from data.load import load
import matplotlib.pyplot as plt
import numpy as np
data = load().ekg()[0:1000]
windowSize = 100
leftOffset = 50
rightOffset = 50
start = leftOffset
end = data.shape[0] - rightOffset
windows = np.zeros(shape=(end - start, windowSize))
#stft = np.zeros(shape=(end - start, windowSize))
for n in range(start, end):
windows[n-start, :] = data[n - leftOffset:n + rightOffset]
#stft[n - start, :] = np.fft.fft(np.hamming(windowSize) * windowVals)
print('')
corrThreshold = 0.9
from data.load import load data = load().ekg() from external.luminol.src import luminol
# clusterer.fit(stfts)
#
#
# plt.plot(data)
#
# labelsX = np.array(range(start,end))
# plt.plot(labelsX, 100*(clusterer.labels_-5))
# plt.show()
if __name__ == '__main__':
from data.load import load
import matplotlib.pyplot as plt
data = load().TICC()[:, 0] # just do univariate for now
data = load().ekg() # already univariate
model = STFT_Cluster(n_clusters=10, windowSize=100)
model.fit_predict(data[0:5000])
plt.figure()
plt.plot(data[0:5000])
plt.plot(100*(model.labels_-5))
# plt.figure()
# results = model.predict(data[5000:6000])
# plt.plot(data[5000:6000])
#
# plt.plot((results-25))
from distance.traj_dist.frechet import frechet
from distance.traj_dist.sspd import e_sspd
from distance.traj_dist.hausdorff import e_hausdorff
from distance.eros import eros
plotSamples = False
runLCSS = False
runDiscretFretchet = False
runFretchet = False
runSSPD = False
runHausdorff = False
runEros = True
allSamples = load().auslan()
wantedLabels = ['how-1', 'how-2', 'how-3', 'answer-3']
samples = []
sampleLabels = []
for label in list(allSamples.keys()):
data = allSamples[label]
#x1, y1, z1 = data['x1'].values, data['y1'].values, data['z1'].values
sample = data[['x1','y1','z1']].values
sampleLabels.append(label)
if segmentType == 'interpolate':
segmenting = fit.interpolate
elif segmentType == 'regression':
segmenting = fit.regression
else:
raise ValueError('Unknown segmenting type')
return window(data, segmenting, error, maxError)
if __name__ == '__main__':
from data.load import load
data = load().s_and_p500()
data = load().TICC()
segments = PLA(data[:,0], windowScale=1000, windowingType='bottom up', segmentType='interpolate')
print(segments)
import matplotlib.pyplot as plt
plt.plot(data[:,0])
for seg in segments:
plt.plot( [seg[0], seg[2]], [seg[1], seg[3]], c='r')
plt.show()