Beispiel #1
0
def so_multiclass(fm_train_real=traindat,
                  label_train_multiclass=label_traindat):
    try:
        from shogun.Features import RealFeatures
        from shogun.Loss import HingeLoss
        from shogun.Structure import MulticlassModel, MulticlassSOLabels, PrimalMosekSOSVM, RealNumber
    except ImportError:
        print("Mosek not available")
        return

    labels = MulticlassSOLabels(label_train_multiclass)
    features = RealFeatures(fm_train_real.T)

    model = MulticlassModel(features, labels)
    loss = HingeLoss()
    sosvm = PrimalMosekSOSVM(model, loss, labels)
    sosvm.train()

    out = sosvm.apply()
    count = 0
    for i in xrange(out.get_num_labels()):
        yi_pred = RealNumber.obtain_from_generic(out.get_label(i))
        if yi_pred.value == label_train_multiclass[i]:
            count = count + 1

    print("Correct classification rate: %0.2f" %
          (100.0 * count / out.get_num_labels()))
Beispiel #2
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def so_multiclass(fm_train_real=traindat,
                  label_train_multiclass=label_traindat):
    labels = MulticlassSOLabels(label_train_multiclass)
    features = RealFeatures(fm_train_real.T)

    model = MulticlassModel(features, labels)
    loss = HingeLoss()
    sosvm = PrimalMosekSOSVM(model, loss, labels)
    sosvm.train()

    out = sosvm.apply()
    count = 0
    for i in xrange(out.get_num_labels()):
        yi_pred = RealNumber.obtain_from_generic(out.get_label(i))
        if yi_pred.value == label_train_multiclass[i]:
            count = count + 1

    print "Correct classification rate: %0.2f" % (100.0 * count /
                                                  out.get_num_labels())
Beispiel #3
0

# Number of classes
M = 3
# Number of samples of each class
N = 1000
# Dimension of the data
dim = 2

X, y = gen_data()

cnt = 250

X2, y2 = fill_data(cnt, np.min(X), np.max(X))

labels = MulticlassSOLabels(y)
features = RealFeatures(X.T)

model = MulticlassModel(features, labels)
loss = HingeLoss()

lambda_ = 1e1
sosvm = DualLibQPBMSOSVM(model, loss, labels, lambda_)

sosvm.set_cleanAfter(
    10
)  # number of iterations that cutting plane has to be inactive for to be removed
sosvm.set_cleanICP(True)  # enables inactive cutting plane removal feature
sosvm.set_TolRel(0.001)  # set relative tolerance
sosvm.set_verbose(True)  # enables verbosity of the solver
sosvm.set_cp_models(16)  # set number of cutting plane models