def iteration_checker(size, test_num, val_range, res_opt=0):
for i in range(0, test_num):
(a, b, x) = generate_linear_system(size, val_range)
a_float = np.array(a, dtype=np.float64)
b_float = np.array(b, dtype=np.float64)
x1 = []
l, u = [], []
(x1, l, u, fra, frb, fr) = rdft.rdft_lu_solver_with_lu(a, b)
x0 = x1
x1_after = np.array(x1)
x1_after = iteration.iteration(fra, l, u, frb, x1_after,
linalg.cond(fra))
x1_after = iteration.remove_imag(x1_after)
(x2, pl, pu, swapped_a, swapped_b) = pp.solve(a_float, b_float)
x3 = iteration.iteration(swapped_a, pl, pu, swapped_b, x2,
linalg.cond(a_float))
#x2 = lib.lu_solver(a,b)
#x2 = lib.direct_solver(a_float,b_float)
#x4 = lib.direct_lu_solver(a,b)
if res_opt == 1:
print("---error---")
print("cond:", linalg.cond(a))
print("[0] error", linalg.norm(b - a.dot(x0)))
print("[1] error", linalg.norm(b - a.dot(x1)))
#print("[2] error", linalg.norm(b - a.dot(x2)))
#print("[3] error", linalg.norm(b - a.dot(x3)))
elif res_opt == 2:
print(
str(linalg.cond(a)) + " " + str(linalg.norm(x - x0)) + " " +
str(linalg.norm(x - x1_before)) + " " +
str(linalg.norm(x - x1_after)) + " " +
str(linalg.norm(x - x5)) + " " + str(linalg.norm(x - x2)) +
" " + str(linalg.norm(x - x3)))
elif res_opt == 3:
step = []
for j in x4_step:
step.append(linalg.norm(x - j))
print(
str(linalg.cond(a)) + " " + str(linalg.norm(x - x0)) + " " +
str(linalg.norm(x - x1_before)) + " " +
str(linalg.norm(x - x1_after)) + " " + str(step) + " " +
str(linalg.norm(x - x2))) # partial pivot only
elif res_opt == 4:
print(
str(linalg.cond(a)) + " " + str(linalg.norm(x - x0)) + " " +
str(linalg.norm(x - x2)) + " " +
str(linalg.norm(x - x4))) # partial pivot only
elif res_opt != 1:
print("---error---")
print("cond:", linalg.cond(a))
print("[0] error", linalg.norm(x - x0))
print("[1] error",
linalg.norm(x - x1_before)) # remove_imag => iteration
print("[2] error",
linalg.norm(x - x1_after)) # iteration => remove_imag
print("[3] error", linalg.norm(x - x2))
def iteration_checker(size, test_num, val_range, res_opt=0):
for i in range(0,test_num):
(a,b,x) = generate_linear_system(size, val_range)
a_float = np.array(a,dtype=np.float64)
b_float = np.array(b,dtype=np.float64)
x1 = []
l,u = [], []
(x1, l, u, fra, frb, fr) = rdft.rdft_lu_solver_with_lu(a,b)
x0 = x1
x1_after = np.array(x1)
x1_after = iteration.iteration(fra, l, u, frb, x1_after, linalg.cond(fra))
x1_after = iteration.remove_imag(x1_after)
(x2, pl, pu, swapped_a, swapped_b) = pp.solve(a_float,b_float)
x3 = iteration.iteration(swapped_a, pl, pu, swapped_b, x2, linalg.cond(a_float))
#x2 = lib.lu_solver(a,b)
#x2 = lib.direct_solver(a_float,b_float)
#x4 = lib.direct_lu_solver(a,b)
if res_opt == 1:
print("---error---")
print("cond:", linalg.cond(a))
print("[0] error", linalg.norm(b - a.dot(x0)))
print("[1] error", linalg.norm(b - a.dot(x1)))
#print("[2] error", linalg.norm(b - a.dot(x2)))
#print("[3] error", linalg.norm(b - a.dot(x3)))
elif res_opt == 2:
print(str(linalg.cond(a)) + " " +
str(linalg.norm(x - x0)) + " " +
str(linalg.norm(x - x1_before)) + " " +
str(linalg.norm(x - x1_after)) + " " +
str(linalg.norm(x - x5)) + " " +
str(linalg.norm(x - x2)) + " " +
str(linalg.norm(x - x3)))
elif res_opt == 3:
step = []
for j in x4_step:
step.append(linalg.norm(x - j))
print(str(linalg.cond(a)) + " " +
str(linalg.norm(x - x0)) + " " +
str(linalg.norm(x - x1_before)) + " " +
str(linalg.norm(x - x1_after)) + " " +
str(step) + " " +
str(linalg.norm(x - x2))) # partial pivot only
elif res_opt == 4:
print(str(linalg.cond(a)) + " " +
str(linalg.norm(x - x0)) + " " +
str(linalg.norm(x - x2)) + " " +
str(linalg.norm(x - x4))) # partial pivot only
elif res_opt != 1:
print("---error---")
print("cond:", linalg.cond(a))
print("[0] error", linalg.norm(x - x0))
print("[1] error", linalg.norm(x - x1_before)) # remove_imag => iteration
print("[2] error", linalg.norm(x - x1_after)) # iteration => remove_imag
print("[3] error", linalg.norm(x - x2))
def solve_rdft(a, b, x):
(size, _) = a.shape
f = rdft.generate_f(size)
r = rdft.generate_r(size)
fr = f.dot(r)
ra = r.dot(a)
(x1, l, u, fra, frb, _) = rdft.rdft_lu_solver_with_lu(a, b, r)
x2 = np.array(x1)
x2 = iteration.iteration(fra, l, u, frb, x2, linalg.cond(fra))
x2 = iteration.remove_imag(x2)
return (x2, fra, fr, ra, x1)
def solve_rdft(a,b,x): (size,_) = a.shape f = rdft.generate_f(size) r = rdft.generate_r(size) fr = f.dot(r) ra = r.dot(a) (x1, l, u, fra, frb, _) = rdft.rdft_lu_solver_with_lu(a,b,r) x2 = np.array(x1) x2 = iteration.iteration(fra, l, u, frb, x2, linalg.cond(fra)) x2 = iteration.remove_imag(x2) return (x2, fra, fr, ra, x1)
def main():
from procedural_city_generation.roadmap.config import config
from copy import copy
singleton = config()
front = copy(singleton.global_lists.vertex_list)
front.pop(0)
front.pop()
vertex_queue = copy(singleton.global_lists.vertex_queue)
from iteration import iteration
singleton.iterationszaehler = 0
if singleton.plot == 1:
if gui is None:
import matplotlib.pyplot as plt
plt.close()
fig = plt.figure()
ax = plt.subplot(111)
fig.canvas.draw()
ax.set_xlim((-singleton.border[0], singleton.border[0]))
ax.set_ylim((-singleton.border[1], singleton.border[1]))
else:
gui.set_xlim((-singleton.border[0], singleton.border[0]))
gui.set_ylim((-singleton.border[1], singleton.border[1]))
i = 0
while (front != [] or singleton.global_lists.vertex_queue != []):
i += 1
front = iteration(front)
if singleton.plot == 1:
if i % singleton.plot_counter == 0:
if gui is None:
plt.pause(0.001)
fig.canvas.blit(ax.bbox)
fig.canvas.flush_events()
else:
gui.update()
singleton.iterationszaehler = 0
from procedural_city_generation.additional_stuff.jsontools import save_vertexlist
print "Roadmap is complete"
# if singleton.plot==1:
# plt.show()
save_vertexlist(singleton.global_lists.vertex_list, "output",
singleton.savefig)
return 0
def main(): from procedural_city_generation.roadmap.config import config from copy import copy singleton=config() front=copy(singleton.global_lists.vertex_list) front.pop(0) front.pop() vertex_queue = copy(singleton.global_lists.vertex_queue) from iteration import iteration singleton.iterationszaehler=0 if singleton.plot==1: import matplotlib.pyplot as plt plt.close() fig=plt.figure() ax=plt.subplot(111) fig.canvas.draw() ax.set_xlim((-singleton.border[0],singleton.border[0])) ax.set_ylim((-singleton.border[1],singleton.border[1])) i=0 while (front!=[] or singleton.global_lists.vertex_queue !=[]): i+=1 front=iteration(front) if singleton.plot==1: if i%singleton.plotabstand==0: plt.pause(0.001) fig.canvas.blit(ax.bbox) fig.canvas.flush_events() singleton.iterationszaehler=0 from procedural_city_generation.additional_stuff.jsontools import save_vertexlist print "Roadmap is complete" if singleton.plot==1: plt.show() save_vertexlist(singleton.global_lists.vertex_list,"output",singleton.savefig) singleton.kill() return 0
# question 1
def g(x):
return (math.sin(math.pi * x) -x**2)/(2.0 * math.pi) + x
def f(x):
return math.sin(math.pi * x) - x**2
# tolerances and number of iterations
tol_err = 1e-5
tol_res = 1e-5
itmax = 20
# try different initial points
x0 = 1.0
print("Trying initial point %f..." % (x0))
x,err,res = iteration(f, g, x0, tol_err, tol_res, itmax)
x0 = 0.0
print("Trying initial point %f..." % (x0))
x,err,res = iteration(f, g, x0, tol_err, tol_res, itmax)
x0 = -0.1
print("Trying initial point %f..." % (x0))
x,err,res = iteration(f, g, x0, tol_err, tol_res, itmax)
# in the final run, the approximate solutions approaches minus infinity...
# now call the recursively programmed function
x0 = 1.0
print("Trying initial point %f with recursion..." % (x0))
x,err,res = recursion(f, g, x0, tol_err, tol_res, itmax)
from config import config
from iteration import iteration
from eta import eta
import matplotlib.pyplot as plt
def set_up():
import numpy as np
config['delta_r'] = config['r_range'] / config['N']
delta_k = np.pi / (config['N'] * config['delta_r'])
beta = np.reciprocal(config['T'] * config['kb'])
config['delta_k'] = delta_k
config['beta'] = beta
if __name__ == '__main__':
set_up()
eta = eta()
h_i = iteration(eta)
gr = h_i + 1.
plt.plot(eta.r, gr)
plt.show()
def turboTop(resFile, rate = 12, it = 5, n = 4, r = 5, p = 48, d = 0, mu = 8, sigma = 6, l = 20, m = 10, q = 8):
""" Turbo decoder top level.
resFile -- files where the results will be saved
rate -- code rate (e.g. 12 for rate 1/2)
it -- number of iterations for the turbo decoding
n -- number of bits for the sampling of the signals - a, b, y1, y2
r -- number of bits for the coding of the extrinsic information
p -- interleaver frame size in bit couples
d -- additional delay through the noiser - 0 means the noiser adds 2 clock cycles
mu -- mean value for the noise distribution (additive noise)
sigma -- standard deviation of the noise distribution (0 means no noise)
l -- first trellis' length
m -- second trellis' length
q -- number of bits for the coding of the accumulated distances
"""
# Signal declarations:
clk = Signal(bool(0))
rst = Signal(bool(1))
flipflop = Signal(bool(0))
aClean = Signal(bool(0))
bClean = Signal(bool(0))
y1Clean = Signal(bool(0))
y2Clean = Signal(bool(0))
aCleanDel = Signal(bool(0))
bCleanDel = Signal(bool(0))
y1CleanDel = Signal(bool(0))
y2CleanDel = Signal(bool(0))
y1IntDel = Signal(bool(0))
y2IntDel = Signal(bool(0))
aNoisy = Signal(int(0))
bNoisy = Signal(int(0))
y1Noisy = Signal(int(0))
y2Noisy = Signal(int(0))
y1IntNoisy = Signal(int(0))
y2IntNoisy = Signal(int(0))
y1Full = Signal(intbv(0, -(2**(n-1)), 2**(n-1)))
y2Full = Signal(intbv(0, -(2**(n-1)), 2**(n-1)))
y1IntFull = Signal(intbv(0, -(2**(n-1)), 2**(n-1)))
y2IntFull = Signal(intbv(0, -(2**(n-1)), 2**(n-1)))
aLim = [Signal(intbv(0, -(2**(n-1)), 2**(n-1))) for i in range(it + 1)]
bLim = [Signal(intbv(0, -(2**(n-1)), 2**(n-1))) for i in range(it + 1)]
y1Lim = [Signal(intbv(0, -(2**(n-1)), 2**(n-1))) for i in range(it + 1)]
y2Lim = [Signal(intbv(0, -(2**(n-1)), 2**(n-1))) for i in range(it + 1)]
y1IntLim = [Signal(intbv(0, -(2**(n-1)), 2**(n-1))) for i in range(it + 1)]
y2IntLim = [Signal(intbv(0, -(2**(n-1)), 2**(n-1))) for i in range(it + 1)]
z = [[Signal(intbv(0, 0, 2**r)) for i in range(4)] for i in range(it + 1)]
zSorted = [Signal(intbv(0, 0, 2**r)) for i in range(4)]
aDec = [Signal(bool(0)) for i in range(it + 1)]
bDec = [Signal(bool(0)) for i in range(it + 1)]
aDel = [Signal(bool(0)) for i in range(it + 1)]
bDel = [Signal(bool(0)) for i in range(it + 1)]
abInt = [Signal(bool(0)) for i in range(2)]
y1Int = Signal(bool(0))
y2Int = Signal(bool(0))
abCleanPerm = [Signal(bool(0)) for i in range(2)]
delayer_ia = [None for i in range(it + 1)]
delayer_ib = [None for i in range(it + 1)]
ber_i = [None for i in range(it + 1)]
iteration_i = [None for i in range(it)]
# Reset and clock generation:
rstGen_i0 = rstGen(rst)
clkGen_i0 = clkGen(clk)
# Random data generation:
randGen_i0 = randGen(clk, rst, aClean, bClean)
# Interleaving and permuting:
clkDiv_i0 = clkDiv(clk, rst, flipflop)
abPermut_i0 = abPermut(flipflop, aClean, bClean, abCleanPerm, 1)
interleaver_i0 = interleaver(clk, rst, abCleanPerm, abInt, p, 0, 0, 2, 2, 0)
# Coder:
coder_i0 = coder(clk, rst, aClean, bClean, y1Clean, y2Clean)
coder_i1 = coder(clk, rst, abInt[1], abInt[0], y1Int, y2Int)
# Additional delay through the channel:
delayer_i0 = delayer(clk, rst, aClean, aCleanDel, d, 0, 2)
delayer_i1 = delayer(clk, rst, bClean, bCleanDel, d, 0, 2)
delayer_i2 = delayer(clk, rst, y1Clean, y1CleanDel, d, 0, 2)
delayer_i3 = delayer(clk, rst, y2Clean, y2CleanDel, d, 0, 2)
delayer_i4 = delayer(clk, rst, y1Int, y1IntDel, d, 0, 2)
delayer_i5 = delayer(clk, rst, y2Int, y2IntDel, d, 0, 2)
# Channel noiser:
noiser_i0 = noiser(clk, rst, aCleanDel, bCleanDel, y1CleanDel, y2CleanDel, y1IntDel, y2IntDel, aNoisy, bNoisy, y1Noisy, y2Noisy, y1IntNoisy, y2IntNoisy, n, mu, sigma)
# Decoder:
limiter_i0 = limiter(aNoisy, bNoisy, y1Noisy, y2Noisy, y1IntNoisy, y2IntNoisy, aLim[0], bLim[0], y1Full, y2Full, y1IntFull, y2IntFull, n)
punct_i0 = punct(clk, rst, y1Full, y2Full, y1IntFull, y2IntFull, y1Lim[0], y2Lim[0], y1IntLim[0], y2IntLim[0], rate)
for i in range(it):
iteration_i[i] = iteration(clk, rst, flipflop, aLim[i], bLim[i], y1Lim[i], y2Lim[i], y1IntLim[i], y2IntLim[i], z[i], z[i + 1], aDec[i + 1], bDec[i + 1], aLim[i + 1], bLim[i + 1], y1Lim[i + 1], y2Lim[i + 1], y1IntLim[i + 1], y2IntLim[i + 1], l, m, q, p, r, n, 2 * i * (l + m + 2) + 2 * i * p + 2)
# Bit Error Rate monitoring:
siho_i0 = siho(aLim[0], bLim[0], aDec[0], bDec[0])
delayer_ia[0] = delayer(clk, rst, aClean, aDel[0], 1 + d, 0, 2)
delayer_ib[0] = delayer(clk, rst, bClean, bDel[0], 1 + d, 0, 2)
ber_i[0] = ber(clk, rst, aDel[0], bDel[0], aDec[0], bDec[0], d + 100, resFile[0])
for i in range(it):
delayer_ia[i + 1] = delayer(clk, rst, aClean, aDel[i + 1], (2 * i + 1) * (l + m + 2) + 2 * i * p + d, 0, 2)
delayer_ib[i + 1] = delayer(clk, rst, bClean, bDel[i + 1], (2 * i + 1) * (l + m + 2) + 2 * i * p + d, 0, 2)
ber_i[i + 1] = ber(clk, rst, aDel[i + 1], bDel[i + 1], aDec[i + 1], bDec[i + 1], (2 * i + 1) * (l + m + 2) + 2 * i * p + d + 100, resFile[i + 1])
return rstGen_i0, clkGen_i0, randGen_i0, clkDiv_i0, abPermut_i0, interleaver_i0, coder_i0, coder_i1, delayer_i0, delayer_i1, delayer_i2, delayer_i3, delayer_i4, delayer_i5, noiser_i0, limiter_i0, punct_i0, siho_i0, delayer_ia, delayer_ib, ber_i, iteration_i
"""
Assignment 1
Question 1 test script for both iteration.py and recursion.py
Author: Hassan Tariq, 100657119
"""
from iteration import iteration
from recursion import recursion
import math
pi = math.pi
# Define both function
def f(x):
return math.sin(pi*x) - x*x
# Define derivative of function
def g(x):
return 1/(2*math.pi) * math.sin(math.pi*x) - 1/(2*math.pi) * x**2 + x
#Initialize variables
x0 = 1
tolx = tolf = 1e-6
kmax = 50
print("\n\tIterative solution for question 1")
iteration(f, g, x0, kmax, tolx, tolf)
# Initialize extra recursion counter variable
i = 0
print("\n\n\tRecursive solution for question 1")
recursion(f, g, x0, tolx, tolf, kmax, i)
.getOrCreate()
import create as tc
import os_file as to
import iteration as it
from pai_pyhdfs import wait_hdfs_file, hdfs_save
f = wait_hdfs_file(work_path, 'distance_matrix.npy', delete=False)
m = np.load(f, allow_pickle=True) #save distance_matrix in m.
def get_res(s1, s2):
if tc.cost(s1, m) < tc.cost(s2, m):
return s1
else:
return s2
while True:
f = wait_hdfs_file(work_path, 'generations.npy', delete=False)
generations = np.load(f)
print("generation information getted. Now setting the gen_rdd.")
gen_rdd = spark.sparkContext.parallelize(generations)
print("rdd setted,now maping.")
res = gen_rdd.map(lambda x: it.iteration(x, m))
print("mapping over,now reducing.")
res2 = res.reduce(get_res)
print("The best solution is:", res2)
c = tc.cost(res2, m)
print("The lowest cost is :", c)
hdfs_save(work_path, 'final_solution.npy', [res2, c])
