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apply changes of v318→v325 #2

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apply changes of v318→v325 #2

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28 changes: 14 additions & 14 deletions SVM/SVM/Prediction.cs
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Original file line number Diff line number Diff line change
Expand Up @@ -46,7 +46,7 @@ public static double Predict(
int correct = 0;
int total = 0;
double error = 0;
double sumv = 0, sumy = 0, sumvv = 0, sumyy = 0, sumvy = 0;
double sump = 0, sumt = 0, sumpp = 0, sumtt = 0, sumpt = 0;
StreamWriter output = outputFile != null ? new StreamWriter(outputFile) : null;

SvmType svm_type = Procedures.svm_get_svm_type(model);
Expand Down Expand Up @@ -77,16 +77,16 @@ public static double Predict(
}
for (int i = 0; i < problem.Count; i++)
{
double target = problem.Y[i];
double target_label = problem.Y[i];
Node[] x = problem.X[i];

double v;
double predict_label;
if (predict_probability && (svm_type == SvmType.C_SVC || svm_type == SvmType.NU_SVC))
{
v = Procedures.svm_predict_probability(model, x, prob_estimates);
predict_label = Procedures.svm_predict_probability(model, x, prob_estimates);
if (output != null)
{
output.Write(v + " ");
output.Write(predict_label + " ");
for (int j = 0; j < nr_class; j++)
{
output.Write(prob_estimates[j] + " ");
Expand All @@ -96,19 +96,19 @@ public static double Predict(
}
else
{
v = Procedures.svm_predict(model, x);
predict_label = Procedures.svm_predict(model, x);
if(output != null)
output.Write(v + "\n");
output.Write(predict_label + "\n");
}

if (v == target)
if (predict_label == target_label)
++correct;
error += (v - target) * (v - target);
sumv += v;
sumy += target;
sumvv += v * v;
sumyy += target * target;
sumvy += v * target;
error += (predict_label - target_label) * (predict_label - target_label);
sump += predict_label;
sumt += target_label;
sumpp += predict_label * predict_label;
sumtt += target_label * target_label;
sumpt += predict_label * target_label;
++total;
}
if(output != null)
Expand Down
44 changes: 32 additions & 12 deletions SVM/SVM/Solver.cs
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Original file line number Diff line number Diff line change
Expand Up @@ -508,7 +508,7 @@ protected virtual int select_working_set(int[] working_set)
}
}

if (Gmax + Gmax2 < eps)
if (Gmax + Gmax2 < eps || Gmin_idx == -1)
return 1;

working_set[0] = Gmax_idx;
Expand Down Expand Up @@ -1849,7 +1849,7 @@ public static Model svm_train(Problem prob, Parameter param)
model.PairwiseProbabilityA = null;
}

int nnz = 0;
int total_sv = 0;
int[] nz_count = new int[nr_class];
model.NumberOfSVPerClass = new int[nr_class];
for (i = 0; i < nr_class; i++)
Expand All @@ -1859,17 +1859,17 @@ public static Model svm_train(Problem prob, Parameter param)
if (nonzero[start[i] + j])
{
++nSV;
++nnz;
++total_sv;
}
model.NumberOfSVPerClass[i] = nSV;
nz_count[i] = nSV;
}

info("Total nSV = " + nnz + "\n");
info("Total nSV = " + total_sv + "\n");

model.SupportVectorCount = nnz;
model.SupportVectors = new Node[nnz][];
model.SupportVectorIndices = new int[nnz];
model.SupportVectorCount = total_sv;
model.SupportVectors = new Node[total_sv][];
model.SupportVectorIndices = new int[total_sv];
p = 0;
for (i = 0; i < l; i++)
if (nonzero[i])
Expand All @@ -1885,7 +1885,7 @@ public static Model svm_train(Problem prob, Parameter param)

model.SupportVectorCoefficients = new double[nr_class - 1][];
for (i = 0; i < nr_class - 1; i++)
model.SupportVectorCoefficients[i] = new double[nnz];
model.SupportVectorCoefficients[i] = new double[total_sv];

p = 0;
for (i = 0; i < nr_class; i++)
Expand Down Expand Up @@ -2172,7 +2172,13 @@ public static double svm_predict_probability(Model model, Node[] x, double[] pro
pairwise_prob[j, i] = 1 - pairwise_prob[i, j];
k++;
}
multiclass_probability(nr_class, pairwise_prob, prob_estimates);
if (nr_class == 2)
{
prob_estimates[0] = pairwise_prob[0, 1];
prob_estimates[1] = pairwise_prob[1, 0];
}
else
multiclass_probability(nr_class, pairwise_prob, prob_estimates);

int prob_max_idx = 0;
for (i = 1; i < nr_class; i++)
Expand All @@ -2189,12 +2195,26 @@ public static String svm_check_parameter(Problem prob, Parameter param)
// svm_type

SvmType svm_type = param.SvmType;

if (svm_type != SvmType.C_SVC &&
svm_type != SvmType.NU_SVC &&
svm_type != SvmType.ONE_CLASS &&
svm_type != SvmType.EPSILON_SVR &&
svm_type != SvmType.NU_SVR)
return "unknown svm type";
// kernel_type, degree

KernelType kernel_type = param.KernelType;

if (param.Gamma < 0)
if (kernel_type != KernelType.LINEAR &&
kernel_type != KernelType.POLY &&
kernel_type != KernelType.RBF &&
kernel_type != KernelType.SIGMOID &&
kernel_type != KernelType.PRECOMPUTED)
return "unknown kernel type";

if ((kernel_type == KernelType.POLY ||
kernel_type == KernelType.RBF ||
kernel_type == KernelType.SIGMOID) &&
param.Gamma < 0)
return "gamma < 0";

if (param.Degree < 0)
Expand Down