?? svm.cs
字號:
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;
}
System.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];
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 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]]);
}
}
public static int svm_get_svm_type(svm_model model)
{
return model.param.svm_type;
}
public static int svm_get_nr_class(svm_model model)
{
return model.nr_class;
}
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];
}
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
{
System.Console.Error.Write("Model doesn't contain information for SVR probability inference\n");
return 0;
}
}
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;
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 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_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)
{
double[] 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;
double[] dec_values = new double[nr_class * (nr_class - 1) / 2];
svm_predict_values(model, x, dec_values);
int[] 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 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[][] 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] = System.Math.Min(System.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);
}
//UPGRADE_NOTE: Final was removed from the declaration of 'svm_type_table'. 'ms-help://MS.VSCC.2003/commoner/redir/redirect.htm?keyword="jlca1003_3"'
internal static readonly System.String[] svm_type_table = new System.String[]{"c_svc", "nu_svc", "one_class", "epsilon_svr", "nu_svr"};
//UPGRADE_NOTE: Final was removed from the declaration of 'kernel_type_table'. 'ms-help://MS.VSCC.2003/commoner/redir/redirect.htm?keyword="jlca1003_3"'
internal static readonly System.String[] kernel_type_table = new System.String[]{"linear", "polynomial", "rbf", "sigmoid"};
public static void svm_save_model(System.String model_file_name, svm_model model)
{
//UPGRADE_TODO: Class 'java.io.DataOutputStream' was converted to 'System.IO.BinaryWriter' which has a different behavior. 'ms-help://MS.VSCC.2003/commoner/redir/redirect.htm?keyword="jlca1073_javaioDataOutputStream_3"'
//UPGRADE_TODO: Constructor 'java.io.FileOutputStream.FileOutputStream' was converted to 'System.IO.FileStream.FileStream' which has a different behavior. 'ms-help://MS.VSCC.2003/commoner/redir/redirect.htm?keyword="jlca1073_javaioFileOutputStreamFileOutputStream_javalangString_3"'
/* Original System.IO.BinaryWriter fp = new System.IO.BinaryWriter(new System.IO.FileStream(model_file_name, System.IO.FileMode.Create));*/
System.IO.StreamWriter fp = new System.IO.StreamWriter(new System.IO.FileStream(model_file_name, System.IO.FileMode.Create), System.Text.Encoding.Default);
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();
}
private static double atof(System.String s)
{
return System.Double.Parse(s);
}
private static int atoi(System.String s)
{
return System.Int32.Parse(s);
}
public static svm_model svm_load_model(System.String model_file_name)
{
//UPGRADE_TODO: The differences in the expected value of parameters for constructor 'java.io.BufferedReader.BufferedReader' may cause compilation errors. 'ms-help://MS.VSCC.2003/commoner/redir/redirect.htm?keyword="jlca1092_3"'
//UPGRADE_WARNING: At least one expression was used more than once in the target code. 'ms-help://MS.VSCC.2003/commoner/redir/redirect.htm?keyword="jlca1181_3"'
//UPGRADE_TODO: Constructor 'java.io.FileReader.FileReader' was converted to 'System.IO.StreamReader' which has a different behavior. 'ms-help://MS.VSCC.2003/commoner/redir/redirect.htm?keyword="jlca1073_3"'
/*Original System.IO.StreamReader fp = new System.IO.StreamReader(new System.IO.StreamReader(model_file_name, System.Text.Encoding.Default).BaseStream, new System.IO.StreamReader(model_file_name, System.Text.Encoding.Default).CurrentEncoding);*/
System.IO.StreamReader fp = new System.IO.StreamReader(new System.IO.FileStream(model_file_name, System.IO.FileMode.Open), System.Text.Encoding.Default);
// read parameter?? 快捷鍵說明
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