def sample_fixmat(newmat, total, position, sample, signal, noise, m, n):
s_list = []
for i in range(sample):
r = round( (newmat[0][i]/total)*((10.0-noise)/10)*(position*sample) )
s_list.append(r/position)
#print "s_list", s_list
mat = [[0 for i in range(sample)]for j in range(position)]
s = somechoose(sample, signal)
for i in range(position):
for j in range(sample):
r = (random.randrange(10)+1)/10.0
if s_list[j] >= r:mat[i][j] = 1
if i == m or i == n:
for z in range(signal):
mat[i][s[z]] = 1
return mat
def sample_fixmat(newmat, total, position, sample, signal, noise, m, n):
s_list = []
for i in range(sample):
r = round((newmat[0][i] / total) * ((10.0 - noise) / 10) *
(position * sample))
s_list.append(r / position)
#print "s_list", s_list
mat = [[0 for i in range(sample)] for j in range(position)]
s = somechoose(sample, signal)
for i in range(position):
for j in range(sample):
r = (random.randrange(10) + 1) / 10.0
if s_list[j] >= r: mat[i][j] = 1
if i == m or i == n:
for z in range(signal):
mat[i][s[z]] = 1
return mat
def normal_dist(sample, position, signal, mean, va, noise, weight, fix_data):
if fix_data == "p": s_rate = [1 for i in range(sample)]
else:
s_rate = []
for s in range(sample):
r = -1
while 0 > r or r > 1:
r = random.normalvariate(mean, va)
s_rate.append(r)
if fix_data == "s": p_rate = [1 for i in range(position)]
else:
p_rate = []
for p in range(position):
r = -1
while 0 > r or r > 1:
r = random.normalvariate(mean, va)
p_rate.append(r)
mat = [[0 for i in range(sample)] for j in range(position)]
total = 0
for s in range(sample):
for p in range(position):
total += s_rate[s] * p_rate[p]
mf = 0
for i in range(position):
for j in range(sample):
#r = random.randrange(10)/10.0
#print "r", r
mat[i][j] = s_rate[j] * p_rate[i] * weight / total
mf += s_rate[j] * p_rate[i] * weight / total
#if r < s_rate[j]*p_rate[i]*weight/total:mat[i][j] = 1
newmat, total = change_prob(mat, sample, position, fix_data)
if fix_data == "b":
"""
pmat = [[0 for i in range(sample)]for j in range(position)]
s = somechoose(sample, signal)
s.sort()
print "somechoose, length", len(s), s
for i in range(position):
for j in range(sample):
r = random.randrange(10)
if r >= noise:pmat[i][j] = 1
if i == m or i == n:
for z in range(signal):
pmat[i][s[z]] = 1
"""
L = []
for i in range(position):
for j in range(sample):
tuple = (i, j)
L.append((tuple, newmat[i][j]))
arr = np.array(newmat)
elnum = position * sample
mat = [[0 for i in range(sample)] for j in range(position)]
"""
for i in range(10):
for j in range(elnum/10):
r = np.unravel_index(arr.argmin(), arr.shape)
mat[r[0]][r[1]] = i*0.1 +0.05
arr[r[0]][r[1]] = 2.0
print "mat", mat
"""
pmat = [[0 for i in range(sample)] for j in range(position)]
s1 = somechoose(sample, signal)
s2 = somechoose(sample, signal)
c = []
while (len(c) < sample * position * (10 - noise) / 10):
choice = random_weight_choice(L)
if choice not in c:
c.append(choice)
for k in range(len(c)):
pmat[c[k][0]][c[k][1]] = 1
for z in range(signal):
r1 = random.randrange(10)
if (r1 < 5):
pmat[0][s1[z]] = 1
pmat[1][s1[z]] = 0
else:
pmat[0][s1[z]] = 0
pmat[1][s1[z]] = 1
r2 = random.randrange(10)
if (r2 < 5):
pmat[3][s2[z]] = 1
pmat[4][s2[z]] = 0
else:
pmat[3][s2[z]] = 0
pmat[4][s2[z]] = 1
"""
for i in range(position):
for j in range(sample):
#r = random.randrange(10)
if mat[i][j] >= (noise)/10.0:pmat[i][j] = 1
if i == m or i == n:
print "signal", signal
for z in range(signal):
print "s[z]", s[z]
pmat[i][s[z]] = 1
"""
if fix_data == "p":
pmat = position_fixmat(newmat, total, position, sample, signal, noise,
m, n)
elif fix_data == "s":
pmat = sample_fixmat(newmat, total, position, sample, signal, noise, m,
n)
return pmat
def normal_dist(sample, position, signal, mean, va, noise, weight, fix_data):
if fix_data == "p":s_rate = [1 for i in range(sample)]
else:
s_rate = []
for s in range(sample):
r = -1
while 0 > r or r > 1:
r = random.normalvariate(mean, va)
s_rate.append(r)
if fix_data == "s":p_rate = [1 for i in range(position)]
else:
p_rate = []
for p in range(position):
r = -1
while 0 > r or r > 1:
r = random.normalvariate(mean, va)
p_rate.append(r)
mat = [[0 for i in range(sample)]for j in range(position)]
total = 0
for s in range(sample):
for p in range(position):
total += s_rate[s]*p_rate[p]
mf = 0
for i in range(position):
for j in range(sample):
#r = random.randrange(10)/10.0
#print "r", r
mat[i][j] = s_rate[j]*p_rate[i]*weight/total
mf += s_rate[j]*p_rate[i]*weight/total
#if r < s_rate[j]*p_rate[i]*weight/total:mat[i][j] = 1
newmat, total = change_prob(mat, sample, position, fix_data)
if fix_data == "b":
"""
pmat = [[0 for i in range(sample)]for j in range(position)]
s = somechoose(sample, signal)
s.sort()
print "somechoose, length", len(s), s
for i in range(position):
for j in range(sample):
r = random.randrange(10)
if r >= noise:pmat[i][j] = 1
if i == m or i == n:
for z in range(signal):
pmat[i][s[z]] = 1
"""
L=[]
for i in range(position):
for j in range(sample):
tuple=(i,j)
L.append((tuple, newmat[i][j]))
arr = np.array(newmat)
elnum = position*sample
mat = [[0 for i in range(sample)]for j in range(position)]
"""
for i in range(10):
for j in range(elnum/10):
r = np.unravel_index(arr.argmin(), arr.shape)
mat[r[0]][r[1]] = i*0.1 +0.05
arr[r[0]][r[1]] = 2.0
print "mat", mat
"""
pmat = [[0 for i in range(sample)]for j in range(position)]
s1 = somechoose(sample, signal)
s2 = somechoose(sample, signal)
c = []
while (len(c) < sample*position*(10-noise)/10):
choice = random_weight_choice(L)
if choice not in c:
c.append(choice)
for k in range(len(c)):
pmat[c[k][0]][c[k][1]] = 1
for z in range(signal):
pmat[0][s1[z]] = 1
pmat[1][s1[z]] = 1
pmat[3][s2[z]] = 1
pmat[4][s2[z]] = 1
"""
for i in range(position):
for j in range(sample):
#r = random.randrange(10)
if mat[i][j] >= (noise)/10.0:pmat[i][j] = 1
if i == m or i == n:
print "signal", signal
for z in range(signal):
print "s[z]", s[z]
pmat[i][s[z]] = 1
"""
if fix_data == "p":pmat = position_fixmat(newmat, total, position, sample, signal, noise, m, n)
elif fix_data == "s":pmat = sample_fixmat(newmat, total, position, sample, signal, noise, m, n)
return pmat
