ClassificationSVM.py 文件源码

def __init__(self, isTrain, isOutlierRemoval=0):
        """
        The linear models ``LinearSVC()`` and ``SVC(kernel='linear')`` yield slightly
        different decision boundaries. This can be a consequence of the following
        differences:
        - ``LinearSVC`` minimizes the squared hinge loss while ``SVC`` minimizes the
          regular hinge loss.

        - ``LinearSVC`` uses the One-vs-All (also known as One-vs-Rest) multiclass
          reduction while ``SVC`` uses the One-vs-One multiclass reduction.
        :return:
        """
        super(ClassificationSVM, self).__init__(isTrain, isOutlierRemoval)

        # data preprocessing
        self.dataPreprocessing()
        self.clf = svm.SVC() # define the SVM classifier

        C = 1.0  # SVM regularization parameter
        self.svc = svm.SVC(kernel='linear', C=C, max_iter=100000)
        self.rbf_svc = svm.SVC(kernel='rbf', gamma=0.7, C=C)
        self.poly_svc = svm.SVC(kernel='poly', coef0=1, degree=3, C=C)
        self.lin_svc = svm.LinearSVC(C=C)