def plot_svm(X,X_s, y, alpha, w0, kernel = 'linearkernel', sigma = 0.5):
    plt.scatter(X[:,0], X[:,1], c = y)

    plt.scatter(X_s[:,0], X_s[:,1], s=350, facecolors='none', edgecolors='black')

    x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1
    y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1
    xx, yy = np.meshgrid(np.arange(x_min, x_max, h),
                         np.arange(y_min, y_max, h))

    X_mesh = np.c_[xx.ravel(), yy.ravel()]


    #print(set(Z - 1))
    #np.sign(
    #Make predictions
    Z = discriminant(alpha,w0,X,y, X_mesh, kernel = kernel, sigma = sigma).reshape(xx.shape)

    plt.contour(xx, yy, Z, [0.0], colors='k', linewidths=1, origin='lower')
    plt.contour(xx, yy, Z + 1, [0.0], colors='grey', linewidths=3, origin='lower')
    plt.contour(xx, yy, Z - 1, [0.0], colors='green', linewidths=3, origin='lower')

    plt.xlim(xx.min(), xx.max())
    plt.ylim(yy.min(), yy.max())
    plt.xticks(())
    plt.yticks(())
    plt.axis("tight")
    plt.show()