public static svm_problem ReadProblem(string input_file_name)
{
var vy = new List<double>();
var vx = new List<svm_node[]>();
using (var sr = new StreamReader(input_file_name))
{
while (true)
{
string line = sr.ReadLine();
if (line == null) break;
string[] st = line.Split(" \t\n\r\f".ToCharArray()).Where(c => c != String.Empty).ToArray();
vy.Add(st[0].ToDouble());
int m = (st.Count() - 1);
var x = new List<svm_node>();
for (int i = 0; i < m; i++)
{
string[] values = st[i + 1].Trim().Split(':');
double value = values[1].ToDouble();
x.Add(new svm_node
{
index = values[0].ToInteger(),
value = value,
});
}
vx.Add(x.ToArray());
}
}
var prob = new svm_problem {l = vy.Count, x = vx.ToArray(), y = vy.ToArray()};
return prob;
}
public void Should_be_able_to_scale_problems()
{
var prob = new svm_problem() {
l = 2,
x = new svm_node[][]
{
new svm_node[]
{
new svm_node(){ index= 1, value=1},
new svm_node(){ index= 2, value=2}
},
new svm_node[]
{
new svm_node(){ index= 1, value=3},
new svm_node(){ index= 2, value=4}
}
},
y = new double[] { 0, 0 }
};
prob = prob.Scale(0, 1);
Assert.IsTrue(prob.x[0].Single(x => x.index == 1).value == 0);
Assert.IsTrue(prob.x[1].Single(x => x.index == 1).value == 1);
Assert.IsTrue(prob.x[0].Single(x => x.index == 2).value == 0);
Assert.IsTrue(prob.x[1].Single(x => x.index == 2).value == 1);
}
public C_SVC_Tests()
{
var current_path = Environment.CurrentDirectory;
var pos = current_path.IndexOf("libsvm.net");
base_path = current_path.Substring(0, pos + 10);
string full_path = System.IO.Path.Combine(base_path, XOR_DATASET);
xor_problem = ProblemHelper.ReadProblem(full_path);
}
public void TestInitialize()
{
var path = Environment.CurrentDirectory;
var pos = path.IndexOf("libsvm.net");
var basePath = path.Substring(0, pos + 10);
training_prob = ProblemHelper.ReadAndScaleProblem(System.IO.Path.Combine(basePath, TRAINING_FILE));
test_prob = ProblemHelper.ReadAndScaleProblem(System.IO.Path.Combine(basePath,TEST_FILE));
full_prob = ProblemHelper.ReadAndScaleProblem(System.IO.Path.Combine(basePath,FULL_FILE));
}
public svm_problem CreateProblem()
{
svm_problem problem = new svm_problem();
problem.l = labels.Length;
problem.y = labels;
problem.x = this.problemSpace.Select(problemVector => MatrixUtil.DoubleToSvmNode(problemVector)).ToArray();
return problem;
}
public void TestInitialize()
{
var path = Environment.CurrentDirectory;
var pos = path.IndexOf("libsvm.net");
var basePath = path.Substring(0, pos + 10);
string fullPath = System.IO.Path.Combine(basePath, LEU_TEST_FILE);
// get data from file
// Note that you should always scale your data
_prob = ProblemHelper.ReadAndScaleProblem(fullPath);
}
/// <summary>
/// Default SVM
/// </summary>
/// <remarks>The class store svm parameters and create the model.
/// This way, you can use it to predict</remarks>
public SVM(svm_problem prob, svm_parameter param)
{
var error = svm.svm_check_parameter(prob, param);
if (error != null)
{
throw new Exception(error);
}
this.prob = prob;
this.param = param;
this.Train();
}
public static svm_problem ScaleProblem(svm_problem prob, double lower = -1.0, double upper = 1.0)
{
var index_max = prob.x.Max(X => X.Max(e=>e.index));
var feature_max = new double[(index_max + 1)];
var feature_min = new double[(index_max + 1)];
int n = prob.l;
for (int i = 0; i <= index_max; i++)
{
feature_max[i] = -Double.MaxValue;
feature_min[i] = Double.MaxValue;
}
for (int i = 0; i < n; i++)
{
var m = prob.x[i].Count();
for (int j = 0; j< m; j++)
{
var index = prob.x[i][j].index;
feature_max[index - 1] = Math.Max(feature_max[index - 1], prob.x[i][j].value);
feature_min[index - 1] = Math.Min(feature_min[index - 1], prob.x[i][j].value);
}
}
var scaledProb = new svm_problem();
scaledProb.l = n;
scaledProb.y = prob.y.ToArray();
scaledProb.x = new svm_node[n][];
for (int i = 0; i < n; i++)
{
var m = prob.x[i].Count();
scaledProb.x[i] = new svm_node[m];
for (int j = 0; j < m; j++)
{
var index = prob.x[i][j].index;
var value = prob.x[i][j].value;
var max = feature_max[index - 1];
var min = feature_min[index - 1];
scaledProb.x[i][j] = new svm_node() { index = index };
if (min == max)
scaledProb.x[i][j].value = 0;
else
scaledProb.x[i][j].value = lower + (upper - lower) * (value - min) / (max - min);
}
}
return scaledProb;
}
public static svm_problem ScaleProblem(svm_problem prob, double lower = -1.0, double upper = 1.0)
{
int indexMax = prob.x.Max(X => X.Max(e => e.index));
var featureMax = new double[(indexMax + 1)];
var featureMin = new double[(indexMax + 1)];
int n = prob.l;
for (int i = 0; i <= indexMax; i++)
{
featureMax[i] = -Double.MaxValue;
featureMin[i] = Double.MaxValue;
}
for (int i = 0; i < n; i++)
{
int m = prob.x[i].Count();
for (int j = 0; j < m; j++)
{
int index = prob.x[i][j].index;
featureMax[index - 1] = Math.Max(featureMax[index - 1], prob.x[i][j].value);
featureMin[index - 1] = Math.Min(featureMin[index - 1], prob.x[i][j].value);
}
}
var scaledProb = new svm_problem {l = n, y = prob.y.ToArray(), x = new svm_node[n][]};
for (int i = 0; i < n; i++)
{
int m = prob.x[i].Count();
scaledProb.x[i] = new svm_node[m];
for (int j = 0; j < m; j++)
{
int index = prob.x[i][j].index;
double value = prob.x[i][j].value;
double max = featureMax[index - 1];
double min = featureMin[index - 1];
scaledProb.x[i][j] = new svm_node {index = index};
if (Math.Abs(min - max) < double.Epsilon)
scaledProb.x[i][j].value = 0;
else
scaledProb.x[i][j].value = lower + (upper - lower)*(value - min)/(max - min);
}
}
return scaledProb;
}
public static svm_problem ReadProblem(string input_file_name)
{
var vy = new List<double>();
var vx = new List<svm_node[]>();
using (StreamReader sr = new StreamReader(input_file_name))
{
while (true)
{
var line = sr.ReadLine();
if (line == null) break;
var st = line.Split(" \t\n\r\f".ToCharArray()).Where(c=>c!=String.Empty).ToArray();
vy.Add(atof(st[0]));
int m = (st.Count()-1);
List<svm_node> x = new List<svm_node>();
for (int i = 0; i < m; i++)
{
var values = st[i + 1].Trim().Split(':');
var value = atof(values[1]);
x.Add( new svm_node()
{
index = atoi(values[0]),
value = value,
});
}
vx.Add(x.ToArray());
}
}
var prob = new svm_problem();
prob.l = vy.Count;
prob.x = vx.ToArray();
prob.y = vy.ToArray();
return prob;
}
/// <summary>
/// Default SVM
/// </summary>
/// <remarks>The class store svm parameters and create the model.
/// This way, you can use it to predict</remarks>
public SVM(svm_problem prob, int svm_type, int kernel_type, int degree,
double C, double gamma, double coef0, double nu, double cache_size,
double eps, double p, int shrinking, int probability, int nr_weight,
int[] weight_label, double[] weight)
:this(prob, new svm_parameter()
{
svm_type = svm_type,
kernel_type = kernel_type,
degree = degree,
C = C,
gamma = gamma,
coef0 = coef0,
nu = nu,
cache_size = cache_size,
eps = eps,
p = p,
shrinking = shrinking,
probability = probability,
nr_weight = nr_weight,
weight_label = weight_label,
weight = weight,
})
{ }
// Return parameter of a Laplace distribution
private static double svm_svr_probability(svm_problem prob, svm_parameter param)
{
int i;
int nr_fold = 5;
double[] ymv = new double[prob.l];
double mae = 0;
svm_parameter newparam = (svm_parameter) param.Clone();
newparam.probability = 0;
svm_cross_validation(prob, newparam, nr_fold, ymv);
for (i = 0; i < prob.l; i++)
{
ymv[i] = prob.y[i] - ymv[i];
mae += System.Math.Abs(ymv[i]);
}
mae /= prob.l;
double std = System.Math.Sqrt(2 * mae * mae);
int count = 0;
mae = 0;
for (i = 0; i < prob.l; i++)
if (System.Math.Abs(ymv[i]) > 5 * std)
count = count + 1;
else
mae += System.Math.Abs(ymv[i]);
mae /= (prob.l - count);
System.Console.Error.Write("Prob. model for test data: target value = predicted value + z,\nz: Laplace distribution e^(-|z|/sigma)/(2sigma),sigma=" + mae + "\n");
return mae;
}
// Cross-validation decision values for probability estimates
private static void svm_binary_svc_probability(svm_problem prob, svm_parameter param, double Cp, double Cn, double[] probAB)
{
int i;
int nr_fold = 5;
int[] perm = new int[prob.l];
double[] dec_values = new double[prob.l];
// random shuffle
for (i = 0; i < prob.l; i++)
perm[i] = i;
for (i = 0; i < prob.l; i++)
{
//UPGRADE_WARNING: Data types in Visual C# might be different. Verify the accuracy of narrowing conversions. 'ms-help://MS.VSCC.2003/commoner/redir/redirect.htm?keyword="jlca1042_3"'
int j = i + (int) (SupportClass.Random.NextDouble() * (prob.l - i));
do
{
int _ = perm[i]; perm[i] = perm[j]; perm[j] = _;
}
while (false);
}
for (i = 0; i < nr_fold; i++)
{
int begin = i * prob.l / nr_fold;
int end = (i + 1) * prob.l / nr_fold;
int j, k;
svm_problem subprob = new svm_problem();
subprob.l = prob.l - (end - begin);
subprob.x = new svm_node[subprob.l][];
subprob.y = new double[subprob.l];
k = 0;
for (j = 0; j < begin; j++)
{
subprob.x[k] = prob.x[perm[j]];
subprob.y[k] = prob.y[perm[j]];
++k;
}
for (j = end; j < prob.l; j++)
{
subprob.x[k] = prob.x[perm[j]];
subprob.y[k] = prob.y[perm[j]];
++k;
}
int p_count = 0, n_count = 0;
for (j = 0; j < k; j++)
if (subprob.y[j] > 0)
p_count++;
else
n_count++;
if (p_count == 0 && n_count == 0)
for (j = begin; j < end; j++)
dec_values[perm[j]] = 0;
else if (p_count > 0 && n_count == 0)
for (j = begin; j < end; j++)
dec_values[perm[j]] = 1;
else if (p_count == 0 && n_count > 0)
for (j = begin; j < end; j++)
dec_values[perm[j]] = - 1;
else
{
svm_parameter subparam = (svm_parameter) param.Clone();
subparam.probability = 0;
subparam.C = 1.0;
subparam.nr_weight = 2;
subparam.weight_label = new int[2];
subparam.weight = new double[2];
subparam.weight_label[0] = + 1;
subparam.weight_label[1] = - 1;
subparam.weight[0] = Cp;
subparam.weight[1] = Cn;
svm_model submodel = svm_train(subprob, subparam);
for (j = begin; j < end; j++)
{
double[] dec_value = new double[1];
svm_predict_values(submodel, prob.x[perm[j]], dec_value);
dec_values[perm[j]] = dec_value[0];
// ensure +1 -1 order; reason not using CV subroutine
dec_values[perm[j]] *= submodel.label[0];
}
}
}
sigmoid_train(prob.l, dec_values, prob.y, probAB);
}
/// <summary>
/// Create and train the Epsilon_SV
/// </summary>
/// <param name="prob">Training Data Set</param>
/// <param name="kernel">Selected Kernel</param>
/// <param name="probability">Specify if probability are needed</param>
/// <param name="cache_size">Indicates the maximum memory that can use the program</param>
public Epsilon_SVR(svm_problem prob, Kernel kernel, double C, double epsilon, bool probability = true, double cache_size = 100)
: base(SvmType.EPSILON_SVR, prob, kernel, C, epsilon, probability, cache_size)
{
}
public static void WriteProblem(string output_file_name, svm_problem problem)
{
using (StreamWriter sw = new StreamWriter(output_file_name))
{
for (int i =0 ; i< problem.l;i++)
{
var sb = new StringBuilder();
sb.AppendFormat("{0} ", problem.y[i] );
for (int j = 0; j < problem.x[i].Count(); j++)
{
var node = problem.x[i][j];
sb.AppendFormat("{0}:{1} ", node.index, node.value);
}
sw.WriteLine(sb.ToString().Trim());
}
sw.Close();
}
}
internal static decision_function svm_train_one(svm_problem prob, svm_parameter param, double Cp, double Cn)
{
double[] alpha = new double[prob.l];
Solver.SolutionInfo si = new Solver.SolutionInfo();
switch (param.svm_type)
{
case svm_parameter.C_SVC:
solve_c_svc(prob, param, alpha, si, Cp, Cn);
break;
case svm_parameter.NU_SVC:
solve_nu_svc(prob, param, alpha, si);
break;
case svm_parameter.ONE_CLASS:
solve_one_class(prob, param, alpha, si);
break;
case svm_parameter.EPSILON_SVR:
solve_epsilon_svr(prob, param, alpha, si);
break;
case svm_parameter.NU_SVR:
solve_nu_svr(prob, param, alpha, si);
break;
}
//Debug.WriteLine("obj = " + si.obj + ", rho = " + si.rho + "\n");
// output SVs
int nSV = 0;
int nBSV = 0;
for (int i = 0; i < prob.l; i++)
{
if (System.Math.Abs(alpha[i]) > 0)
{
++nSV;
if (prob.y[i] > 0)
{
if (System.Math.Abs(alpha[i]) >= si.upper_bound_p)
++nBSV;
}
else
{
if (System.Math.Abs(alpha[i]) >= si.upper_bound_n)
++nBSV;
}
}
}
//Debug.WriteLine("nSV = " + nSV + ", nBSV = " + nBSV + "\n");
decision_function f = new decision_function();
f.alpha = alpha;
f.rho = si.rho;
return f;
}
public static System.String svm_check_parameter(svm_problem prob, svm_parameter param)
{
// svm_type
int svm_type = param.svm_type;
if (svm_type != svm_parameter.C_SVC && svm_type != svm_parameter.NU_SVC && svm_type != svm_parameter.ONE_CLASS && svm_type != svm_parameter.EPSILON_SVR && svm_type != svm_parameter.NU_SVR)
return "unknown svm type";
// kernel_type
int kernel_type = param.kernel_type;
if (kernel_type != svm_parameter.LINEAR && kernel_type != svm_parameter.POLY && kernel_type != svm_parameter.RBF && kernel_type != svm_parameter.SIGMOID)
return "unknown kernel type";
// cache_size,eps,C,nu,p,shrinking
if (param.cache_size <= 0)
return "cache_size <= 0";
if (param.eps <= 0)
return "eps <= 0";
if (svm_type == svm_parameter.C_SVC || svm_type == svm_parameter.EPSILON_SVR || svm_type == svm_parameter.NU_SVR)
if (param.C <= 0)
return "C <= 0";
if (svm_type == svm_parameter.NU_SVC || svm_type == svm_parameter.ONE_CLASS || svm_type == svm_parameter.NU_SVR)
if (param.nu < 0 || param.nu > 1)
return "nu < 0 or nu > 1";
if (svm_type == svm_parameter.EPSILON_SVR)
if (param.p < 0)
return "p < 0";
if (param.shrinking != 0 && param.shrinking != 1)
return "shrinking != 0 and shrinking != 1";
if (param.probability != 0 && param.probability != 1)
return "probability != 0 and probability != 1";
if (param.probability == 1 && svm_type == svm_parameter.ONE_CLASS)
return "one-class SVM probability output not supported yet";
// check whether nu-svc is feasible
if (svm_type == svm_parameter.NU_SVC)
{
int l = prob.l;
int max_nr_class = 16;
int nr_class = 0;
int[] label = new int[max_nr_class];
int[] count = new int[max_nr_class];
int i;
for (i = 0; i < l; i++)
{
//UPGRADE_WARNING: Data types in Visual C# might be different. Verify the accuracy of narrowing conversions. 'ms-help://MS.VSCC.2003/commoner/redir/redirect.htm?keyword="jlca1042_3"'
int this_label = (int) prob.y[i];
int j;
for (j = 0; j < nr_class; j++)
if (this_label == label[j])
{
++count[j];
break;
}
if (j == nr_class)
{
if (nr_class == max_nr_class)
{
max_nr_class *= 2;
int[] new_data = new int[max_nr_class];
Array.Copy(label, 0, new_data, 0, label.Length);
label = new_data;
new_data = new int[max_nr_class];
Array.Copy(count, 0, new_data, 0, count.Length);
count = new_data;
}
label[nr_class] = this_label;
count[nr_class] = 1;
++nr_class;
}
}
for (i = 0; i < nr_class; i++)
{
int n1 = count[i];
for (int j = i + 1; j < nr_class; j++)
{
int n2 = count[j];
if (param.nu * (n1 + n2) / 2 > System.Math.Min(n1, n2))
return "specified nu is infeasible";
}
}
}
return null;
}
internal SVC_Q(svm_problem prob, svm_parameter param, sbyte[] y_)
: base(prob.l, prob.x, param)
{
y = new sbyte[y_.Length];
y_.CopyTo(y, 0);
//UPGRADE_WARNING: Data types in Visual C# might be different. Verify the accuracy of narrowing conversions. 'ms-help://MS.VSCC.2003/commoner/redir/redirect.htm?keyword="jlca1042_3"'
cache = new Cache(prob.l, (int) (param.cache_size * (1 << 20)));
}
/// <summary>
/// Create and train the Epsilon_SV
/// </summary>
/// <param name="prob">Training Data Set</param>
/// <param name="kernel">Selected Kernel</param>
/// <param name="probability">Specify if probability are needed</param>
/// <param name="cache_size">Indicates the maximum memory that can use the program</param>
public Epsilon_SVR(svm_problem prob, Kernel kernel, double C, double epsilon, bool probability = true, double cache_size = 100)
: base(SvmType.EPSILON_SVR, prob, kernel, C, epsilon, probability, cache_size)
{
}
public static svm_problem Scale(this svm_problem prob, double lower = -1.0, double upper = 1.0)
{
return(ScaleProblem(prob, lower, upper));
}
/// <summary>
/// Runs polynomial-kernel SVM C-classifier on a specified digit vs all other digits
/// </summary>
/// <param name="digit"></param>
/// <returns>
/// Ein, # of support vectors
/// </returns>
static Tuple<double, int> Run1VsAll(int digit)
{
var prob = new svm_problem()
{
x = trainingData.Select(v =>
new svm_node[] {
new svm_node() { index = 0, value = v[1] },
new svm_node() { index = 1, value = v[2] } }).ToArray(),
y = trainingData.Select(v => (v[0] == digit ? 1.0 : -1.0)).ToArray(),
l = trainingData.Length
};
var model = svm.svm_train(prob, new svm_parameter()
{
svm_type = (int)SvmType.C_SVC,
kernel_type = (int)KernelType.POLY,
C = 0.01,
degree = 2,
coef0 = 1,
gamma = 1,
eps = 0.001
});
return Tuple.Create((prob.x.Zip(prob.y, (v, u) => new { x = v, y = u }).Count(v =>
Math.Sign(svm.svm_predict(model, v.x)) != Math.Sign(v.y)) + 0.0) / prob.x.Length,
model.l);
}
public void TrainLibSVM(double[][] vektoren, double[] labels, double currentC, double currentG, out int errorCount)
{
int nrdocs = vektoren.Length;
svm_problem prob = new svm_problem();
prob.l = vektoren.Length - 1;
prob.y = labels;
svm_node[][] nodes = new svm_node[nrdocs][];
for (int i = 0; i < vektoren.Length; i++)
{
int dim = vektoren[i].Length;
nodes[i] = new svm_node[dim + 1];
for (int j = 0; j < dim; j++)
{
svm_node n = new svm_node();
n.index = j;
n.value_Renamed = vektoren[i][j];
nodes[i][j] = n;
}
svm_node ln = new svm_node();
ln.index = -1;
ln.value_Renamed = 0;
nodes[i][dim] = ln;
}
prob.x = nodes;
svm_parameter param = new svm_parameter();
param.cache_size = 256.0;
param.C = 1000.0;
//param.weight = new double[] { 1.0, 1.0, 1.0, 1.0, 1.0 };
//param.weight_label = new int[] { 1, 1, 1, 1, 1 };
param.svm_type = svm_parameter.C_SVC;
param.kernel_type = svm_parameter.SIGMOID;
param.gamma = 0.00000001;
param.eps = 0.0001;
//param.nr_weight = 0;
param.probability = 1;
//double[] cs;
//double[] gs;
double[] cergs = new double[labels.Length];
int minfehler = labels.Length;
int fehler = 0;
double c = 0.0;
double g = 0.0;
#region Parameterabstimmung
//cs = new double[] { Math.Pow(2.0, -15.0), Math.Pow(2.0, -11.0), Math.Pow(2.0, -9.0), Math.Pow(2.0, -7.0), Math.Pow(2.0, -5.0), Math.Pow(2.0, -3.0), Math.Pow(2.0, -1.0), Math.Pow(2.0, 1.0), Math.Pow(2.0, 3.0), Math.Pow(2.0, 5.0), Math.Pow(2.0, 7.0), Math.Pow(2.0, 12.0), Math.Pow(2.0, 15.0) };
//gs = new double[] { Math.Pow(2.0, -15.0), Math.Pow(2.0, -12.0), Math.Pow(2.0, -9.0), Math.Pow(2.0, -7.0), Math.Pow(2.0, -5.0), Math.Pow(2.0, -3.0), Math.Pow(2.0, -1.0), Math.Pow(2.0, 1.0), Math.Pow(2.0, 3.0) };
//cs = new double[] { Math.Pow(2.0, -3.0), Math.Pow(2.0, -1.0), Math.Pow(2.0, 1.0), Math.Pow(2.0, 3.0), Math.Pow(2.0, 5.0), Math.Pow(2.0, 7.0), Math.Pow(2.0, 12.0) };
//gs = new double[] { Math.Pow(2.0, -7.0), Math.Pow(2.0, -5.0), Math.Pow(2.0, -3.0), Math.Pow(2.0, -1.0), Math.Pow(2.0, 1.0), Math.Pow(2.0, 3.0) };
//for (int i = 0; i < cs.Length; i++)
//{
// param.C = cs[i];
// for (int j = 0; j < gs.Length; j++)
// {
// fehler = 0;
// param.gamma = gs[j];
// string res = svm.svm_check_parameter(prob, param);
// if (res == null)
// {
// svm.svm_cross_validation(prob, param, vektoren.Length/4, cergs);
// for (int k = 0; k < labels.Length; k++)
// {
// if (cergs[k] != labels[k])
// fehler++;
// }
// if (fehler < minfehler)
// {
// minfehler = fehler;
// c = param.C;
// g = param.gamma;
// }
// }
// }
//}
param.C = currentC;
fehler = 0;
param.gamma = currentG;
string res = svm.svm_check_parameter(prob, param);
if (res == null)
{
svm.svm_cross_validation(prob, param, vektoren.Length / 4, cergs);
for (int k = 0; k < labels.Length; k++)
{
if (cergs[k] != labels[k])
{
fehler++;
}
}
if (fehler < minfehler)
{
minfehler = fehler;
c = param.C;
g = param.gamma;
}
}
#endregion
#region Feinabstimmung
//cs = new double[] { c * 0.3, c * 0.4, c * 0.5, c * 0.6, c * 0.7, c * 0.8, c * 0.9, c, c * 2.0, c * 3.0, c * 4.0, c * 5.0, c * 6.0 };
//gs = new double[] { g * 0.5, g * 0.6, g * 0.7, g * 0.8, g * 0.9, g, g * 2.0, g * 3.0, g * 4.0 };
double[] csF = new double[] { c * 0.6, c * 0.7, c * 0.8, c * 0.9, c, c * 2.0, c * 3.0 };
double[] gsF = new double[] { g * 0.7, g * 0.8, g * 0.9, g, g * 2.0, g * 3.0 };
for (int i = 0; i < csF.Length; i++)
{
param.C = csF[i];
for (int j = 0; j < gsF.Length; j++)
{
fehler = 0;
param.gamma = gsF[j];
res = svm.svm_check_parameter(prob, param);
if (res == null)
{
svm.svm_cross_validation(prob, param, vektoren.Length / 4, cergs);
for (int k = 0; k < labels.Length; k++)
{
if (cergs[k] != labels[k])
{
fehler++;
}
}
if (fehler < minfehler)
{
minfehler = fehler;
c = param.C;
g = param.gamma;
}
}
//Thread.Sleep(1);
}
//Thread.Sleep(10);
}
#endregion
#region Superfeinabstimmung
//cs = new double[] { c - 7.0, c - 6.0, c - 5.0, c - 4.0, c - 3.0, c - 2.0, c - 1.0, c, c + 1.0, c + 2.0, c + 3.0, c + 4.0, c + 5.0 };
//gs = new double[] { g - 5.0, g - 4.0, g - 3.0, g - 2.0, g - 1.0, g, g + 1.0, g + 2.0, g + 3.0 };
/*cs = new double[] { c - 1.0, c - 0.3, c - 0.1, c, c + 0.1, c + 0.3, c + 1.0, };
* gs = new double[] { g - 1.0, g - 0.3, g - 0.1, g, g + 0.1, g + 0.3, g + 1.0 };
* for (int i = 0; i < cs.Length; i++)
* {
* param.C = cs[i];
*
* for (int j = 0; j < gs.Length; j++)
* {
* fehler = 0;
* param.gamma = gs[j];
* string res = svm.svm_check_parameter(prob, param);
* if (res == null)
* {
* svm.svm_cross_validation(prob, param, 6, cergs);
*
* for (int k = 0; k < labels.Length; k++)
* {
* if (cergs[k] != labels[k])
* fehler++;
* }
* if (fehler < minfehler)
* {
* minfehler = fehler;
* c = param.C;
* g = param.gamma;
* }
* }
* }
* }*/
#endregion
param.C = c;
param.gamma = g;
this._model = new svm_model();
this._model = svm.svm_train(prob, param);
int anzKlassen = svm.svm_get_nr_class(this._model);
double[] probs = new double[anzKlassen];
double erg;
erg = svm.svm_predict_probability(this._model, nodes[0], probs);
//erg = svm.svm_predict_probability(this._model, nodes[11], probs);
//klazzifiziere(this.testvektor);
//klazzifiziere(vektoren[6]);
errorCount = minfehler;
}
public SVR(SvmType svm_type, svm_problem prob, Kernel kernel, double C, double eps, bool probability, double cache_size)
: base(prob, (int)svm_type, kernel, C, 0.0, cache_size, 1e-3, 0.1, 1, probability ? 1 : 0, 0, new int[0], new double[0])
{
}
public SVC(SvmType svm_type, svm_problem prob, Kernel kernel, double C, double cache_size = 100, int probability = 0)
: base(prob, (int)svm_type, kernel, C, 0.0, cache_size, 1e-3, 0.1, 1, probability, 0, new int[0], new double[0])
{
}
public static void svm_cross_validation(svm_problem prob, svm_parameter param, int nr_fold, double[] target)
{
int i;
int[] perm = new int[prob.l];
// random shuffle
for (i = 0; i < prob.l; i++)
perm[i] = i;
for (i = 0; i < prob.l; i++)
{
//UPGRADE_WARNING: Data types in Visual C# might be different. Verify the accuracy of narrowing conversions. 'ms-help://MS.VSCC.2003/commoner/redir/redirect.htm?keyword="jlca1042_3"'
int j = i + (int) (SupportClass.Random.NextDouble() * (prob.l - i));
do
{
int _ = perm[i]; perm[i] = perm[j]; perm[j] = _;
}
while (false);
}
for (i = 0; i < nr_fold; i++)
{
int begin = i * prob.l / nr_fold;
int end = (i + 1) * prob.l / nr_fold;
int j, k;
svm_problem subprob = new svm_problem();
subprob.l = prob.l - (end - begin);
subprob.x = new svm_node[subprob.l][];
subprob.y = new double[subprob.l];
k = 0;
for (j = 0; j < begin; j++)
{
subprob.x[k] = prob.x[perm[j]];
subprob.y[k] = prob.y[perm[j]];
++k;
}
for (j = end; j < prob.l; j++)
{
subprob.x[k] = prob.x[perm[j]];
subprob.y[k] = prob.y[perm[j]];
++k;
}
svm_model submodel = svm_train(subprob, param);
if (param.probability == 1 && (param.svm_type == svm_parameter.C_SVC || param.svm_type == svm_parameter.NU_SVC))
{
double[] prob_estimates = new double[svm_get_nr_class(submodel)];
for (j = begin; j < end; j++)
target[perm[j]] = svm_predict_probability(submodel, prob.x[perm[j]], prob_estimates);
}
else
for (j = begin; j < end; j++)
target[perm[j]] = svm_predict(submodel, prob.x[perm[j]]);
}
}
internal ONE_CLASS_Q(svm_problem prob, svm_parameter param)
: base(prob.l, prob.x, param)
{
//UPGRADE_WARNING: Data types in Visual C# might be different. Verify the accuracy of narrowing conversions. 'ms-help://MS.VSCC.2003/commoner/redir/redirect.htm?keyword="jlca1042_3"'
cache = new Cache(prob.l, (int) (param.cache_size * (1 << 20)));
}
//
// construct and solve various formulations
//
private static void solve_c_svc(svm_problem prob, svm_parameter param, double[] alpha, Solver.SolutionInfo si, double Cp, double Cn)
{
int l = prob.l;
double[] minus_ones = new double[l];
sbyte[] y = new sbyte[l];
int i;
for (i = 0; i < l; i++)
{
alpha[i] = 0;
minus_ones[i] = - 1;
if (prob.y[i] > 0)
y[i] = (sbyte) (+ 1);
else
y[i] = - 1;
}
Solver s = new Solver();
s.Solve(l, new SVC_Q(prob, param, y), minus_ones, y, alpha, Cp, Cn, param.eps, si, param.shrinking);
double sum_alpha = 0;
for (i = 0; i < l; i++)
sum_alpha += alpha[i];
//if (Cp == Cn)
//Debug.WriteLine("nu = " + sum_alpha / (Cp * prob.l) + "\n");
for (i = 0; i < l; i++)
alpha[i] *= y[i];
}
internal SVR_Q(svm_problem prob, svm_parameter param)
: base(prob.l, prob.x, param)
{
l = prob.l;
//UPGRADE_WARNING: Data types in Visual C# might be different. Verify the accuracy of narrowing conversions. 'ms-help://MS.VSCC.2003/commoner/redir/redirect.htm?keyword="jlca1042_3"'
cache = new Cache(l, (int) (param.cache_size * (1 << 20)));
sign = new sbyte[2 * l];
index = new int[2 * l];
for (int k = 0; k < l; k++)
{
sign[k] = 1;
sign[k + l] = - 1;
index[k] = k;
index[k + l] = k;
}
buffer = new float[2][];
for (int i = 0; i < 2; i++)
{
buffer[i] = new float[2 * l];
}
next_buffer = 0;
}
private static void solve_epsilon_svr(svm_problem prob, svm_parameter param, double[] alpha, Solver.SolutionInfo si)
{
int l = prob.l;
double[] alpha2 = new double[2 * l];
double[] linear_term = new double[2 * l];
sbyte[] y = new sbyte[2 * l];
int i;
for (i = 0; i < l; i++)
{
alpha2[i] = 0;
linear_term[i] = param.p - prob.y[i];
y[i] = 1;
alpha2[i + l] = 0;
linear_term[i + l] = param.p + prob.y[i];
y[i + l] = - 1;
}
Solver s = new Solver();
s.Solve(2 * l, new SVR_Q(prob, param), linear_term, y, alpha2, param.C, param.C, param.eps, si, param.shrinking);
double sum_alpha = 0;
for (i = 0; i < l; i++)
{
alpha[i] = alpha2[i] - alpha2[i + l];
sum_alpha += System.Math.Abs(alpha[i]);
}
//Debug.WriteLine("nu = " + sum_alpha / (param.C * l) + "\n");
}
//
// Interface functions
//
public static svm_model svm_train(svm_problem prob, svm_parameter param, TrainingProgressEvent progressEvent = null)
{
svm_model model = new svm_model();
model.param = param;
if (param.svm_type == svm_parameter.ONE_CLASS || param.svm_type == svm_parameter.EPSILON_SVR || param.svm_type == svm_parameter.NU_SVR)
{
// regression or one-class-svm
model.nr_class = 2;
model.label = null;
model.nSV = null;
model.probA = null; model.probB = null;
model.sv_coef = new double[1][];
if (param.probability == 1 && (param.svm_type == svm_parameter.EPSILON_SVR || param.svm_type == svm_parameter.NU_SVR))
{
model.probA = new double[1];
model.probA[0] = svm_svr_probability(prob, param);
}
decision_function f = svm_train_one(prob, param, 0, 0);
model.rho = new double[1];
model.rho[0] = f.rho;
int nSV = 0;
int i;
for (i = 0; i < prob.l; i++)
if (System.Math.Abs(f.alpha[i]) > 0)
++nSV;
model.l = nSV;
model.SV = new svm_node[nSV][];
model.sv_coef[0] = new double[nSV];
int j = 0;
for (i = 0; i < prob.l; i++)
if (System.Math.Abs(f.alpha[i]) > 0)
{
model.SV[j] = prob.x[i];
model.sv_coef[0][j] = f.alpha[i];
++j;
}
}
else
{
// classification
// find out the number of classes
int l = prob.l;
int max_nr_class = 16;
int nr_class = 0;
int[] label = new int[max_nr_class];
int[] count = new int[max_nr_class];
int[] index = new int[l];
int i;
for (i = 0; i < l; i++)
{
//UPGRADE_WARNING: Data types in Visual C# might be different. Verify the accuracy of narrowing conversions. 'ms-help://MS.VSCC.2003/commoner/redir/redirect.htm?keyword="jlca1042_3"'
int this_label = (int)prob.y[i];
int j;
for (j = 0; j < nr_class; j++)
if (this_label == label[j])
{
++count[j];
break;
}
index[i] = j;
if (j == nr_class)
{
if (nr_class == max_nr_class)
{
max_nr_class *= 2;
int[] new_data = new int[max_nr_class];
Array.Copy(label, 0, new_data, 0, label.Length);
label = new_data;
new_data = new int[max_nr_class];
Array.Copy(count, 0, new_data, 0, count.Length);
count = new_data;
}
label[nr_class] = this_label;
count[nr_class] = 1;
++nr_class;
}
}
// group training data of the same class
int[] start = new int[nr_class];
start[0] = 0;
for (i = 1; i < nr_class; i++)
start[i] = start[i - 1] + count[i - 1];
svm_node[][] x = new svm_node[l][];
for (i = 0; i < l; i++)
{
x[start[index[i]]] = prob.x[i];
++start[index[i]];
}
start[0] = 0;
for (i = 1; i < nr_class; i++)
start[i] = start[i - 1] + count[i - 1];
// calculate weighted C
double[] weighted_C = new double[nr_class];
for (i = 0; i < nr_class; i++)
weighted_C[i] = param.C;
for (i = 0; i < param.nr_weight; i++)
{
int j;
for (j = 0; j < nr_class; j++)
if (param.weight_label[i] == label[j])
break;
if (j == nr_class)
System.Console.Error.Write("warning: class label " + param.weight_label[i] + " specified in weight is not found\n");
else
weighted_C[j] *= param.weight[i];
}
// train k*(k-1)/2 models
bool[] nonzero = new bool[l];
for (i = 0; i < l; i++)
nonzero[i] = false;
decision_function[] f = new decision_function[nr_class * (nr_class - 1) / 2];
double[] probA = null, probB = null;
if (param.probability == 1)
{
probA = new double[nr_class * (nr_class - 1) / 2];
probB = new double[nr_class * (nr_class - 1) / 2];
}
int p = 0;
for (i = 0; i < nr_class; i++)
for (int j = i + 1; j < nr_class; j++)
{
svm_problem sub_prob = new svm_problem();
int si = start[i], sj = start[j];
int ci = count[i], cj = count[j];
sub_prob.l = ci + cj;
sub_prob.x = new svm_node[sub_prob.l][];
sub_prob.y = new double[sub_prob.l];
int k;
for (k = 0; k < ci; k++)
{
sub_prob.x[k] = x[si + k];
sub_prob.y[k] = +1;
}
for (k = 0; k < cj; k++)
{
sub_prob.x[ci + k] = x[sj + k];
sub_prob.y[ci + k] = -1;
}
if (param.probability == 1)
{
double[] probAB = new double[2];
svm_binary_svc_probability(sub_prob, param, weighted_C[i], weighted_C[j], probAB);
probA[p] = probAB[0];
probB[p] = probAB[1];
}
f[p] = svm_train_one(sub_prob, param, weighted_C[i], weighted_C[j]);
for (k = 0; k < ci; k++)
if (!nonzero[si + k] && System.Math.Abs(f[p].alpha[k]) > 0)
nonzero[si + k] = true;
for (k = 0; k < cj; k++)
if (!nonzero[sj + k] && System.Math.Abs(f[p].alpha[ci + k]) > 0)
nonzero[sj + k] = true;
++p;
}
// build output
model.nr_class = nr_class;
model.label = new int[nr_class];
for (i = 0; i < nr_class; i++)
model.label[i] = label[i];
model.rho = new double[nr_class * (nr_class - 1) / 2];
for (i = 0; i < nr_class * (nr_class - 1) / 2; i++)
model.rho[i] = f[i].rho;
if (param.probability == 1)
{
model.probA = new double[nr_class * (nr_class - 1) / 2];
model.probB = new double[nr_class * (nr_class - 1) / 2];
for (i = 0; i < nr_class * (nr_class - 1) / 2; i++)
{
model.probA[i] = probA[i];
model.probB[i] = probB[i];
}
}
else
{
model.probA = null;
model.probB = null;
}
int nnz = 0;
int[] nz_count = new int[nr_class];
model.nSV = new int[nr_class];
for (i = 0; i < nr_class; i++)
{
int nSV = 0;
for (int j = 0; j < count[i]; j++)
if (nonzero[start[i] + j])
{
++nSV;
++nnz;
}
model.nSV[i] = nSV;
nz_count[i] = nSV;
}
//Debug.WriteLine("Total nSV = " + nnz + "\n");
model.l = nnz;
model.SV = new svm_node[nnz][];
p = 0;
for (i = 0; i < l; i++)
if (nonzero[i])
model.SV[p++] = x[i];
int[] nz_start = new int[nr_class];
nz_start[0] = 0;
for (i = 1; i < nr_class; i++)
nz_start[i] = nz_start[i - 1] + nz_count[i - 1];
model.sv_coef = new double[nr_class - 1][];
for (i = 0; i < nr_class - 1; i++)
model.sv_coef[i] = new double[nnz];
p = 0;
for (i = 0; i < nr_class; i++)
for (int j = i + 1; j < nr_class; j++)
{
// classifier (i,j): coefficients with
// i are in sv_coef[j-1][nz_start[i]...],
// j are in sv_coef[i][nz_start[j]...]
int si = start[i];
int sj = start[j];
int ci = count[i];
int cj = count[j];
int q = nz_start[i];
int k;
for (k = 0; k < ci; k++)
if (nonzero[si + k])
model.sv_coef[j - 1][q++] = f[p].alpha[k];
q = nz_start[j];
for (k = 0; k < cj; k++)
if (nonzero[sj + k])
model.sv_coef[i][q++] = f[p].alpha[ci + k];
++p;
}
}
return model;
}
private static void solve_nu_svc(svm_problem prob, svm_parameter param, double[] alpha, Solver.SolutionInfo si)
{
int i;
int l = prob.l;
double nu = param.nu;
sbyte[] y = new sbyte[l];
for (i = 0; i < l; i++)
if (prob.y[i] > 0)
y[i] = (sbyte) (+ 1);
else
y[i] = - 1;
double sum_pos = nu * l / 2;
double sum_neg = nu * l / 2;
for (i = 0; i < l; i++)
if (y[i] == + 1)
{
alpha[i] = System.Math.Min(1.0, sum_pos);
sum_pos -= alpha[i];
}
else
{
alpha[i] = System.Math.Min(1.0, sum_neg);
sum_neg -= alpha[i];
}
double[] zeros = new double[l];
for (i = 0; i < l; i++)
zeros[i] = 0;
Solver_NU s = new Solver_NU();
s.Solve(l, new SVC_Q(prob, param, y), zeros, y, alpha, 1.0, 1.0, param.eps, si, param.shrinking);
double r = si.r;
//Debug.WriteLine("C = " + 1 / r + "\n");
for (i = 0; i < l; i++)
alpha[i] *= y[i] / r;
si.rho /= r;
si.obj /= (r * r);
si.upper_bound_p = 1 / r;
si.upper_bound_n = 1 / r;
}
/// <summary>
/// Classification SVM
/// Supports multi-class classification
/// </summary>
/// <param name="prob">Training Data Set</param>
/// <param name="kernel">Selected Kernel</param>
/// <param name="C">Cost parameter </param>
/// <param name="cache_size">Indicates the maximum memory that can use the program</param>
public C_SVC(svm_problem prob, Kernel kernel, double C, double cache_size = 100)
: base(SvmType.C_SVC, prob, kernel, C, cache_size)
{
}
private static void solve_nu_svr(svm_problem prob, svm_parameter param, double[] alpha, Solver.SolutionInfo si)
{
int l = prob.l;
double C = param.C;
double[] alpha2 = new double[2 * l];
double[] linear_term = new double[2 * l];
sbyte[] y = new sbyte[2 * l];
int i;
double sum = C * param.nu * l / 2;
for (i = 0; i < l; i++)
{
alpha2[i] = alpha2[i + l] = System.Math.Min(sum, C);
sum -= alpha2[i];
linear_term[i] = - prob.y[i];
y[i] = 1;
linear_term[i + l] = prob.y[i];
y[i + l] = - 1;
}
Solver_NU s = new Solver_NU();
s.Solve(2 * l, new SVR_Q(prob, param), linear_term, y, alpha2, C, C, param.eps, si, param.shrinking);
//Debug.WriteLine("epsilon = " + (- si.r) + "\n");
for (i = 0; i < l; i++)
alpha[i] = alpha2[i] - alpha2[i + l];
}
// read in a problem (in svmlight format)
private void read_problem()
{
/* UPGRADE_TODO: Expected value of parameters of constructor
* 'java.io.BufferedReader.BufferedReader' are different in the equivalent in .NET.
* 'ms-help://MS.VSCC.2003/commoner/redir/redirect.htm?keyword="jlca1092"'
*/
System.IO.StreamReader fp = new System.IO.StreamReader(input_file_name);
System.Collections.ArrayList vy = new System.Collections.ArrayList(10);
System.Collections.ArrayList vx = new System.Collections.ArrayList(10);
int max_index = 0;
while (true)
{
System.String line = fp.ReadLine();
if ((System.Object) line == null)
break;
SupportClass.Tokenizer st = new SupportClass.Tokenizer(line, " \t\n\r\f:");
vy.Add(st.NextToken());
int m = st.Count / 2;
svm_node[] x = new svm_node[m];
for (int j = 0; j < m; j++)
{
x[j] = new svm_node();
x[j].index = atoi(st.NextToken());
x[j].value_Renamed = atof(st.NextToken());
}
if (m > 0)
max_index = System.Math.Max(max_index, x[m - 1].index);
vx.Add(x);
}
prob = new svm_problem();
prob.l = vy.Count;
prob.x = new svm_node[prob.l][];
for (int i = 0; i < prob.l; i++)
prob.x[i] = (svm_node[]) vx[i];
prob.y = new double[prob.l];
for (int i = 0; i < prob.l; i++)
prob.y[i] = atof((System.String) vy[i]);
if (param.gamma == 0)
param.gamma = 1.0 / max_index;
fp.Close();
}
private static void solve_one_class(svm_problem prob, svm_parameter param, double[] alpha, Solver.SolutionInfo si)
{
int l = prob.l;
double[] zeros = new double[l];
sbyte[] ones = new sbyte[l];
int i;
//UPGRADE_WARNING: Data types in Visual C# might be different. Verify the accuracy of narrowing conversions. 'ms-help://MS.VSCC.2003/commoner/redir/redirect.htm?keyword="jlca1042_3"'
int n = (int) (param.nu * prob.l); // # of alpha's at upper bound
for (i = 0; i < n; i++)
alpha[i] = 1;
alpha[n] = param.nu * prob.l - n;
for (i = n + 1; i < l; i++)
alpha[i] = 0;
for (i = 0; i < l; i++)
{
zeros[i] = 0;
ones[i] = 1;
}
Solver s = new Solver();
s.Solve(l, new ONE_CLASS_Q(prob, param), zeros, ones, alpha, 1.0, 1.0, param.eps, si, param.shrinking);
}
