def fit(self):
'''
First finds the wavelet in types that fits the data the best => The smallest Euclidean distance between the
approximation signal and under-sampled signal
After finding the best Wavelet, uses it to decompose data into levels detail signals and builds a AR(p)
model per approximation signal and detail signals using order as p for each corresponding
'''
self.bestType = 'db1'
bestDist = np.inf
for t in self.types:
approx_levels = pywt.wavedec(self.data, wavelet=t, level=self.levels)
idx = np.int_(np.linspace(0, self.data.shape[0]-1, num=len(approx_levels[0])))
samples = self.data[idx]
dist = np.linalg.norm(approx_levels[0]-samples)
if dist < bestDist:
bestDist = dist
self.bestType = t
self.coefs = pywt.wavedec(self.data, wavelet=self.bestType, level=self.levels)
for i in range(len(self.order)):
# model = AR_model(approx_levels[i], order=self.order[i])
model = Markov_model(approx_levels[i], maximum=np.max(approx_levels[i]))
model.fit()
self.models.append(model)
def __init__(self,
data,
maximum,
corr_tresh=0.85,
mean_ratio=0.1,
match_rate=1.0,
filter_window=21):
'''
Create a Signature Pattern recognizer Markov based on PRESS's signature-driven method
Params
------
@param data: Training data
@type data: Array-like
@param corr_tresh: Patterns should have a Pearson-Correlation above corr_tresh
@type corr_tresh: float
@param mean_ratio:
'''
self.history = data[:]
self.data = data[:]
self.corr_thres = corr_tresh
self.mr = mean_ratio
self.window = filter_window
self.match = match_rate
self.N = data.shape[0]
self.contain_pat = False
self.warp = False
self.markov = Markov_model(data=data, maximum=maximum)
