Instance inst = insts.instance(j); double[] vals = new double[2]; vals[0] = SVMOutput(-1, inst); if (inst.classValue() == cl2) { vals[1] = 1; BinarySMO smo = new BinarySMO(); smo.setKernel(Kernel.makeCopy(SMO.this.m_kernel)); smo.buildClassifier(train, cl1, cl2, false, -1, -1); Instances test = insts.testCV(numFolds, i); for (int j = 0; j < test.numInstances(); j++) { double[] vals = new double[2]; vals[0] = smo.SVMOutput(-1, test.instance(j)); if (test.instance(j).classValue() == cl2) { vals[1] = 1;
fitCalibrator(insts, cl1, cl2, numFolds, new Random(randomSeed)); if (examineAll) { for (int i = 0; i < m_alpha.length; i++) { if (examineExample(i)) { numChanged++; if ((m_alpha[i] > 0) && (m_alpha[i] < m_C * m_data.instance(i).weight())) { if (examineExample(i)) { numChanged++; fitCalibrator(insts, cl1, cl2, numFolds, new Random(randomSeed));
fitCalibrator(insts, cl1, cl2, numFolds, new Random(randomSeed)); if (examineAll) { for (int i = 0; i < m_alpha.length; i++) { if (examineExample(i)) { numChanged++; if ((m_alpha[i] > 0) && (m_alpha[i] < m_C * m_data.instance(i).weight())) { if (examineExample(i)) { numChanged++; fitCalibrator(insts, cl1, cl2, numFolds, new Random(randomSeed));
for (int i = 0; i < insts.numClasses(); i++) { for (int j = i + 1; j < insts.numClasses(); j++) { m_classifiers[i][j] = new BinarySMO(); m_classifiers[i][j].setKernel(Kernel.makeCopy(getKernel())); Instances data = new Instances(insts, insts.numInstances()); for (int k = 0; k < subsets[i].numInstances(); k++) { m_classifiers[i][j].buildClassifier(data, i, j, m_fitCalibratorModels, m_numFolds, m_randomSeed);
for (int i = 0; i < insts.numClasses(); i++) { for (int j = i + 1; j < insts.numClasses(); j++) { m_classifiers[i][j] = new BinarySMO(); m_classifiers[i][j].setKernel(Kernel.makeCopy(getKernel())); Instances data = new Instances(insts, insts.numInstances()); for (int k = 0; k < subsets[i].numInstances(); k++) { m_classifiers[i][j].buildClassifier(data, i, j, m_fitCalibratorModels, m_numFolds, m_randomSeed);
if ((m_classifiers[i][j].m_alpha != null) || (m_classifiers[i][j].m_sparseWeights != null)) { double output = m_classifiers[i][j].SVMOutput(-1, inst); if (output > 0) { result[j] += 1; newInst[0] = m_classifiers[0][1].SVMOutput(-1, inst); newInst[1] = Utils.missingValue(); DenseInstance d = new DenseInstance(1, newInst); (m_classifiers[i][j].m_sparseWeights != null)) { double[] newInst = new double[2]; newInst[0] = m_classifiers[i][j].SVMOutput(-1, inst); newInst[1] = Utils.missingValue(); DenseInstance d = new DenseInstance(1, newInst);
if ((m_classifiers[i][j].m_alpha != null) || (m_classifiers[i][j].m_sparseWeights != null)) { double output = m_classifiers[i][j].SVMOutput(-1, inst); if (output > 0) { result[j] += 1; newInst[0] = m_classifiers[0][1].SVMOutput(-1, inst); newInst[1] = Utils.missingValue(); DenseInstance d = new DenseInstance(1, newInst); (m_classifiers[i][j].m_sparseWeights != null)) { double[] newInst = new double[2]; newInst[0] = m_classifiers[i][j].SVMOutput(-1, inst); newInst[1] = Utils.missingValue(); DenseInstance d = new DenseInstance(1, newInst);
Instance inst = insts.instance(j); double[] vals = new double[2]; vals[0] = SVMOutput(-1, inst); if (inst.classValue() == cl2) { vals[1] = 1; BinarySMO smo = new BinarySMO(); smo.setKernel(Kernel.makeCopy(SMO.this.m_kernel)); smo.buildClassifier(train, cl1, cl2, false, -1, -1); Instances test = insts.testCV(numFolds, i); for (int j = 0; j < test.numInstances(); j++) { double[] vals = new double[2]; vals[0] = smo.SVMOutput(-1, test.instance(j)); if (test.instance(j).classValue() == cl2) { vals[1] = 1;
for (int i = 0; i < inst.numClasses(); i++) { for (int j = i + 1; j < inst.numClasses(); j++) { double output = m_classifiers[i][j].SVMOutput(-1, inst); if (output > 0) { votes[j] += 1;
for (int i = 0; i < inst.numClasses(); i++) { for (int j = i + 1; j < inst.numClasses(); j++) { double output = m_classifiers[i][j].SVMOutput(-1, inst); if (output > 0) { votes[j] += 1;
double output = SVMOutput(i, m_data.instance(i)); if (Utils.eq(m_alpha[i], 0)) { if (Utils.sm(m_class[i] * output, 1)) {
double output = SVMOutput(i, m_data.instance(i)); if (Utils.eq(m_alpha[i], 0)) { if (Utils.sm(m_class[i] * output, 1)) {
/** * Computes SVM output for given instance. * * @param index the instance for which output is to be computed * @param inst the instance * @return the output of the SVM for the given instance * @throws Exception in case of an error */ protected double output(int index, Instance inst) throws Exception { double output = 0; output = m_classifiers[0][1].SVMOutput(index, inst); return output; }