From e9a93150a2650eb08d6a56f630a47dcf62d53d21 Mon Sep 17 00:00:00 2001 From: remilepriol Date: Sat, 7 Jan 2017 17:08:00 +0100 Subject: [PATCH] private variables with one leading underscore --- IsingModel/Ising2.py | 93 ++-- Poster/Poster.pptx | Bin 0 -> 1405825 bytes Poster/ising_model.svg | 794 ++++++++++++++++++++++++++++++ Poster/rgising1.png | Bin 0 -> 45126 bytes Poster/scratch.pptx | Bin 0 -> 82305 bytes Rapport/scratch.pptx | Bin 0 -> 82305 bytes Test_Ising2.ipynb | 167 +++++-- images/LBP-message-passing.png | Bin 0 -> 15880 bytes images/LBPobs1/frame0000.png | Bin 0 -> 6828 bytes images/LBPobs1/frame0001.png | Bin 0 -> 7022 bytes images/LBPobs1/frame0002.png | Bin 0 -> 7132 bytes images/LBPobs1/frame0003.png | Bin 0 -> 7146 bytes images/LBPobs1/frame0004.png | Bin 0 -> 7287 bytes images/LBPobs1/frame0005.png | Bin 0 -> 7392 bytes images/LBPobs1/frame0006.png | Bin 0 -> 7499 bytes images/LBPobs1/frame0007.png | Bin 0 -> 7681 bytes images/LBPobs1/frame0008.png | Bin 0 -> 7737 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images/LBPobs1/frame0195.png create mode 100644 images/LBPobs1/frame0196.png create mode 100644 images/LBPobs1/frame0197.png create mode 100644 images/LBPobs1/frame0198.png create mode 100644 images/LBPobs1/frame0199.png create mode 100644 images/compare_gs_mf_speed.png create mode 100644 images/compare_mf_lb.png create mode 100644 images/compare_mf_lb_1.png create mode 100644 images/compare_mf_lb_2.png create mode 100644 images/compare_mf_lb_3.png create mode 100644 images/compare_mf_lb_4.png create mode 100644 images/gibbs_sampling.png create mode 100644 images/meanfield_qui_chie_a10.png diff --git a/IsingModel/Ising2.py b/IsingModel/Ising2.py index 87276c6..c9691ca 100644 --- a/IsingModel/Ising2.py +++ b/IsingModel/Ising2.py @@ -29,7 +29,7 @@ def __init__(self, height, width): self.vertical_correlations = np.zeros([height - 1, width]) self.horizontal_correlations = np.zeros([height, width - 1]) self.mean_parameters = np.zeros([height, width]) - self.__canonical_parameters = np.zeros([height, width, 5]) + self._canonical_parameters = np.zeros([height, width, 5]) def random_init(self): self.linear_factors = 2 * np.random.rand(self.height, self.width) - 1 @@ -66,14 +66,14 @@ def energy(self, grid): enrg -= 1 / 2 * np.sum(self.horizontal_correlations * grid[:, 1:] * grid[:, :-1]) return enrg - def __set_canonical(self): - self.__canonical_parameters[:, :, 0] = self.linear_factors - self.__canonical_parameters[:-1, :, 1] = self.vertical_correlations # with lower neighbor - self.__canonical_parameters[1:, :, 2] = self.vertical_correlations # with upper neighbor - self.__canonical_parameters[:, :-1, 3] = self.horizontal_correlations # with right neighbor - self.__canonical_parameters[:, 1:, 4] = self.horizontal_correlations # with left neighbor + def _set_canonical(self): + self._canonical_parameters[:, :, 0] = self.linear_factors + self._canonical_parameters[:-1, :, 1] = self.vertical_correlations # with lower neighbor + self._canonical_parameters[1:, :, 2] = self.vertical_correlations # with upper neighbor + self._canonical_parameters[:, :-1, 3] = self.horizontal_correlations # with right neighbor + self._canonical_parameters[:, 1:, 4] = self.horizontal_correlations # with left neighbor - def __sum_neighbors(self, grid): + def _sum_neighbors(self, grid): """ return a new grid with the weighted sum of all neighbors on each point """ @@ -82,12 +82,12 @@ def __sum_neighbors(self, grid): + np.pad(grid[:, 1:] * self.horizontal_correlations, ((0, 0), (0, 1)), mode='constant') + np.pad(grid[:, :-1] * self.horizontal_correlations, ((0, 0), (1, 0)), mode='constant')) - def __gibbs_update(self, grid, mask): + def _gibbs_update(self, grid, mask): """ return a new grid where nodes that are not masked have been re sampled conditionally to their neighbors """ # how to specify type in python 3 annotation - probagrid = logistic(self.linear_factors + self.__sum_neighbors(grid)) + probagrid = logistic(self.linear_factors + self._sum_neighbors(grid)) return mask * grid + (1 - mask) * (2 * (np.random.rand(self.height, self.width) < probagrid) - 1) def gibbs_sampling(self, grid): @@ -103,9 +103,9 @@ def gibbs_sampling(self, grid): # mask half of the nodes in a checkerboard pattern mask = np.fromfunction(lambda x, y: (x + y) % 2, (self.height, self.width)) # update nodes that are not touching each other - grid = self.__gibbs_update(grid, mask) + grid = self._gibbs_update(grid, mask) # update the other half - grid = self.__gibbs_update(grid, 1 - mask) + grid = self._gibbs_update(grid, 1 - mask) energylist.append(self.energy(grid)) return grid, energylist[1:] @@ -120,9 +120,9 @@ def gibbs_video(self, grid, time_max=100): # mask half of the nodes in a checkerboard pattern mask = np.fromfunction(lambda x, y: (x + y) % 2, (self.height, self.width)) # update nodes that are not touching each other - grid = self.__gibbs_update(grid, mask) + grid = self._gibbs_update(grid, mask) # update the other half - grid = self.__gibbs_update(grid, 1 - mask) + grid = self._gibbs_update(grid, 1 - mask) video[t, :, :] = grid energylist.append(self.energy(grid)) return video, energylist[1:] @@ -144,27 +144,27 @@ def meanfields(self, initial_grid, max_iter=100): while abs(means_energy[-2] - means_energy[-1]) > epsilon and countiter < max_iter: countiter += 1 # update the means similarly to gibbs sampling - grid = 2 * logistic(self.linear_factors + self.__sum_neighbors(grid)) - 1 + grid = 2 * logistic(self.linear_factors + self._sum_neighbors(grid)) - 1 grid = self.observations + (1 - np.absolute(self.observations)) * grid means_energy.append(self.energy(grid)) self.mean_parameters = grid return means_energy[1:] @staticmethod - def __repeat_symmetric(grid): + def _repeat_symmetric(grid): tmp = np.expand_dims(grid, axis=-1) return np.concatenate((tmp, - tmp), axis=-1) - def __messages2means(self, messages): + def _messages2means(self, messages): self.mean_parameters = np.product(messages, axis=0) self.mean_parameters = self.mean_parameters[:, :, 0] / (np.sum(self.mean_parameters, axis=-1) + 1e-10) self.mean_parameters = 2 * self.mean_parameters - 1 - def loopybelief(self, max_iter=25, damping=0.5): + def loopybelief(self, max_iter=25, damping=0.5, update_order='cyclic'): """ update the mean_parameters field with the probability given by the sum product algorithm - :param max_iter: - :param damping: momentum coefficient + :param max_iter: maximum number of iteration, should depend on the damping + :param damping: momentum coefficient, from 0 to 1 :return: means_energy: the sequence of mean parameters energy """ messages = np.ones([5, self.height, self.width, 2]) @@ -178,34 +178,41 @@ def loopybelief(self, max_iter=25, damping=0.5): # 4 : log-potentials in each point # potentials are stored in an additional channel # if +1 is observed, set potential for -1 to 0 - messages[4, :, :, 1] = (1 - (self.observations == 1)) * (-1) * self.linear_factors / 2 + messages[4, :, :, 1] = (1 - (self.observations == 1)) * np.exp(- self.linear_factors / 2) # if -1 is observed, set potential for +1 to 0 - messages[4, :, :, 0] = (1 - (self.observations == -1)) * self.linear_factors / 2 + messages[4, :, :, 0] = (1 - (self.observations == -1)) * np.exp(self.linear_factors / 2) correlations = [self.vertical_correlations, self.horizontal_correlations, self.vertical_correlations, self.horizontal_correlations] - means_energy = [] # we stop when we reach max_iter iterations. # Murphy et al. found that in average, max_iter = 15 is sufficient - for _ in range(max_iter): - old_messages = messages.copy() - for k, (newmessages, oldmessages) in zip([0, 2, 1, 3], - [(messages[:, :-1, :], old_messages[:, 1:, :]), - (messages[:, 1:, :], old_messages[:, :-1, :]), - (messages[:, :, 1:], old_messages[:, :, :-1]), - (messages[:, :, :-1], old_messages[:, :, 1:])]): - # sum of messages coming to the source node, except the one coming from the destination node - newmessages[k] = oldmessages[(k - 1)] * oldmessages[k] * oldmessages[(k + 1) % 4] * oldmessages[4] - corr = self.__repeat_symmetric(correlations[k] / 2) - # x_i=+1 in newmessages[k, :, :, 0] - tmp = np.sum(newmessages[k] * np.exp(corr), axis=-1) - # x_i=-1 in newmessages[k, :, :, 1] - newmessages[k, :, :, 1] = np.sum(newmessages[k] * np.exp(- corr), axis=-1) - newmessages[k, :, :, 0] = tmp - # normalization of messages to 1 - newmessages[k] /= (np.expand_dims(np.sum(newmessages[k], axis=-1), axis=-1) + 1e-10) + video = np.empty([max_iter, self.height, self.width]) + means_energy = [] + old_messages = messages.copy() + list_new_old = [(messages[:, :-1, :], old_messages[:, 1:, :]), + (messages[:, :, 1:], old_messages[:, :, :-1]), + (messages[:, 1:, :], old_messages[:, :-1, :]), + (messages[:, :, :-1], old_messages[:, :, 1:])] + if update_order == 'cyclic': + list_update = np.array([0, 1, 2, 3] * (max_iter // 4)) + else: + list_update = np.random.randint(0, 4, max_iter) + for count_iter, k in enumerate(list_update): + newmessages, oldmessages = list_new_old[k] + # sum of messages coming to the source node, except the one coming from the destination node + newmessages[k] = oldmessages[(k - 1) % 4] * oldmessages[k] * oldmessages[(k + 1) % 4] * oldmessages[4] + corr = self._repeat_symmetric(correlations[k] / 2) + # x_i=+1 in newmessages[k, :, :, 0] + tmp = np.sum(newmessages[k] * np.exp(corr), axis=-1) + # x_i=-1 in newmessages[k, :, :, 1] + newmessages[k, :, :, 1] = np.sum(newmessages[k] * np.exp(- corr), axis=-1) + newmessages[k, :, :, 0] = tmp # damping - messages[:4] = damping * messages[:4] + (1 - damping) * old_messages[:4] - self.__messages2means(messages) + messages[k] = damping * messages[k] + (1 - damping) * old_messages[k] + # normalization of messages to 1 + newmessages[k] /= (np.expand_dims(np.sum(newmessages[k], axis=-1), axis=-1) + 1e-10) + old_messages[k] = messages[k].copy() + self._messages2means(messages) means_energy.append(self.energy(self.mean_parameters)) - return means_energy + video[count_iter] = self.mean_parameters + 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