public override int GetHashCode()
{
unchecked
{
return((Rho.GetHashCode() * 397) ^ Theta.GetHashCode());
}
}
public override bool Equals(object obj)
{
Model test = obj as Model;
if (test == null)
{
return(false);
}
bool same = ClassLabels.IsEqual(test.ClassLabels);
same = same && NumberOfClasses == test.NumberOfClasses;
same = same && NumberOfSVPerClass.IsEqual(test.NumberOfSVPerClass);
if (PairwiseProbabilityA != null)
{
same = same && PairwiseProbabilityA.IsEqual(test.PairwiseProbabilityA);
}
if (PairwiseProbabilityB != null)
{
same = same && PairwiseProbabilityB.IsEqual(test.PairwiseProbabilityB);
}
same = same && Parameter.Equals(test.Parameter);
same = same && Rho.IsEqual(test.Rho);
same = same && SupportVectorCoefficients.IsEqual(test.SupportVectorCoefficients);
same = same && SupportVectorCount == test.SupportVectorCount;
same = same && SupportVectors.IsEqual(test.SupportVectors);
return(same);
}
public override void Optimize()
{
GradientClipper.Clip(this);
foreach (var data in Data)
{
if (data.Variable.Type == VariableType.Parameter)
{
if (data.Variable.DataType == typeof(float))
{
var a = _weights[data.Variable].Cast <float>();
var p = data.Tensor.Cast <float>();
var g = data.Gradient.Cast <float>();
Context.Assign(a, Rho.AsScalar <float>() * a + (1.0.AsScalar <float>() - Rho.AsScalar <float>()) * g * g);
Context.Assign(p, p - LearningRate.AsScalar <float>() * g / (Sqrt(a) + Epsilon.AsScalar <float>()));
}
else if (data.Variable.DataType == typeof(double))
{
var a = _weights[data.Variable].Cast <double>();
var p = data.Tensor.Cast <double>();
var g = data.Gradient.Cast <double>();
Context.Assign(a, Rho.AsScalar <double>() * a + (1.0.AsScalar <double>() - Rho.AsScalar <double>()) * g * g);
Context.Assign(p, p - LearningRate.AsScalar <double>() * g / (Sqrt(a) + Epsilon.AsScalar <double>()));
}
else
{
throw new InvalidOperationException();
}
}
}
}
override public void initialize()
{
setProblemClass();
int total_nodes = this.Ranks.Length;
maxcells = Convert.ToInt32(Math.Sqrt(total_nodes));
ncells = Convert.ToInt32(Math.Sqrt(total_nodes));
MAX_CELL_DIM = (problem_size / maxcells) + 1;
IMAX = JMAX = KMAX = MAX_CELL_DIM;
_grid_points_[0] = _grid_points_[1] = _grid_points_[2] = problem_size;
IMAXP = IMAX / 2 * 2 + 1;
JMAXP = JMAX / 2 * 2 + 1;
set_constants(0, dt_default);
U.initialize_field("u", maxcells, KMAX + 4, JMAXP + 4, IMAXP + 4, 5);
Rhs.initialize_field("rhs", maxcells, KMAX + 2, JMAXP + 2, IMAXP + 2, 5);
Lhs.initialize_field("lhs", maxcells, KMAX + 2, JMAXP + 2, IMAXP + 2, 15);
Forcing.initialize_field("forcing", maxcells, KMAX + 2, JMAXP + 2, IMAXP + 2, 5);
Us.initialize_field("us", maxcells, KMAX + 3, JMAX + 3, IMAX + 3);
Vs.initialize_field("vs", maxcells, KMAX + 3, JMAX + 3, IMAX + 3);
Ws.initialize_field("ws", maxcells, KMAX + 3, JMAX + 3, IMAX + 3);
Qs.initialize_field("qs", maxcells, KMAX + 3, JMAX + 3, IMAX + 3);
Ainv.initialize_field("ainv", maxcells, KMAX + 3, JMAX + 3, IMAX + 3);
Rho.initialize_field("rho", maxcells, KMAX + 3, JMAX + 3, IMAX + 3);
Speed.initialize_field("speed", maxcells, KMAX + 3, JMAX + 3, IMAX + 3);
Square.initialize_field("square", maxcells, KMAX + 3, JMAX + 3, IMAX + 3);
//Lhsa.initialize_field("lhsa", maxcells, KMAX+2, JMAXP+2, IMAXP+2, 15);
//Lhsb.initialize_field("lhsb", maxcells, KMAX+2, JMAXP+2, IMAXP+2, 15);
//Lhsc.initialize_field("lhsc", maxcells, KMAX+2, JMAXP+2, IMAXP+2, 15);
}
protected override void EncodeParameterCore(ref string res)
{
res += OutScale.ToString() + " ";
res += KernelSize.ToString() + " ";
res += KernelExpand.ToString() + " ";
res += OptimizerType.ToString() + " ";
res += Rho.ToString() + " ";
}
protected bool Equals(Greeks other)
{
return(Gamma.AlmostEqual(other.Gamma, 4) &&
Delta.AlmostEqual(other.Delta, 4) &&
Theta.AlmostEqual(other.Theta, 4) &&
Vega.AlmostEqual(other.Vega, 4) &&
Rho.AlmostEqual(other.Rho, 4) &&
RhoFx.AlmostEqual(other.RhoFx, 4) &&
Sigma.AlmostEqual(other.Sigma, 4));
}
public SVMModel Clone()
{
SVMModel y = new SVMModel();
if (Parameter != null)
{
y.Parameter = Parameter.Clone();
}
y.ClassCount = ClassCount;
y.TotalSVCount = TotalSVCount;
if (SV != null)
{
y.SV = SV.Select(a => a.Select(b => b.Clone()).ToArray()).ToList();
}
if (SVCoefs != null)
{
y.SVCoefs = SVCoefs.Select(a => a.Select(b => b).ToArray()).ToList();
}
if (Rho != null)
{
y.Rho = Rho.Select(a => a).ToArray();
}
if (ProbabilityA != null)
{
y.ProbabilityA = ProbabilityA.Select(a => a).ToArray();
}
if (ProbabilityB != null)
{
y.ProbabilityB = ProbabilityB.Select(a => a).ToArray();
}
if (SVIndices != null)
{
y.SVIndices = SVIndices.Select(a => a).ToArray();
}
if (Labels != null)
{
y.Labels = Labels.Select(a => a).ToArray();
}
if (SVCounts != null)
{
y.SVCounts = SVCounts.Select(a => a).ToArray();
}
y.Creation = Creation;
return(y);
}
public override int GetHashCode()
{
return(ClassLabels.ComputeHashcode() +
NumberOfClasses.GetHashCode() +
NumberOfSVPerClass.ComputeHashcode() +
PairwiseProbabilityA.ComputeHashcode() +
PairwiseProbabilityB.ComputeHashcode() +
Parameter.GetHashCode() +
Rho.ComputeHashcode() +
SupportVectorCoefficients.ComputeHashcode() +
SupportVectorCount.GetHashCode() +
SupportVectors.ComputeHashcode());
}
public override int GetHashCode()
{
unchecked
{
int hashCode = Gamma.GetHashCode();
hashCode = (hashCode * 397) ^ Delta.GetHashCode();
hashCode = (hashCode * 397) ^ Theta.GetHashCode();
hashCode = (hashCode * 397) ^ Vega.GetHashCode();
hashCode = (hashCode * 397) ^ Rho.GetHashCode();
hashCode = (hashCode * 397) ^ RhoFx.GetHashCode();
hashCode = (hashCode * 397) ^ Sigma.GetHashCode();
return(hashCode);
}
}
public override string ToString()
{
string returnString = "";
returnString += "T: " + EnginesGUI.GUIUnitsSettings.TemperatureUnits.Format(T);
returnString += " P: " + EnginesGUI.GUIUnitsSettings.PressureUnits.Format(P);
returnString += " Rho: " + Rho.ToString("F2");
returnString += "\n FF: " + FF.ToString("F3");
returnString += "\n Cp: " + Cp.ToString("F2");
returnString += " Cv: " + Cv.ToString("F2");
returnString += "\nGamma: " + Gamma.ToString("F2");
returnString += " R: " + R.ToString("F2");
return(returnString);
}
private Dictionary <char, string> MakeDictionary()
{
var dict = new Dictionary <char, string>();
dict['s'] = "Sample " + Samples;
dict['f'] = "Feature " + Dimension;
dict['n'] = "Nu " + Nu;
dict['t'] = "Time " + ExecutionTime;
dict['i'] = "Iteration " + Iteration;
dict['v'] = "#SupportVector " + CountSupportVectors;
dict['w'] = MakeStringW();
dict['b'] = "B " + B.ToString();
dict['r'] = "Rho " + Rho.ToString();
dict['c'] = "C " + C.ToString();
dict['o'] = "OptimalValue " + OptValue;
dict['a'] = "Accuracy " + Accuracy;
dict['l'] = MakeLable();
return(dict);
}
protected bool Equals(PolarPointF other)
{
return(Rho.Equals(other.Rho) && Theta.Equals(other.Theta));
}
protected override void EncodeParameterCore(ref string res)
{
res += OptimizerType.ToString() + " ";
res += Rho.ToString() + " ";
}
public static ValPosPlr ValPosPlr(Theta t, Rho r, double value = 0) => new ValPosPlr(t, r, value);
