public static svm_model svm_load_model(String model_file_name) throws IOException
{
BufferedReader fp = new BufferedReader(new FileReader(model_file_name));
// read parameters
svm_model model = new svm_model();
svm_parameter param = new svm_parameter();
model.param = param;
model.label = null;
model.nSV = null;
while(true)
{
String cmd = fp.readLine();
String arg = cmd.substring(cmd.indexOf(' ')+1);
if(cmd.startsWith("svm_type"))
{
int i;
for(i=0;i<svm_type_table.length;i++)
{
if(arg.indexOf(svm_type_table[i])!=-1)
{
param.svm_type=i;
break;
}
}
if(i == svm_type_table.length)
{
System.err.print("unknown svm type.\n");
System.exit(1);
}
}
else if(cmd.startsWith("kernel_type"))
{
int i;
for(i=0;i<kernel_type_table.length;i++)
{
if(arg.indexOf(kernel_type_table[i])!=-1)
{
param.kernel_type=i;
break;
}
}
if(i == kernel_type_table.length)
{
System.err.print("unknown kernel function.\n");
System.exit(1);
}
}
else if(cmd.startsWith("degree"))
param.degree = atof(arg);
else if(cmd.startsWith("gamma"))
param.gamma = atof(arg);
else if(cmd.startsWith("coef0"))
param.coef0 = atof(arg);
else if(cmd.startsWith("nr_class"))
model.nr_class = atoi(arg);
else if(cmd.startsWith("total_sv"))
model.l = atoi(arg);
else if(cmd.startsWith("rho"))
{
int n = model.nr_class * (model.nr_class-1)/2;
model.rho = new double[n];
StringTokenizer st = new StringTokenizer(arg);
for(int i=0;i<n;i++)
model.rho[i] = atof(st.nextToken());
}
else if(cmd.startsWith("label"))
{
int n = model.nr_class;
model.label = new int[n];
StringTokenizer st = new StringTokenizer(arg);
for(int i=0;i<n;i++)
model.label[i] = atoi(st.nextToken());
}
else if(cmd.startsWith("nr_sv"))
{
int n = model.nr_class;
model.nSV = new int[n];
StringTokenizer st = new StringTokenizer(arg);
for(int i=0;i<n;i++)
model.nSV[i] = atoi(st.nextToken());
}
else if(cmd.startsWith("SV"))
{
break;
}
else
{
System.err.print("unknown text in model file\n");
System.exit(1);
}
}
// read sv_coef and SV
int m = model.nr_class - 1;
int l = model.l;
model.sv_coef = new double[m][l];
model.SV = new svm_node[l][];
for(int i=0;i<l;i++)
{
String line = fp.readLine();
StringTokenizer st = new StringTokenizer(line," \t\n\r\f:");
for(int k=0;k<m;k++)
model.sv_coef[k][i] = atof(st.nextToken());
int n = st.countTokens()/2;
model.SV[i] = new svm_node[n];
for(int j=0;j<n;j++)
{
model.SV[i][j] = new svm_node();
model.SV[i][j].index = atoi(st.nextToken());
model.SV[i][j].value = atof(st.nextToken());
}
}
fp.close();
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.sv_coef = new double[1][];
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;
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++)
{
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];
System.arraycopy(label,0,new_data,0,label.length);
label = new_data;
new_data = new int[max_nr_class];
System.arraycopy(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.err.print("warning: class label "+param.weight_label[i]+" specified in weight is not found\n");
else
weighted_C[j] *= param.weight[i];
}
// train n*(n-1)/2 models
boolean[] nonzero = new boolean[l];
for(i=0;i<l;i++)
nonzero[i] = false;
decision_function[] f = new decision_function[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;
}
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;
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;
}
System.out.print("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;
}
