def gradient_it(m, eps):
cnt = 0
(A, B) = ut.split_at(m)
x0 = [0] * len(B)
r0 = ut.minus(B, ut.subst(A, x0))
p0 = r0
z0 = r0
s0 = r0
for i in range(0, 2 * len(B)):
sub = ut.subst(A, z0)
prev_p = p0
prev_r = r0
At = ut.transpose_matrix(A)
# print(At)
a = ut.scalar_product(p0, r0) / ut.scalar_product(s0, sub)
x0 = ut.plus(x0, ut.mul(a, z0))
r0 = ut.minus(r0, ut.mul(a, sub))
p0 = ut.minus(p0, ut.mul(a, ut.subst(At, s0)))
b = ut.scalar_product(p0, r0) / ut.scalar_product(prev_p, prev_r)
z0 = ut.plus(r0, ut.mul(b, z0))
s0 = ut.plus(p0, ut.mul(b, s0))
if abs(ut.norm(r0) / ut.norm(B)) < eps:
break
cnt += 1
return (x0, cnt)
def get_bit(self, port):
if port.ibuffer:
self.pipes[port] += port.ibuffer
port.ibuffer = ''
else:
self.pipes[port] += self.noise_bit()
bit, self.pipes[port] = utils.split_at(self.pipes[port], 1)
return bit
def load_fc_weights_from_vgg16bn(model):
"Load weights for model from the dense layers of the Vgg16BN model."
# See imagenet_batchnorm.ipynb for info on how the weights for
# Vgg16BN can be generated from the standard Vgg16 weights.
from vgg16bn import Vgg16BN
vgg16_bn = Vgg16BN()
_, fc_layers = utils.split_at(vgg16_bn.model, Convolution2D)
utils.copy_weights(fc_layers, model.layers)
def get_bit(self, port):
if port.ibuffer:
self.pipes[port] += port.ibuffer
port.ibuffer = ''
else:
self.pipes[port] += self.noise_bit()
bit, self.pipes[port] = utils.split_at(self.pipes[port], 1)
return bit
def jacobi(m, eps):
(A, f) = split_at(m)
(B, c) = to_iter(A, f)
prev = c
x = plus(subst(B, prev), c)
cnt = 1
n = norma(B)
if (n >= 1):
print("norma > 1, Jacobi error")
return ([[]], 0)
e1 = (1 - n) / n * eps
while (myNorm(minus(x, prev)) > e1):
cnt += 1
prev = x
x = plus(subst(B, prev), c)
return (x, cnt)
def handoff(self):
if not self.peer:
return
sent, self.obuffer = utils.split_at(self.obuffer,
min(self.rate, self.peer.rate))
self.peer.ibuffer += sent
def get(self, nbits=None):
if not nbits:
nbits = len(self.ibuffer)
ret, self.ibuffer = utils.split_at(self.ibuffer, nbits)
return ret
f = open(path, 'r')
m = []
for line in f:
m.append([float(x) for x in line.split()])
return m
eps = 0.00001
# m = read_matrix("./tests/test4.in")
m = utils.gilbert_test(10)
m_g = copy.deepcopy(m)
m_j = copy.deepcopy(m)
m_gr = copy.deepcopy(m)
m_test = copy.deepcopy(m)
(clean_m, ldsfj) = utils.split_at(m)
print("".join([
"Rank matrix ",
str(task6.matrix_rate(clean_m)), ", condition_number ",
str(task6.condition_number(clean_m))
]))
gauss_res = task1.gauss(m_g)
print("".join(["Gauss Result==", str(gauss_res)]))
# print(m_g)
# print(m_j)split_at
(jacob_res, j_it) = task2.jacobi(m_j, eps)
print("".join([
"Jacobi Result==",
str(jacob_res), " with eps==",
def handoff(self):
if not self.peer:
return
sent, self.obuffer = utils.split_at(self.obuffer,
min(self.rate, self.peer.rate))
self.peer.ibuffer += sent
def get(self, nbits=None):
if not nbits:
nbits = len(self.ibuffer)
ret, self.ibuffer = utils.split_at(self.ibuffer, nbits)
return ret
