slearn.fABBA
- class slearn.fABBA(tol=0.1, alpha=0.5, sorting='2-norm', scl=1, verbose=1, max_len=inf)[source]
- __init__(tol=0.1, alpha=0.5, sorting='2-norm', scl=1, verbose=1, max_len=inf)[source]
- tol - float
Control tolerence for compression, default as 0.1.
- scl - int
Scale for length, default as 1, means 2d-digitization, otherwise implement 1d-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, alpha, sorting, scl, ...])Parameters tol - float Control tolerence for compression, default as 0.1. scl - int Scale for length, default as 1, means 2d-digitization, otherwise implement 1d-digitization. verbose - int Control logs print, default as 1, print logs. max_len - int The max length for each segment, default as np.inf.
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)