public static double svm_predict(svm_model model, svm_node[] x)
{
int nr_class = model.nr_class;
double[] dec_values;
if(model.param.svm_type == svm_parameter.ONE_CLASS ||
model.param.svm_type == svm_parameter.EPSILON_SVR ||
model.param.svm_type == svm_parameter.NU_SVR)
dec_values = new double[1];
else
dec_values = new double[nr_class*(nr_class-1)/2];
double pred_result = svm_predict_values(model, x, dec_values);
return pred_result;
}
public static double svm_predict_probability(svm_model model, svm_node[] x, double[] prob_estimates)
{
if ((model.param.svm_type == svm_parameter.C_SVC || model.param.svm_type == svm_parameter.NU_SVC) &&
model.probA!=null && model.probB!=null)
{
int i;
int nr_class = model.nr_class;
double[] dec_values = new double[nr_class*(nr_class-1)/2];
svm_predict_values(model, x, dec_values);
double min_prob=1e-7;
double[,] pairwise_prob=new double[nr_class, nr_class];
int k=0;
for(i=0;i<nr_class;i++)
{
for (int j = i + 1; j < nr_class; j++)
{
pairwise_prob[i,j] =
Math.Min(Math.Max(sigmoid_predict(dec_values[k], model.probA[k], model.probB[k]), min_prob), 1 - min_prob);
pairwise_prob[j,i] = 1 - pairwise_prob[i,j];
k++;
}
}
multiclass_probability(nr_class,pairwise_prob,prob_estimates);
int prob_max_idx = 0;
for(i=1;i<nr_class;i++)
if(prob_estimates[i] > prob_estimates[prob_max_idx])
prob_max_idx = i;
return model.label[prob_max_idx];
}
else
return svm_predict(model, x);
}
public static double svm_predict_values(svm_model model, svm_node[] x, double[] dec_values)
{
int i;
if(model.param.svm_type == svm_parameter.ONE_CLASS ||
model.param.svm_type == svm_parameter.EPSILON_SVR ||
model.param.svm_type == svm_parameter.NU_SVR)
{
double[] sv_coef = model.sv_coef[0];
double sum = 0;
for(i=0;i<model.l;i++)
sum += sv_coef[i] * Kernel.k_function(x,model.SV[i],model.param);
sum -= model.rho[0];
dec_values[0] = sum;
if(model.param.svm_type == svm_parameter.ONE_CLASS)
return (sum>0)?1:-1;
else
return sum;
}
else
{
int nr_class = model.nr_class;
int l = model.l;
double[] kvalue = new double[l];
for(i=0;i<l;i++)
kvalue[i] = Kernel.k_function(x,model.SV[i],model.param);
int[] start = new int[nr_class];
start[0] = 0;
for(i=1;i<nr_class;i++)
start[i] = start[i-1]+model.nSV[i-1];
int[] vote = new int[nr_class];
for(i=0;i<nr_class;i++)
vote[i] = 0;
int p=0;
for(i=0;i<nr_class;i++)
for(int j=i+1;j<nr_class;j++)
{
double sum = 0;
int si = start[i];
int sj = start[j];
int ci = model.nSV[i];
int cj = model.nSV[j];
int k;
double[] coef1 = model.sv_coef[j-1];
double[] coef2 = model.sv_coef[i];
for(k=0;k<ci;k++)
sum += coef1[si+k] * kvalue[si+k];
for(k=0;k<cj;k++)
sum += coef2[sj+k] * kvalue[sj+k];
sum -= model.rho[p];
dec_values[p] = sum;
if(dec_values[p] > 0)
++vote[i];
else
++vote[j];
p++;
}
int vote_max_idx = 0;
for(i=1;i<nr_class;i++)
if(vote[i] > vote[vote_max_idx])
vote_max_idx = i;
return model.label[vote_max_idx];
}
}
public static double k_function(svm_node[] x, svm_node[] y,
svm_parameter param)
{
switch(param.kernel_type)
{
case svm_parameter.LINEAR:
return dot(x,y);
case svm_parameter.POLY:
return powi(param.gamma*dot(x,y)+param.coef0,param.degree);
case svm_parameter.RBF:
{
double sum = 0;
int xlen = x.Length;
int ylen = y.Length;
int i = 0;
int j = 0;
while(i < xlen && j < ylen)
{
if(x[i].index == y[j].index)
{
double d = x[i++].value - y[j++].value;
sum += d*d;
}
else if(x[i].index > y[j].index)
{
sum += y[j].value * y[j].value;
++j;
}
else
{
sum += x[i].value * x[i].value;
++i;
}
}
while(i < xlen)
{
sum += x[i].value * x[i].value;
++i;
}
while(j < ylen)
{
sum += y[j].value * y[j].value;
++j;
}
return Math.Exp(-param.gamma*sum);
}
case svm_parameter.SIGMOID:
return Math.Tanh(param.gamma*dot(x,y)+param.coef0);
case svm_parameter.PRECOMPUTED:
return x[(int)(y[0].value)].value;
default:
return 0; // java
}
}
static double dot(svm_node[] x, svm_node[] y)
{
double sum = 0;
int xlen = x.Length;
int ylen = y.Length;
int i = 0;
int j = 0;
while(i < xlen && j < ylen)
{
if(x[i].index == y[j].index)
sum += x[i++].value * y[j++].value;
else
{
if(x[i].index > y[j].index)
++j;
else
++i;
}
}
return sum;
}
//
// 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];
model.sv_indices = new int[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];
model.sv_indices[j] = i+1;
++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.\n");
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.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] && 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+"\n");
model.l = nnz;
model.SV = new svm_node[nnz][];
model.sv_indices = new int[nnz];
p = 0;
for(i=0;i<l;i++)
if(nonzero[i])
{
model.SV[p] = x[i];
model.sv_indices[p++] = perm[i] + 1;
}
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;
}
public Kernel(int l, svm_node[][] x_, svm_parameter param)
{
this.kernel_type = param.kernel_type;
this.degree = param.degree;
this.gamma = param.gamma;
this.coef0 = param.coef0;
x = (svm_node[][])x_.Clone();
if(kernel_type == svm_parameter.RBF)
{
x_square = new double[l];
for(int i=0;i<l;i++)
x_square[i] = dot(x[i],x[i]);
}
else x_square = null;
}