sys.path.append(os.getcwd()[:-5])
from src.timeseries.TimeSeriesLoader import load
from src.transformation.SFA import *
symbols = 8
wordLength = 16
normMean = False
def sfaToWord(word):
word_string = ""
for w in word:
word_string += chr(w + 97)
return word_string
train, test, train_labels, test_labels = load("CBF", "\t")
sfa = SFA("EQUI_DEPTH")
sfa.fitTransform(train, train_labels, wordLength, symbols, normMean)
sfa.printBins()
for i in range(test.shape[0]):
wordList = sfa.transform2(test.iloc[i, :], "null")
print(
str(i) + "-th transformed time series SFA word " + "\t" +
sfaToWord(wordList))
if FIXED_PARAMETERS['test'] == 'Shotgun':
logger.Log("Test: Shotgun")
from src.classification.ShotgunClassifier import *
shotgun = ShotgunClassifier(FIXED_PARAMETERS, logger)
scoreShotgun = shotgun.eval(train, test)[0]
logger.Log("%s: %s" % (FIXED_PARAMETERS['dataset'], scoreShotgun))
##=========================================================================================
## SFA Word Tests
##=========================================================================================
if FIXED_PARAMETERS['test'] == 'SFAWordTest':
logger.Log("Test: SFAWordTest")
from src.transformation.SFA import *
sfa = SFA(FIXED_PARAMETERS["histogram_type"], logger=logger)
sfa.fitTransform(train, FIXED_PARAMETERS['wordLength'],
FIXED_PARAMETERS['symbols'],
FIXED_PARAMETERS['normMean'])
logger.Log(sfa.__dict__)
for i in range(test["Samples"]):
wordList = sfa.transform2(test[i].data, "null", str_return=True)
logger.Log("%s-th transformed TEST time series SFA word \t %s " %
(i, wordList))
if FIXED_PARAMETERS['test'] == 'SFAWordWindowingTest':
logger.Log("Test: SFAWordWindowingTest")
from src.transformation.SFA import *
sfa = SFA(FIXED_PARAMETERS["histogram_type"], logger=logger)
sfa.fitWindowing(train, FIXED_PARAMETERS['windowLength'],
FIXED_PARAMETERS['wordLength'],
