Function main

python_examples/svm_struct.py:34–61

()

def main():
    # In this example, we have three types of samples: class 0, 1, or 2.  That
    # is, each of our sample vectors falls into one of three classes.  To keep
    # this example very simple, each sample vector is zero everywhere except at
    # one place.  The non-zero dimension of each vector determines the class of
    # the vector.  So for example, the first element of samples has a class of 1
    # because samples[0][1] is the only non-zero element of samples[0].
    samples = [[0, 2, 0], [1, 0, 0], [0, 4, 0], [0, 0, 3]]
    # Since we want to use a machine learning method to learn a 3-class
    # classifier we need to record the labels of our samples.  Here samples[i]
    # has a class label of labels[i].
    labels = [1, 0, 1, 2]

    # Now that we have some training data we can tell the structural SVM to
    # learn the parameters of our 3-class classifier model.  The details of this
    # will be explained later.  For now, just note that it finds the weights
    # (i.e. a vector of real valued parameters) such that predict_label(weights,
    # sample) always returns the correct label for a sample vector.
    problem = ThreeClassClassifierProblem(samples, labels)
    weights = dlib.solve_structural_svm_problem(problem)

    # Print the weights and then evaluate predict_label() on each of our
    # training samples. Note that the correct label is predicted for each
    # sample.
    print(weights)
    for k, s in enumerate(samples):
        print("Predicted label for sample[{0}]: {1}".format(
            k, predict_label(weights, s)))


def predict_label(weights, sample):