//
// Interface functions
//
public static svm_model svm_train(svm_problem prob, svm_parameter param)
{
var 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 (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 (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;
var label = new int[max_nr_class];
var count = new int[max_nr_class];
var 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"'
var 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;
var 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
var start = new int[nr_class];
start[0] = 0;
for (i = 1; i < nr_class; i++)
start[i] = start[i - 1] + count[i - 1];
var 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
var 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)
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
var nonzero = new bool[l];
for (i = 0; i < l; i++)
nonzero[i] = false;
var 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++)
{
var 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)
{
var 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] && Math.Abs(f[p].alpha[k]) > 0)
nonzero[si + k] = true;
for (k = 0; k < cj; k++)
if (!nonzero[sj + k] && 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;
var 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;
}
Console.Out.Write("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];
var 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;
}
internal static decision_function svm_train_one(svm_problem prob, svm_parameter param, double Cp, double Cn)
{
var alpha = new double[prob.l];
var 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;
}
Console.Out.Write("obj = " + si.obj + ", rho = " + si.rho + "\n");
// output SVs
int nSV = 0;
int nBSV = 0;
for (int i = 0; i < prob.l; i++)
{
if (Math.Abs(alpha[i]) > 0)
{
++nSV;
if (prob.y[i] > 0)
{
if (Math.Abs(alpha[i]) >= si.upper_bound_p)
++nBSV;
}
else
{
if (Math.Abs(alpha[i]) >= si.upper_bound_n)
++nBSV;
}
}
}
Console.Out.Write("nSV = " + nSV + ", nBSV = " + nBSV + "\n");
var f = new decision_function();
f.alpha = alpha;
f.rho = si.rho;
return f;
}
static decision_function svm_train_one(Problem prob, Parameter param, double Cp, double Cn)
{
double[] alpha = new double[prob.Count];
Solver.SolutionInfo si = new Solver.SolutionInfo();
switch (param.SvmType)
{
case SvmType.C_SVC:
solve_c_svc(prob, param, alpha, si, Cp, Cn);
break;
case SvmType.NU_SVC:
solve_nu_svc(prob, param, alpha, si);
break;
case SvmType.ONE_CLASS:
solve_one_class(prob, param, alpha, si);
break;
case SvmType.EPSILON_SVR:
solve_epsilon_svr(prob, param, alpha, si);
break;
case SvmType.NU_SVR:
solve_nu_svr(prob, param, alpha, si);
break;
}
Procedures.info("obj = " + si.obj + ", rho = " + si.rho + "\n");
// output SVs
int nSV = 0;
int nBSV = 0;
for (int i = 0; i < prob.Count; i++)
{
if (Math.Abs(alpha[i]) > 0)
{
++nSV;
if (prob.Y[i] > 0)
{
if (Math.Abs(alpha[i]) >= si.upper_bound_p)
++nBSV;
}
else
{
if (Math.Abs(alpha[i]) >= si.upper_bound_n)
++nBSV;
}
}
}
Procedures.info("nSV = " + nSV + ", nBSV = " + nBSV + "\n");
decision_function f = new decision_function();
f.alpha = alpha;
f.rho = si.rho;
return f;
}
//
// Interface functions
//
public static Model svm_train(Problem prob, Parameter param)
{
Model model = new Model();
model.Parameter = param;
if (param.SvmType == SvmType.ONE_CLASS ||
param.SvmType == SvmType.EPSILON_SVR ||
param.SvmType == SvmType.NU_SVR)
{
// regression or one-class-svm
model.NumberOfClasses = 2;
model.ClassLabels = null;
model.NumberOfSVPerClass = null;
model.PairwiseProbabilityA = null; model.PairwiseProbabilityB = null;
model.SupportVectorCoefficients = new double[1][];
if (param.Probability &&
(param.SvmType == SvmType.EPSILON_SVR ||
param.SvmType == SvmType.NU_SVR))
{
model.PairwiseProbabilityA = new double[1];
model.PairwiseProbabilityA[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.Count; i++)
if (Math.Abs(f.alpha[i]) > 0) ++nSV;
model.SupportVectorCount = nSV;
model.SupportVectors = new Node[nSV][];
model.SupportVectorCoefficients[0] = new double[nSV];
int j = 0;
for (i = 0; i < prob.Count; i++)
if (Math.Abs(f.alpha[i]) > 0)
{
model.SupportVectors[j] = prob.X[i];
model.SupportVectorCoefficients[0][j] = f.alpha[i];
++j;
}
}
else
{
// classification
int l = prob.Count;
int[] tmp_nr_class = new int[1];
int[][] tmp_label = new int[1][];
int[][] tmp_start = new int[1][];
int[][] tmp_count = new int[1][];
int[] perm = new int[l];
// group training data of the same class
svm_group_classes(prob, tmp_nr_class, tmp_label, tmp_start, tmp_count, perm);
int nr_class = tmp_nr_class[0];
int[] label = tmp_label[0];
int[] start = tmp_start[0];
int[] count = tmp_count[0];
Node[][] x = new Node[l][];
int i;
for (i = 0; i < l; i++)
x[i] = prob.X[perm[i]];
// calculate weighted C
double[] weighted_C = new double[nr_class];
for (i = 0; i < nr_class; i++)
weighted_C[i] = param.C;
foreach (int weightedLabel in param.Weights.Keys)
{
int index = Array.IndexOf<int>(label, weightedLabel);
if (index < 0)
Console.Error.WriteLine("warning: class label " + weightedLabel + " specified in weight is not found");
else weighted_C[index] *= param.Weights[weightedLabel];
}
// 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)
{
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++)
{
Problem sub_prob = new Problem();
int si = start[i], sj = start[j];
int ci = count[i], cj = count[j];
sub_prob.Count = ci + cj;
sub_prob.X = new Node[sub_prob.Count][];
sub_prob.Y = new double[sub_prob.Count];
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)
{
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] && Math.Abs(f[p].alpha[k]) > 0)
nonzero[si + k] = true;
for (k = 0; k < cj; k++)
if (!nonzero[sj + k] && Math.Abs(f[p].alpha[ci + k]) > 0)
nonzero[sj + k] = true;
++p;
}
// build output
model.NumberOfClasses = nr_class;
model.ClassLabels = new int[nr_class];
for (i = 0; i < nr_class; i++)
model.ClassLabels[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)
{
model.PairwiseProbabilityA = new double[nr_class * (nr_class - 1) / 2];
model.PairwiseProbabilityB = new double[nr_class * (nr_class - 1) / 2];
for (i = 0; i < nr_class * (nr_class - 1) / 2; i++)
{
model.PairwiseProbabilityA[i] = probA[i];
model.PairwiseProbabilityB[i] = probB[i];
}
}
else
{
model.PairwiseProbabilityA = null;
model.PairwiseProbabilityB = null;
}
int nnz = 0;
int[] nz_count = new int[nr_class];
model.NumberOfSVPerClass = 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.NumberOfSVPerClass[i] = nSV;
nz_count[i] = nSV;
}
Procedures.info("Total nSV = " + nnz + "\n");
model.SupportVectorCount = nnz;
model.SupportVectors = new Node[nnz][];
p = 0;
for (i = 0; i < l; i++)
if (nonzero[i]) model.SupportVectors[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.SupportVectorCoefficients = new double[nr_class - 1][];
for (i = 0; i < nr_class - 1; i++)
model.SupportVectorCoefficients[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.SupportVectorCoefficients[j - 1][q++] = f[p].alpha[k];
q = nz_start[j];
for (k = 0; k < cj; k++)
if (nonzero[sj + k])
model.SupportVectorCoefficients[i][q++] = f[p].alpha[ci + k];
++p;
}
}
return model;
}
//
// Interface functions
//
public static svm_model svm_train(svm_problem prob, svm_parameter param) {
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 (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 (Math.Abs(f.alpha[i]) > 0) {
model.SV[j] = prob.x[i];
model.sv_coef[0][j] = f.alpha[i];
++j;
}
} else {
// classification
int l = prob.l;
int[] tmp_nr_class = new int[1];
int[][] tmp_label = new int[1][];
int[][] tmp_start = new int[1][];
int[][] tmp_count = new int[1][];
int[] perm = new int[l];
// group training data of the same class
svm_group_classes(prob, tmp_nr_class, tmp_label, tmp_start, tmp_count, perm);
int nr_class = tmp_nr_class[0];
int[] label = tmp_label[0];
int[] start = tmp_start[0];
int[] count = tmp_count[0];
if (nr_class == 1)
svm.info("WARNING: training data in only one class. See README for details." + Environment.NewLine);
svm_node[][] x = new svm_node[l][];
int i;
for (i = 0; i < l; i++)
x[i] = prob.x[perm[i]];
// 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)
Console.Error.WriteLine("WARNING: class label " + param.weight_label[i] +
" specified in weight is not found");
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] && Math.Abs(f[p].alpha[k]) > 0)
nonzero[si + k] = true;
for (k = 0; k < cj; k++)
if (!nonzero[sj + k] && 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;
}
svm.info("Total nSV = " + nnz + Environment.NewLine);
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;
}
