/**
* initializes the algorithm
*
* @param data the data to work with
* @throws Exception if m_SVM is null
*/
protected void init(Instances data) throws Exception {
if (m_SVM == null) {
throw new Exception("SVM not initialized in optimizer. Use RegOptimizer.setSVMReg()");
}
m_C = m_SVM.getC();
m_data = data;
m_classIndex = data.classIndex();
m_nInstances = data.numInstances();
// Initialize kernel
m_kernel = Kernel.makeCopy(m_SVM.getKernel());
m_kernel.buildKernel(data);
// init m_target
m_target = new double[m_nInstances];
for (int i = 0; i < m_nInstances; i++) {
m_target[i] = data.instance(i).classValue();
}
m_random = new Random(m_nSeed);
// initialize alpha and alpha* array to all zero
m_alpha = new double[m_target.length];
m_alphaStar = new double[m_target.length];
m_supportVectors = new SMOset(m_nInstances);
m_b = 0.0;
m_nEvals = 0;
m_nCacheHits = -1;
}
/**
* Builds a model using the current Kernel using the given data and returns the produced output.
*
* @param data the instances to test the Kernel on
* @return a String containing the output of the Kernel.
*/
protected String useKernel(Instances data) throws Exception {
Kernel kernel = null;
StringBuffer text = new StringBuffer();
try {
kernel = Kernel.makeCopy(m_Kernel);
} catch (Exception e) {
e.printStackTrace();
fail("Problem setting up to use Kernel: " + e);
}
kernel.buildKernel(data);
for (int n = 0; n < data.numInstances(); n++) {
for (int i = n; i < data.numInstances(); i++) {
text.append((n + 1) + "-" + (i + 1) + ": " + kernel.eval(n, i, data.instance(i)) + "\n");
}
}
return text.toString();
}
