def jacobi(A, B, tol=0.005, forcedIter=0):
'''itteratively solving for matrix A with solution vector B
tol = tolerance for dh/h
init_val = array of initial values to use in the solver
'''
h = np.zeros(np.shape(B), float)
dmax = 1.0
n = 0
tmp0 = np.empty(np.shape(A), float)
tmp1 = np.empty(np.shape(B), float)
AD = np.diagonal(A)
#np.timer_reset()
#np.evalflush()
t1 = time.time()
while (forcedIter and forcedIter > n) or \
(forcedIter == 0 and dmax > tol):
n += 1
np.multiply(A, h, tmp0)
np.add.reduce(tmp0, 1, out=tmp1)
tmp2 = AD
np.subtract(B, tmp1, tmp1)
np.divide(tmp1, tmp2, tmp1)
hnew = h + tmp1
np.subtract(hnew, h, tmp2)
np.divide(tmp2, h, tmp1)
np.absolute(tmp1, tmp1)
dmax = np.maximum.reduce(tmp1)
h = hnew
print dmax
#np.evalflush()
t1 = time.time() - t1
print 'Iter: ', n, ' size: ', np.shape(B), ' time: ', t1,
print "(Dist) notes: %s" % sys.argv[4]
#if A.dist():
# print "(Dist) notes: %s"%sys.argv[4]
#else:
# print "(Non-Dist) notes: %s"%sys.argv[4]
return h
def jacobi(A, B, tol=0.005, forcedIter=0):
"""itteratively solving for matrix A with solution vector B
tol = tolerance for dh/h
init_val = array of initial values to use in the solver
"""
h = np.zeros(np.shape(B), float)
dmax = 1.0
n = 0
tmp0 = np.empty(np.shape(A), float)
tmp1 = np.empty(np.shape(B), float)
AD = np.diagonal(A)
# np.timer_reset()
# np.evalflush()
t1 = time.time()
while (forcedIter and forcedIter > n) or (forcedIter == 0 and dmax > tol):
n += 1
np.multiply(A, h, tmp0)
np.add.reduce(tmp0, 1, out=tmp1)
tmp2 = AD
np.subtract(B, tmp1, tmp1)
np.divide(tmp1, tmp2, tmp1)
hnew = h + tmp1
np.subtract(hnew, h, tmp2)
np.divide(tmp2, h, tmp1)
np.absolute(tmp1, tmp1)
dmax = np.maximum.reduce(tmp1)
h = hnew
print dmax
# np.evalflush()
t1 = time.time() - t1
print "Iter: ", n, " size: ", np.shape(B), " time: ", t1,
print "(Dist) notes: %s" % sys.argv[4]
# if A.dist():
# print "(Dist) notes: %s"%sys.argv[4]
# else:
# print "(Non-Dist) notes: %s"%sys.argv[4]
return h
def MC_int(c, s):
x = np.empty([s], dtype=np.double)
y = np.empty([s], dtype=np.double)
sum=0.0
#np.timer_reset()
#np.evalflush()
start=time.time()
for i in range(c):
#np.ufunc_random(x,x)
#np.ufunc_random(y,y)
x = np.random.random_sample([s])
y = np.random.random_sample([s])
np.square(x,x)
np.square(y,y)
np.add(x,y,x)
z = np.less_equal(x, 1)
sum += np.add.reduce(z)*4.0/s
sum = sum / c
#np.evalflush()
stop=time.time()
print 'Pi: ', sum, ' with ', s,' samples in sec: ', stop-start,
print "(Dist) notes: %s"%sys.argv[3]
