How can I weight each point in one-class SVM?

If I understood correctly, you are tring to apply more weight in advance to certain points which you consider (based on domain knowledge?) that are likely to be anomalies, correct? Your one-class support vector machine is meant to give you that insight, instead of specifying it in advance, so you could check if those points are actually far from the "normality" decision surface found by the algorithm itself, to confirm that those are novelties, also quantitative via the decision_function method:

(source)

Here you can find a more detailed answer on how it works, in case you want to have a look.