def run():
ser_loc = localload.load(ser_info_path)
ser_loc_m = localload.load_locmore(ser_info_more_path)
res = {}
res1 = {}
for serid in ser_loc:
sn = ser_loc[serid][1]
if sn not in res:
res[sn] = 0
res[sn] = res[sn] + 1
an = ser_loc_m[sn][0]
if an not in res1:
res1[an] = 0
res1[an] = res1[an] + 1
res = sorted(res.items(), key=lambda p: p[1], reverse=True)
Y = []
for i, item in enumerate(res):
print(i, item)
Y.append(item[1])
Y = np.array(Y)
X = np.arange(len(Y))
plt.bar(X, Y)
plt.ylabel('number of services')
plt.show()
print()
def run():
ser_loc = localload.load(ser_info_path)
ser_loc_m = localload.load_locmore(ser_info_more_path)
ser_name = localload.load_location_name(ser_info_path)
oridata = np.loadtxt(origin_path, np.float)
_, s = np.where(oridata)
res = {}
res1 = {}
for sn in ser_name:
res[sn] = 0
res1['SA'] = 0
res1['A'] = 0
res1['O'] = 0
res1['E'] = 0
res1['NA'] = 0
res1['ME'] = 0
res1['SAF'] = 0
for sid in s:
sn = ser_loc[sid][1]
res[sn] = res[sn] + 1
an = ser_loc_m[sn][0]
res1[an] = res1[an] + 1
for i in res:
print(i, res[i])
print()
for i in res1:
print(i, res1[i])
pass
def run():
ser_loc = localload.load(ser_info_path)
ser_loc_m = localload.load_locmore(ser_info_more_path)
os.remove(loc_class_out)
data = []
names = []
area = []
k = 5
for sn in ser_loc_m:
data.append(ser_loc_m[sn][1])
names.append(sn)
area.append(ser_loc_m[sn][0])
data = np.array(data)
# np.random.shuffle(data);
cent, res = simple_km(data, k)
print(cent)
print(res)
for i in range(k):
tmp = []
tmp2 = []
for id in res[i]:
tmp.append(area[id])
tmp2.append(names[id])
print(tmp)
print(tmp2)
print()
classf(tmp2, ser_loc)
pass
def run():
ser_loc = localload.load(ser_info_path);
ser_loc_m = localload.load_locmore(ser_info_more_path);
os.remove(loc_class_out);
data=[];
names=[];
area=[];
k=3;
for sn in ser_loc_m:
data.append(ser_loc_m[sn][1]);
names.append(sn);
area.append(ser_loc_m[sn][0])
data=np.array(data);
cent,res = simple_km(data,k);
print(cent);
print(res);
for i in range(k):
tmp=[];
tmp2=[];
for id in res[i]:
tmp.append(area[id]);
tmp2.append(names[id]);
print(tmp)
print(tmp2);
print();
classf(tmp2,ser_loc);
pass;
def run():
ser_loc = localload.load(ser_info_path);
ser_loc_m = localload.load_locmore(ser_info_more_path);
user_class = localtools.load_classif(loc_class_for_user);
R = np.loadtxt(origin_path,np.float);
if os.path.isfile(loc_class_out):
os.remove(loc_class_out);
idx = np.where(R<0);
R[idx]=0;
user_mean = [];
for uc in user_class:
UR= R[uc];
ser_sum = np.sum(UR,axis=0);
ser_cot = np.count_nonzero(UR, axis=0);
uc_ser_mean = np.divide(ser_sum,ser_cot,
out=np.zeros_like(ser_sum),where=ser_cot!=0);
all_mean = np.sum(ser_sum)/np.sum(ser_cot);
uc_ser_mean[np.where(ser_cot==0)] = all_mean;
user_mean.append(uc_ser_mean);
data=[];
names=[];
area=[];
k=6;
for sid in range(5825):
sn = ser_loc[sid][1];
names.append(sn);
area.append(ser_loc_m[sn][0])
lc = [];
lc.extend(ser_loc_m[sn][1]);
for um in user_mean:
lc.append(um[sid]);
data.append(lc);
data=np.array(data);
cent,res = simple_km2(data,k,1);
print(cent);
print(res);
for i in range(k):
tmp=[];
tmp2=[];
for id in res[i]:
if names[id] not in tmp2:
tmp2.append(names[id]);
tmp.append(area[id]);
print(tmp)
print(tmp2);
print();
write2file(res);
pass;
def run():
user_loc = localload.load_userinfo(user_info_path)
user_loc_m = localload.load_locmore(user_info_more_path);
R = np.loadtxt(origin_path,np.float);
if os.path.isfile(loc_class_out):
os.remove(loc_class_out);
idx = np.where(R<0);
R[idx]=0;
user_sum = np.sum(R,axis=1);
user_cot = np.count_nonzero(R, axis=1);
user_mean = np.divide(user_sum,user_cot,
out=np.zeros_like(user_sum),where=user_cot!=0);
all_mean = np.sum(user_sum)/np.sum(user_cot);
user_mean[np.where(user_cot==0)] = all_mean;
data=[];
names=[];
area=[];
k=3;
for uid in range(339):
un = user_loc[uid][1];
names.append(un);
area.append(user_loc_m[un][0])
lc = [];
lc.extend(user_loc[uid][2]);
lc.extend(user_loc[uid][3][:2]);#ip前两域
lc.append(user_mean[uid]);
data.append(lc);
data=np.array(data);
cent,res = simple_km2(data,k,k);
print(cent);
print(res);
write2file(res);
for i in range(k):
tmp=[];
tmp2=[];
for id in res[i]:
if names[id] not in tmp2:
tmp2.append(names[id]);
tmp.append(area[id]);
print(tmp)
print(tmp2);
print();
pass;
def run():
ser_loc = localload.load(ser_info_path)
ser_loc_m = localload.load_locmore(ser_info_more_path)
R = np.loadtxt(origin_path, np.float)
os.remove(loc_class_out)
idx = np.where(R < 0)
R[idx] = 0
ser_sum = np.sum(R, axis=0)
ser_cot = np.count_nonzero(R, axis=0)
ser_mean = np.divide(ser_sum,
ser_cot,
out=np.zeros_like(ser_sum),
where=ser_cot != 0)
all_mean = np.sum(ser_sum) / np.sum(ser_cot)
ser_mean[np.where(ser_cot == 0)] = all_mean
data = []
names = []
area = []
k = 8
for sid in range(5825):
sn = ser_loc[sid][1]
names.append(sn)
area.append(ser_loc_m[sn][0])
lc = []
lc.extend(ser_loc_m[sn][1])
lc.append(ser_mean[sid])
data.append(lc)
data = np.array(data)
# np.random.shuffle(data);
cent, res = simple_km(data, k, 8)
print(cent)
print(res)
for i in range(k):
tmp = []
tmp2 = []
for id in res[i]:
if names[id] not in tmp2:
tmp2.append(names[id])
tmp.append(area[id])
print(tmp)
print(tmp2)
print()
write2file(res)
pass
def run():
user_loc = localload.load_userinfo(user_info_path)
user_loc_m = localload.load_locmore(user_info_more_path)
R = np.loadtxt(origin_path, np.float)
if os.path.isfile(loc_class_out):
os.remove(loc_class_out)
idx = np.where(R < 0)
R[idx] = 0
user_sum = np.sum(R, axis=1)
user_cot = np.count_nonzero(R, axis=1)
user_mean = np.divide(user_sum,
user_cot,
out=np.zeros_like(user_sum),
where=user_cot != 0)
all_mean = np.sum(user_sum) / np.sum(user_cot)
user_mean[np.where(user_cot == 0)] = all_mean
data = []
names = []
area = []
k = 3
for uid in range(339):
un = user_loc[uid][1]
names.append(un)
area.append(user_loc_m[un][0])
lc = []
lc.extend(user_loc[uid][2])
lc.append(user_mean[uid])
data.append(lc)
data = np.array(data)
cent, res = simple_km2(data, k, k)
print(cent)
print(res)
write2file(res)
for i in range(k):
tmp = []
tmp2 = []
for id in res[i]:
if names[id] not in tmp2:
tmp2.append(names[id])
tmp.append(area[id])
print(tmp)
print(tmp2)
print()
pass
def run():
ser_loc = localload.load(ser_info_path)
user_loc = localload.load_userinfo(user_info_path)
ser_loc_m = localload.load_locmore(ser_info_more_path)
user_class = localtools.load_classif(loc_class_for_user)
R = np.loadtxt(origin_path, np.float)
# os.remove(loc_class_out);
idx = np.where(R > 0)
u = idx[0].astype(np.int)
s = idx[1].astype(np.int)
dataize = len(u)
data = []
names = []
area = []
k = 8
for sid in range(5825):
sn = ser_loc[sid][1]
names.append(sn)
area.append(ser_loc_m[sn][0])
for did in range(dataize):
sn = ser_loc[s[did]][1]
cl = []
cl.extend(user_loc[u[did]][2])
cl.extend(ser_loc_m[sn][1])
cl.append(R[u[did], s[did]])
data.append(cl)
data = np.array(data)
cent, res = simple_km(data, k, 6)
print(cent)
# print(res);
for i in range(k):
tmp = []
tmp2 = []
for id in res[i]:
if names[id] not in tmp2:
tmp2.append(names[id])
tmp.append(area[id])
print(tmp)
print(tmp2)
print()
write2file(res)
pass
def run():
ser_loc = localload.load(ser_info_path);
ser_loc_m = localload.load_locmore(ser_info_more_path);
res={};
res1={};
for serid in ser_loc:
sn = ser_loc[serid][1];
if sn not in res:
res[sn]=0;
res[sn]=res[sn]+1;
an = ser_loc_m[sn][0];
if an not in res1:
res1[an]=0;
res1[an]=res1[an]+1;
for i in res:
print(i,res[i]);
print();
for i in res1:
print(i,res1[i]);
pass;
def run():
ser_loc = localload.load(ser_info_path)
ser_loc_m = localload.load_locmore(ser_info_more_path)
user_class = localtools.load_classif(loc_class_for_user)
R = np.loadtxt(origin_path, np.float)
idx = np.where(R < 0)
R[idx] = 0
user_mean = []
for uc in user_class:
UR = R[uc]
ser_sum = np.sum(UR, axis=0)
ser_cot = np.count_nonzero(UR, axis=0)
uc_ser_mean = np.divide(ser_sum,
ser_cot,
out=np.zeros_like(ser_sum),
where=ser_cot != 0)
all_mean = np.sum(ser_sum) / np.sum(ser_cot)
uc_ser_mean[np.where(ser_cot == 0)] = all_mean
user_mean.append(uc_ser_mean)
data = []
names = []
area = []
k = 6
di = 3
for sid in range(5825):
sn = ser_loc[sid][1]
names.append(sn)
area.append(ser_loc_m[sn][0])
lc = []
lc.extend(ser_loc_m[sn][1])
for um in user_mean:
lc.append(um[sid])
data.append(lc)
data = np.array(data)
# np.random.shuffle(data);
cent, res, dis_rate = simple_km(data, k, di)
print(cent)
print(res)
for i in range(k):
tmp = []
tmp2 = []
for id in res[i]:
if names[id] not in tmp2:
tmp2.append(names[id])
tmp.append(area[id])
print(tmp)
print(tmp2)
print()
# 计算类别距离
dis_rate = 1 / dis_rate
print(dis_rate)
print(np.sort(dis_rate, axis=1))
dis_rate = np.exp(dis_rate)
dis_sum = np.sum(dis_rate, axis=0)
dis_rate /= dis_sum
print(dis_rate)
print(np.sort(dis_rate, axis=1))
np.savetxt(loc_class_dis_rate_out, dis_rate, '%.8f')
os.remove(loc_class_out)
write2file(res)
pass
def run():
ser_loc = localload.load(ser_info_path)
ser_loc_m = localload.load_locmore(ser_info_more_path)
user_class = localtools.load_classif(loc_class_for_user)
R = np.loadtxt(origin_path, np.float)
if os.path.isfile(loc_class_out):
os.remove(loc_class_out)
idx = np.where(R < 0)
R[idx] = 0
user_mean = []
for uc in user_class:
UR = R[uc]
ser_sum = np.sum(UR, axis=0)
ser_cot = np.count_nonzero(UR, axis=0)
uc_ser_mean = np.divide(ser_sum,
ser_cot,
out=np.zeros_like(ser_sum),
where=ser_cot != 0)
all_mean = np.sum(ser_sum) / np.sum(ser_cot)
uc_ser_mean[np.where(ser_cot == 0)] = all_mean
user_mean.append(uc_ser_mean)
data = []
names = []
area = []
k = 6
for sid in range(5825):
sn = ser_loc[sid][1]
names.append(sn)
area.append(ser_loc_m[sn][0])
lc = []
lc.extend(ser_loc_m[sn][1])
# 添加ip
lc.extend(ser_loc[sid][2][:2])
for um in user_mean:
lc.append(um[sid])
data.append(lc)
data = np.array(data)
cent, res = simple_km2(data, k, 1)
print(cent)
print(res)
for i in range(k):
tmp = []
tmp2 = []
for id in res[i]:
if names[id] not in tmp2:
tmp2.append(names[id])
tmp.append(area[id])
print(tmp)
print(tmp2)
print()
write2file(res)
pass
