def Run(self, W, x, eps, seed): x = x.flatten() prng = np.random.RandomState(seed) striped_mapping = mapper.Striped(self.domain, self.stripe_dim).mapping() x_sub_list = meta.SplitByPartition(striped_mapping).transform(x) Ms = [] ys = [] scale_factors = [] group_idx = sorted(set(striped_mapping)) for i in group_idx: x_i = x_sub_list[group_idx.index(i)] P_i = support.projection_matrix(striped_mapping, i) M_bar = selection.HB(x_i.shape).select() y_i = measurement.Laplace(M_bar, eps).measure(x_i, prng) noise_scale_factor = laplace_scale_factor(M_bar, eps) M_i = M_bar * P_i Ms.append(M_i) ys.append(y_i) scale_factors.append(noise_scale_factor) x_hat = inference.LeastSquares().infer(Ms, ys, scale_factors) return x_hat
def Run(self, W, x, eps, seed): x = x.flatten() prng = np.random.RandomState(seed) striped_vectors = mapper.Striped(self.domain, self.stripe_dim).partitions() hd_vector = support.combine_all(striped_vectors) striped_mapping = hd_vector.flatten() x_sub_list = meta.SplitByPartition(striped_mapping).transform(x) Ms = [] ys = [] scale_factors = [] group_idx = sorted(set(striped_mapping)) # Given a group id on the full vector, recover the group id for each partition # put back in loop to save memory self.subgroups = {} for i in group_idx: selected_idx = np.where(hd_vector == i) ans = [p[i[0]] for p, i in zip(striped_vectors, selected_idx)] self.subgroups[i] = ans for i in group_idx: x_i = x_sub_list[group_idx.index(i)] # overwriting standard projection for efficiency W_i = self.project_workload(W, striped_vectors, hd_vector, i) dawa = pmapper.Dawa(eps, self.ratio, self.approx) mapping = dawa.mapping(x_i, prng) reducer = transformation.ReduceByPartition(mapping) x_bar = reducer.transform(x_i) W_bar = W_i * support.expansion_matrix(mapping) M_bar = selection.GreedyH(x_bar.shape, W_bar).select() if not isinstance(M_bar, np.ndarray): M_bar = M_bar.toarray() y_i = measurement.Laplace( M_bar, eps * (1 - self.ratio)).measure(x_bar, prng) noise_scale_factor = laplace_scale_factor( M_bar, eps * (1 - self.ratio)) # convert the measurement back to the original domain for inference P_i = support.projection_matrix(striped_mapping, i) M_i = (M_bar * support.reduction_matrix(mapping)) * P_i Ms.append(M_i) ys.append(y_i) scale_factors.append(noise_scale_factor) x_hat = inference.LeastSquares().infer(Ms, ys, scale_factors) return x_hat
def Run(self, W, x, eps, seed): x = x.flatten() prng = np.random.RandomState(seed) striped_mapping = mapper.Striped(self.domain, self.stripe_dim).mapping() x_sub_list = meta.SplitByPartition(striped_mapping).transform(x) Ms = [] ys = [] scale_factors = [] group_idx = sorted(set(striped_mapping)) W = get_matrix(W) for i in group_idx: x_i = x_sub_list[group_idx.index(i)] P_i = support.projection_matrix(striped_mapping, i) W_i = W * P_i.T dawa = pmapper.Dawa(eps, self.ratio, self.approx) mapping = dawa.mapping(x_i, prng) reducer = transformation.ReduceByPartition(mapping) x_bar = reducer.transform(x_i) W_bar = W_i * support.expansion_matrix(mapping) M_bar = selection.GreedyH(x_bar.shape, W_bar).select() if not isinstance(M_bar, np.ndarray): M_bar = M_bar.toarray() y_i = measurement.Laplace( M_bar, eps * (1 - self.ratio)).measure(x_bar, prng) noise_scale_factor = laplace_scale_factor( M_bar, eps * (1 - self.ratio)) M_i = (M_bar * support.reduction_matrix(mapping)) * P_i Ms.append(M_i) ys.append(y_i) scale_factors.append(noise_scale_factor) x_hat = inference.LeastSquares().infer(Ms, ys, scale_factors) return x_hat