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, domain_shape, weight=1.0, pretty_name='identity'):
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, 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, ))
from dpcomp_core.dataset import DatasetFromFile
import numpy as np
if False:
n = 1024
x = DatasetFromFile('PATENT', n).payload
#x = np.load('fnlwgt.npy')
if n == 128:
A = np.kron(np.eye(128), np.ones(8192 // 128))
x = A.dot(x).astype(int)
ranges = [ndRangeUnion().add1DRange(i, i + 31) for i in range(n - 32 + 1)]
Q = Prefix1D(n)
Q = Workload(ranges, (n, ))
P = Q.get_matrix('dense')
error = 0.00
trials = 25
for i in range(trials):
print i
xest = dawa.dawa_engine().Run(Q, x, 1.0, i)
diff = P.dot(x - xest)
error += np.dot(diff, diff) / trials
baseline = 2 * np.trace(np.dot(P.T, P))