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
