@staticmethod
    def object_model(objects, Xim):
        sigma_o = 1
        # Image of objects label
        labels_im = np.zeros(Xim.shape[1:], dtype=int)
        label = 0
        for k in xrange(len(objects)):
            o = objects[k]
            label += 1
            o.label = label
            # A) Add object latent variable
            mask_o = o.get_mask()
            labels_im[mask_o] = o.label
            obj_latent = np.random.normal(0, sigma_o, Xim.shape[0])
            obj_latent -= obj_latent.mean()  # - 0
#            obj_latent /= obj_latent.std() * sigma_o
            obj_latent *= 1.0 / (obj_latent.std() * sigma_o)
            std = o.get_std()
            Xim[:, mask_o] = (std * obj_latent + Xim[:, mask_o].T).T
        return Xim, labels_im
Return the last update.
dataset/ETHPy150Open neurospin/pylearn-parsimony/parsimony/datasets/utils.py/ObjImage.object_model--crossover with--dataset/ETHPy150Open home-assistant/home-assistant/homeassistant/components/sensor/tellduslive.py/TelldusLiveSensor._last_updated