Implement Hyperparameter Tuning in SVM

Question

I would like to implement a function for SVM with the requirement:

Consider the binary classification that consists of distinguishing class 6 from the rest of the data points. Use SVMs combined with polynomial kernels to solve this classification problem. For each value of the polynomial degree, $d$ = 1, 2, 3, 4, plot the average 5-fold cross-validation error plus or minus one standard deviation as a function of $C$ (let the other parameters of the polynomial kernels be equal to their default values) on the training data.

def cross_validation_score(X, y, c_vals, n_folds, d_vals): """ Calculates the cross validation error and returns its mean and standard deviation.

Args:
    X: features
    y: labels
    c_vals: list of C values
    n_folds: number of cross-validation folds
    d_vals: list of degrees of the polynomial kernel

Returns:
    Tuple of (list of error_mean, list of error_std)       
"""

error_mean = np.zeros((len(c_vals),len(d_vals)))
error_std = np.zeros((len(c_vals),len(d_vals)))

////code in here

return error_mean, error_std

Can anyone help me to implement that function? Thanks!

I have try to follow cross_val_error function from scikit-learn but it's quite complicated for me.