public SupportVectorMachine(svm_model theModel)
{
svm_node[][] sV;
int num;
svm_node[] _nodeArray3;
int num2;
this._model = theModel;
this._paras = this._model.param;
if (8 != 0)
{
goto Label_00A8;
}
return;
Label_002C:
if (num < sV.Length)
{
_nodeArray3 = sV[num];
num2 = 0;
}
else
{
if ((0 == 0) && ((((uint) num2) + ((uint) num2)) > uint.MaxValue))
{
goto Label_002C;
}
if ((((uint) num2) - ((uint) num2)) >= 0)
{
return;
}
goto Label_00A8;
}
Label_005F:
while (num2 < _nodeArray3.Length)
{
svm_node _node = _nodeArray3[num2];
this._inputCount = Math.Max(_node.index, this._inputCount);
num2++;
}
num++;
goto Label_002C;
Label_00A8:
this._inputCount = 0;
sV = this._model.SV;
num = 0;
if (((uint) num2) < 0)
{
goto Label_005F;
}
goto Label_002C;
}
public static void svm_save_model(StreamWriter fp, svm_model model)
{
svm_parameter param = model.param;
fp.Write("svm_type " + svm_type_table[param.svm_type] + "\n");
fp.Write("kernel_type " + kernel_type_table[param.kernel_type] + "\n");
if (param.kernel_type == svm_parameter.POLY)
fp.Write("degree " + param.degree + "\n");
if (param.kernel_type == svm_parameter.POLY || param.kernel_type == svm_parameter.RBF ||
param.kernel_type == svm_parameter.SIGMOID)
fp.Write("gamma " + param.gamma + "\n");
if (param.kernel_type == svm_parameter.POLY || param.kernel_type == svm_parameter.SIGMOID)
fp.Write("coef0 " + param.coef0 + "\n");
int nr_class = model.nr_class;
int l = model.l;
fp.Write("nr_class " + nr_class + "\n");
fp.Write("total_sv " + l + "\n");
{
fp.Write("rho");
for (int i = 0; i < nr_class*(nr_class - 1)/2; i++)
fp.Write(" " + model.rho[i]);
fp.Write("\n");
}
if (model.label != null)
{
fp.Write("label");
for (int i = 0; i < nr_class; i++)
fp.Write(" " + model.label[i]);
fp.Write("\n");
}
if (model.probA != null)
// regression has probA only
{
fp.Write("probA");
for (int i = 0; i < nr_class*(nr_class - 1)/2; i++)
fp.Write(" " + model.probA[i]);
fp.Write("\n");
}
if (model.probB != null)
{
fp.Write("probB");
for (int i = 0; i < nr_class*(nr_class - 1)/2; i++)
fp.Write(" " + model.probB[i]);
fp.Write("\n");
}
if (model.nSV != null)
{
fp.Write("nr_sv");
for (int i = 0; i < nr_class; i++)
fp.Write(" " + model.nSV[i]);
fp.Write("\n");
}
fp.Write("SV\n");
double[][] sv_coef = model.sv_coef;
svm_node[][] SV = model.SV;
for (int i = 0; i < l; i++)
{
for (int j = 0; j < nr_class - 1; j++)
fp.Write(sv_coef[j][i] + " ");
svm_node[] p = SV[i];
for (int j = 0; j < p.Length; j++)
fp.Write(p[j].index + ":" + p[j].value_Renamed + " ");
fp.Write("\n");
}
fp.Close();
}
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;
var dec_values = new double[nr_class*(nr_class - 1)/2];
svm_predict_values(model, x, dec_values);
double min_prob = 1e-7;
var tmpArray = new double[nr_class][];
for (int i2 = 0; i2 < nr_class; i2++)
{
tmpArray[i2] = new double[nr_class];
}
double[][] pairwise_prob = tmpArray;
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(svm_model model, svm_node[] x)
{
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)
{
var res = new double[1];
svm_predict_values(model, x, res);
if (model.param.svm_type == svm_parameter.ONE_CLASS)
return (res[0] > 0) ? 1 : - 1;
else
return res[0];
}
else
{
int i;
int nr_class = model.nr_class;
var dec_values = new double[nr_class*(nr_class - 1)/2];
svm_predict_values(model, x, dec_values);
var vote = new int[nr_class];
for (i = 0; i < nr_class; i++)
vote[i] = 0;
int pos = 0;
for (i = 0; i < nr_class; i++)
for (int j = i + 1; j < nr_class; j++)
{
if (dec_values[pos++] > 0)
++vote[i];
else
++vote[j];
}
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 void svm_predict_values(svm_model model, svm_node[] x, 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)
{
double[] sv_coef = model.sv_coef[0];
double sum = 0;
for (int 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;
}
else
{
int i;
int nr_class = model.nr_class;
int l = model.l;
var kvalue = new double[l];
for (i = 0; i < l; i++)
kvalue[i] = Kernel.k_function(x, model.SV[i], model.param);
var 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 p = 0;
int pos = 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[pos++] = sum;
}
}
}
public static double svm_get_svr_probability(svm_model model)
{
if ((model.param.svm_type == svm_parameter.EPSILON_SVR || model.param.svm_type == svm_parameter.NU_SVR) &&
model.probA != null)
return model.probA[0];
else
{
Console.Error.Write("Model doesn't contain information for SVR probability inference\n");
return 0;
}
}
public static void svm_get_labels(svm_model model, int[] label)
{
if (model.label != null)
for (int i = 0; i < model.nr_class; i++)
label[i] = model.label[i];
}
/// <summary>
/// Load the models.
/// </summary>
/// <param name="xmlin">Where to read the models from.</param>
/// <param name="network">Where the models are read into.</param>
private void HandleModels(ReadXML xmlin, SVMNetwork network)
{
int index = 0;
while (xmlin.ReadToTag())
{
if (xmlin.IsIt(SVMNetworkPersistor.TAG_MODEL, true))
{
svm_parameter param = new svm_parameter();
svm_model model = new svm_model();
model.param = param;
network.Models[index] = model;
HandleModel(xmlin, network.Models[index]);
index++;
}
else if (xmlin.IsIt(SVMNetworkPersistor.TAG_MODELS, false))
{
break;
}
}
}
public static int svm_get_svm_type(svm_model model)
{
return model.param.svm_type;
}
//
// 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;
}
/// <summary>
/// Construct a SVM from a model.
/// </summary>
///
/// <param name="theModel">The model.</param>
public SupportVectorMachine(svm_model theModel)
{
_model = theModel;
_paras = _model.param;
_inputCount = 0;
// determine the input count
foreach (var element in _model.SV)
{
foreach (svm_node t in element)
{
_inputCount = Math.Max(t.index, _inputCount);
}
}
//
}
/// <summary>
/// Save a model.
/// </summary>
/// <param name="xmlout">Where to save a model to.</param>
/// <param name="model">The model to save to.</param>
public static void SaveModel(WriteXML xmlout, svm_model model)
{
if (model != null)
{
xmlout.BeginTag(SVMNetworkPersistor.TAG_MODEL);
svm_parameter param = model.param;
xmlout.AddProperty(SVMNetworkPersistor.TAG_TYPE_SVM,
svm_type_table[param.svm_type]);
xmlout.AddProperty(SVMNetworkPersistor.TAG_TYPE_KERNEL,
kernel_type_table[param.kernel_type]);
if (param.kernel_type == svm_parameter.POLY)
{
xmlout.AddProperty(SVMNetworkPersistor.TAG_DEGREE, param.degree);
}
if (param.kernel_type == svm_parameter.POLY
|| param.kernel_type == svm_parameter.RBF
|| param.kernel_type == svm_parameter.SIGMOID)
{
xmlout.AddProperty(SVMNetworkPersistor.TAG_GAMMA, param.gamma);
}
if (param.kernel_type == svm_parameter.POLY
|| param.kernel_type == svm_parameter.SIGMOID)
{
xmlout.AddProperty(SVMNetworkPersistor.TAG_COEF0, param.coef0);
}
int nr_class = model.nr_class;
int l = model.l;
xmlout.AddProperty(SVMNetworkPersistor.TAG_NUMCLASS, nr_class);
xmlout.AddProperty(SVMNetworkPersistor.TAG_TOTALSV, l);
xmlout.AddProperty(SVMNetworkPersistor.TAG_RHO, model.rho, nr_class
* (nr_class - 1) / 2);
xmlout.AddProperty(SVMNetworkPersistor.TAG_LABEL, model.label,
nr_class);
xmlout.AddProperty(SVMNetworkPersistor.TAG_PROB_A, model.probA,
nr_class * (nr_class - 1) / 2);
xmlout.AddProperty(SVMNetworkPersistor.TAG_PROB_B, model.probB,
nr_class * (nr_class - 1) / 2);
xmlout.AddProperty(SVMNetworkPersistor.TAG_NSV, model.nSV, nr_class);
xmlout.BeginTag(SVMNetworkPersistor.TAG_DATA);
double[][] sv_coef = model.sv_coef;
svm_node[][] SV = model.SV;
StringBuilder line = new StringBuilder();
for (int i = 0; i < l; i++)
{
line.Length = 0;
for (int j = 0; j < nr_class - 1; j++)
line.Append(sv_coef[j][i] + " ");
svm_node[] p = SV[i];
//if (param.kernel_type == svm_parameter.PRECOMPUTED)
//{
// line.Append("0:" + (int) (p[0].value));
//}
//else
for (int j = 0; j < p.Length; j++)
line.Append(p[j].index + ":" + p[j].value_Renamed + " ");
xmlout.AddProperty(SVMNetworkPersistor.TAG_ROW, line.ToString());
}
xmlout.EndTag();
xmlout.EndTag();
}
}
/// <summary>
/// Load the data from a model.
/// </summary>
/// <param name="xmlin">Where to read the data from.</param>
/// <param name="model">The model to load data into.</param>
private void HandleData(ReadXML xmlin, svm_model model)
{
int i = 0;
int m = model.nr_class - 1;
int l = model.l;
model.sv_coef = EngineArray.AllocateDouble2D(m, l);
model.SV = new svm_node[l][];
while (xmlin.ReadToTag())
{
if (xmlin.IsIt(SVMNetworkPersistor.TAG_ROW, true))
{
String line = xmlin.ReadTextToTag();
String[] st = xmlin.ReadTextToTag().Split(',');
for (int k = 0; k < m; k++)
model.sv_coef[k][i] = Double.Parse(st[i]);
int n = st.Length / 2;
model.SV[i] = new svm_node[n];
int idx = 0;
for (int j = 0; j < n; j++)
{
model.SV[i][j] = new svm_node();
model.SV[i][j].index = int.Parse(st[idx++]);
model.SV[i][j].value_Renamed = Double.Parse(st[idx++]);
}
i++;
}
else if (xmlin.IsIt(SVMNetworkPersistor.TAG_DATA, false))
{
break;
}
}
}
/// <summary>
/// Handle a model.
/// </summary>
/// <param name="xmlin">Where to read the model from.</param>
/// <param name="model">Where to load the model into.</param>
private void HandleModel(ReadXML xmlin, svm_model model)
{
while (xmlin.ReadToTag())
{
if (xmlin.IsIt(SVMNetworkPersistor.TAG_TYPE_SVM, true))
{
int i = EngineArray.FindStringInArray(
SVMNetworkPersistor.svm_type_table, xmlin.ReadTextToTag());
model.param.svm_type = i;
}
else if (xmlin.IsIt(SVMNetworkPersistor.TAG_DEGREE, true))
{
model.param.degree = int.Parse(xmlin.ReadTextToTag());
}
else if (xmlin.IsIt(SVMNetworkPersistor.TAG_GAMMA, true))
{
model.param.gamma = double.Parse(xmlin.ReadTextToTag());
}
else if (xmlin.IsIt(SVMNetworkPersistor.TAG_COEF0, true))
{
model.param.coef0 = double.Parse(xmlin.ReadTextToTag());
}
else if (xmlin.IsIt(SVMNetworkPersistor.TAG_NUMCLASS, true))
{
model.nr_class = int.Parse(xmlin.ReadTextToTag());
}
else if (xmlin.IsIt(SVMNetworkPersistor.TAG_TOTALSV, true))
{
model.l = int.Parse(xmlin.ReadTextToTag());
}
else if (xmlin.IsIt(SVMNetworkPersistor.TAG_RHO, true))
{
int n = model.nr_class * (model.nr_class - 1) / 2;
model.rho = new double[n];
String[] st = xmlin.ReadTextToTag().Split(',');
for (int i = 0; i < n; i++)
model.rho[i] = double.Parse(st[i]);
}
else if (xmlin.IsIt(SVMNetworkPersistor.TAG_LABEL, true))
{
int n = model.nr_class;
model.label = new int[n];
String[] st = xmlin.ReadTextToTag().Split(',');
for (int i = 0; i < n; i++)
model.label[i] = int.Parse(st[i]);
}
else if (xmlin.IsIt(SVMNetworkPersistor.TAG_PROB_A, true))
{
int n = model.nr_class * (model.nr_class - 1) / 2;
model.probA = new double[n];
String[] st = xmlin.ReadTextToTag().Split(',');
for (int i = 0; i < n; i++)
model.probA[i] = Double.Parse(st[i]);
}
else if (xmlin.IsIt(SVMNetworkPersistor.TAG_PROB_B, true))
{
int n = model.nr_class * (model.nr_class - 1) / 2;
model.probB = new double[n];
String[] st = xmlin.ReadTextToTag().Split(',');
for (int i = 0; i < n; i++)
model.probB[i] = Double.Parse(st[i]);
}
else if (xmlin.IsIt(SVMNetworkPersistor.TAG_NSV, true))
{
int n = model.nr_class;
model.nSV = new int[n];
String[] st = xmlin.ReadTextToTag().Split(',');
for (int i = 0; i < n; i++)
model.nSV[i] = int.Parse(st[i]);
}
else if (xmlin.IsIt(SVMNetworkPersistor.TAG_TYPE_KERNEL, true))
{
int i = EngineArray.FindStringInArray(
SVMNetworkPersistor.kernel_type_table, xmlin
.ReadTextToTag());
model.param.kernel_type = i;
}
else if (xmlin.IsIt(SVMNetworkPersistor.TAG_DATA, true))
{
HandleData(xmlin, model);
}
else if (xmlin.IsIt(SVMNetworkPersistor.TAG_MODEL, false))
{
break;
}
}
}
public static svm_model svm_load_model(StringReader fp)
{
// read parameters
var model = new svm_model();
var param = new svm_parameter();
model.param = param;
model.rho = null;
model.probA = null;
model.probB = null;
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)
{
Console.Error.Write("unknown svm type.\n");
return null;
}
}
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)
{
Console.Error.Write("unknown kernel function.\n");
return null;
}
}
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];
var st = new SupportClass.Tokenizer(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];
var st = new SupportClass.Tokenizer(arg);
for (int i = 0; i < n; i++)
model.label[i] = atoi(st.NextToken());
}
else if (cmd.StartsWith("probA"))
{
int n = model.nr_class*(model.nr_class - 1)/2;
model.probA = new double[n];
var st = new SupportClass.Tokenizer(arg);
for (int i = 0; i < n; i++)
model.probA[i] = atof(st.NextToken());
}
else if (cmd.StartsWith("probB"))
{
int n = model.nr_class*(model.nr_class - 1)/2;
model.probB = new double[n];
var st = new SupportClass.Tokenizer(arg);
for (int i = 0; i < n; i++)
model.probB[i] = atof(st.NextToken());
}
else if (cmd.StartsWith("nr_sv"))
{
int n = model.nr_class;
model.nSV = new int[n];
var st = new SupportClass.Tokenizer(arg);
for (int i = 0; i < n; i++)
model.nSV[i] = atoi(st.NextToken());
}
else if (cmd.StartsWith("SV"))
{
break;
}
else
{
Console.Error.Write("unknown text in model file\n");
return null;
}
}
// read sv_coef and SV
int m = model.nr_class - 1;
int l = model.l;
model.sv_coef = new double[m][];
for (int i = 0; i < m; i++)
{
model.sv_coef[i] = new double[l];
}
model.SV = new svm_node[l][];
for (int i = 0; i < l; i++)
{
String line = fp.ReadLine();
var st = new SupportClass.Tokenizer(line, " \t\n\r\f:");
for (int k = 0; k < m; k++)
model.sv_coef[k][i] = atof(st.NextToken());
int n = st.Count/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_Renamed = atof(st.NextToken());
}
}
return model;
}
public static int svm_check_probability_model(svm_model model)
{
if (((model.param.svm_type == svm_parameter.C_SVC || model.param.svm_type == svm_parameter.NU_SVC) &&
model.probA != null && model.probB != null) ||
((model.param.svm_type == svm_parameter.EPSILON_SVR || model.param.svm_type == svm_parameter.NU_SVR) &&
model.probA != null))
return 1;
else
return 0;
}
public static int svm_get_nr_class(svm_model model)
{
return model.nr_class;
}
