def __init__(self, short_name="GreedyH"): self.init_params = util.init_params_from_locals(locals()) self._max_block_size = None self._partition_engine = None self._estimate_engine = greedyH.greedyH_engine() self.short_name = short_name
def __init__(self, c = 10,gz=0,short_name ="UGrid"): # c is the constant parameter controling the number of cells self.init_params = util.init_params_from_locals(locals()) self.c = c self.gz= gz self.short_name = short_name
def __init__(self, index = 'DEFAULT1D', short_name = 'AHP*'): self.init_params = util.init_params_from_locals(locals()) self._vSEP = Lfunction[index]['vSEP'] self._vRatio = Lfunction[index]['vRatio'] self._vEta = Lfunction[index]['vEta'] self.short_name = short_name
def __init__(self, shape_list, domain_shape, size, seed=9001, pretty_name='random range'): self.init_params = util.init_params_from_locals(locals()) self.shape_list = copy.deepcopy(shape_list) self.seed = seed self.pretty_name = pretty_name self._size = size prng = numpy.random.RandomState(seed) if shape_list == None: shapes = randomQueryShapes(domain_shape, prng) else: prng.shuffle(self.shape_list) shapes = itertools.cycle( self.shape_list) # infinite iterable over shapes in shape_list queries = [] for i in range(size): lb, ub = placeRandomly(next(shapes), domain_shape, prng) # seed must be None or repeats queries.append(ndRangeUnion().addRange(lb, ub, 1.0)) super(RandomRange, self).__init__(queries, domain_shape)
def __init__(self, gz=11, alpha=0.5, short_name="DPCube"): # gz control the grid size for partition, alpha is the ratio of budget split for computing partition points self.init_params = util.init_params_from_locals(locals()) self.gz = gz self.alpha = alpha self.short_name = short_name
def __init__(self, index = 'DEFALUT1D',ratio = 0.5, updateround = 100, short_name = 'MWEM*'): self.init_params = util.init_params_from_locals(locals()) self._vSEP = Lfunction[index][0] self._vRound = Lfunction[index][1] self._updateround = updateround self._ratio = ratio self.short_name = short_name
def __init__(self, E): # move this to the highest subclass self.init_params = util.init_params_from_locals(locals()) self.E = E self.X = self.E.X self.X_hat = None self.W = self.E.W
def __init__(self, ratio=0.25, short_name='DAWA'): # set ratio to 0 if no partition needed => GREEDYH_LINEAR,else DAWA_LINEAR. self.init_params = util.init_params_from_locals(locals()) self._ratio = ratio self._partition_engine = l1partition.l1partition_approx_engine() self._estimate_engine = greedyH.greedyH_engine() self.short_name = short_name
def __init__(self, nickname, sample_to_scale, reduce_to_dom_shape=None, seed=None): self.init_params = util.init_params_from_locals(locals()) self.fname = nickname assert nickname in filenameDict, 'Filename parameter not recognized: %s' % nickname hist = load(filenameDict[self.fname]) dist = util.old_div(hist, float(hist.sum())) super(DatasetSampledFromFile,self).__init__(dist, sample_to_scale, reduce_to_dom_shape, seed)
def __init__(self, domain_shape, weight=1.0, pretty_name='identity'): self.init_params = util.init_params_from_locals(locals()) self.weight = weight self.pretty_name = pretty_name indexes = itertools.product(*[list(range(i)) for i in domain_shape ]) # generate index tuples queries = [ndRangeUnion().addRange(i, i, weight) for i in indexes] super(self.__class__, self).__init__(queries, domain_shape)
def __init__(self, c=10, c2=5, alpha=0.5, short_name='AGrid'): # c,c2 are the constant parameter controling the number of cells in each level # alpha is the ratio of budget split on the first level self.init_params = util.init_params_from_locals(locals()) self.c = c self.c2 = c2 self.alpha = alpha self.short_name = short_name
def __init__(self, domain_shape_int, pretty_name='prefix 1D'): self.init_params = util.init_params_from_locals(locals()) self.pretty_name = pretty_name queries = [ ndRangeUnion().add1DRange(0, c, 1.0) for c in range(domain_shape_int) ] super(self.__class__, self).__init__(queries, (domain_shape_int, ))
def __init__(self, branch=2, granu=100): """Setup the branching factor and granularity in numerical search. branch(2) - the branching factor of the hierarchy granu(100) - the granularity in numerical search """ self.init_params = util.init_params_from_locals(locals()) self._branch = branch self._granu = granu
def __init__(self, ratio=0.5, max_block_size=None, short_name="DAWA"): """ c,c2 are the constant parameter controlling the number of cells in each level alpha is the ratio of budget split on the first level beside total budget""" self.init_params = util.init_params_from_locals(locals()) self._ratio = ratio self._max_block_size = max_block_size self._partition_engine = l1partition.l1partition_approx_engine() self._estimate_engine = greedyH.greedyH_engine() self.short_name = short_name
def __init__(self, X, W, A, epsilon, seed=None): self.init_params = util.init_params_from_locals(locals()) self.X = X self.W = W self.A = A self.epsilon = epsilon self.seed = seed self.X_hat = None self.state_op_log = None
def __init__(self, nrounds = 10, ratio = 0.5, updateround = 100,short_name ='MWEM'): """Set up parameters for MWEM. nrounds(10) - how many rounds are MWEM run. ratio(0.5) - the ratio of privacy budget used for query selection. updateround(100) - the number of iterations in each update. """ self.init_params = util.init_params_from_locals(locals()) self._updateround = updateround super(type(self), self).__init__(nrounds, ratio) self.short_name = short_name
def __init__(self, short_name='operator'): self.init_params = util.init_params_from_locals(locals()) self.short_name = short_name
def __init__(self, short_name="QuadTree"): self.init_params = util.init_params_from_locals(locals()) self.short_name = short_name
def __init__(self, short_name="Privelet"): self.init_params = util.init_params_from_locals(locals()) self.short_name = short_name
def __init__(self,short_name="hierarchical_complete"): self.init_params = util.init_params_from_locals(locals()) self.short_name = short_name
def __init__(self, foo, bar, short_name='my alg'): self.init_params = util.init_params_from_locals(locals()) self.short_name = short_name
def __init__(self, br=0, short_name="HB"): #b control the branching, if b == 0, compute it based on expression in the paper self.init_params = util.init_params_from_locals(locals()) self.br = br self.short_name = short_name
def __init__(self, ratio=0.85, eta=0.35, short_name = 'AHP'): self.init_params = util.init_params_from_locals(locals()) self._ratio = ratio self._eta = eta self.short_name = short_name
def __init__(self, short_name='Identity'): self.init_params = util.init_params_from_locals(locals()) self.short_name = short_name
def __init__(self): self.init_params = util.init_params_from_locals(locals())
def __init__(self, short_name="Uniform"): self.init_params = util.init_params_from_locals(locals()) self.short_name = short_name