SVM RBF核可视化

def solve():
    import pandas as pd
    import matplotlib.pyplot as plt
    import numpy as np
    from sklearn.svm import SVC
    from sklearn.model_selection import train_test_split
    df = pd.read_csv(____)
    X = df[["____","____"]].values; y = df["____"].values
    X_train, X_test, y_train, y_test = train_test_split(____)
    svc = SVC(____).fit(____)
    h = ____; x_min, x_max = X[:,____].min()-____, X[:,____].max()+____
    y_min, y_max = X[:,____].min()-____, X[:,____].max()+____
    xx, yy = np.meshgrid(np.arange(____), np.arange(____))
    Z = svc.predict(np.c_[xx.ravel(), yy.ravel()]).reshape(____)
    plt.contourf(____); plt.scatter(____)
    plt.title(____); plt.show()
    print(____)