def encrypt(self, x, same_type=False):
"""Encrypts x. X can be either an encrypted int or a numpy
vector/matrix/tensor."""
if (type(x) == int or type(x) == float or type(x) == np.float64):
if (same_type):
return NotImplemented
return Float(self, x)
elif (type(x) == TensorBase):
if (x.encrypted or same_type):
return NotImplemented
return BVTensor(self, x.data)
elif (type(x) == np.ndarray):
sh = x.shape
x_ = x.reshape(-1)
out = list()
for v in x_:
out.append(Float(self, v))
if (same_type):
return np.array(out).reshape(sh)
else:
return BVTensor(self, np.array(out).reshape(sh))
else:
print("format not recognized:" + str(type(x)))
return NotImplemented
return self.pk.encrypt(x)
def test_add(self):
a = BVTensor(self.keys.pk(), np.asarray([40,2]), True, self.keys.sk())
# integers only, for now!
b = a + 2
p = self.keys.sk().decrypt(b)
print(p)
def test_mul_by_scalarinv(self):
a = BVTensor(self.keys.pk, [21], False, self.keys.sk)
b = a * 2
def test_add(self):
a = BVTensor(self.keys.pk, [40,2], False, self.keys.sk)
b = a + 2.0
def test_mul_by_scalarinv(self):
a = BVTensor(self.keys.pk(), np.asarray([21]), True, self.keys.sk)
b = a * 2
def test_add(self):
a = BVTensor(self.keys.pk(), np.asarray([40, 2]), True, self.keys.sk)
b = a + 2.0
def test_mul_by_scalar(self):
a = BVTensor(self.keys.pk(), np.asarray([21]), True, self.keys.sk())
# Integers only, for now!
b = a * 2