internal xf094e3229d63c9be(int l, svm_node[][] x_, svm_parameter param)
{
int num;
Label_00C5:
this.xbcd9612004129925 = param.kernel_type;
Label_0075:
if ((((uint) num) | 0x80000000) != 0)
{
this.x83735b10b6a3d76d = param.degree;
do
{
this.xc7c4e9c099884228 = param.gamma;
this.x987b3666f8a37f90 = param.coef0;
if (0xff != 0)
{
this.x08db3aeabb253cb1 = (svm_node[][]) x_.Clone();
if (this.xbcd9612004129925 != 2)
{
this.x4f9e236643454e9f = null;
if (0 == 0)
{
if (0 == 0)
{
return;
}
goto Label_00C5;
}
goto Label_0075;
}
this.x4f9e236643454e9f = new double[l];
}
}
while (2 == 0);
}
for (num = 0; num < l; num++)
{
this.x4f9e236643454e9f[num] = x99240096a9e3842c(this.x08db3aeabb253cb1[num], this.x08db3aeabb253cb1[num]);
}
}
internal 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 Math.Pow(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_Renamed - y[j++].value_Renamed;
sum += d*d;
}
else if (x[i].index > y[j].index)
{
sum += y[j].value_Renamed*y[j].value_Renamed;
++j;
}
else
{
sum += x[i].value_Renamed*x[i].value_Renamed;
++i;
}
}
while (i < xlen)
{
sum += x[i].value_Renamed*x[i].value_Renamed;
++i;
}
while (j < ylen)
{
sum += y[j].value_Renamed*y[j].value_Renamed;
++j;
}
return Math.Exp((- param.gamma)*sum);
}
case svm_parameter.SIGMOID:
return tanh(param.gamma*dot(x, y) + param.coef0);
default:
return 0; // java
}
}
internal 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_Renamed*y[j++].value_Renamed;
else
{
if (x[i].index > y[j].index)
++j;
else
++i;
}
}
return sum;
}
internal Kernel(int l, svm_node[][] x_, svm_parameter param)
{
kernel_type = param.kernel_type;
degree = param.degree;
gamma = param.gamma;
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;
}
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;
}
}
}
//
// 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>
/// Convert regular Encog MLData into the "sparse" data needed by an SVM.
/// </summary>
///
/// <param name="data">The data to convert.</param>
/// <returns>The SVM sparse data.</returns>
public svm_node[] MakeSparse(IMLData data)
{
var result = new svm_node[data.Count];
for (int i = 0; i < data.Count; i++)
{
result[i] = new svm_node {index = i + 1, value_Renamed = data[i]};
}
return result;
}
/// <summary>
/// Convert regular Encog NeuralData into the "sparse" data needed by an SVM.
/// </summary>
/// <param name="data">The data to convert.</param>
/// <returns>The SVM sparse data.</returns>
public svm_node[] MakeSparse(INeuralData data)
{
svm_node[] result = new svm_node[data.Count];
for (int i = 0; i < data.Count; i++)
{
result[i] = new svm_node();
result[i].index = i + 1;
result[i].value_Renamed = data[i];
}
return result;
}
public svm_node[] MakeSparse(IMLData data)
{
svm_node[] _nodeArray = new svm_node[data.Count];
for (int i = 0; i < data.Count; i++)
{
_nodeArray[i] = new svm_node { index = i + 1, value_Renamed = data[i] };
if (0 == 0)
{
}
}
return _nodeArray;
}
internal static double x99240096a9e3842c(svm_node[] x08db3aeabb253cb1, svm_node[] x1e218ceaee1bb583)
{
int num4;
int num5;
double num = 0.0;
int length = x08db3aeabb253cb1.Length;
int num3 = x1e218ceaee1bb583.Length;
if (0 == 0)
{
num4 = 0;
num5 = 0;
}
goto Label_007F;
Label_000F:
if ((((uint) num5) - ((uint) num4)) <= uint.MaxValue)
{
if (0x7fffffff != 0)
{
}
return num;
}
Label_007F:
if (num4 < length)
{
goto Label_00BC;
}
if (((((uint) length) | 4) != 0) && ((((uint) length) + ((uint) num)) >= 0))
{
if ((((uint) num) & 0) != 0)
{
goto Label_00A4;
}
if ((((uint) num3) + ((uint) num5)) >= 0)
{
return num;
}
goto Label_00D0;
}
goto Label_000F;
Label_00A4:
if ((((uint) num) - ((uint) num)) >= 0)
{
goto Label_007F;
}
Label_00BC:
if (num5 < num3)
{
if (x08db3aeabb253cb1[num4].index == x1e218ceaee1bb583[num5].index)
{
num += x08db3aeabb253cb1[num4++].value_Renamed * x1e218ceaee1bb583[num5++].value_Renamed;
}
else if ((((uint) length) + ((uint) num5)) <= uint.MaxValue)
{
goto Label_00D0;
}
goto Label_007F;
}
goto Label_000F;
Label_00D0:
if (x08db3aeabb253cb1[num4].index > x1e218ceaee1bb583[num5].index)
{
num5++;
goto Label_007F;
}
num4++;
goto Label_00A4;
}
internal static double xc60aa42cfbd0c1ed(svm_node[] x08db3aeabb253cb1, svm_node[] x1e218ceaee1bb583, svm_parameter x0d173b5435b4d6ad)
{
double num;
int length;
int num3;
int num4;
int num5;
int num7 = x0d173b5435b4d6ad.kernel_type;
switch (num7)
{
case 0:
return x99240096a9e3842c(x08db3aeabb253cb1, x1e218ceaee1bb583);
case 1:
return Math.Pow((x0d173b5435b4d6ad.gamma * x99240096a9e3842c(x08db3aeabb253cb1, x1e218ceaee1bb583)) + x0d173b5435b4d6ad.coef0, x0d173b5435b4d6ad.degree);
case 2:
num = 0.0;
length = x08db3aeabb253cb1.Length;
num3 = x1e218ceaee1bb583.Length;
num4 = 0;
num5 = 0;
goto Label_0095;
case 3:
return xcb156489dc62ed23((x0d173b5435b4d6ad.gamma * x99240096a9e3842c(x08db3aeabb253cb1, x1e218ceaee1bb583)) + x0d173b5435b4d6ad.coef0);
default:
goto Label_021B;
}
Label_008D:
if (num4 < length)
{
num += x08db3aeabb253cb1[num4].value_Renamed * x08db3aeabb253cb1[num4].value_Renamed;
if ((((uint) length) - ((uint) num)) <= uint.MaxValue)
{
goto Label_0111;
}
goto Label_00DF;
}
if ((((uint) length) + ((uint) num4)) < 0)
{
goto Label_021B;
}
while (num5 < num3)
{
num += x1e218ceaee1bb583[num5].value_Renamed * x1e218ceaee1bb583[num5].value_Renamed;
num5++;
}
return Math.Exp(-x0d173b5435b4d6ad.gamma * num);
Label_0095:
if (num4 < length)
{
if (num5 < num3)
{
if (x08db3aeabb253cb1[num4].index != x1e218ceaee1bb583[num5].index)
{
goto Label_00DF;
}
double num6 = x08db3aeabb253cb1[num4++].value_Renamed - x1e218ceaee1bb583[num5++].value_Renamed;
num += num6 * num6;
goto Label_0095;
}
goto Label_008D;
}
if ((((uint) num5) + ((uint) num7)) < 0)
{
goto Label_00F2;
}
if ((((uint) num) - ((uint) length)) <= uint.MaxValue)
{
goto Label_008D;
}
Label_00DF:
if (x08db3aeabb253cb1[num4].index > x1e218ceaee1bb583[num5].index)
{
num += x1e218ceaee1bb583[num5].value_Renamed * x1e218ceaee1bb583[num5].value_Renamed;
num5++;
goto Label_0095;
}
Label_00F2:
num += x08db3aeabb253cb1[num4].value_Renamed * x08db3aeabb253cb1[num4].value_Renamed;
num4++;
if (0xff != 0)
{
goto Label_0095;
}
Label_0111:
if ((((uint) num5) - ((uint) length)) <= uint.MaxValue)
{
num4++;
}
goto Label_008D;
Label_021B:
return 0.0;
}
public static svm_problem Encode(IMLDataSet training, int outputIndex)
{
svm_problem _problem3;
try
{
svm_problem _problem;
int num;
int num2;
svm_problem _problem2 = new svm_problem();
goto Label_0158;
Label_000C:
if (1 == 0)
{
return _problem3;
}
if ((((uint) num2) + ((uint) num2)) < 0)
{
goto Label_018B;
}
Label_0031:
if (num >= _problem.l)
{
num2 = 0;
using (IEnumerator<IMLDataPair> enumerator = training.GetEnumerator())
{
IMLDataPair pair;
IMLData input;
IMLData data2;
int num3;
svm_node _node;
goto Label_0083;
Label_0049:
num3++;
Label_004F:
if (num3 < input.Count)
{
goto Label_00CA;
}
_problem.y[num2] = data2[outputIndex];
if (((uint) outputIndex) < 0)
{
return _problem;
}
num2++;
Label_0083:
if (enumerator.MoveNext())
{
goto Label_0100;
}
return _problem;
Label_008E:
data2 = pair.Ideal;
if ((((uint) num3) + ((uint) num2)) >= 0)
{
_problem.x[num2] = new svm_node[input.Count];
num3 = 0;
goto Label_004F;
}
Label_00CA:
_node = new svm_node();
_node.index = num3 + 1;
_node.value_Renamed = input[num3];
_problem.x[num2][num3] = _node;
goto Label_0049;
Label_0100:
pair = enumerator.Current;
input = pair.Input;
goto Label_008E;
}
}
_problem.x[num] = new svm_node[training.InputSize];
num++;
if ((((uint) outputIndex) & 0) == 0)
{
goto Label_000C;
}
return _problem3;
Label_0158:
_problem2.l = (int) training.Count;
_problem = _problem2;
_problem.y = new double[_problem.l];
_problem.x = new svm_node[_problem.l][];
Label_018B:
num = 0;
goto Label_0031;
}
catch (OutOfMemoryException)
{
throw new EncogError("SVM Model - Out of Memory");
}
return _problem3;
}