Utility.py 文件源码

def kernel_matrix(svm_model, original_X):

        if (svm_model.svm_kernel == 'polynomial_kernel' or svm_model.svm_kernel == 'soft_polynomial_kernel'):
            K = (svm_model.zeta + svm_model.gamma * np.dot(original_X, original_X.T)) ** svm_model.Q
        elif (svm_model.svm_kernel == 'gaussian_kernel' or svm_model.svm_kernel == 'soft_gaussian_kernel'):
            pairwise_dists = squareform(pdist(original_X, 'euclidean'))
            K = np.exp(-svm_model.gamma * (pairwise_dists ** 2))

        '''
        K = np.zeros((svm_model.data_num, svm_model.data_num))

        for i in range(svm_model.data_num):
            for j in range(svm_model.data_num):
                if (svm_model.svm_kernel == 'polynomial_kernel' or svm_model.svm_kernel == 'soft_polynomial_kernel'):
                    K[i, j] = Kernel.polynomial_kernel(svm_model, original_X[i], original_X[j])
                elif (svm_model.svm_kernel == 'gaussian_kernel' or svm_model.svm_kernel == 'soft_gaussian_kernel'):
                    K[i, j] = Kernel.gaussian_kernel(svm_model, original_X[i], original_X[j])
        '''

        return K