Using libSVM programmatically - libsvm

Question

I have started using libSVM (java: https://github.com/cjlin1/libsvm) programmatically. I wrote the following code to test it:
svm_parameter param = new svm_parameter();
// default values
param.svm_type = svm_parameter.C_SVC;
param.kernel_type = svm_parameter.RBF;
param.degree = 3;
param.gamma = 0;
param.coef0 = 0;
param.nu = 0.5;
param.cache_size = 40;
param.C = 1;
param.eps = 1e-3;
param.p = 0.1;
param.shrinking = 1;
param.probability = 0;
param.nr_weight = 0;
param.weight_label = new int[0];
param.weight = new double[0];
svm_problem prob = new svm_problem();
prob.l = 4;
prob.y = new double[prob.l];
prob.x = new svm_node[prob.l][2];
for(int i = 0; i < prob.l; i++)
{
prob.x[i][0] = new svm_node();
prob.x[i][1] = new svm_node();
prob.x[i][0].index = 1;
prob.x[i][1].index = 2;
prob.x[i][0].value = (i%2!=0)?-1:1;
prob.x[i][1].value = (i/2%2==0)?-1:1;
prob.y[i] = (prob.x[i][0].value == 1 && prob.x[i][1].value == 1)?1:-1;
System.out.println("X = [ " + prob.x[i][0].value + ", " + prob.x[i][1].value + " ] \t -> " + prob.y[i] );
}
svm_model model = svm.svm_train(prob, param);
int test_length = 4;
for( int i = 0; i < test_length; i++)
{
svm_node[] x_test = new svm_node[2];
x_test[0] = new svm_node();
x_test[1] = new svm_node();
x_test[0].index = 1;
x_test[0].value = (i%2!=0)?-1:1;
x_test[1].index = 2;
x_test[1].value = (i/2%2==0)?-1:1;
double d = svm.svm_predict(model, x_test);
System.out.println("X[0] = " + x_test[0].value + " X[1] = " + x_test[1].value + "\t\t\t Y = "
+ ((x_test[0].value == 1 && x_test[1].value == 1)?1:-1) + "\t\t\t The predicton = " + d);
}
Since I am testing on the same training data, I'd expect to get 100% accuracy, but the output that I get, is the following:
X = [ 1.0, -1.0 ] -> -1.0
X = [ -1.0, -1.0 ] -> -1.0
X = [ 1.0, 1.0 ] -> 1.0
X = [ -1.0, 1.0 ] -> -1.0
*
optimization finished, #iter = 1
nu = 0.5
obj = -20000.0, rho = 1.0
nSV = 2, nBSV = 2
Total nSV = 2
X[0] = 1.0 X[1] = -1.0 Y = -1 The predicton = -1.0
X[0] = -1.0 X[1] = -1.0 Y = -1 The predicton = -1.0
X[0] = 1.0 X[1] = 1.0 Y = 1 The predicton = -1.0
X[0] = -1.0 X[1] = 1.0 Y = -1 The predicton = -1.0
We can see that the following prediction is erroneous:
X[0] = 1.0 X[1] = 1.0 Y = 1 The predicton = -1.0
Anyone knows what is the mistake in my code?

Answer 1

You're using Radial Basis Function (param.kernel_type = svm_parameter.RBF) which uses gamma. Setting 'param.gamma = 1' should yield 100% accuracy.