Construct a MaStream instance specifying:
- tree_no: number of h:d-Trees in the ensemble
- tree_train_size: number of data entries used for constructing each tree
- max_lvl: maximum tree depth
- horizon: stream speed as number of data instances per time unit
from mastream.MaStream import MaStream mastream = MaStream(tree_no=20, tree_train_size=45, max_lvl=10, horizon=1000)
Consume a stream:
for idx, entry in enumerate(stream):
mastream.parse_entry(idx, entry)
After each time unit, the identified labels can be retrieved via:
mastream.get_labels()
Clustering Data Streams Using Mass Estimation
Sabau, Andrei Sorin. Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), 2013 15th International Symposium on. IEEE, 2013.