def precompute_z_norm(self, data): """ Stores the z-norm of every possible shapelet from data in self.z_data. :param data: list of training examples :type data: np.array """ self.z_data = dict() for w in self.windows: for ts_id, ts in enumerate(data): self.z_data[ts_id, w] = z_normalize(subsequences(ts, w))
def estimate_sigma_min(self): """ Estimates $\sigma_{min}$ by using the maximum standard deviation of shapelets in time series without label. """ if self.sigma_min is None: sigma_min = 0 for id, labels in enumerate(self.target): if len(labels) == 0: ts_subs = subsequences(self.data[id], min(self.windows)) sigma_min = max(sigma_min, ts_subs.std(axis=1).max()) print("sigma_min set to {}".format(sigma_min)) self.sigma_min = sigma_min shapelet_utils.sigma_min = self.sigma_min