def solve_getinfo(a, b, x, sample):
mat = np.array(a)
(size, _) = a.shape
result = []
a_cond = linalg.cond(mat)
(a_maxcond, _, a_subcond) = rdft.get_leading_maxcond(mat)
for i in range(0, sample):
# RDFT solve
(x1, fra, fr, ra, x1_orig) = solve_rdft(a, b, x)
(fra_maxcond, _, fra_subcond) = rdft.get_leading_maxcond(fra)
fra_cond = linalg.cond(fra)
err_rdft_iter = linalg.norm(x1 - x)
err_rdft = linalg.norm(x1_orig - x)
# RDFT improved solve
(x1_imp, fra_imp, fr_imp, ra_imp,
x1_imp_orig) = solve_rdft_improved(a, b, x)
(fra_maxcond_imp, _, fra_subcond_imp) = rdft.get_leading_maxcond(fra)
fra_cond_imp = linalg.cond(fra_imp)
err_rdft_imp_iter = linalg.norm(x1_imp - x)
err_rdft_imp = linalg.norm(x1_imp_orig - x)
# RDFT givens solve
(x5_imp, x5_imp_orig) = solve_rdft_givens(a, b, x)
err_rdft_givens_iter = linalg.norm(x5_imp - x)
err_rdft_givens = linalg.norm(x5_imp_orig - x)
# RDFT givens solve
(x7_imp, x7_imp_orig) = solve_rdft_givens_both(a, b, x)
err_rdft_givens_both_iter = linalg.norm(x7_imp - x)
err_rdft_givens_both = linalg.norm(x7_imp_orig - x)
# GAUSS solve
(x2, ga, _, x2_orig) = solve_gauss(a, b, x)
(ga_maxcond, _, ga_subcond) = rdft.get_leading_maxcond(fra)
ga_cond = linalg.cond(ga)
err_gauss_iter = linalg.norm(x2 - x)
err_gauss = linalg.norm(x2_orig - x)
# Partial Pivot
a_float = np.array(a, dtype=np.float64)
b_float = np.array(b, dtype=np.float64)
(x3, pl, pu, swapped_a, swapped_b) = pp.solve(a_float, b_float)
err_pp = linalg.norm(x3 - x)
##x3_i = iteration.iteration(swapped_a, pl, pu, swapped_b, x3, linalg.cond(a_float))
#result make
result.append(
([
a_maxcond / a_cond, fra_maxcond / fra_cond, fra, a_subcond,
fra_subcond, fr, ra, err_rdft_iter, err_rdft,
err_rdft_imp_iter, err_rdft_imp
], [err_rdft_givens_iter, err_rdft_givens
], [err_rdft_givens_both_iter, err_rdft_givens_both],
[ga_maxcond / ga_cond, ga, ga_subcond, err_gauss_iter,
err_gauss], [err_pp]))
return result
def const_r():
for i in range(0, 50):
size = 200
mat = np.zeros((size, size))
r = rdft.generate_r(size) # use constant r
for j in range(0, size):
for k in range(0, size):
mat[j, k] = random.uniform(-100, 100)
f = rdft.generate_f(size)
fr = f.dot(r)
fra = fr.dot(mat)
(a_maxcond, _, _) = rdft.get_leading_maxcond(mat)
(fra_maxcond, _, _) = rdft.get_leading_maxcond(fra)
a_cond = linalg.cond(mat)
fra_cond = linalg.cond(fra)
print("A: ", a_maxcond / a_cond)
print("FRA:", fra_maxcond / fra_cond)
def const_r():
for i in range(0, 50):
size = 200
mat = np.zeros((size,size))
r = rdft.generate_r(size) # use constant r
for j in range(0,size):
for k in range(0,size):
mat[j,k] = random.uniform(-100,100)
f = rdft.generate_f(size)
fr = f.dot(r)
fra = fr.dot(mat)
(a_maxcond,_,_) = rdft.get_leading_maxcond(mat)
(fra_maxcond,_,_) = rdft.get_leading_maxcond(fra)
a_cond = linalg.cond(mat)
fra_cond = linalg.cond(fra)
print("A: ", a_maxcond/a_cond)
print("FRA:", fra_maxcond/fra_cond)
def const_a(sample, size, rand_range):
result = []
mat = np.zeros((size,size))
for j in range(0,size):
for k in range(0,size):
mat[j,k] = random.uniform(-rand_range,rand_range)
for i in range(0, sample):
f = rdft.generate_f(size)
r = rdft.generate_r(size)
fr = f.dot(r)
fra = fr.dot(mat)
(a_maxcond,_,a_subcond) = rdft.get_leading_maxcond(mat)
(fra_maxcond,_,fra_subcond) = rdft.get_leading_maxcond(fra)
a_cond = linalg.cond(mat)
fra_cond = linalg.cond(fra)
result.append([mat, a_maxcond/a_cond, fra_maxcond/fra_cond, fra, a_subcond, fra_subcond])
#print("A: ", a_maxcond/a_cond)
#print("FRA:", fra_maxcond/fra_cond)
return result
def solve_getinfo(a,b,x,sample):
mat = np.array(a)
(size,_) = a.shape
result = []
a_cond = linalg.cond(mat)
(a_maxcond,_,a_subcond) = rdft.get_leading_maxcond(mat)
for i in range(0, sample):
# RDFT solve
(x1, fra, fr, ra, x1_orig) = solve_rdft(a,b,x)
(fra_maxcond,_,fra_subcond) = rdft.get_leading_maxcond(fra)
fra_cond = linalg.cond(fra)
err_rdft_iter = linalg.norm(x1-x)
err_rdft = linalg.norm(x1_orig-x)
# RDFT improved solve
(x1_imp, fra_imp, fr_imp, ra_imp, x1_imp_orig) = solve_rdft_improved(a,b,x)
(fra_maxcond_imp,_,fra_subcond_imp) = rdft.get_leading_maxcond(fra)
fra_cond_imp = linalg.cond(fra_imp)
err_rdft_imp_iter = linalg.norm(x1_imp-x)
err_rdft_imp = linalg.norm(x1_imp_orig-x)
# RDFT givens solve
(x5_imp, x5_imp_orig) = solve_rdft_givens(a,b,x)
err_rdft_givens_iter = linalg.norm(x5_imp-x)
err_rdft_givens = linalg.norm(x5_imp_orig-x)
# RDFT givens solve
(x7_imp, x7_imp_orig) = solve_rdft_givens_both(a,b,x)
err_rdft_givens_both_iter = linalg.norm(x7_imp-x)
err_rdft_givens_both = linalg.norm(x7_imp_orig-x)
# GAUSS solve
(x2, ga, _, x2_orig) = solve_gauss(a,b,x)
(ga_maxcond,_,ga_subcond) = rdft.get_leading_maxcond(fra)
ga_cond = linalg.cond(ga)
err_gauss_iter = linalg.norm(x2-x)
err_gauss = linalg.norm(x2_orig-x)
# Partial Pivot
a_float = np.array(a,dtype=np.float64)
b_float = np.array(b,dtype=np.float64)
(x3, pl, pu, swapped_a, swapped_b) = pp.solve(a_float,b_float)
err_pp = linalg.norm(x3-x)
##x3_i = iteration.iteration(swapped_a, pl, pu, swapped_b, x3, linalg.cond(a_float))
#result make
result.append(([a_maxcond/a_cond, fra_maxcond/fra_cond, fra, a_subcond, fra_subcond, fr, ra, err_rdft_iter, err_rdft,err_rdft_imp_iter,err_rdft_imp],[err_rdft_givens_iter,err_rdft_givens],[err_rdft_givens_both_iter,err_rdft_givens_both],[ga_maxcond/ga_cond, ga, ga_subcond, err_gauss_iter, err_gauss],[err_pp]))
return result
def const_a(sample, size, rand_range):
result = []
mat = np.zeros((size, size))
for j in range(0, size):
for k in range(0, size):
mat[j, k] = random.uniform(-rand_range, rand_range)
for i in range(0, sample):
f = rdft.generate_f(size)
r = rdft.generate_r(size)
fr = f.dot(r)
fra = fr.dot(mat)
(a_maxcond, _, a_subcond) = rdft.get_leading_maxcond(mat)
(fra_maxcond, _, fra_subcond) = rdft.get_leading_maxcond(fra)
a_cond = linalg.cond(mat)
fra_cond = linalg.cond(fra)
result.append([
mat, a_maxcond / a_cond, fra_maxcond / fra_cond, fra, a_subcond,
fra_subcond
])
#print("A: ", a_maxcond/a_cond)
#print("FRA:", fra_maxcond/fra_cond)
return result
def const_a_test(test_num, sample, size, rand_range, way=-1, way_option=[]):
counter = 0
sample_counter = 0
for i in range(0, test_num):
[cd, fs, mat, mca, mcfra, fras, a_subcond,
fra_subconds] = const_a_detection(sample, size, rand_range)
a = np.array(mat)
if way == 0:
mat = mat.T
elif way == 1:
for (k, j) in way_option:
mat = pp.swap(mat, k, j)
if cd > sample * (2 / 3):
np.savetxt("./test_result/test_result" + str(counter) + "_a.txt",
a)
os.mkdir("./test_result/fra_" + str(counter))
for i in range(0, len(fras)):
np.savetxt(
"./test_result/fra_" + str(counter) + "/" + str(i) +
".txt", fras[i])
output = open(
"./test_result/test_result" + str(counter) + "_a_subcond.txt",
'w')
output.write(str(a_subcond))
output.close()
output = open(
"./test_result/test_result" + str(counter) +
"_fra_subcond.txt", 'w')
output.write(str(fra_subconds))
output.close()
output = open(
"./test_result/test_result" + str(counter) + "_mca.txt", 'w')
output.write(str(mca))
output = open(
"./test_result/test_result" + str(counter) + "_mcfra.txt", 'w')
output.write(str(mcfra))
output.close()
output = open(
"./test_result/test_result" + str(counter) + "_det.txt", 'w')
output.write(str(np.linalg.det(a)))
output.close()
output = open(
"./test_result/test_result" + str(counter) + "_cd.txt", 'w')
output.write(str(cd))
output.close()
output = open(
"./test_result/test_result" + str(counter) + "_cdnum.txt", 'w')
output.write(str(linalg.cond(a)))
output.close()
sample_counter = 0
a_subcond, fra_subconds = [], []
while sample_counter < 100:
f = rdft.generate_f(size)
r = rdft.generate_r(size)
fr = f.dot(r)
fra = fr.dot(mat)
(a_maxcond, _, a_subcond) = rdft.get_leading_maxcond(mat)
(fra_maxcond, _, fra_subcond) = rdft.get_leading_maxcond(fra)
fra_subconds.append(fra_subcond)
sample_counter = sample_counter + 1
np.savetxt(
"./test_result/test_result" + str(counter) + "_z_mod_a.txt",
mat)
os.mkdir("./test_result/fra_" + str(counter) + "_" +
str(sample_counter))
for i in range(0, len(fras)):
np.savetxt(
"./test_result/fra_" + str(counter) + "_" +
str(sample_counter) + "/" + str(i) + ".txt", fras[i])
output = open(
"./test_result/test_result" + str(counter) +
"_z_a_subcond.txt", 'w')
output.write(str(a_subcond))
output.close()
output = open(
"./test_result/test_result" + str(counter) +
"_z_fra_subcond.txt", 'w')
output.write(str(fra_subconds))
output.close()
output = open(
"./test_result/test_result" + str(counter) + "_z_mca.txt", 'w')
output.write(str(mca))
output = open(
"./test_result/test_result" + str(counter) + "_z_mcfra.txt",
'w')
output.write(str(mcfra))
output.close()
output = open(
"./test_result/test_result" + str(counter) + "_z_det.txt", 'w')
output.write(str(np.linalg.det(mat)))
output.close()
output = open(
"./test_result/test_result" + str(counter) + "_z_cdnum.txt",
'w')
output.write(str(linalg.cond(mat)))
output.close()
counter = counter + 1
print(str(counter) + "detected")
def const_a_test(test_num, sample, size, rand_range, way=-1, way_option=[]):
counter = 0
sample_counter = 0
for i in range(0, test_num):
[cd, fs, mat, mca, mcfra, fras, a_subcond, fra_subconds] = const_a_detection(sample, size, rand_range)
a = np.array(mat)
if way == 0:
mat = mat.T
elif way == 1:
for (k,j) in way_option:
mat = pp.swap(mat,k,j)
if cd > sample*(2/3):
np.savetxt("./test_result/test_result" + str(counter) + "_a.txt",a)
os.mkdir("./test_result/fra_" + str(counter))
for i in range(0,len(fras)):
np.savetxt("./test_result/fra_" + str(counter) + "/" + str(i) + ".txt", fras[i])
output = open("./test_result/test_result" + str(counter) + "_a_subcond.txt",'w')
output.write(str(a_subcond))
output.close()
output = open("./test_result/test_result" + str(counter) + "_fra_subcond.txt",'w')
output.write(str(fra_subconds))
output.close()
output = open("./test_result/test_result" + str(counter) + "_mca.txt",'w')
output.write(str(mca))
output = open("./test_result/test_result" + str(counter) + "_mcfra.txt",'w')
output.write(str(mcfra))
output.close()
output = open("./test_result/test_result" + str(counter) + "_det.txt",'w')
output.write(str(np.linalg.det(a)))
output.close()
output = open("./test_result/test_result" + str(counter) + "_cd.txt",'w')
output.write(str(cd))
output.close()
output = open("./test_result/test_result" + str(counter) + "_cdnum.txt",'w')
output.write(str(linalg.cond(a)))
output.close()
sample_counter = 0
a_subcond, fra_subconds = [], []
while sample_counter < 100:
f = rdft.generate_f(size)
r = rdft.generate_r(size)
fr = f.dot(r)
fra = fr.dot(mat)
(a_maxcond,_,a_subcond) = rdft.get_leading_maxcond(mat)
(fra_maxcond,_,fra_subcond) = rdft.get_leading_maxcond(fra)
fra_subconds.append(fra_subcond)
sample_counter = sample_counter + 1
np.savetxt("./test_result/test_result" + str(counter) + "_z_mod_a.txt",mat)
os.mkdir("./test_result/fra_" + str(counter) + "_" + str(sample_counter))
for i in range(0,len(fras)):
np.savetxt("./test_result/fra_" + str(counter) + "_" + str(sample_counter) + "/" + str(i) + ".txt", fras[i])
output = open("./test_result/test_result" + str(counter) + "_z_a_subcond.txt",'w')
output.write(str(a_subcond))
output.close()
output = open("./test_result/test_result" + str(counter) + "_z_fra_subcond.txt",'w')
output.write(str(fra_subconds))
output.close()
output = open("./test_result/test_result" + str(counter) + "_z_mca.txt",'w')
output.write(str(mca))
output = open("./test_result/test_result" + str(counter) + "_z_mcfra.txt",'w')
output.write(str(mcfra))
output.close()
output = open("./test_result/test_result" + str(counter) + "_z_det.txt",'w')
output.write(str(np.linalg.det(mat)))
output.close()
output = open("./test_result/test_result" + str(counter) + "_z_cdnum.txt",'w')
output.write(str(linalg.cond(mat)))
output.close()
counter = counter + 1
print(str(counter) + "detected")
