Support Vector Classifier and cost C
SVC aims to find a separating hyperplane. That data you have is separable. Thus the hyperplane will go between x3 and x4. This are your support vectors. Now C is a parameter which allows you to make a trade off between errors allowed and the width of separating hyperplane. But your data does not need this trade off since it is already separable. Because of that the needed error to be accepted is zero. This penalty error in svc equations is multiplied with C. Because C is multiplied with 0 it does not matter the value of C, their product will be zero anyway.