public static void Main(string[] args)
{
int nc = 10;
int m = 50;
int n = 3;
int seed = 1;
double variance = 1.0;
double mean = 1.0;
Random gen = new Random(seed);
int nump = gen.Next(m);
double [] y = new double[m];
for (int i = 0; i < nump; i++)
{
y[i] = 1.0;
}
for (int i = nump; i < m; i++)
{
y[i] = -1.0;
}
double [,] X = new double[m, n];
for (int i = 0; i < nump; i++)
{
for (int j = 0; j < n; j++)
{
double xx = Math.Sqrt(-2.0 * Math.Log(gen.NextDouble()));
double yy = Math.Sin(2.0 * Math.PI * gen.NextDouble());
X[i, j] = (xx * yy) * variance + mean;
}
}
Model M = new Model("Primal SVM");
try
{
Console.WriteLine("Number of data : {0}\n", m);
Console.WriteLine("Number of features: {0}\n", n);
Variable w = M.Variable(n, Domain.Unbounded());
Variable t = M.Variable(1, Domain.Unbounded());
Variable b = M.Variable(1, Domain.Unbounded());
Variable xi = M.Variable(m, Domain.GreaterThan(0.0));
M.Constraint(Expr.Add(Expr.MulElm(y, Expr.Sub(Expr.Mul(X, w), Var.Repeat(b, m))), xi),
Domain.GreaterThan(1.0));
M.Constraint(Expr.Vstack(1.0, t, w), Domain.InRotatedQCone());
M.AcceptedSolutionStatus(AccSolutionStatus.NearOptimal);
Console.WriteLine(" c | b | w");
for (int i = 0; i < nc; i++)
{
double c = 500.0 * i;
//TAG:begin-svm-obj-fun
M.Objective(ObjectiveSense.Minimize, Expr.Add(t, Expr.Mul(c, Expr.Sum(xi))));
//TAG:end-svm-obj-fun
M.Solve();
Console.WriteLine("{0} | {1} |", c, b.Level()[0]);
for (int j = 0; j < n; j++)
{
Console.Write(" {0}", w.Level()[j]);
}
Console.WriteLine("\n");
}
}
finally
{
M.Dispose();
}
}
public static void Main(String[] argv)
{
Model M = new Model();
// (Optional) set a log stream
// M.SetLogHandler(Console.Out);
// (Optional) uncomment to see what happens when solution status is unknown
// M.SetSolverParam("intpntMaxIterations", 1);
// In this example we set up a small conic problem
SetupExample(M);
// Optimize
try
{
M.Solve();
// We expect solution status OPTIMAL (this is also default)
M.AcceptedSolutionStatus(AccSolutionStatus.Optimal);
Variable x = M.GetVariable("x");
long xsize = x.GetSize();
double[] xVal = x.Level();
Console.Write("Optimal value of x = ");
for (int i = 0; i < xsize; ++i)
{
Console.Write(xVal[i] + " ");
}
Console.WriteLine("\nOptimal primal objective: " + M.PrimalObjValue());
// .. continue analyzing the solution
}
catch (OptimizeError e)
{
Console.WriteLine("Optimization failed. Error: " + e.ToString());
}
catch (SolutionError)
{
// The solution with at least the expected status was not available.
// We try to diagnoze why.
Console.WriteLine("Requested solution was not available.");
ProblemStatus prosta = M.GetProblemStatus();
switch (prosta)
{
case ProblemStatus.DualInfeasible:
Console.WriteLine("Dual infeasibility certificate found.");
break;
case ProblemStatus.PrimalInfeasible:
Console.WriteLine("Primal infeasibility certificate found.");
break;
case ProblemStatus.Unknown:
// The solutions status is unknown. The termination code
// indicates why the optimizer terminated prematurely.
Console.WriteLine("The solution status is unknown.");
StringBuilder symname = new StringBuilder();
StringBuilder desc = new StringBuilder();
Env.getcodedesc((rescode)M.GetSolverIntInfo("optimizeResponse"), symname, desc);
Console.WriteLine(" Termination code: {0} {1}", symname, desc);
break;
default:
Console.WriteLine("Another unexpected problem status: " + prosta);
break;
}
}
catch (Exception e)
{
Console.WriteLine("Unexpected error: " + e.ToString());
}
M.Dispose();
}
