slearn.ABBA
- class slearn.ABBA(tol=0.1, k_cluster=10, verbose=1, max_len=inf)[source]
- __init__(tol=0.1, k_cluster=10, verbose=1, max_len=inf)[source]
- tol - float
Control tolerence for compression, default as 0.1.
- k_cluster - int
Number of symbols used for digitization.
- verbose - int
Control logs print, default as 1, print logs.
- max_len - int
The max length for each segment, default as np.inf.
Methods
__init__([tol, k_cluster, verbose, max_len])Parameters tol - float Control tolerence for compression, default as 0.1.
digitize(pieces[, early_stopping])Greedy 2D clustering of pieces (a Nx2 numpy array), using tolernce tol and len/inc scaling parameter scl.
fit_transform(series)Compress and digitize the time series together.
inverse_compress(pieces, start)Modified from ABBA package, please see ABBA package to see guidance.
inverse_digitize(strings, centers, hashmap)inverse_transform(strings[, start])quantize(pieces)