def Run(self, W, x, eps, seed): x = x.flatten() prng = np.random.RandomState(seed) if self.workload_based: mapping = mapper.WorkloadBased(W).mapping() reducer = transformation.ReduceByPartition(mapping) x = reducer.transform(x) # Reduce workload # W = support.reduce_queries(mapping, W) W = W * support.expansion_matrix(mapping) # Orange AHPparition(PA) operator in paper can be expressed # as the following sequence of simpler opeartors M = selection.Identity(x.shape).select() y = measurement.Laplace(M, self.ratio * eps).measure(x, prng) xest = inference.AHPThresholding(self.eta, self.ratio).infer(M, y, eps) mapping = mapper.AHPCluster(xest, (1 - self.ratio) * eps).mapping() # TR reducer = transformation.ReduceByPartition(mapping) x_bar = reducer.transform(x) # SI LM LS M_bar = selection.Identity(x_bar.shape).select() y_bar = measurement.Laplace(M_bar, eps * (1 - self.ratio)).measure( x_bar, prng) x_bar_hat = inference.LeastSquares().infer(M_bar, y_bar) x_hat = support.expansion_matrix(mapping) * x_bar_hat return x_hat
def Run(self, W, x, eps, seed): x = x.flatten() prng = np.random.RandomState(seed) if self.workload_based: W = get_matrix(W) mapping = mapper.WorkloadBased(W).mapping() reducer = transformation.ReduceByPartition(mapping) x = reducer.transform(x) # Reduce workload # W = support.reduce_queries(mapping, W) W = W * support.expansion_matrix(mapping) self.domain_shape = x.shape if len(self.domain_shape) == 2: # apply hilbert transform to convert 2d domain into 1d hilbert_mapping = mapper.HilbertTransform(self.domain_shape).mapping() domain_reducer = transformation.ReduceByPartition(hilbert_mapping) x = domain_reducer.transform(x) W = get_matrix(W) W = W * support.expansion_matrix(hilbert_mapping) dawa = pmapper.Dawa(eps, self.ratio, self.approx) mapping = dawa.mapping(x, prng) elif len(self.domain_shape) == 1: W = get_matrix(W) dawa = pmapper.Dawa(eps, self.ratio, self.approx) mapping = dawa.mapping(x, prng) reducer = transformation.ReduceByPartition(mapping) x_bar = reducer.transform(x) W_bar = W * 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 = measurement.Laplace(M_bar, eps*(1-self.ratio)).measure(x_bar, prng) x_bar_hat = inference.LeastSquares().infer(M_bar, y) x_bar_hat_exp = support.expansion_matrix(mapping) * x_bar_hat if len(self.domain_shape) == 1: return x_bar_hat_exp elif len(self.domain_shape) == 2: return support.expansion_matrix(hilbert_mapping) * x_bar_hat_exp
def Run(self, W, x, eps, seed): x = x.flatten() prng = np.random.RandomState(seed) if self.workload_based: mapping = mapper.WorkloadBased(W).mapping() reducer = transformation.ReduceByPartition(mapping) x = reducer.transform(x) # Reduce workload # W = support.reduce_queries(mapping, W) W = W * support.expansion_matrix(mapping) M = selection.Identity(x.shape).select() y = measurement.Laplace(M, eps).measure(x, prng) x_hat = inference.LeastSquares().infer(M, y) return x_hat
def Run(self, W, x, eps, seed): x = x.flatten() prng = np.random.RandomState(seed) if self.workload_based: W = get_matrix(W) mapping = mapper.WorkloadBased(W).mapping() reducer = transformation.ReduceByPartition(mapping) x = reducer.transform(x) # Reduce workload # W = support.reduce_queries(mapping, W) W = W * support.expansion_matrix(mapping) self.domain_shape = x.shape M = selection.HB(self.domain_shape).select() if not isinstance(M, np.ndarray): M = M.toarray() y = measurement.Laplace(M, eps).measure(x, prng) x_hat = inference.LeastSquares().infer(M, y) return x_hat