def train(dataset):
db = DB(join(dbpath,'train.db'))
tbl_manifest = '%s_man'%dataset
tbl_rss = '%s_rss'%dataset
n_feature = int(db.queryone('value',tbl_manifest,'key="n_feature"')[0])
data = []
cls = []
for y,entry in db.query(['p_id','entry'], tbl_rss):
dic = json.loads(entry)
x = [-100]*n_feature
for m_id,rss in dic.items():
x[int(m_id)] = float(rss)
data.append(x)
cls.append(y)
neigh = KNeighborsClassifier(n_neighbors=5)
neigh.fit(data,cls)
fout = open('model.dat','w')
pickle.dump(neigh,fout)
fout.close()
#!/usr/bin/env python
import pickle
from dbhelper import DB
db = DB('train.db')
n_feature = int(db.queryone('value','manifest','key="n_feature"')[0])
fin = open('model.dat')
clf = pickle.load(fin)
fin.close
def predict(dic):
data = [-100]*n_feature
for m_id, rss in dic.items():
data[int(m_id)] = float(rss)
proba = clf.predict_proba([data]).tolist()[0]
print 'proba--', proba
return proba
#!/usr/bin/env python
import pickle
from dbhelper import DB
import os
from os.path import join
rootpath = os.getenv('WLAN_ROOT')
datapath = join(rootpath,'raw_data')
dbpath = join(datapath,'DB')
db = DB(join(dbpath,'train.db'))
dataset = db.queryone('value','meta','key="current"')[0]
tbl_manifest = '%s_man'%dataset
n_feature = int(db.queryone('value',tbl_manifest,'key="n_feature"')[0])
fin = open('model.dat')
clf = pickle.load(fin)
fin.close
def predict(dic):
data = [-100]*n_feature
for m_id, rss in dic.items():
data[int(m_id)] = float(rss)
proba = clf.predict_proba([data]).tolist()[0]
return proba
map_coord = {}
for line in open('raw_data/coord.txt'):
pt,xy = line.strip().split('\t')
x,y = xy.split(' ')
map_coord[pt] = (x,y)
db.insert(('n_feature',len(map_mac)), 'manifest')
db.insertmany([ (m_id,mac) for mac,m_id in map_mac.items()], 'map_mac')
db.insertmany([(p_id,pt)+map_coord[pt] for pt,p_id in map_pt.items()], 'map_pt')
db.commit()
#-------------------------------split data----------------------------
db_train = DB('train.db')
db_test = DB('test.db')
for device in db.queryone('value', 'manifest', 'key="device"'):
db_train.new_table(device, ('r_id integer','p_id integer','entry text'))
db_test.new_table(device, ('r_id integer','p_id integer','entry text'))
for record in db.queryall(device):
if random.random() > 0.3:
db_train.insert(record, device)
else:
db_test.insert(record, device)
db_train.clone_from(db, 'manifest')
db_train.clone_from(db, 'map_mac')
db_train.clone_from(db, 'map_pt')
db_test.clone_from(db, 'manifest')
db_test.clone_from(db, 'map_mac')
db_test.clone_from(db, 'map_pt')
#!/usr/bin/env python
from dbhelper import DB
import json
map_mac = {}
map_pt = {}
feas_map = {}
db = DB('train.db')
mac_dic = dict([(mac,m_id) for mac,m_id in db.query(['mac','m_id'],'map_mac')])
pid_dic = dict([(p_id,pt) for p_id,pt in db.query(['p_id','pt'],'map_pt')])
coord_dic = dict([(p_id,(int(x),int(y))) for p_id,x,y in db.query(['p_id','x','y'],'map_pt')])
n_point = int(db.queryone('value','manifest','key="n_point"')[0])
p2p_matrix = [[float('inf')]*n_point for i in range(n_point)]
for i in range(n_point):
p2p_matrix[i][i] = 0.0
for i,j,dis in db.query(['src_pt','dest_pt','distance'],'relation'):
p2p_matrix[i][j] = p2p_matrix[j][i] = dis
def get_macid(mac):
return mac_dic[mac]
def get_pointname(pid):
return pid_dic[pid]
def get_coordinate(pid):
return coord_dic[pid]
def has_mac(mac):
return mac in mac_dic
def transform(entry):
