/// </summary>
///
public override sealed void Iteration()
{
if (!_samplesLoaded)
{
_network.Samples = new BasicMLDataSet(_training);
_samplesLoaded = true;
}
var globalMinimum = new GlobalMinimumSearch();
var dermin = new DeriveMinimum();
int k;
if (_network.OutputMode == PNNOutputMode.Classification)
{
k = _network.OutputCount;
}
else
{
k = _network.OutputCount + 1;
}
_dsqr = new double[_network.InputCount];
_v = new double[_network.InputCount * k];
_w = new double[_network.InputCount * k];
var x = new double[_network.InputCount];
var bs = new double[_network.InputCount];
var direc = new double[_network.InputCount];
var g = new double[_network.InputCount];
var h = new double[_network.InputCount];
var dwk2 = new double[_network.InputCount];
if (_network.Trained)
{
for (int i = 0; i < _network.InputCount; i++)
{
x[i] = _network.Sigma[i];
}
globalMinimum.Y2 = 1.0e30d;
}
else
{
globalMinimum.FindBestRange(_sigmaLow, _sigmaHigh,
_numSigmas, true, _maxError, this);
for (int i = 0; i < _network.InputCount; i++)
{
x[i] = globalMinimum.X2;
}
}
double d = dermin.Calculate(32767, _maxError, 1.0e-8d,
_minImprovement, this, _network.InputCount, x,
globalMinimum.Y2, bs, direc, g, h, dwk2);
globalMinimum.Y2 = d;
for (int i = 0; i < _network.InputCount; i++)
{
_network.Sigma[i] = x[i];
}
_network.Error = Math.Abs(globalMinimum.Y2);
_network.Trained = true; // Tell other routines net is trained
return;
}
public double Calculate(int maxIterations, double maxError, double eps, double tol, ICalculationCriteria network, int n, double[] x, double ystart, double[] bs, double[] direc, double[] g, double[] h, double[] deriv2)
{
double num;
double num2;
int num3;
int num4;
int num5;
int num6;
double num7;
double num8;
double num9;
double num10;
double num11;
int num12;
double num13;
int num14;
int num16;
double num19;
GlobalMinimumSearch search = new GlobalMinimumSearch();
if ((((uint) ystart) | 8) != 0)
{
num = network.CalcErrorWithMultipleSigma(x, direc, deriv2, true);
num2 = 1E+30;
num3 = 0;
goto Label_07E4;
}
goto Label_03E1;
Label_003D:
if (num6 >= maxIterations)
{
if ((((uint) num19) + ((uint) num11)) < 0)
{
if (((uint) num7) <= uint.MaxValue)
{
goto Label_005E;
}
goto Label_003D;
}
if (((uint) maxError) < 0)
{
goto Label_0080;
}
if (((uint) num10) <= uint.MaxValue)
{
return num;
}
goto Label_039C;
}
Label_005E:
if (num >= maxError)
{
goto Label_071B;
}
return num;
Label_0080:
if (num5 >= 6)
{
goto Label_0096;
}
Label_0085:
xbfc8da7bffe4bca2(n, num19, g, h, direc);
num6++;
goto Label_003D;
Label_0096:
num5 = 0;
num19 = 0.0;
goto Label_0085;
Label_009D:
if (num5 < 2)
{
goto Label_0080;
}
Label_00A2:
if (num19 <= 1.0)
{
goto Label_0080;
}
Label_00DF:
num19 = 1.0;
goto Label_0080;
Label_0213:
if (num16 < n)
{
x[num16] = bs[num16] + (search.X2 * direc[num16]);
if (((((uint) num3) | 0xfffffffe) == 0) || (x[num16] < 1E-10))
{
x[num16] = 1E-10;
}
goto Label_0276;
}
double num17 = (num2 - num) / num2;
if ((((uint) num10) - ((uint) ystart)) < 0)
{
goto Label_045B;
}
if ((((uint) num6) - ((uint) num14)) > uint.MaxValue)
{
goto Label_04C5;
}
if (num < maxError)
{
return num;
}
int index = 0;
Label_019D:
if (index < n)
{
direc[index] = -direc[index];
if ((((uint) n) - ((uint) num13)) >= 0)
{
index++;
goto Label_019D;
}
}
else
{
num19 = xdf79f321ca5c3af5(n, g, direc);
}
if (4 != 0)
{
if (num19 < 0.0)
{
num19 = 0.0;
}
if (num19 > 10.0)
{
num19 = 10.0;
}
else if ((((uint) maxError) | 0xff) == 0)
{
goto Label_06FD;
}
if (num17 >= 0.001)
{
num5 = 0;
}
else
{
if ((((uint) num10) | 1) == 0)
{
goto Label_0096;
}
num5++;
}
goto Label_009D;
}
if ((((uint) num12) & 0) == 0)
{
goto Label_029C;
}
Label_0276:
num16++;
if (0 == 0)
{
if (((uint) maxError) > uint.MaxValue)
{
goto Label_031E;
}
goto Label_0213;
}
Label_029C:
num = search.Brentmin(10, maxError, 1E-06, 1E-05, network, search.Y2);
Label_02C0:
num16 = 0;
if ((((uint) maxIterations) - ((uint) maxError)) > uint.MaxValue)
{
goto Label_07E9;
}
goto Label_0213;
Label_031E:
if (num4 > 0)
{
num = search.Brentmin(20, maxError, eps, 1E-07, network, search.Y2);
goto Label_02C0;
}
if ((((uint) num16) + ((uint) num2)) <= uint.MaxValue)
{
goto Label_029C;
}
goto Label_0276;
Label_0365:
if (search.Y2 >= maxError)
{
goto Label_031E;
}
Label_039C:
if (search.Y2 < num)
{
num14 = 0;
goto Label_041C;
}
int num15 = 0;
Label_0391:
if (num15 < n)
{
x[num15] = bs[num15];
num15++;
if ((((uint) n) + ((uint) num15)) >= 0)
{
goto Label_0391;
}
}
else
{
return num;
}
if ((((uint) num13) - ((uint) n)) >= 0)
{
goto Label_031E;
}
goto Label_0365;
Label_03E1:
x[num14] = bs[num14] + (search.Y2 * direc[num14]);
if (x[num14] < 1E-10)
{
x[num14] = 1E-10;
}
num14++;
Label_041C:
if (num14 < n)
{
goto Label_03E1;
}
return search.Y2;
Label_045B:
num13 = 10.0 * num11;
Label_0469:
num2 = num;
search.Y2 = num;
search.FindBestRange(0.0, 2.0 * num13, -3, false, maxError, network);
goto Label_0365;
Label_04C5:
num13 = num11;
goto Label_0469;
Label_04CB:
if (num13 < 0.0)
{
goto Label_04C5;
}
if ((((uint) num17) + ((uint) maxIterations)) >= 0)
{
if (((uint) num9) >= 0)
{
if (num13 >= (0.1 * num11))
{
if (num13 <= (10.0 * num11))
{
goto Label_0469;
}
}
else
{
num13 = 0.1 * num11;
if ((((uint) num11) - ((uint) num16)) < 0)
{
goto Label_04CB;
}
goto Label_0469;
}
}
goto Label_045B;
}
Label_04F0:
num11 = 1.5 / num11;
if (((((uint) tol) + ((uint) eps)) >= 0) && (num11 >= 0.0001))
{
goto Label_04CB;
}
Label_0516:
num11 = 0.0001;
goto Label_04CB;
Label_05C6:
if (num12 < n)
{
bs[num12] = x[num12];
if (((uint) num8) > uint.MaxValue)
{
goto Label_009D;
}
}
else
{
if (Math.Abs(num8) < 1E-07)
{
if ((((uint) num14) + ((uint) num4)) > uint.MaxValue)
{
goto Label_07E4;
}
num13 = 0.0;
if ((((uint) num19) - ((uint) ystart)) < 0)
{
goto Label_0516;
}
goto Label_04F0;
}
num13 = num10 / num8;
if ((((uint) num13) - ((uint) num15)) >= 0)
{
goto Label_04F0;
}
goto Label_05C6;
}
Label_0640:
if (deriv2[num12] > num11)
{
num11 = deriv2[num12];
if (((uint) num) < 0)
{
goto Label_00A2;
}
}
num10 += direc[num12] * g[num12];
if ((((uint) num8) + ((uint) num8)) > uint.MaxValue)
{
goto Label_071B;
}
num8 += (direc[num12] * direc[num12]) * deriv2[num12];
num9 += direc[num12] * direc[num12];
num12++;
goto Label_05C6;
Label_06B9:
if (++num4 >= 3)
{
return num;
}
Label_06D9:
num8 = 0.0;
num9 = 0.0;
num10 = num8 = num9 = 0.0;
num11 = 0.0001;
num12 = 0;
goto Label_05C6;
Label_06F7:
num7 = tol * num2;
Label_06FD:
if ((num2 - num) > num7)
{
num4 = 0;
if ((((uint) num14) & 0) != 0)
{
goto Label_06B9;
}
goto Label_06D9;
}
if (((uint) num9) <= uint.MaxValue)
{
goto Label_06B9;
}
return num;
Label_071B:
if (num2 > 1.0)
{
if ((((uint) num7) + ((uint) eps)) < 0)
{
goto Label_003D;
}
if ((((uint) num8) - ((uint) num14)) <= uint.MaxValue)
{
goto Label_06F7;
}
}
else
{
num7 = tol;
goto Label_06FD;
}
Label_0762:
num5 = 0;
num6 = 0;
goto Label_003D;
Label_07D7:
direc[num3] = -direc[num3];
num3++;
Label_07E4:
if (num3 < n)
{
goto Label_07D7;
}
Label_07E9:
if (((uint) num15) > uint.MaxValue)
{
if ((((uint) num11) + ((uint) num13)) < 0)
{
goto Label_0640;
}
goto Label_00DF;
}
do
{
EngineArray.ArrayCopy(direc, g);
EngineArray.ArrayCopy(direc, h);
if ((((uint) num5) + ((uint) maxError)) < 0)
{
goto Label_06F7;
}
num4 = 0;
if ((((uint) num5) - ((uint) num10)) <= uint.MaxValue)
{
goto Label_0762;
}
}
while ((((uint) index) - ((uint) num17)) > uint.MaxValue);
goto Label_07D7;
}
