Implementation of the Streaming Half--Space--Trees (HS--Trees) [1]_, a fast one-class anomaly detector for evolving data streams. It requires only normal data for training and works well when anomalous data are rare. [1] S.C.Tan, K.M.Ting, and T.F.Liu, “Fast anomaly detection for streaming data,” in IJCAI Proceedings - International Joint Conference on Artificial Intelligence, 2011, vol. 22, no. 1, pp. 1511–1516. The model features an ensemble of random HS--Trees, and the tree structure is constructed without any data. This makes the method highly efficient because it requires no model restructuring when adapting to evolving data streams.